data
dict |
|---|
{
"proceeding": {
"id": "1CAThh2drSE",
"title": "2021 11th International Conference on Information Technology in Medicine and Education (ITME)",
"acronym": "itme",
"groupId": "1002567",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1CATBZ3QJeU",
"doi": "10.1109/ITME53901.2021.00101",
"title": "Interpretability Analysis of Academic Achievement Prediction Based on Machine Learning",
"normalizedTitle": "Interpretability Analysis of Academic Achievement Prediction Based on Machine Learning",
"abstract": "In recent years, with the development of artificial intelligence and information technology, we are gradually stepping into the era of big data, in which education-related data has developed sufficiently in terms of quantity and content. To be able to use machine learning techniques to assist educators to help improve the current quality of education and teaching, more and more researchers have started to data-mine educational data. In this paper, various algorithms of machine learning are applied to the field of education to process the data of students' teaching performance and then model it using various algorithms of machine learning to predict the students' performance and provide some suggestions to the teachers to improve the students' performance. The main contributions of this paper are as follows: Firstly, this paper carries out necessary preprocessing operations on the original data to remove some dirty data or missing data. Then, a variety of machine learning algorithms are used to model students' academic performance. By comparing the prediction accuracy, recall rate, and F1 score of the model, the Gradient Boosting Decision Tree Classifier is finally obtained as the optimal model. We then integrated the three best machine learning models as the base models and proposed a new Stacking learning method with better results. Finally, this paper analyzes the interpretability of the Gradient Boosting Decision Tree Classifier, evaluates the importance of different characteristics, and finally concludes that “Visited resources”, “Raised hand”, “Student Absence Days”, and “Viewing announcements” are the most important factors affecting students' performance. This model has an advanced effect and good interpretability.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In recent years, with the development of artificial intelligence and information technology, we are gradually stepping into the era of big data, in which education-related data has developed sufficiently in terms of quantity and content. To be able to use machine learning techniques to assist educators to help improve the current quality of education and teaching, more and more researchers have started to data-mine educational data. In this paper, various algorithms of machine learning are applied to the field of education to process the data of students' teaching performance and then model it using various algorithms of machine learning to predict the students' performance and provide some suggestions to the teachers to improve the students' performance. The main contributions of this paper are as follows: Firstly, this paper carries out necessary preprocessing operations on the original data to remove some dirty data or missing data. Then, a variety of machine learning algorithms are used to model students' academic performance. By comparing the prediction accuracy, recall rate, and F1 score of the model, the Gradient Boosting Decision Tree Classifier is finally obtained as the optimal model. We then integrated the three best machine learning models as the base models and proposed a new Stacking learning method with better results. Finally, this paper analyzes the interpretability of the Gradient Boosting Decision Tree Classifier, evaluates the importance of different characteristics, and finally concludes that “Visited resources”, “Raised hand”, “Student Absence Days”, and “Viewing announcements” are the most important factors affecting students' performance. This model has an advanced effect and good interpretability.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In recent years, with the development of artificial intelligence and information technology, we are gradually stepping into the era of big data, in which education-related data has developed sufficiently in terms of quantity and content. To be able to use machine learning techniques to assist educators to help improve the current quality of education and teaching, more and more researchers have started to data-mine educational data. In this paper, various algorithms of machine learning are applied to the field of education to process the data of students' teaching performance and then model it using various algorithms of machine learning to predict the students' performance and provide some suggestions to the teachers to improve the students' performance. The main contributions of this paper are as follows: Firstly, this paper carries out necessary preprocessing operations on the original data to remove some dirty data or missing data. Then, a variety of machine learning algorithms are used to model students' academic performance. By comparing the prediction accuracy, recall rate, and F1 score of the model, the Gradient Boosting Decision Tree Classifier is finally obtained as the optimal model. We then integrated the three best machine learning models as the base models and proposed a new Stacking learning method with better results. Finally, this paper analyzes the interpretability of the Gradient Boosting Decision Tree Classifier, evaluates the importance of different characteristics, and finally concludes that “Visited resources”, “Raised hand”, “Student Absence Days”, and “Viewing announcements” are the most important factors affecting students' performance. This model has an advanced effect and good interpretability.",
"fno": "067900a475",
"keywords": [
"Computer Aided Instruction",
"Decision Trees",
"Educational Administrative Data Processing",
"Learning Artificial Intelligence",
"Teaching",
"Academic Achievement Prediction",
"Artificial Intelligence",
"Information Technology",
"Big Data",
"Education Related Data",
"Teaching",
"Data Mine Educational Data",
"Dirty Data",
"Machine Learning Algorithms",
"Model Students",
"Gradient Boosting Decision Tree Classifier",
"Optimal Model",
"Machine Learning Models",
"Base Models",
"Stacking Learning Method",
"Student Absence Days",
"Analytical Models",
"Machine Learning Algorithms",
"Education",
"Machine Learning",
"Predictive Models",
"Boosting",
"Prediction Algorithms",
"Academic Performance Prodections",
"Interpretability Analysis",
"Feature Selection",
"Data Visualization",
"Machine Learning"
],
"authors": [
{
"affiliation": "Shandong Normal University,School of Information Science and Engineering,Jinan,China",
"fullName": "Jie Yang",
"givenName": "Jie",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shandong Normal University,School of Information Science and Engineering,Jinan,China",
"fullName": "Hong Wang",
"givenName": "Hong",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "itme",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-11-01T00:00:00",
"pubType": "proceedings",
"pages": "475-479",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-0679-6",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "067900a470",
"articleId": "1CATrJjLKc8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "067900a480",
"articleId": "1CATlpgT7Nu",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/eitt/2021/2757/0/275700a316",
"title": "Research on the Prediction of Students' Academic Performance Based on XGBoost",
"doi": null,
"abstractUrl": "/proceedings-article/eitt/2021/275700a316/1AFsrEeEAcE",
"parentPublication": {
"id": "proceedings/eitt/2021/2757/0",
"title": "2021 Tenth International Conference of Educational Innovation through Technology (EITT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/itme/2021/0679/0/067900a569",
"title": "Fusion of Machine Learning for Teaching Case Research on Algorithm Course",
"doi": null,
"abstractUrl": "/proceedings-article/itme/2021/067900a569/1CATr2RBp1S",
"parentPublication": {
"id": "proceedings/itme/2021/0679/0",
"title": "2021 11th International Conference on Information Technology in Medicine and Education (ITME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/assic/2022/6109/0/10088305",
"title": "Multimodal Machine Learning approaches for Career Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/assic/2022/10088305/1M4rJ3ICT4Y",
"parentPublication": {
"id": "proceedings/assic/2022/6109/0",
"title": "2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/assic/2022/6109/0/10088294",
"title": "Employing Machine Learning Techniques to Categorize users in a Fitness Application",
"doi": null,
"abstractUrl": "/proceedings-article/assic/2022/10088294/1M4rOyGyTfO",
"parentPublication": {
"id": "proceedings/assic/2022/6109/0",
"title": "2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/csde/2022/5305/0/10089330",
"title": "Understanding the learning of disabled students: An exploration of machine learning approaches",
"doi": null,
"abstractUrl": "/proceedings-article/csde/2022/10089330/1M7L9BVTl2U",
"parentPublication": {
"id": "proceedings/csde/2022/5305/0",
"title": "2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpbd&is/2020/6512/0/09130599",
"title": "Performance Prediction of Physical Computer Systems Using Simulation-Based Hardware Models",
"doi": null,
"abstractUrl": "/proceedings-article/hpbd&is/2020/09130599/1l6SS8ueRtS",
"parentPublication": {
"id": "proceedings/hpbd&is/2020/6512/0",
"title": "2020 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "letters/lc/2020/02/09134722",
"title": "Contrastive Fairness in Machine Learning",
"doi": null,
"abstractUrl": "/journal/lc/2020/02/09134722/1lE0lZ5SpP2",
"parentPublication": {
"id": "letters/lc",
"title": "IEEE Letters of the Computer Society",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbase/2020/9619/0/961900a433",
"title": "An interpretability research of the Xgboost algorithm in remaining useful life prediction",
"doi": null,
"abstractUrl": "/proceedings-article/icbase/2020/961900a433/1t2nsTI3baw",
"parentPublication": {
"id": "proceedings/icbase/2020/9619/0",
"title": "2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icceai/2021/3960/0/396000a431",
"title": "Teaching Quality Evaluation of “Data Structure” Courses Based on Principal Component Analysis and Support Vector Machine",
"doi": null,
"abstractUrl": "/proceedings-article/icceai/2021/396000a431/1xqyJm3zEME",
"parentPublication": {
"id": "proceedings/icceai/2021/3960/0",
"title": "2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaml/2020/9264/0/926400a386",
"title": "Research on Constructing Learning-centered Interactive Teaching Design with the Support of Information Technology",
"doi": null,
"abstractUrl": "/proceedings-article/icaml/2020/926400a386/1yLP5qj5pkY",
"parentPublication": {
"id": "proceedings/icaml/2020/9264/0",
"title": "2020 2nd International Conference on Applied Machine Learning (ICAML)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1cdNWThm9GM",
"title": "2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)",
"acronym": "cbms",
"groupId": "1000153",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1cdNZ7L3f0c",
"doi": "10.1109/CBMS.2019.00065",
"title": "Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques",
"normalizedTitle": "Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques",
"abstract": "Although complex machine learning models (e.g., Random Forest, Neural Networks) are commonly outperforming the traditional simple interpretable models (e.g., Linear Regression, Decision Tree), in the healthcare domain, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. With the new General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. To tackle this challenge, recently, several machine learning interpretability techniques have been developed and introduced. In general, the main aim of these interpretability techniques is to shed light and provide insights into the predictions process of the machine learning models and explain how the model predictions have resulted. However, in practice, assessing the quality of the explanations provided by the various interpretability techniques is still questionable. In this paper, we present a comprehensive experimental evaluation of three recent and popular local model agnostic interpretability techniques, namely, LIME, SHAP and Anchors on different types of real-world healthcare data. Our experimental evaluation covers different aspects for its comparison including identity, stability, separability, similarity, execution time and bias detection. The results of our experiments show that LIME achieves the lowest performance for the identity metric and the highest performance for the separability metric across all datasets included in this study. On average, SHAP has the smallest average time to output explanation across all datasets included in this study. For detecting the bias, SHAP enables the participants to better detect the bias.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Although complex machine learning models (e.g., Random Forest, Neural Networks) are commonly outperforming the traditional simple interpretable models (e.g., Linear Regression, Decision Tree), in the healthcare domain, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. With the new General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. To tackle this challenge, recently, several machine learning interpretability techniques have been developed and introduced. In general, the main aim of these interpretability techniques is to shed light and provide insights into the predictions process of the machine learning models and explain how the model predictions have resulted. However, in practice, assessing the quality of the explanations provided by the various interpretability techniques is still questionable. In this paper, we present a comprehensive experimental evaluation of three recent and popular local model agnostic interpretability techniques, namely, LIME, SHAP and Anchors on different types of real-world healthcare data. Our experimental evaluation covers different aspects for its comparison including identity, stability, separability, similarity, execution time and bias detection. The results of our experiments show that LIME achieves the lowest performance for the identity metric and the highest performance for the separability metric across all datasets included in this study. On average, SHAP has the smallest average time to output explanation across all datasets included in this study. For detecting the bias, SHAP enables the participants to better detect the bias.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Although complex machine learning models (e.g., Random Forest, Neural Networks) are commonly outperforming the traditional simple interpretable models (e.g., Linear Regression, Decision Tree), in the healthcare domain, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. With the new General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. To tackle this challenge, recently, several machine learning interpretability techniques have been developed and introduced. In general, the main aim of these interpretability techniques is to shed light and provide insights into the predictions process of the machine learning models and explain how the model predictions have resulted. However, in practice, assessing the quality of the explanations provided by the various interpretability techniques is still questionable. In this paper, we present a comprehensive experimental evaluation of three recent and popular local model agnostic interpretability techniques, namely, LIME, SHAP and Anchors on different types of real-world healthcare data. Our experimental evaluation covers different aspects for its comparison including identity, stability, separability, similarity, execution time and bias detection. The results of our experiments show that LIME achieves the lowest performance for the identity metric and the highest performance for the separability metric across all datasets included in this study. On average, SHAP has the smallest average time to output explanation across all datasets included in this study. For detecting the bias, SHAP enables the participants to better detect the bias.",
"fno": "228600a275",
"keywords": [
"Decision Trees",
"Health Care",
"Learning Artificial Intelligence",
"Regression Analysis",
"Popular Local Model Agnostic Interpretability Techniques",
"Real World Healthcare Data",
"Local Machine",
"Complex Machine Learning Models",
"Traditional Simple Interpretable Models",
"Complex Models",
"Model Predictions",
"General Data Protection Regulation",
"Model Agnostic Interpretability Techniques",
"GDPR",
"LIME",
"SHAP",
"Anchors",
"Measurement",
"Predictive Models",
"Machine Learning",
"Testing",
"Diabetes",
"Data Models",
"Training",
"Machine Learning",
"Black Box Model",
"Machine Learning Interpretability",
"Model Agnostic Interpretability"
],
"authors": [
{
"affiliation": "University of Tartu",
"fullName": "Radwa El Shawi",
"givenName": "Radwa",
"surname": "El Shawi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Zewail City",
"fullName": "Youssef Sherif",
"givenName": "Youssef",
"surname": "Sherif",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Houston Methodist Center Houston",
"fullName": "Mouaz Al-Mallah",
"givenName": "Mouaz",
"surname": "Al-Mallah",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Tartu University",
"fullName": "Sherif Sakr",
"givenName": "Sherif",
"surname": "Sakr",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cbms",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-06-01T00:00:00",
"pubType": "proceedings",
"pages": "275-280",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-2286-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "228600a652",
"articleId": "1cdO2HsCCMo",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "228600a664",
"articleId": "1cdO3tamS8E",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/big-data/2021/3902/0/09671501",
"title": "Evaluating Attribution Methods in Machine Learning Interpretability",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2021/09671501/1A8jnddrCEw",
"parentPublication": {
"id": "proceedings/big-data/2021/3902/0",
"title": "2021 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09767606",
"title": "Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09767606/1D4MJ8H1fk4",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iri/2022/6603/0/660300a297",
"title": "Adding Explainability to Machine Learning Models to Detect Chronic Kidney Disease",
"doi": null,
"abstractUrl": "/proceedings-article/iri/2022/660300a297/1GvdSa8VIk0",
"parentPublication": {
"id": "proceedings/iri/2022/6603/0",
"title": "2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956192",
"title": "Going Beyond Accuracy: Interpretability Metrics for CNN Representations of Physiological Signals",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956192/1IHpcIzGlnW",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icsme/2022/7956/0/795600a444",
"title": "Why Don’t XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions",
"doi": null,
"abstractUrl": "/proceedings-article/icsme/2022/795600a444/1JeF9u9MQ8M",
"parentPublication": {
"id": "proceedings/icsme/2022/7956/0",
"title": "2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cogmi/2022/7406/0/740600a074",
"title": "Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning",
"doi": null,
"abstractUrl": "/proceedings-article/cogmi/2022/740600a074/1Lu4hB2vbBm",
"parentPublication": {
"id": "proceedings/cogmi/2022/7406/0",
"title": "2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2019/3798/0/379800a432",
"title": "Agnostic Local Explanation for Time Series Classification",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2019/379800a432/1hrLV9nJ1KM",
"parentPublication": {
"id": "proceedings/ictai/2019/3798/0",
"title": "2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icict/2020/7283/0/728300a078",
"title": "New Perspective of Interpretability of Deep Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/icict/2020/728300a078/1jPb572WT84",
"parentPublication": {
"id": "proceedings/icict/2020/7283/0",
"title": "2020 3rd International Conference on Information and Computer Technologies (ICICT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbms/2020/9429/0/942900a007",
"title": "Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models",
"doi": null,
"abstractUrl": "/proceedings-article/cbms/2020/942900a007/1mLMlF3jiVO",
"parentPublication": {
"id": "proceedings/cbms/2020/9429/0",
"title": "2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2020/5382/0/09374376",
"title": "Augmenting Decision Competence in Healthcare Using AI-based Cognitive Models",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2020/09374376/1rUIYj20jF6",
"parentPublication": {
"id": "proceedings/ichi/2020/5382/0",
"title": "2020 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1sZ3bB8vbji",
"title": "2020 International Conference on Big Data Economy and Information Management (BDEIM)",
"acronym": "bdeim",
"groupId": "1841306",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1sZ3deBOAfe",
"doi": "10.1109/BDEIM52318.2020.00011",
"title": "Research on the Innovation of China's Pension Insurance Transfer and Succession Model——Based on Big Data Technology",
"normalizedTitle": "Research on the Innovation of China's Pension Insurance Transfer and Succession Model——Based on Big Data Technology",
"abstract": "In the process of rapid economic development, the state must ensure the rationality and scientific nature of the relevant guarantee system, so as to promote stable economic development. However, there are some problems in the current transfer and continuation model of China's pension insurance, which will affect the flow of labor to a certain extent. The transfer and continuation of pension insurance will cause migrant workers to give up corresponding insurance rights and affect the vigorous development of China's labor market. Therefore, we must study and analyze the transfer and continuation model of China's pension insurance. The application of big data technology has a certain significance in the innovation research work of pension insurance transfer and continuation model. Big data technology can not only provide comprehensive data information for the pension insurance transfer and continuation model, but also can effectively improve the pension insurance transfer and continuation model. This requires us to research and analyze the problems of pension insurance transfer and continuation models, and build a new intelligent pension insurance model based on big data. Only in this way can it be ensured that the pension insurance transfer and continuation model meets the mobility needs of the Chinese labor market and promotes the stable development of the Chinese economy.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In the process of rapid economic development, the state must ensure the rationality and scientific nature of the relevant guarantee system, so as to promote stable economic development. However, there are some problems in the current transfer and continuation model of China's pension insurance, which will affect the flow of labor to a certain extent. The transfer and continuation of pension insurance will cause migrant workers to give up corresponding insurance rights and affect the vigorous development of China's labor market. Therefore, we must study and analyze the transfer and continuation model of China's pension insurance. The application of big data technology has a certain significance in the innovation research work of pension insurance transfer and continuation model. Big data technology can not only provide comprehensive data information for the pension insurance transfer and continuation model, but also can effectively improve the pension insurance transfer and continuation model. This requires us to research and analyze the problems of pension insurance transfer and continuation models, and build a new intelligent pension insurance model based on big data. Only in this way can it be ensured that the pension insurance transfer and continuation model meets the mobility needs of the Chinese labor market and promotes the stable development of the Chinese economy.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In the process of rapid economic development, the state must ensure the rationality and scientific nature of the relevant guarantee system, so as to promote stable economic development. However, there are some problems in the current transfer and continuation model of China's pension insurance, which will affect the flow of labor to a certain extent. The transfer and continuation of pension insurance will cause migrant workers to give up corresponding insurance rights and affect the vigorous development of China's labor market. Therefore, we must study and analyze the transfer and continuation model of China's pension insurance. The application of big data technology has a certain significance in the innovation research work of pension insurance transfer and continuation model. Big data technology can not only provide comprehensive data information for the pension insurance transfer and continuation model, but also can effectively improve the pension insurance transfer and continuation model. This requires us to research and analyze the problems of pension insurance transfer and continuation models, and build a new intelligent pension insurance model based on big data. Only in this way can it be ensured that the pension insurance transfer and continuation model meets the mobility needs of the Chinese labor market and promotes the stable development of the Chinese economy.",
"fno": "033100a013",
"keywords": [
"Big Data",
"Insurance",
"Labour Resources",
"Pensions",
"Continuation Model",
"Big Data Technology",
"Pension Insurance Transfer",
"Intelligent Pension Insurance Model",
"Current Transfer",
"Succession Model",
"China",
"Labor Market",
"Chinese Economy",
"Economics",
"Analytical Models",
"Technological Innovation",
"Insurance",
"Big Data",
"Data Models",
"Pensions",
"Pension Insurance Transfer",
"Succession Model",
"Innovative Analysis",
"Big Data Technology"
],
"authors": [
{
"affiliation": "Huainan Normal University,School of Economy and Management,Huainan,China,232038",
"fullName": "Guoqiang Wu",
"givenName": "Guoqiang",
"surname": "Wu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Huainan Normal University,School of Economy and Management,Huainan,China,232038",
"fullName": "Qingping Li",
"givenName": "Qingping",
"surname": "Li",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bdeim",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-12-01T00:00:00",
"pubType": "proceedings",
"pages": "13-16",
"year": "2020",
"issn": null,
"isbn": "978-1-6654-0331-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "033100a009",
"articleId": "1sZ3eOwRgf6",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "033100a017",
"articleId": "1sZ3dk2sQvK",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iciii/2011/4523/3/4523c054",
"title": "The Establishment and Perfection of New Rural Social Pension Insurance System in China: Based on the View from Public Finance",
"doi": null,
"abstractUrl": "/proceedings-article/iciii/2011/4523c054/12OmNC2OSKB",
"parentPublication": {
"id": "proceedings/iciii/2011/4523/3",
"title": "International Conference on Information Management, Innovation Management and Industrial Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bife/2013/4777/0/4777a315",
"title": "Longevity Risk and Capital Accumulation under the Current China Pension System",
"doi": null,
"abstractUrl": "/proceedings-article/bife/2013/4777a315/12OmNs5rkMk",
"parentPublication": {
"id": "proceedings/bife/2013/4777/0",
"title": "2013 Sixth International Conference on Business Intelligence and Financial Engineering (BIFE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cso/2012/1365/0/06274787",
"title": "Individual Pension Account Fund Investing Strategy",
"doi": null,
"abstractUrl": "/proceedings-article/cso/2012/06274787/12OmNxZBSyI",
"parentPublication": {
"id": "proceedings/cso/2012/1365/0",
"title": "2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mines/2011/4559/0/4559a248",
"title": "System Design for China Further Implementing a New Old-Age Insurance System for Rural Residents and Policy Suggestions -- The Clustering Analysis Based on 31 Provinces, Autonomous Regions and Municipalities",
"doi": null,
"abstractUrl": "/proceedings-article/mines/2011/4559a248/12OmNy5zsrr",
"parentPublication": {
"id": "proceedings/mines/2011/4559/0",
"title": "Multimedia Information Networking and Security, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2017/2715/0/08258131",
"title": "A preliminary study on deep learning for predicting social insurance payment behavior",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2017/08258131/17D45XeKgpO",
"parentPublication": {
"id": "proceedings/big-data/2017/2715/0",
"title": "2017 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdsba/2021/4590/0/459000a389",
"title": "Research on the Replacement Rate of Pension",
"doi": null,
"abstractUrl": "/proceedings-article/icdsba/2021/459000a389/1AH7COhf0M8",
"parentPublication": {
"id": "proceedings/icdsba/2021/4590/0",
"title": "2021 5th Annual International Conference on Data Science and Business Analytics (ICDSBA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pmis/2021/3872/0/387200a167",
"title": "A Study on the Sustainability of Provincial Urban Employees ' Basic Pension Insurance Funds",
"doi": null,
"abstractUrl": "/proceedings-article/pmis/2021/387200a167/1t2n4WY1KhO",
"parentPublication": {
"id": "proceedings/pmis/2021/3872/0",
"title": "2021 International Conference on Public Management and Intelligent Society (PMIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pmis/2021/3872/0/387200a139",
"title": "Research on the Poverty Reduction Effect of the Basic Pension Insurance System for Urban and Rural Residents---Based on a Survey of 13 Provinces in the East, Central and West",
"doi": null,
"abstractUrl": "/proceedings-article/pmis/2021/387200a139/1t2n5vapjVK",
"parentPublication": {
"id": "proceedings/pmis/2021/3872/0",
"title": "2021 International Conference on Public Management and Intelligent Society (PMIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icemme/2020/9144/0/914400a202",
"title": "Research on Motor Vehicle Insurance in China",
"doi": null,
"abstractUrl": "/proceedings-article/icemme/2020/914400a202/1tV9kQR1UMU",
"parentPublication": {
"id": "proceedings/icemme/2020/9144/0",
"title": "2020 2nd International Conference on Economic Management and Model Engineering (ICEMME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dfhmc/2020/1518/0/151800a325",
"title": "China's pension gap optimization path: based on regression model and policy analysis",
"doi": null,
"abstractUrl": "/proceedings-article/dfhmc/2020/151800a325/1tcjYIamhAk",
"parentPublication": {
"id": "proceedings/dfhmc/2020/1518/0",
"title": "2020 16th Dahe Fortune China Forum and Chinese High-educational Management Annual Academic Conference (DFHMC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1xgBjtA24tq",
"title": "2021 International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA)",
"acronym": "caibda",
"groupId": "1843185",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1xgBlH67oti",
"doi": "10.1109/CAIBDA53561.2021.00010",
"title": "Research on Mobile Caravan Insurance Recommendation Method Based on Machine Learning",
"normalizedTitle": "Research on Mobile Caravan Insurance Recommendation Method Based on Machine Learning",
"abstract": "With the continuous progress of computer computing performance, network transmission and other information technology, machine learning methods are more widely used in various industries. In this paper, we study the caravan insurance recommendation algorithm based on machine learning methods for the insurance product recommendation business problem. The article analyses data exploration, data pre-processing, building classification models and balancing data sets, focusing on the taking, distribution and visualisation of the various types of feature fields of the data to show the distribution of variables. Based on the construction of a preliminary logistic regression model, this paper performs a balancing dataset operation to address the problem of dataset imbalance. The results of the model tests show that: user characteristics social class and rental house characteristics have a significant negative effect on the purchase of mobile caravan insurance; private insurance, public equipment and the number of fire insurance policies taken out have a significant positive effect on the purchase of mobile caravan insurance.",
"abstracts": [
{
"abstractType": "Regular",
"content": "With the continuous progress of computer computing performance, network transmission and other information technology, machine learning methods are more widely used in various industries. In this paper, we study the caravan insurance recommendation algorithm based on machine learning methods for the insurance product recommendation business problem. The article analyses data exploration, data pre-processing, building classification models and balancing data sets, focusing on the taking, distribution and visualisation of the various types of feature fields of the data to show the distribution of variables. Based on the construction of a preliminary logistic regression model, this paper performs a balancing dataset operation to address the problem of dataset imbalance. The results of the model tests show that: user characteristics social class and rental house characteristics have a significant negative effect on the purchase of mobile caravan insurance; private insurance, public equipment and the number of fire insurance policies taken out have a significant positive effect on the purchase of mobile caravan insurance.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "With the continuous progress of computer computing performance, network transmission and other information technology, machine learning methods are more widely used in various industries. In this paper, we study the caravan insurance recommendation algorithm based on machine learning methods for the insurance product recommendation business problem. The article analyses data exploration, data pre-processing, building classification models and balancing data sets, focusing on the taking, distribution and visualisation of the various types of feature fields of the data to show the distribution of variables. Based on the construction of a preliminary logistic regression model, this paper performs a balancing dataset operation to address the problem of dataset imbalance. The results of the model tests show that: user characteristics social class and rental house characteristics have a significant negative effect on the purchase of mobile caravan insurance; private insurance, public equipment and the number of fire insurance policies taken out have a significant positive effect on the purchase of mobile caravan insurance.",
"fno": "249000a009",
"keywords": [
"Data Mining",
"Insurance",
"Learning Artificial Intelligence",
"Pattern Classification",
"Recommender Systems",
"Regression Analysis",
"Preliminary Logistic Regression Model",
"Balancing Dataset Operation",
"Model Tests",
"User Characteristics Social Class",
"Private Insurance",
"Fire Insurance Policies",
"Mobile Caravan Insurance Recommendation Method",
"Continuous Progress",
"Computer Computing Performance",
"Network Transmission",
"Information Technology",
"Machine Learning Methods",
"Caravan Insurance Recommendation Algorithm",
"Insurance Product Recommendation Business Problem",
"Article Analyses Data Exploration",
"Data Pre Processing",
"Building Classification Models",
"Balancing Data Sets",
"Industries",
"Machine Learning Algorithms",
"Insurance",
"Focusing",
"Machine Learning",
"Data Models",
"Numerical Models",
"Artificial Intelligence",
"Machine Learning",
"Recommendation Algorithms",
"Building Models"
],
"authors": [
{
"affiliation": "Beijing University Of Chemical Technology,Beijing,China",
"fullName": "Hongwei Li",
"givenName": "Hongwei",
"surname": "Li",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "caibda",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-05-01T00:00:00",
"pubType": "proceedings",
"pages": "9-15",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-2490-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "249000a005",
"articleId": "1xgBmKjBkmk",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "249000a016",
"articleId": "1xgBkBfHHVe",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bife/2013/4777/0/4777a324",
"title": "Effect of Affordability on Insurance Purchase in Hong Kong",
"doi": null,
"abstractUrl": "/proceedings-article/bife/2013/4777a324/12OmNvSbBI9",
"parentPublication": {
"id": "proceedings/bife/2013/4777/0",
"title": "2013 Sixth International Conference on Business Intelligence and Financial Engineering (BIFE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iceitsa/2021/1300/0/130000a478",
"title": "Machine Learning in Insurance Underwriting Context",
"doi": null,
"abstractUrl": "/proceedings-article/iceitsa/2021/130000a478/1B2HqYhnS9y",
"parentPublication": {
"id": "proceedings/iceitsa/2021/1300/0",
"title": "2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmtma/2022/9978/0/997800a725",
"title": "Agricultural insurance demand forecasting model based on support vector machine",
"doi": null,
"abstractUrl": "/proceedings-article/icmtma/2022/997800a725/1Byem6zXua4",
"parentPublication": {
"id": "proceedings/icmtma/2022/9978/0",
"title": "2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aemcse/2022/8474/0/847400a301",
"title": "Insurance Product Recommendation Based on Neural Network",
"doi": null,
"abstractUrl": "/proceedings-article/aemcse/2022/847400a301/1IlNYNyH4QM",
"parentPublication": {
"id": "proceedings/aemcse/2022/8474/0",
"title": "2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2022/8045/0/10020230",
"title": "POSGen: Personalized Opening Sentence Generation for Online Insurance Sales",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2022/10020230/1KfRKuhriOA",
"parentPublication": {
"id": "proceedings/big-data/2022/8045/0",
"title": "2022 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmew/2019/9214/0/921400a471",
"title": "Causation-Driven Visualizations for Insurance Recommendation",
"doi": null,
"abstractUrl": "/proceedings-article/icmew/2019/921400a471/1cJ0zVHel7q",
"parentPublication": {
"id": "proceedings/icmew/2019/9214/0",
"title": "2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvris/2019/5050/0/505000a211",
"title": "Design and Research of Insurance Survey Claims System Based on Big Data Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icvris/2019/505000a211/1fHk7IOQAb6",
"parentPublication": {
"id": "proceedings/icvris/2019/5050/0",
"title": "2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mlbdbi/2019/5094/0/509400a308",
"title": "Research on Recommendation of Insurance Products Based on Random Forest",
"doi": null,
"abstractUrl": "/proceedings-article/mlbdbi/2019/509400a308/1gjRIxXfJQs",
"parentPublication": {
"id": "proceedings/mlbdbi/2019/5094/0",
"title": "2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/asonam/2019/6868/0/09073106",
"title": "A Hierarchical Insurance Recommendation Framework Using GraphOLAM Approach",
"doi": null,
"abstractUrl": "/proceedings-article/asonam/2019/09073106/1jjA5nAIBpu",
"parentPublication": {
"id": "proceedings/asonam/2019/6868/0",
"title": "2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iri/2021/3875/0/387500a049",
"title": "Agentless Insurance Model Based on Modern Artificial Intelligence",
"doi": null,
"abstractUrl": "/proceedings-article/iri/2021/387500a049/1yBGa9m4NUc",
"parentPublication": {
"id": "proceedings/iri/2021/3875/0",
"title": "2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1lgoo3dQcBa",
"title": "2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)",
"acronym": "wi-iat",
"groupId": "1001411",
"volume": "2",
"displayVolume": "2",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNvq5jv8",
"doi": "10.1109/WI-IAT.2014.138",
"title": "Uncertainty Reasoning Based Formal Framework for Big Video Data Understanding",
"normalizedTitle": "Uncertainty Reasoning Based Formal Framework for Big Video Data Understanding",
"abstract": "It is worthwhile to incorporate human knowledge with conventional machine learning approaches for big data analytics. Focusing on big video data understanding, this paper presents a formal scenario recognition framework where knowledge-based logic representation and reasoning is combined with data-based learning approach to enhance scenario recognition capabilities. This is achieved via multi-layered (hierarchical) processing. This approach constructs the hierarchical representation structure based on the semantic understanding of considered scenario, and transforms the structure into logic formulas. After applying conventional computer vision methods for low-level events classification, we apply logic based uncertainty reasoning to determine scene content. Experimental results on a benchmark dataset are provided to show the rationality of the proposed approach.",
"abstracts": [
{
"abstractType": "Regular",
"content": "It is worthwhile to incorporate human knowledge with conventional machine learning approaches for big data analytics. Focusing on big video data understanding, this paper presents a formal scenario recognition framework where knowledge-based logic representation and reasoning is combined with data-based learning approach to enhance scenario recognition capabilities. This is achieved via multi-layered (hierarchical) processing. This approach constructs the hierarchical representation structure based on the semantic understanding of considered scenario, and transforms the structure into logic formulas. After applying conventional computer vision methods for low-level events classification, we apply logic based uncertainty reasoning to determine scene content. Experimental results on a benchmark dataset are provided to show the rationality of the proposed approach.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "It is worthwhile to incorporate human knowledge with conventional machine learning approaches for big data analytics. Focusing on big video data understanding, this paper presents a formal scenario recognition framework where knowledge-based logic representation and reasoning is combined with data-based learning approach to enhance scenario recognition capabilities. This is achieved via multi-layered (hierarchical) processing. This approach constructs the hierarchical representation structure based on the semantic understanding of considered scenario, and transforms the structure into logic formulas. After applying conventional computer vision methods for low-level events classification, we apply logic based uncertainty reasoning to determine scene content. Experimental results on a benchmark dataset are provided to show the rationality of the proposed approach.",
"fno": "4143b487",
"keywords": [
"Cognition",
"Semantics",
"Computer Vision",
"Uncertainty",
"Optical Imaging",
"Image Motion Analysis",
"Vectors",
"Uncertainty Reasoning",
"Video Data Understanding",
"Scenario Recognition",
"Formal Logical Representation",
"Hierarchical Structure"
],
"authors": [
{
"affiliation": null,
"fullName": "Shuwei Chen",
"givenName": "Shuwei",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Kathy Clawson",
"givenName": "Kathy",
"surname": "Clawson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Min Jing",
"givenName": "Min",
"surname": "Jing",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jun Liu",
"givenName": "Jun",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Hui Wang",
"givenName": "Hui",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Bryan Scotney",
"givenName": "Bryan",
"surname": "Scotney",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "wi-iat",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-08-01T00:00:00",
"pubType": "proceedings",
"pages": "487-494",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-4143-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4143b481",
"articleId": "12OmNwB2dYc",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4143b495",
"articleId": "12OmNwcCIM3",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icse/2012/1066/0/06227159",
"title": "Partial models: Towards modeling and reasoning with uncertainty",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2012/06227159/12OmNBgQFSA",
"parentPublication": {
"id": "proceedings/icse/2012/1066/0",
"title": "2012 34th International Conference on Software Engineering (ICSE 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iri/2014/5880/0/07051884",
"title": "Uncertainty reasoning for the \"big data\" semantic web",
"doi": null,
"abstractUrl": "/proceedings-article/iri/2014/07051884/12OmNBp52DY",
"parentPublication": {
"id": "proceedings/iri/2014/5880/0",
"title": "2014 IEEE International Conference on Information Reuse and Integration (IRI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/time/2011/1242/0/06065234",
"title": "Interleaved Programs and Rely-Guarantee Reasoning with ITL",
"doi": null,
"abstractUrl": "/proceedings-article/time/2011/06065234/12OmNBzRNpN",
"parentPublication": {
"id": "proceedings/time/2011/1242/0",
"title": "2011 Eighteenth International Symposium on Temporal Representation and Reasoning (TIME 2011)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/time/2016/3825/0/3825a196",
"title": "Stream Reasoning Using Temporal Logic and Predictive Probabilistic State Models",
"doi": null,
"abstractUrl": "/proceedings-article/time/2016/3825a196/12OmNCdTePT",
"parentPublication": {
"id": "proceedings/time/2016/3825/0",
"title": "2016 23rd International Symposium on Temporal Representation and Reasoning (TIME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2014/5666/0/07004482",
"title": "Semantic HMC for big data analysis",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2014/07004482/12OmNwF0BOT",
"parentPublication": {
"id": "proceedings/big-data/2014/5666/0",
"title": "2014 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cmpeur/1992/2760/0/00218473",
"title": "A robust logic for rule-based reasoning under uncertainty",
"doi": null,
"abstractUrl": "/proceedings-article/cmpeur/1992/00218473/12OmNyY4rlZ",
"parentPublication": {
"id": "proceedings/cmpeur/1992/2760/0",
"title": "1992 Proceedings Computer Systems and Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2016/04/mex2016040030",
"title": "Satisfiability Degree Analysis and Deductive Reasoning",
"doi": null,
"abstractUrl": "/magazine/ex/2016/04/mex2016040030/13rRUx0geDN",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2018/2666/1/266601a787",
"title": "Possibilistic Reasoning About Actions in Agent Systems",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2018/266601a787/144U9b07hJQ",
"parentPublication": {
"id": "proceedings/compsac/2018/2666/2",
"title": "2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/2.812E131",
"title": "Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/2.812E131/1BmHu3IcZxe",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2022/06/09942307",
"title": "A Framework for Reasoning About Uncertainty in Ontologies",
"doi": null,
"abstractUrl": "/magazine/ex/2022/06/09942307/1I8NWcath6g",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1gjRKqf75Ha",
"title": "2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS)",
"acronym": "icis",
"groupId": "1001200",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1gjRQLb3Tkk",
"doi": "10.1109/ICIS46139.2019.8940309",
"title": "Visualization Analysis of Social Networks of Chinese Historical Figures : Take the Study of Literati of Song Dynasty as an Example",
"normalizedTitle": "Visualization Analysis of Social Networks of Chinese Historical Figures : Take the Study of Literati of Song Dynasty as an Example",
"abstract": "In the thousands of years Chinese history, countless important figures have emerged. Some of their life experience, works and research results are data that can be processed by programs and visualized. China Biographical Database (CBDB) is a relational database on Chinese historical figures started by Harvard University. This paper using the above database, taking the literati of Song Dynasty as an example, constructed and visualized a social network based on the social relations and correspondence between the characters. PageRank algorithm is used to evaluate the importance of people in the social network.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In the thousands of years Chinese history, countless important figures have emerged. Some of their life experience, works and research results are data that can be processed by programs and visualized. China Biographical Database (CBDB) is a relational database on Chinese historical figures started by Harvard University. This paper using the above database, taking the literati of Song Dynasty as an example, constructed and visualized a social network based on the social relations and correspondence between the characters. PageRank algorithm is used to evaluate the importance of people in the social network.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In the thousands of years Chinese history, countless important figures have emerged. Some of their life experience, works and research results are data that can be processed by programs and visualized. China Biographical Database (CBDB) is a relational database on Chinese historical figures started by Harvard University. This paper using the above database, taking the literati of Song Dynasty as an example, constructed and visualized a social network based on the social relations and correspondence between the characters. PageRank algorithm is used to evaluate the importance of people in the social network.",
"fno": "08940309",
"keywords": [
"History",
"Information Retrieval",
"Internet",
"Relational Databases",
"Search Engines",
"Social Networking Online",
"Visualization Analysis",
"Social Network",
"Chinese Historical Figures",
"Literati",
"Song Dynasty",
"Life Experience",
"Relational Database",
"Social Relations",
"China Biographical Database",
"Social Networking Online",
"Databases",
"Web Pages",
"Data Visualization",
"History",
"Biographies",
"Python",
"CBDB",
"Social Network",
"Visualization",
"Page Rank"
],
"authors": [
{
"affiliation": "Communication University of China,School of Computer and Cyberspace Security,Beijing,China",
"fullName": "Chunfang Li",
"givenName": "Chunfang",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Communication University of China,School of Computer and Cyberspace Security,Beijing,China",
"fullName": "Tianyi Zuo",
"givenName": "Tianyi",
"surname": "Zuo",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icis",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-06-01T00:00:00",
"pubType": "proceedings",
"pages": "502-506",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-0801-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "08940243",
"articleId": "1gjRKZDRzq0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08940156",
"articleId": "1gjRNNlwnvO",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/big-data/2015/9926/0/07363931",
"title": "Mining local gazetteers of literary Chinese with CRF and pattern based methods for biographical information in Chinese history",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2015/07363931/12OmNrJ11Gp",
"parentPublication": {
"id": "proceedings/big-data/2015/9926/0",
"title": "2015 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2014/4288/2/06972090",
"title": "Exploratory Information Extraction from a Historical Dictionary",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2014/06972090/12OmNyrIaJC",
"parentPublication": {
"id": "proceedings/e-science/2014/4288/2",
"title": "2014 IEEE 10th International Conference on e-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/latice/2015/9967/0/9967a022",
"title": "Teaching High School Computer Science with Videos of Historical Figures -- An Augmented Reality Approach",
"doi": null,
"abstractUrl": "/proceedings-article/latice/2015/9967a022/12OmNzUxOcF",
"parentPublication": {
"id": "proceedings/latice/2015/9967/0",
"title": "2015 International Conference on Learning and Teaching in Computing and Engineering (LaTiCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ficloudw/2018/7810/0/781000a095",
"title": "Computing Len for Exploring the Historical People's Social Network",
"doi": null,
"abstractUrl": "/proceedings-article/ficloudw/2018/781000a095/14jQfQxNoEG",
"parentPublication": {
"id": "proceedings/ficloudw/2018/7810/0",
"title": "2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/06/09695348",
"title": "Visual Reasoning for Uncertainty in Spatio-Temporal Events of Historical Figures",
"doi": null,
"abstractUrl": "/journal/tg/2023/06/09695348/1AvqJHyHeO4",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/01/09912359",
"title": "CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data",
"doi": null,
"abstractUrl": "/journal/tg/2023/01/09912359/1HeiT0PQBWg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icsc/2020/6332/0/633200a101",
"title": "A Statistical Method for Constructing Tang Poet Social Networks",
"doi": null,
"abstractUrl": "/proceedings-article/icsc/2020/633200a101/1iffyQName4",
"parentPublication": {
"id": "proceedings/icsc/2020/6332/0",
"title": "2020 IEEE 14th International Conference on Semantic Computing (ICSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/10/09382844",
"title": "Interactive Visual Exploration of Longitudinal Historical Career Mobility Data",
"doi": null,
"abstractUrl": "/journal/tg/2022/10/09382844/1saZr0JHX5C",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmeim/2020/9623/0/962300a421",
"title": "An Empirical Study on the Communication Effect of Mental Health Related WeChat Official Account Take “Yi Xinli” as An Example",
"doi": null,
"abstractUrl": "/proceedings-article/icmeim/2020/962300a421/1syvhj1L3ck",
"parentPublication": {
"id": "proceedings/icmeim/2020/9623/0",
"title": "2020 International Conference on Modern Education and Information Management (ICMEIM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/visap/2021/4021/0/402100a087",
"title": "Invisible Pixel: Short Video Narratives from Machine Perspective",
"doi": null,
"abstractUrl": "/proceedings-article/visap/2021/402100a087/1yNiPswSOUo",
"parentPublication": {
"id": "proceedings/visap/2021/4021/0",
"title": "2021 IEEE VIS Arts Program (VISAP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1qyxP4fUhfG",
"title": "2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)",
"acronym": "cogmi",
"groupId": "1835524",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1qyxR7c0BEs",
"doi": "10.1109/CogMI50398.2020.00023",
"title": "Causality and Uncertainty of Information for Content Understanding",
"normalizedTitle": "Causality and Uncertainty of Information for Content Understanding",
"abstract": "Tasks require a clear picture of the context or the backdrop that frames the circumstances. Additionally tasks require a clear understanding of the content, the information available that will be used for completion of the task. Often the task involves a single or a set of decisions along the way. However, obtaining that content is not a perfect one. Understanding the content with is possible constraints, limitations, uncertainties adds to the challenge. To attempt to generate and express this the idea of an uncertainty of information concept that includes key aspects of causal reasoning is presented in this paper. In the paper the uncertainty of information (UoI) idea is discussed and how causality can be infused into this concept to not just provide another value for uncertainty be the causes. Moreover, can a causal UoI concept expand the idea so that a computational expression can capture the nuances of causal reasoning? This paper presents a possible vision.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Tasks require a clear picture of the context or the backdrop that frames the circumstances. Additionally tasks require a clear understanding of the content, the information available that will be used for completion of the task. Often the task involves a single or a set of decisions along the way. However, obtaining that content is not a perfect one. Understanding the content with is possible constraints, limitations, uncertainties adds to the challenge. To attempt to generate and express this the idea of an uncertainty of information concept that includes key aspects of causal reasoning is presented in this paper. In the paper the uncertainty of information (UoI) idea is discussed and how causality can be infused into this concept to not just provide another value for uncertainty be the causes. Moreover, can a causal UoI concept expand the idea so that a computational expression can capture the nuances of causal reasoning? This paper presents a possible vision.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Tasks require a clear picture of the context or the backdrop that frames the circumstances. Additionally tasks require a clear understanding of the content, the information available that will be used for completion of the task. Often the task involves a single or a set of decisions along the way. However, obtaining that content is not a perfect one. Understanding the content with is possible constraints, limitations, uncertainties adds to the challenge. To attempt to generate and express this the idea of an uncertainty of information concept that includes key aspects of causal reasoning is presented in this paper. In the paper the uncertainty of information (UoI) idea is discussed and how causality can be infused into this concept to not just provide another value for uncertainty be the causes. Moreover, can a causal UoI concept expand the idea so that a computational expression can capture the nuances of causal reasoning? This paper presents a possible vision.",
"fno": "414400a109",
"keywords": [
"Data Handling",
"Feature Selection",
"Inference Mechanisms",
"Uncertainty Handling",
"Causal Uo I Concept",
"Computational Expression",
"Causal Reasoning",
"Information Causality",
"Information Concept",
"Content Understanding",
"Uncertainty Of Information",
"Data Preprocessing",
"Feature Selection",
"Uncertainty",
"Cognition",
"Task Analysis",
"Taxonomy",
"Decision Making",
"Data Integration",
"Information Systems",
"Causality",
"Reasoning",
"Uncertainty",
"Data Transformation",
"Data Preprocessing",
"Feature Selection"
],
"authors": [
{
"affiliation": "Army Research Laboratory,Adelphi,MD,USA",
"fullName": "Adrienne Raglin",
"givenName": "Adrienne",
"surname": "Raglin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Arizona State University,Tempe,Az,USA",
"fullName": "Raha Moraffah",
"givenName": "Raha",
"surname": "Moraffah",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Arizona State University,Tempe,Az,USA",
"fullName": "Huan Liu",
"givenName": "Huan",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cogmi",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-10-01T00:00:00",
"pubType": "proceedings",
"pages": "109-113",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-4144-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "414400a101",
"articleId": "1qyxPuaeikM",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "414400a114",
"articleId": "1qyxQgeY3eg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/wi-iat/2014/4143/2/4143b487",
"title": "Uncertainty Reasoning Based Formal Framework for Big Video Data Understanding",
"doi": null,
"abstractUrl": "/proceedings-article/wi-iat/2014/4143b487/12OmNvq5jv8",
"parentPublication": {
"id": "wi-iat/2014/4143/2",
"title": "2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mise/2015/7055/0/7055a007",
"title": "Modularity for Uncertainty",
"doi": null,
"abstractUrl": "/proceedings-article/mise/2015/7055a007/12OmNxG1yWW",
"parentPublication": {
"id": "proceedings/mise/2015/7055/0",
"title": "2015 IEEE/ACM 7th International Workshop on Modeling in Software Engineering (MiSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/re/2014/3031/0/06912245",
"title": "Supporting early decision-making in the presence of uncertainty",
"doi": null,
"abstractUrl": "/proceedings-article/re/2014/06912245/12OmNzICEVd",
"parentPublication": {
"id": "proceedings/re/2014/3031/0",
"title": "2014 IEEE 22nd International Requirements Engineering Conference (RE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ssiai/2018/6568/0/08470359",
"title": "FUSED REASONING UNDER UNCERTAINTY FOR SOLDIER CENTRIC HUMAN-AGENT DECISION MAKING",
"doi": null,
"abstractUrl": "/proceedings-article/ssiai/2018/08470359/13WBGNbHGd0",
"parentPublication": {
"id": "proceedings/ssiai/2018/6568/0",
"title": "2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2017/2715/0/08257906",
"title": "Collective subjective logic: Scalable uncertainty-based opinion inference",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2017/08257906/17D45Vu1Tya",
"parentPublication": {
"id": "proceedings/big-data/2017/2715/0",
"title": "2017 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2019/01/08457476",
"title": "In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation",
"doi": null,
"abstractUrl": "/journal/tg/2019/01/08457476/17D45WaTkcP",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cic/2019/6739/0/673900a266",
"title": "On the Use of Operations Research for Decision Making with Uncertainty for IoT Devices in Battlefield Situations",
"doi": null,
"abstractUrl": "/proceedings-article/cic/2019/673900a266/1hrMeCtdgeQ",
"parentPublication": {
"id": "proceedings/cic/2019/6739/0",
"title": "2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acsos/2020/7277/0/09196226",
"title": "Understanding Uncertainty in Self-adaptive Systems",
"doi": null,
"abstractUrl": "/proceedings-article/acsos/2020/09196226/1n912R7ChJS",
"parentPublication": {
"id": "proceedings/acsos/2020/7277/0",
"title": "2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cogmi/2020/4144/0/414400a082",
"title": "On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems",
"doi": null,
"abstractUrl": "/proceedings-article/cogmi/2020/414400a082/1qyxQjrWRpu",
"parentPublication": {
"id": "proceedings/cogmi/2020/4144/0",
"title": "2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pacificvis/2021/3931/0/393100a041",
"title": "Visualising Temporal Uncertainty: A Taxonomy and Call for Systematic Evaluation",
"doi": null,
"abstractUrl": "/proceedings-article/pacificvis/2021/393100a041/1tTtqZZkSzu",
"parentPublication": {
"id": "proceedings/pacificvis/2021/3931/0",
"title": "2021 IEEE 14th Pacific Visualization Symposium (PacificVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1B12DGrwoyQ",
"title": "2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"acronym": "wacv",
"groupId": "1000040",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1B13HiwSCBy",
"doi": "10.1109/WACV51458.2022.00104",
"title": "Late-resizing: A Simple but Effective Sketch Extraction Strategy for Improving Generalization of Line-art Colorization",
"normalizedTitle": "Late-resizing: A Simple but Effective Sketch Extraction Strategy for Improving Generalization of Line-art Colorization",
"abstract": "Automatic line-art colorization is a demanding research field owing to its expensive and labor-intensive workload. Learning-based approaches have lately emerged to improve the quality of colorization. To handle the lack of paired data in line art and color images, sketch extraction has been widely adopted. This study primarily focuses on the resizing process applied within the sketch extraction procedure, which is essential for normalizing input sketches of various sizes to the target size of the colorization model. We first analyze the inherent risk in a conventional resizing strategy, i.e., early-resizing, which places the resizing step before the line detection process to ensure the practicality. Although the strategy is extensively used, it involves an often overlooked risk of significantly degrading the generalization of the colorization model. Thus, we propose a late-resizing strategy in which resizing is applied after the line detection step. The proposed late-resizing strategy has three advantages: prevention of a quality degradation in the color image, augmentation for downsizing artifacts, and alleviation of look-ahead bias. In conclusion, we present both quantitative and qualitative evaluations on representative learning-based line-art colorization methods, which verify the effectiveness of the proposed method in the generalization of the colorization model.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Automatic line-art colorization is a demanding research field owing to its expensive and labor-intensive workload. Learning-based approaches have lately emerged to improve the quality of colorization. To handle the lack of paired data in line art and color images, sketch extraction has been widely adopted. This study primarily focuses on the resizing process applied within the sketch extraction procedure, which is essential for normalizing input sketches of various sizes to the target size of the colorization model. We first analyze the inherent risk in a conventional resizing strategy, i.e., early-resizing, which places the resizing step before the line detection process to ensure the practicality. Although the strategy is extensively used, it involves an often overlooked risk of significantly degrading the generalization of the colorization model. Thus, we propose a late-resizing strategy in which resizing is applied after the line detection step. The proposed late-resizing strategy has three advantages: prevention of a quality degradation in the color image, augmentation for downsizing artifacts, and alleviation of look-ahead bias. In conclusion, we present both quantitative and qualitative evaluations on representative learning-based line-art colorization methods, which verify the effectiveness of the proposed method in the generalization of the colorization model.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Automatic line-art colorization is a demanding research field owing to its expensive and labor-intensive workload. Learning-based approaches have lately emerged to improve the quality of colorization. To handle the lack of paired data in line art and color images, sketch extraction has been widely adopted. This study primarily focuses on the resizing process applied within the sketch extraction procedure, which is essential for normalizing input sketches of various sizes to the target size of the colorization model. We first analyze the inherent risk in a conventional resizing strategy, i.e., early-resizing, which places the resizing step before the line detection process to ensure the practicality. Although the strategy is extensively used, it involves an often overlooked risk of significantly degrading the generalization of the colorization model. Thus, we propose a late-resizing strategy in which resizing is applied after the line detection step. The proposed late-resizing strategy has three advantages: prevention of a quality degradation in the color image, augmentation for downsizing artifacts, and alleviation of look-ahead bias. In conclusion, we present both quantitative and qualitative evaluations on representative learning-based line-art colorization methods, which verify the effectiveness of the proposed method in the generalization of the colorization model.",
"fno": "091500a965",
"keywords": [
"Image Colour Analysis",
"Learning Artificial Intelligence",
"Generalization",
"Automatic Line Art Colorization",
"Expensive Labor Intensive Workload",
"Color Images",
"Sketch Extraction Procedure",
"Input Sketches",
"Colorization Model",
"Early Resizing",
"Resizing Step",
"Late Resizing Strategy",
"Line Detection Step",
"Representative Learning Based Line Art Colorization",
"Degradation",
"Computer Vision",
"Art",
"Computational Modeling",
"Color",
"Data Mining",
"Deep Learning Multimedia Applications"
],
"authors": [
{
"affiliation": "Naver Webtoon Corporation,Seoul,Korea",
"fullName": "Dohyun Kim",
"givenName": "Dohyun",
"surname": "Kim",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Naver Webtoon Corporation,Seoul,Korea",
"fullName": "Dajung Je",
"givenName": "Dajung",
"surname": "Je",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Naver Webtoon Corporation,Seoul,Korea",
"fullName": "Kwangjin Lee",
"givenName": "Kwangjin",
"surname": "Lee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Naver Webtoon Corporation,Seoul,Korea",
"fullName": "Moohyun Kim",
"givenName": "Moohyun",
"surname": "Kim",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Naver Webtoon Corporation,Seoul,Korea",
"fullName": "Han Kim",
"givenName": "Han",
"surname": "Kim",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "wacv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-01-01T00:00:00",
"pubType": "proceedings",
"pages": "965-974",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-0915-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [
{
"id": "1B13HeFALXa",
"name": "pwacv202209150-09706675s1-mm_091500a965.zip",
"size": "10.4 MB",
"location": "https://www.computer.org/csdl/api/v1/extra/pwacv202209150-09706675s1-mm_091500a965.zip",
"__typename": "WebExtraType"
}
],
"adjacentArticles": {
"previous": {
"fno": "091500a955",
"articleId": "1B12OZCI3JK",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "091500a975",
"articleId": "1B12K4frGKs",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2012/2216/0/06460810",
"title": "Patch-based image colorization",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2012/06460810/12OmNBigFy1",
"parentPublication": {
"id": "proceedings/icpr/2012/2216/0",
"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/934600b818",
"title": "Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/934600b818/1KxVvkNfNss",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/crv/2019/1838/0/183800a189",
"title": "Automatic Temporally Coherent Video Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/crv/2019/183800a189/1cMGuXaHeGQ",
"parentPublication": {
"id": "proceedings/crv/2019/1838/0",
"title": "2019 16th Conference on Computer and Robot Vision (CRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300j055",
"title": "Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300j055/1hVlpf5xOp2",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2019/5023/0/502300d157",
"title": "Artist-Guided Semiautomatic Animation Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2019/502300d157/1i5mP2ezeqQ",
"parentPublication": {
"id": "proceedings/iccvw/2019/5023/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800f800",
"title": "Reference-Based Sketch Image Colorization Using Augmented-Self Reference and Dense Semantic Correspondence",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800f800/1m3nh4A8M8g",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccst/2020/8138/0/813800a435",
"title": "Cartoon image colorization based on emotion recognition and superpixel color resolution",
"doi": null,
"abstractUrl": "/proceedings-article/iccst/2020/813800a435/1p1gtwbDSH6",
"parentPublication": {
"id": "proceedings/iccst/2020/8138/0",
"title": "2020 International Conference on Culture-oriented Science & Technology (ICCST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412756",
"title": "Stylized-Colorization for Line Arts",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412756/1tmiCa6wp8c",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2021/0477/0/047700d871",
"title": "Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2021/047700d871/1uqGPvnkBUI",
"parentPublication": {
"id": "proceedings/wacv/2021/0477/0",
"title": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2021/4899/0/489900d941",
"title": "Line Art Colorization with Concatenated Spatial Attention",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2021/489900d941/1yVzYqkncJy",
"parentPublication": {
"id": "proceedings/cvprw/2021/4899/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1i5mkDyiIUg",
"title": "2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)",
"acronym": "iccvw",
"groupId": "1800041",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1i5mP2ezeqQ",
"doi": "10.1109/ICCVW.2019.00388",
"title": "Artist-Guided Semiautomatic Animation Colorization",
"normalizedTitle": "Artist-Guided Semiautomatic Animation Colorization",
"abstract": "There is a delicate balance between automating repetitive work in creative domains while staying true to an artist's vision. The animation industry regularly outsources large animation workloads to foreign countries where labor is inexpensive and long hours are common. Automating part of this process can be incredibly useful for reducing costs and creating manageable workloads for major animation studios and outsourced artists. We present a method for automating line art colorization by keeping artists in the loop to successfully reduce this workload while staying true to an artist's vision. By incorporating color hints and temporal information to an adversarial image-to-image framework, we show that it is possible to meet the balance between automation and authenticity through artist's input to generate colored frames with temporal consistency.",
"abstracts": [
{
"abstractType": "Regular",
"content": "There is a delicate balance between automating repetitive work in creative domains while staying true to an artist's vision. The animation industry regularly outsources large animation workloads to foreign countries where labor is inexpensive and long hours are common. Automating part of this process can be incredibly useful for reducing costs and creating manageable workloads for major animation studios and outsourced artists. We present a method for automating line art colorization by keeping artists in the loop to successfully reduce this workload while staying true to an artist's vision. By incorporating color hints and temporal information to an adversarial image-to-image framework, we show that it is possible to meet the balance between automation and authenticity through artist's input to generate colored frames with temporal consistency.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "There is a delicate balance between automating repetitive work in creative domains while staying true to an artist's vision. The animation industry regularly outsources large animation workloads to foreign countries where labor is inexpensive and long hours are common. Automating part of this process can be incredibly useful for reducing costs and creating manageable workloads for major animation studios and outsourced artists. We present a method for automating line art colorization by keeping artists in the loop to successfully reduce this workload while staying true to an artist's vision. By incorporating color hints and temporal information to an adversarial image-to-image framework, we show that it is possible to meet the balance between automation and authenticity through artist's input to generate colored frames with temporal consistency.",
"fno": "502300d157",
"keywords": [
"Art",
"Computer Animation",
"Image Colour Analysis",
"Outsourcing",
"Outsourced Artists",
"Line Art Colorization",
"Color Hints",
"Image To Image Framework",
"Authenticity",
"Artist Guided Semiautomatic Animation Colorization",
"Creative Domains",
"Animation Industry",
"Foreign Countries",
"Animation Studios",
"Frames Color",
"Image Color Analysis",
"Art",
"Animation",
"Generators",
"Production",
"Training",
"Gallium Nitride",
"Animation",
"Colorization",
"Artificial Intelligence",
"Temporal Consistency"
],
"authors": [
{
"affiliation": "University of Ontario Institute of Technology, Canada",
"fullName": "Harrish Thasarathan",
"givenName": "Harrish",
"surname": "Thasarathan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Ontario Institute of Technology, Canada",
"fullName": "Mehran Ebrahimi",
"givenName": "Mehran",
"surname": "Ebrahimi",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccvw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-10-01T00:00:00",
"pubType": "proceedings",
"pages": "3157-3160",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-5023-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "502300d153",
"articleId": "1i5mE5jbGxy",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "502300d161",
"articleId": "1i5mujf2ACY",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ca/1994/6240/0/00324000",
"title": "Visually guided animation",
"doi": null,
"abstractUrl": "/proceedings-article/ca/1994/00324000/12OmNAoUTrm",
"parentPublication": {
"id": "proceedings/ca/1994/6240/0",
"title": "Proceedings of Computer Animation '94",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2013/2246/0/2246a346",
"title": "Some New Possibilities for In-World Virtual Art Gallery Configuration",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2013/2246a346/12OmNBBhN6K",
"parentPublication": {
"id": "proceedings/cw/2013/2246/0",
"title": "2013 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sibgrapi/2011/4548/0/4548a009",
"title": "Colorization and Illumination of 2D Animations Based on a Region-Tree Representation",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/2011/4548a009/12OmNBiygCw",
"parentPublication": {
"id": "proceedings/sibgrapi/2011/4548/0",
"title": "2011 24th SIBGRAPI Conference on Graphics, Patterns and Images",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2004/2177/0/21770959",
"title": "Digital-Primitive Art Research: Animation Permeates Centuries-old Rawhides",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2004/21770959/12OmNyuPKYE",
"parentPublication": {
"id": "proceedings/iv/2004/2177/0",
"title": "Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004.",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2022/5365/0/536500a935",
"title": "Immersive Animation Authoring in Industrial VR Applications",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2022/536500a935/1J7Wg05jJeM",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2022/5365/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icris/2019/2632/0/263200a102",
"title": "Research on Visual Reproduction and Communication of Dongba Character Symbols in Animation Technology Production",
"doi": null,
"abstractUrl": "/proceedings-article/icris/2019/263200a102/1cI6kfrAa4M",
"parentPublication": {
"id": "proceedings/icris/2019/2632/0",
"title": "2019 International Conference on Robots & Intelligent System (ICRIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sbgames/2019/4637/0/463700a182",
"title": "Towards Machine-Learning Assisted Asset Generation for Games: A Study on Pixel Art Sprite Sheets",
"doi": null,
"abstractUrl": "/proceedings-article/sbgames/2019/463700a182/1fHHqne4cgg",
"parentPublication": {
"id": "proceedings/sbgames/2019/4637/0",
"title": "2019 18th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300j055",
"title": "Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300j055/1hVlpf5xOp2",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2020/6553/0/09093474",
"title": "ICface: Interpretable and Controllable Face Reenactment Using GANs",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2020/09093474/1jPbcapGK7m",
"parentPublication": {
"id": "proceedings/wacv/2020/6553/0",
"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2021/0477/0/047700d871",
"title": "Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2021/047700d871/1uqGPvnkBUI",
"parentPublication": {
"id": "proceedings/wacv/2021/0477/0",
"title": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1m3n9N02qgE",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1m3nh4A8M8g",
"doi": "10.1109/CVPR42600.2020.00584",
"title": "Reference-Based Sketch Image Colorization Using Augmented-Self Reference and Dense Semantic Correspondence",
"normalizedTitle": "Reference-Based Sketch Image Colorization Using Augmented-Self Reference and Dense Semantic Correspondence",
"abstract": "This paper tackles the automatic colorization task of a sketch image given an already-colored reference image. Colorizing a sketch image is in high demand in comics, animation, and other content creation applications, but it suffers from information scarcity of a sketch image. To address this, a reference image can render the colorization process in a reliable and user-driven manner. However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e.g., coloring a sketch of an originally blue car given a reference green car). To tackle this challenge, we propose to utilize the identical image with geometric distortion as a virtual reference, which makes it possible to secure the ground truth for a colored output image. Furthermore, it naturally provides the ground truth for dense semantic correspondence, which we utilize in our internal attention mechanism for color transfer from reference to sketch input. We demonstrate the effectiveness of our approach in various types of sketch image colorization via quantitative as well as qualitative evaluation against existing methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper tackles the automatic colorization task of a sketch image given an already-colored reference image. Colorizing a sketch image is in high demand in comics, animation, and other content creation applications, but it suffers from information scarcity of a sketch image. To address this, a reference image can render the colorization process in a reliable and user-driven manner. However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e.g., coloring a sketch of an originally blue car given a reference green car). To tackle this challenge, we propose to utilize the identical image with geometric distortion as a virtual reference, which makes it possible to secure the ground truth for a colored output image. Furthermore, it naturally provides the ground truth for dense semantic correspondence, which we utilize in our internal attention mechanism for color transfer from reference to sketch input. We demonstrate the effectiveness of our approach in various types of sketch image colorization via quantitative as well as qualitative evaluation against existing methods.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper tackles the automatic colorization task of a sketch image given an already-colored reference image. Colorizing a sketch image is in high demand in comics, animation, and other content creation applications, but it suffers from information scarcity of a sketch image. To address this, a reference image can render the colorization process in a reliable and user-driven manner. However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e.g., coloring a sketch of an originally blue car given a reference green car). To tackle this challenge, we propose to utilize the identical image with geometric distortion as a virtual reference, which makes it possible to secure the ground truth for a colored output image. Furthermore, it naturally provides the ground truth for dense semantic correspondence, which we utilize in our internal attention mechanism for color transfer from reference to sketch input. We demonstrate the effectiveness of our approach in various types of sketch image colorization via quantitative as well as qualitative evaluation against existing methods.",
"fno": "716800f800",
"keywords": [
"Image Colour Analysis",
"Reference Based Sketch Image Colorization",
"Dense Semantic Correspondence",
"Automatic Colorization Task",
"Ground Truth",
"Virtual Reference",
"Colored Output Image",
"Color Transfer",
"Sketch Input",
"Internal Attention Mechanism",
"Image Color Analysis",
"Task Analysis",
"Visualization",
"Semantics",
"Feature Extraction",
"Training",
"Color"
],
"authors": [
{
"affiliation": "KAIST",
"fullName": "Junsoo Lee",
"givenName": "Junsoo",
"surname": "Lee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "KAIST",
"fullName": "Eungyeup Kim",
"givenName": "Eungyeup",
"surname": "Kim",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Korea University",
"fullName": "Yunsung Lee",
"givenName": "Yunsung",
"surname": "Lee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "KAIST",
"fullName": "Dongjun Kim",
"givenName": "Dongjun",
"surname": "Kim",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "NAVER WEBTOON Corp",
"fullName": "Jaehyuk Chang",
"givenName": "Jaehyuk",
"surname": "Chang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "KAIST",
"fullName": "Jaegul Choo",
"givenName": "Jaegul",
"surname": "Choo",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-06-01T00:00:00",
"pubType": "proceedings",
"pages": "5800-5809",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-7168-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "716800f790",
"articleId": "1m3nxZc4396",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "716800f810",
"articleId": "1m3nWgB7c2Y",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/wacv/2022/0915/0/091500a965",
"title": "Late-resizing: A Simple but Effective Sketch Extraction Strategy for Improving Generalization of Line-art Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2022/091500a965/1B13HiwSCBy",
"parentPublication": {
"id": "proceedings/wacv/2022/0915/0",
"title": "2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/934600b818",
"title": "Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/934600b818/1KxVvkNfNss",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2019/2506/0/250600c233",
"title": "NTIRE 2019 Challenge on Image Colorization: Report",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2019/250600c233/1iTvkbVhHlm",
"parentPublication": {
"id": "proceedings/cvprw/2019/2506/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/02/09143503",
"title": "Active Colorization for Cartoon Line Drawings",
"doi": null,
"abstractUrl": "/journal/tg/2022/02/09143503/1lxmsQXZ36U",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h965",
"title": "Instance-Aware Image Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h965/1m3nNZyhYXe",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800j360",
"title": "Stylization-Based Architecture for Fast Deep Exemplar Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800j360/1m3oarBqEnK",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccst/2020/8138/0/813800a435",
"title": "Cartoon image colorization based on emotion recognition and superpixel color resolution",
"doi": null,
"abstractUrl": "/proceedings-article/iccst/2020/813800a435/1p1gtwbDSH6",
"parentPublication": {
"id": "proceedings/iccst/2020/8138/0",
"title": "2020 International Conference on Culture-oriented Science & Technology (ICCST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412507",
"title": "Anime Sketch Colorization by Component-based Matching using Deep Appearance Features and Graph Representation",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412507/1tmhNNPsnrG",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdh/2020/9234/0/923400a001",
"title": "Automatic Image Colorization via Weighted Sparse Representation Learning",
"doi": null,
"abstractUrl": "/proceedings-article/icdh/2020/923400a001/1uGXZvzfk4w",
"parentPublication": {
"id": "proceedings/icdh/2020/9234/0",
"title": "2020 8th International Conference on Digital Home (ICDH)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2021/0477/0/047700d871",
"title": "Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2021/047700d871/1uqGPvnkBUI",
"parentPublication": {
"id": "proceedings/wacv/2021/0477/0",
"title": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1tmhi3ly74c",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"acronym": "icpr",
"groupId": "1000545",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1tmiCa6wp8c",
"doi": "10.1109/ICPR48806.2021.9412756",
"title": "Stylized-Colorization for Line Arts",
"normalizedTitle": "Stylized-Colorization for Line Arts",
"abstract": "We address a novel problem of stylized-colorization which colorizes a given line art using a given coloring style in text. This problem can be stated as multi-domain image translation and is more challenging than the current colorization problem because it requires not only capturing the illustration distribution but also satisfying the required coloring styles specific to anime such as lightness, shading, or saturation. We propose a GAN-based end-to-end model for stylized-colorization where the model has one generator and two discriminators. Our generator is based on the U-Net architecture and receives a pair of a line art and a coloring style in text as its input to produce a stylized-colorization image of the line art. Two discriminators, on the other hand, share weights at early layers to judge the stylized-colorization image in two different aspects: one for color and one for style. One generator and two discriminators are jointly trained in an adversarial and end-to-end manner. Extensive experiments demonstrate the effectiveness of our proposed model.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We address a novel problem of stylized-colorization which colorizes a given line art using a given coloring style in text. This problem can be stated as multi-domain image translation and is more challenging than the current colorization problem because it requires not only capturing the illustration distribution but also satisfying the required coloring styles specific to anime such as lightness, shading, or saturation. We propose a GAN-based end-to-end model for stylized-colorization where the model has one generator and two discriminators. Our generator is based on the U-Net architecture and receives a pair of a line art and a coloring style in text as its input to produce a stylized-colorization image of the line art. Two discriminators, on the other hand, share weights at early layers to judge the stylized-colorization image in two different aspects: one for color and one for style. One generator and two discriminators are jointly trained in an adversarial and end-to-end manner. Extensive experiments demonstrate the effectiveness of our proposed model.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We address a novel problem of stylized-colorization which colorizes a given line art using a given coloring style in text. This problem can be stated as multi-domain image translation and is more challenging than the current colorization problem because it requires not only capturing the illustration distribution but also satisfying the required coloring styles specific to anime such as lightness, shading, or saturation. We propose a GAN-based end-to-end model for stylized-colorization where the model has one generator and two discriminators. Our generator is based on the U-Net architecture and receives a pair of a line art and a coloring style in text as its input to produce a stylized-colorization image of the line art. Two discriminators, on the other hand, share weights at early layers to judge the stylized-colorization image in two different aspects: one for color and one for style. One generator and two discriminators are jointly trained in an adversarial and end-to-end manner. Extensive experiments demonstrate the effectiveness of our proposed model.",
"fno": "09412756",
"keywords": [
"Art",
"Image Colour Analysis",
"Neural Net Architecture",
"Line Art",
"Multidomain Image Translation",
"GAN Based End To End Model",
"Discriminators",
"Stylized Colorization Image",
"Coloring Styles",
"U Net Architecture",
"Art",
"Image Color Analysis",
"Face Recognition",
"Training Data",
"Generators",
"Data Models",
"Task Analysis"
],
"authors": [
{
"affiliation": "National Tsing Hua University,Hsinchu,Taiwan",
"fullName": "Tzu-Ting Fang",
"givenName": "Tzu-Ting",
"surname": "Fang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "SOKENDAI,Tokyo,Japan",
"fullName": "Duc Minh Vo",
"givenName": "Duc Minh",
"surname": "Vo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "National Institute of Informatics,Tokyo,Japan",
"fullName": "Akihiro Sugimoto",
"givenName": "Akihiro",
"surname": "Sugimoto",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "National Tsing Hua University,Hsinchu,Taiwan",
"fullName": "Shang-Hong Lai",
"givenName": "Shang-Hong",
"surname": "Lai",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-01-01T00:00:00",
"pubType": "proceedings",
"pages": "2033-2040",
"year": "2021",
"issn": "1051-4651",
"isbn": "978-1-7281-8808-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09413042",
"articleId": "1tmjCMk1CtG",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09412629",
"articleId": "1tmhITmGble",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/vis/2005/9462/0/01532859",
"title": "Visualization with stylized line primitives",
"doi": null,
"abstractUrl": "/proceedings-article/vis/2005/01532859/12OmNxAlzZw",
"parentPublication": {
"id": "proceedings/vis/2005/9462/0",
"title": "IEEE Visualization 2005",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2022/0915/0/091500a965",
"title": "Late-resizing: A Simple but Effective Sketch Extraction Strategy for Improving Generalization of Line-art Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2022/091500a965/1B13HiwSCBy",
"parentPublication": {
"id": "proceedings/wacv/2022/0915/0",
"title": "2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/crv/2019/1838/0/183800a189",
"title": "Automatic Temporally Coherent Video Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/crv/2019/183800a189/1cMGuXaHeGQ",
"parentPublication": {
"id": "proceedings/crv/2019/1838/0",
"title": "2019 16th Conference on Computer and Robot Vision (CRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300j055",
"title": "Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300j055/1hVlpf5xOp2",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvrv/2019/4752/0/09212835",
"title": "Interactive Grayscale Image Colorization with Generative Adversarial Networks",
"doi": null,
"abstractUrl": "/proceedings-article/icvrv/2019/09212835/1nHRVtisZb2",
"parentPublication": {
"id": "proceedings/icvrv/2019/4752/0",
"title": "2019 International Conference on Virtual Reality and Visualization (ICVRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2020/6497/0/649700a033",
"title": "ArchGANs: stylized colorization prototyping for architectural line drawing",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2020/649700a033/1olHzfivvJm",
"parentPublication": {
"id": "proceedings/cw/2020/6497/0",
"title": "2020 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccst/2020/8138/0/813800a435",
"title": "Cartoon image colorization based on emotion recognition and superpixel color resolution",
"doi": null,
"abstractUrl": "/proceedings-article/iccst/2020/813800a435/1p1gtwbDSH6",
"parentPublication": {
"id": "proceedings/iccst/2020/8138/0",
"title": "2020 International Conference on Culture-oriented Science & Technology (ICCST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2021/0477/0/047700d871",
"title": "Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2021/047700d871/1uqGPvnkBUI",
"parentPublication": {
"id": "proceedings/wacv/2021/0477/0",
"title": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2021/4899/0/489900d941",
"title": "Line Art Colorization with Concatenated Spatial Attention",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2021/489900d941/1yVzYqkncJy",
"parentPublication": {
"id": "proceedings/cvprw/2021/4899/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2021/0898/0/089800a729",
"title": "Bilateral Res-Unet for Image Colorization with Limited Data via GANs",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2021/089800a729/1zw6jdm4tSo",
"parentPublication": {
"id": "proceedings/ictai/2021/0898/0",
"title": "2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1yeHGyRsuys",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1yeJWoXWd7W",
"doi": "10.1109/CVPR46437.2021.00976",
"title": "User-Guided Line Art Flat Filling with Split Filling Mechanism",
"normalizedTitle": "User-Guided Line Art Flat Filling with Split Filling Mechanism",
"abstract": "Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the \"influence areas\" of the user color scribbles, i.e., the areas where the user scribbles should propagate and influence. This framework explicitly controls such scribble influence areas for artists to manipulate the colors of image details and avoid color leakage/contamination between scribbles, and simultaneously, leverages data-driven color generation to facilitate content creation. This framework is based on a Split Filling Mechanism (SFM), which first splits the user scribbles into individual groups and then independently processes the colors and influence areas of each group with a Convolutional Neural Network (CNN). Learned from more than a million illustrations, the framework can estimate the scribble influence areas in a content-aware manner, and can smartly generate visually pleasing colors to assist the daily works of artists. We show that our proposed framework is easy to use, allowing even amateurs to obtain professional-quality results on a wide variety of line arts.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the \"influence areas\" of the user color scribbles, i.e., the areas where the user scribbles should propagate and influence. This framework explicitly controls such scribble influence areas for artists to manipulate the colors of image details and avoid color leakage/contamination between scribbles, and simultaneously, leverages data-driven color generation to facilitate content creation. This framework is based on a Split Filling Mechanism (SFM), which first splits the user scribbles into individual groups and then independently processes the colors and influence areas of each group with a Convolutional Neural Network (CNN). Learned from more than a million illustrations, the framework can estimate the scribble influence areas in a content-aware manner, and can smartly generate visually pleasing colors to assist the daily works of artists. We show that our proposed framework is easy to use, allowing even amateurs to obtain professional-quality results on a wide variety of line arts.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the \"influence areas\" of the user color scribbles, i.e., the areas where the user scribbles should propagate and influence. This framework explicitly controls such scribble influence areas for artists to manipulate the colors of image details and avoid color leakage/contamination between scribbles, and simultaneously, leverages data-driven color generation to facilitate content creation. This framework is based on a Split Filling Mechanism (SFM), which first splits the user scribbles into individual groups and then independently processes the colors and influence areas of each group with a Convolutional Neural Network (CNN). Learned from more than a million illustrations, the framework can estimate the scribble influence areas in a content-aware manner, and can smartly generate visually pleasing colors to assist the daily works of artists. We show that our proposed framework is easy to use, allowing even amateurs to obtain professional-quality results on a wide variety of line arts.",
"fno": "450900j884",
"keywords": [
"Art",
"Image Colour Analysis",
"Learning Artificial Intelligence",
"Neural Nets",
"User Guided Line Art Flat Filling",
"User Color Scribbles",
"User Scribbles",
"Scribble Influence Areas",
"Leverages Data Driven Color Generation",
"Split Filling Mechanism",
"Visually Pleasing Colors",
"Line Arts",
"Digital Artistic Content Creation",
"Flat Colors",
"Deep Learning Framework",
"Deep Learning",
"Computer Vision",
"Art",
"Image Color Analysis",
"Filling",
"Pattern Recognition",
"Convolutional Neural Networks"
],
"authors": [
{
"affiliation": "Soochow University,Style2Paints Research,China",
"fullName": "Lvmin Zhang",
"givenName": "Lvmin",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "The Chinese University of Hong Kong,Style2Paints Research,China",
"fullName": "Chengze Li",
"givenName": "Chengze",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Waseda University,JST PRESTO,Japan",
"fullName": "Edgar Simo-Serra",
"givenName": "Edgar",
"surname": "Simo-Serra",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Soochow University,China",
"fullName": "Yi Ji",
"givenName": "Yi",
"surname": "Ji",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "The Chinese University of Hong Kong,China",
"fullName": "Tien-Tsin Wong",
"givenName": "Tien-Tsin",
"surname": "Wong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Soochow University,China",
"fullName": "Chunping Liu",
"givenName": "Chunping",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-06-01T00:00:00",
"pubType": "proceedings",
"pages": "9884-9893",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-4509-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "450900j873",
"articleId": "1yeJD4GAcU0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "450900j894",
"articleId": "1yeI8Wv8BwY",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccvw/2013/3022/0/3022a874",
"title": "Mixing Paints for Generating Metamerism Art under 2 Lights and 3 Object Colors",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2013/3022a874/12OmNARRYnj",
"parentPublication": {
"id": "proceedings/iccvw/2013/3022/0",
"title": "2013 IEEE International Conference on Computer Vision Workshops (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ats/2009/3864/0/3864a105",
"title": "New Scheme of Reducing Shift and Capture Power Using the X-Filling Methodology",
"doi": null,
"abstractUrl": "/proceedings-article/ats/2009/3864a105/12OmNBfZSn7",
"parentPublication": {
"id": "proceedings/ats/2009/3864/0",
"title": "2009 Asian Test Symposium",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2014/5209/0/5209b073",
"title": "Superpixel-Based Disocclusion Filling in Depth Image Based Rendering",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/5209b073/12OmNBqdr4j",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/prdc/2009/3849/0/3849a081",
"title": "A GA-Based Method for High-Quality X-Filling to Reduce Launch Switching Activity in At-speed Scan Testing",
"doi": null,
"abstractUrl": "/proceedings-article/prdc/2009/3849a081/12OmNqJ8tnX",
"parentPublication": {
"id": "proceedings/prdc/2009/3849/0",
"title": "Pacific Rim International Symposium on Dependable Computing, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/visual/1994/6627/0/00346317",
"title": "Restorer: a visualization technique for handling missing data",
"doi": null,
"abstractUrl": "/proceedings-article/visual/1994/00346317/12OmNwErpVf",
"parentPublication": {
"id": "proceedings/visual/1994/6627/0",
"title": "Proceedings Visualization '94",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2021/9489/0/948900a333",
"title": "Bidirectional Information Transfer Scheme for Joint Intent Detection and Slot Filling",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2021/948900a333/1AUpLsfmhrO",
"parentPublication": {
"id": "proceedings/cis/2021/9489/0",
"title": "2021 17th International Conference on Computational Intelligence and Security (CIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2022/5365/0/536500a667",
"title": "Art Rich: Place Your AR Artwork",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2022/536500a667/1J7WguJqzWE",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2022/5365/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cisce/2020/9761/0/976100a450",
"title": "An Algorithm for the Hole Filling of Motion Foreground Based on Superpixel Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cisce/2020/976100a450/1oUCUOsgxqM",
"parentPublication": {
"id": "proceedings/cisce/2020/9761/0",
"title": "2020 International Conference on Communications, Information System and Computer Engineering (CISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icedme/2021/3596/0/359600a114",
"title": "Simulation of the EPC filling speed based on PLC control",
"doi": null,
"abstractUrl": "/proceedings-article/icedme/2021/359600a114/1tMPNud8Xza",
"parentPublication": {
"id": "proceedings/icedme/2021/3596/0",
"title": "2021 4th International Conference on Electron Device and Mechanical Engineering (ICEDME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900f116",
"title": "Image Inpainting with External-internal Learning and Monochromic Bottleneck",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900f116/1yeKdepFXq0",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNAkEU46",
"title": "2012 26th International Conference on Advanced Information Networking and Applications Workshops",
"acronym": "waina",
"groupId": "1001766",
"volume": "0",
"displayVolume": "0",
"year": "2012",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNC4eSz4",
"doi": "10.1109/WAINA.2012.140",
"title": "Characterizing Obfuscated JavaScript Using Abstract Syntax Trees: Experimenting with Malicious Scripts",
"normalizedTitle": "Characterizing Obfuscated JavaScript Using Abstract Syntax Trees: Experimenting with Malicious Scripts",
"abstract": "Obfuscation, code transformations that make the code unintelligible, is still an issue for web malware analysts and is still a weapon of choice for attackers. Worse, some researchers have arbitrarily decided to consider obfuscated contents as malicious although it has been proven wrong. Yet, we can assume than some web attack kits only feature a fraction of existing obfuscating transformations which may make it easy to detect malicious scripting contents. However, because of the undecidability on obfuscated contents, we propose to survey, classify and design deobfuscation methods for each obfuscating transformation. In this paper, we apply abstract syntax tree (AST) based methods to characterize obfuscating transformations found in malicious JavaScript samples. We are able to classify similar obfuscated codes based on AST fingerprints regardless of the original attack code. We are also able to quickly detect these obfuscating transformations by matching these in an analyzed sample's AST using a pushdown automaton (PDA). The PDA accepts a set of sub trees representing obfuscating transformations previously learned. Such quick and lightweight sub tree matching algorithm has the potential to detect obfuscated pieces of code in a script, to be later extracted for deobfuscation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Obfuscation, code transformations that make the code unintelligible, is still an issue for web malware analysts and is still a weapon of choice for attackers. Worse, some researchers have arbitrarily decided to consider obfuscated contents as malicious although it has been proven wrong. Yet, we can assume than some web attack kits only feature a fraction of existing obfuscating transformations which may make it easy to detect malicious scripting contents. However, because of the undecidability on obfuscated contents, we propose to survey, classify and design deobfuscation methods for each obfuscating transformation. In this paper, we apply abstract syntax tree (AST) based methods to characterize obfuscating transformations found in malicious JavaScript samples. We are able to classify similar obfuscated codes based on AST fingerprints regardless of the original attack code. We are also able to quickly detect these obfuscating transformations by matching these in an analyzed sample's AST using a pushdown automaton (PDA). The PDA accepts a set of sub trees representing obfuscating transformations previously learned. Such quick and lightweight sub tree matching algorithm has the potential to detect obfuscated pieces of code in a script, to be later extracted for deobfuscation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Obfuscation, code transformations that make the code unintelligible, is still an issue for web malware analysts and is still a weapon of choice for attackers. Worse, some researchers have arbitrarily decided to consider obfuscated contents as malicious although it has been proven wrong. Yet, we can assume than some web attack kits only feature a fraction of existing obfuscating transformations which may make it easy to detect malicious scripting contents. However, because of the undecidability on obfuscated contents, we propose to survey, classify and design deobfuscation methods for each obfuscating transformation. In this paper, we apply abstract syntax tree (AST) based methods to characterize obfuscating transformations found in malicious JavaScript samples. We are able to classify similar obfuscated codes based on AST fingerprints regardless of the original attack code. We are also able to quickly detect these obfuscating transformations by matching these in an analyzed sample's AST using a pushdown automaton (PDA). The PDA accepts a set of sub trees representing obfuscating transformations previously learned. Such quick and lightweight sub tree matching algorithm has the potential to detect obfuscated pieces of code in a script, to be later extracted for deobfuscation.",
"fno": "4652a344",
"keywords": [
"Abstract Syntax Tree",
"Java Script",
"Obfuscation"
],
"authors": [
{
"affiliation": null,
"fullName": "Gregory Blanc",
"givenName": "Gregory",
"surname": "Blanc",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Daisuke Miyamoto",
"givenName": "Daisuke",
"surname": "Miyamoto",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Mitsuaki Akiyama",
"givenName": "Mitsuaki",
"surname": "Akiyama",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Youki Kadobayashi",
"givenName": "Youki",
"surname": "Kadobayashi",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "waina",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2012-03-01T00:00:00",
"pubType": "proceedings",
"pages": "344-351",
"year": "2012",
"issn": null,
"isbn": "978-0-7695-4652-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4652a339",
"articleId": "12OmNzgwmL3",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4652a352",
"articleId": "12OmNBSjJ7I",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/wcre/2000/0881/0/08810284",
"title": "E/R Schema for the Datrix C/C++/Java Exchange Format",
"doi": null,
"abstractUrl": "/proceedings-article/wcre/2000/08810284/12OmNA14AeW",
"parentPublication": {
"id": "proceedings/wcre/2000/0881/0",
"title": "Proceedings Seventh Working Conference on Reverse Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dsn/2018/5596/0/559601a478",
"title": "Specification-Based Protocol Obfuscation",
"doi": null,
"abstractUrl": "/proceedings-article/dsn/2018/559601a478/12OmNqzu6Xa",
"parentPublication": {
"id": "proceedings/dsn/2018/5596/0",
"title": "2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isise/2008/3494/2/3494b749",
"title": "Implementing of Gaussian Syntax-Analyzer Using ANTLR",
"doi": null,
"abstractUrl": "/proceedings-article/isise/2008/3494b749/12OmNrJRPbp",
"parentPublication": {
"id": "proceedings/isise/2008/3494/2",
"title": "2008 International Symposium on Information Science and Engineering (ISISE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ncm/2009/3769/0/3769a635",
"title": "WebVaccine: A Client-Side Realtime Prevention System against Obfuscated Malicious Web Pages",
"doi": null,
"abstractUrl": "/proceedings-article/ncm/2009/3769a635/12OmNroij13",
"parentPublication": {
"id": "proceedings/ncm/2009/3769/0",
"title": "Networked Computing and Advanced Information Management, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icis/2010/4147/0/4147a625",
"title": "Mapping of Dynamic Language Constructs into Static Abstract Syntax Trees",
"doi": null,
"abstractUrl": "/proceedings-article/icis/2010/4147a625/12OmNvSbBIQ",
"parentPublication": {
"id": "proceedings/icis/2010/4147/0",
"title": "Computer and Information Science, ACIS International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wi-iat/2010/4191/3/4191c410",
"title": "Homology Detection Based on Abstract Syntax Tree Combined Simple Semantics Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/wi-iat/2010/4191c410/12OmNwbukgv",
"parentPublication": {
"id": "proceedings/wi-iat/2010/4191/2",
"title": "Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2008/3262/0/3262a092",
"title": "Ontology Model-Based Static Analysis on Java Programs",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2008/3262a092/12OmNxWuira",
"parentPublication": {
"id": "proceedings/compsac/2008/3262/0",
"title": "2008 32nd Annual IEEE International Computer Software and Applications Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/trustcom/2012/4745/0/4745b370",
"title": "RedJsod: A Readable JavaScript Obfuscation Detector Using Semantic-based Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/trustcom/2012/4745b370/12OmNzaQoGe",
"parentPublication": {
"id": "proceedings/trustcom/2012/4745/0",
"title": "2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2005/11/e0955",
"title": "A Method for Detecting Obfuscated Calls in Malicious Binaries",
"doi": null,
"abstractUrl": "/journal/ts/2005/11/e0955/13rRUxd2b0g",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dsn/2022/1693/0/169300a295",
"title": "Invoke-Deobfuscation: AST-Based and Semantics-Preserving Deobfuscation for PowerShell Scripts",
"doi": null,
"abstractUrl": "/proceedings-article/dsn/2022/169300a295/1Fixfv3B624",
"parentPublication": {
"id": "proceedings/dsn/2022/1693/0",
"title": "2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNCdk2Yv",
"title": "Visualization Conference, IEEE",
"acronym": "ieee-vis",
"groupId": "1000796",
"volume": "0",
"displayVolume": "0",
"year": "2000",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzBwGt1",
"doi": "10.1109/VISUAL.2000.885741",
"title": "Visualizing Volume Data using Physical Models",
"normalizedTitle": "Visualizing Volume Data using Physical Models",
"abstract": "Visualization techniques enable scientists to interactively explore 3D data sets, segmenting and cutting them to reveal inner structure. While powerful, these techniques suffer from one serious flaw - the images they create are displayed on a flat piece of glass or paper. It is not really 3D - it only can be made to appear 3D. In this paper we describe the construction of 3D physical models from volumetric data. Using solid freeform fabrication equipment, these models are built as separate interlocking pieces that express in physical form the segmentation and cutting operations common in display-based visualization.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Visualization techniques enable scientists to interactively explore 3D data sets, segmenting and cutting them to reveal inner structure. While powerful, these techniques suffer from one serious flaw - the images they create are displayed on a flat piece of glass or paper. It is not really 3D - it only can be made to appear 3D. In this paper we describe the construction of 3D physical models from volumetric data. Using solid freeform fabrication equipment, these models are built as separate interlocking pieces that express in physical form the segmentation and cutting operations common in display-based visualization.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Visualization techniques enable scientists to interactively explore 3D data sets, segmenting and cutting them to reveal inner structure. While powerful, these techniques suffer from one serious flaw - the images they create are displayed on a flat piece of glass or paper. It is not really 3D - it only can be made to appear 3D. In this paper we describe the construction of 3D physical models from volumetric data. Using solid freeform fabrication equipment, these models are built as separate interlocking pieces that express in physical form the segmentation and cutting operations common in display-based visualization.",
"fno": "64780055",
"keywords": [
"Scene Graphs",
"Volume Graphics",
"Volume Visualization",
"Physical Models"
],
"authors": [
{
"affiliation": "San Diego Supercomputer Center",
"fullName": "David R. Nadeau",
"givenName": "David R.",
"surname": "Nadeau",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "San Diego Supercomputer Center",
"fullName": "Michael J. Bailey",
"givenName": "Michael J.",
"surname": "Bailey",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "ieee-vis",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2000-10-01T00:00:00",
"pubType": "proceedings",
"pages": "55",
"year": "2000",
"issn": "1070-2385",
"isbn": "0-7803-6478-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "64780054",
"articleId": "12OmNAWH9DZ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "64780056",
"articleId": "12OmNxYbT1d",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ieee-vis/2005/2766/0/27660035",
"title": "High Performance Volume Splatting for Visualization of Neurovascular Data",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/2005/27660035/12OmNrYlmLf",
"parentPublication": {
"id": "proceedings/ieee-vis/2005/2766/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/2004/8788/0/87880059",
"title": "Visualizing Gyrokinetic Simulations",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/2004/87880059/12OmNvjgWA5",
"parentPublication": {
"id": "proceedings/ieee-vis/2004/8788/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wsc/2006/0500/0/04117884",
"title": "Volume Fusion: A Unified Framework for Multi-Scale Dynamic and Heterogeneous Scenes",
"doi": null,
"abstractUrl": "/proceedings-article/wsc/2006/04117884/12OmNwFicYp",
"parentPublication": {
"id": "proceedings/wsc/2006/0500/0",
"title": "2006 Winter Simulation Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pacificvis/2010/6685/0/05429595",
"title": "Explorable images for visualizing volume data",
"doi": null,
"abstractUrl": "/proceedings-article/pacificvis/2010/05429595/12OmNxwnchK",
"parentPublication": {
"id": "proceedings/pacificvis/2010/6685/0",
"title": "2010 IEEE Pacific Visualization Symposium (PacificVis 2010)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/1998/9176/0/91760319",
"title": "Image-Based Transfer Function Design for Data Exploration in Volume Visualization",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/1998/91760319/12OmNy5hRlW",
"parentPublication": {
"id": "proceedings/ieee-vis/1998/9176/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pvg/2003/2091/0/20910010",
"title": "Real-Time Volume Rendering of Time-Varying Data Using a Fragment-Shader Compression Approach",
"doi": null,
"abstractUrl": "/proceedings-article/pvg/2003/20910010/12OmNyaGeMm",
"parentPublication": {
"id": "proceedings/pvg/2003/2091/0",
"title": "Parallel and Large-Data Visualization and Graphics, IEEE Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/1997/8262/0/82620089",
"title": "Visualization of height field data with physical models and texture photomapping",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/1997/82620089/12OmNz61d4m",
"parentPublication": {
"id": "proceedings/ieee-vis/1997/8262/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2004/06/v0664",
"title": "Interactive Volume Rendering of Thin Thread Structures within Multivalued Scientific Data Sets",
"doi": null,
"abstractUrl": "/journal/tg/2004/06/v0664/13rRUwbaqUG",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cs/2003/02/c2034",
"title": "Visualizing Time-Varying Volume Data",
"doi": null,
"abstractUrl": "/magazine/cs/2003/02/c2034/13rRUxNmPJy",
"parentPublication": {
"id": "mags/cs",
"title": "Computing in Science & Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/1999/04/v0333",
"title": "Using Linked Volumes to Model Object Collisions, Deformation, Cutting, Carving, and Joining",
"doi": null,
"abstractUrl": "/journal/tg/1999/04/v0333/13rRUyft7CW",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNwDAC4K",
"title": "2013 29th IEEE International Conference on Data Engineering (ICDE 2013)",
"acronym": "icde",
"groupId": "1000178",
"volume": "0",
"displayVolume": "0",
"year": "2013",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzcPAuw",
"doi": "10.1109/ICDE.2013.6544882",
"title": "Logical provenance in data-oriented workflows?",
"normalizedTitle": "Logical provenance in data-oriented workflows?",
"abstract": "We consider the problem of defining, generating, and tracing provenance in data-oriented workflows, in which input data sets are processed by a graph of transformations to produce output results. We first give a new general definition of provenance for general transformations, introducing the notions of correctness, precision, and minimality. We then determine when properties such as correctness and minimality carry over from the individual transformations' provenance to the workflow provenance. We describe a simple logical-provenance specification language consisting of attribute mappings and filters. We provide an algorithm for provenance tracing in workflows where logical provenance for each transformation is specified using our language. We consider logical provenance in the relational setting, observing that for a class of Select-Project-Join (SPJ) transformations, logical provenance specifications encode minimal provenance. We have built a prototype system supporting the features and algorithms presented in the paper, and we report a few preliminary experimental results.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We consider the problem of defining, generating, and tracing provenance in data-oriented workflows, in which input data sets are processed by a graph of transformations to produce output results. We first give a new general definition of provenance for general transformations, introducing the notions of correctness, precision, and minimality. We then determine when properties such as correctness and minimality carry over from the individual transformations' provenance to the workflow provenance. We describe a simple logical-provenance specification language consisting of attribute mappings and filters. We provide an algorithm for provenance tracing in workflows where logical provenance for each transformation is specified using our language. We consider logical provenance in the relational setting, observing that for a class of Select-Project-Join (SPJ) transformations, logical provenance specifications encode minimal provenance. We have built a prototype system supporting the features and algorithms presented in the paper, and we report a few preliminary experimental results.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We consider the problem of defining, generating, and tracing provenance in data-oriented workflows, in which input data sets are processed by a graph of transformations to produce output results. We first give a new general definition of provenance for general transformations, introducing the notions of correctness, precision, and minimality. We then determine when properties such as correctness and minimality carry over from the individual transformations' provenance to the workflow provenance. We describe a simple logical-provenance specification language consisting of attribute mappings and filters. We provide an algorithm for provenance tracing in workflows where logical provenance for each transformation is specified using our language. We consider logical provenance in the relational setting, observing that for a class of Select-Project-Join (SPJ) transformations, logical provenance specifications encode minimal provenance. We have built a prototype system supporting the features and algorithms presented in the paper, and we report a few preliminary experimental results.",
"fno": "06544882",
"keywords": [
"Portable Computers",
"Debugging",
"Writing",
"Data Mining",
"IP Networks",
"Integrated Circuits",
"Educational Institutions"
],
"authors": [
{
"affiliation": "Stanford Univ., Stanford, CA, USA",
"fullName": "R. Ikeda",
"givenName": "R.",
"surname": "Ikeda",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Stanford Univ., Stanford, CA, USA",
"fullName": "Akash Das Sarma",
"givenName": "Akash",
"surname": "Das Sarma",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Stanford Univ., Stanford, CA, USA",
"fullName": "J. Widom",
"givenName": "J.",
"surname": "Widom",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icde",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2013-04-01T00:00:00",
"pubType": "proceedings",
"pages": "877-888",
"year": "2013",
"issn": "1063-6382",
"isbn": "978-1-4673-4909-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06544881",
"articleId": "12OmNBBzohf",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06544883",
"articleId": "12OmNylsZXX",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibmw/2012/2746/0/06470215",
"title": "Managing data provenance in genome project workflows",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2012/06470215/12OmNAsk4Bz",
"parentPublication": {
"id": "proceedings/bibmw/2012/2746/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2013/5083/0/06683906",
"title": "Dependency Provenance in Agent Based Modeling",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2013/06683906/12OmNBZ85ZM",
"parentPublication": {
"id": "proceedings/e-science/2013/5083/0",
"title": "2013 IEEE 9th International Conference on eScience (eScience)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/grid/2011/4572/0/4572a224",
"title": "HisT/PLIER: A Two-Fold Provenance Approach for Grid-Enabled Scientific Workflows Using WS-VLAM",
"doi": null,
"abstractUrl": "/proceedings-article/grid/2011/4572a224/12OmNyLiuvH",
"parentPublication": {
"id": "proceedings/grid/2011/4572/0",
"title": "Grid Computing, IEEE/ACM International Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbd/2013/3261/0/3261a197",
"title": "Research of Data Provenance Semantic Annotation for Dependency Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/cbd/2013/3261a197/12OmNyYDDyg",
"parentPublication": {
"id": "proceedings/cbd/2013/3261/0",
"title": "2013 International Conference on Advanced Cloud and Big Data (CBD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/works/2010/8990/0/05671873",
"title": "A history-tracing XML-based provenance framework for workflows",
"doi": null,
"abstractUrl": "/proceedings-article/works/2010/05671873/12OmNz3bdEO",
"parentPublication": {
"id": "proceedings/works/2010/8990/0",
"title": "2010 5th Workshop on Workflows in Support of Large-Scale Science (WORKS 2010)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2012/4747/0/4747b249",
"title": "Provenance-Based Debugging and Drill-Down in Data-Oriented Workflows",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2012/4747b249/12OmNz5apEc",
"parentPublication": {
"id": "proceedings/icde/2012/4747/0",
"title": "2012 IEEE 28th International Conference on Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nwesp/2006/2664/0/04090002",
"title": "Incorporating Provenance in Service Oriented Architecture",
"doi": null,
"abstractUrl": "/proceedings-article/nwesp/2006/04090002/17D45VsBU3w",
"parentPublication": {
"id": "proceedings/nwesp/2006/2664/0",
"title": "International Conference on Next Generation Web Services Practices",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2022/0883/0/088300d178",
"title": "How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine–Grained Provenance Analysis for SQL",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2022/088300d178/1FwFn3RfhFm",
"parentPublication": {
"id": "proceedings/icde/2022/0883/0",
"title": "2022 IEEE 38th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/escience/2019/2451/0/245100a560",
"title": "Active Provenance for Data-Intensive Workflows: Engaging Users and Developers",
"doi": null,
"abstractUrl": "/proceedings-article/escience/2019/245100a560/1ike37hlouk",
"parentPublication": {
"id": "proceedings/escience/2019/2451/0",
"title": "2019 15th International Conference on eScience (eScience)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse/2020/7121/0/712100b086",
"title": "Improving Data Scientist Efficiency with Provenance",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2020/712100b086/1pK5ki4uCfS",
"parentPublication": {
"id": "proceedings/icse/2020/7121/0",
"title": "2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1KOv807mslW",
"title": "2022 29th Asia-Pacific Software Engineering Conference (APSEC)",
"acronym": "apsec",
"groupId": "1000681",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1KOvgB6mKUE",
"doi": "10.1109/APSEC57359.2022.00065",
"title": "Visualizing Contributor Code Competency for PyPI Libraries: Preliminary Results",
"normalizedTitle": "Visualizing Contributor Code Competency for PyPI Libraries: Preliminary Results",
"abstract": "Python is known to be used by beginners to professional programmers. Python provides functionality to its community of users through PyPI libraries, which allows developers to reuse functionalities to an application. However, it is unknown the extent to which these PyPI libraries require proficient code in their implementation. We conjecture that PyPI contributors may decide to implement more advanced Pythonic code, or stick with more basic Python code. Are complex codes only committed by few contributors, or only to specific files? The new idea in this paper is to confirm who and where complex code is implemented. Hence, we present a visualization to show the relationship between proficient code, contributors, and files. Analyzing four PyPI projects, we are able to explore which files contain more elegant code, and which contributors committed to these files. Our results show that most files contain more basic competency files, and that not every contributor contributes competent code. We show how our visualization is able to summarize such information, and opens up different possibilities for understanding how to make elegant contributions.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Python is known to be used by beginners to professional programmers. Python provides functionality to its community of users through PyPI libraries, which allows developers to reuse functionalities to an application. However, it is unknown the extent to which these PyPI libraries require proficient code in their implementation. We conjecture that PyPI contributors may decide to implement more advanced Pythonic code, or stick with more basic Python code. Are complex codes only committed by few contributors, or only to specific files? The new idea in this paper is to confirm who and where complex code is implemented. Hence, we present a visualization to show the relationship between proficient code, contributors, and files. Analyzing four PyPI projects, we are able to explore which files contain more elegant code, and which contributors committed to these files. Our results show that most files contain more basic competency files, and that not every contributor contributes competent code. We show how our visualization is able to summarize such information, and opens up different possibilities for understanding how to make elegant contributions.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Python is known to be used by beginners to professional programmers. Python provides functionality to its community of users through PyPI libraries, which allows developers to reuse functionalities to an application. However, it is unknown the extent to which these PyPI libraries require proficient code in their implementation. We conjecture that PyPI contributors may decide to implement more advanced Pythonic code, or stick with more basic Python code. Are complex codes only committed by few contributors, or only to specific files? The new idea in this paper is to confirm who and where complex code is implemented. Hence, we present a visualization to show the relationship between proficient code, contributors, and files. Analyzing four PyPI projects, we are able to explore which files contain more elegant code, and which contributors committed to these files. Our results show that most files contain more basic competency files, and that not every contributor contributes competent code. We show how our visualization is able to summarize such information, and opens up different possibilities for understanding how to make elegant contributions.",
"fno": "553700a472",
"keywords": [
"Program Visualisation",
"Public Domain Software",
"Python",
"Software Libraries",
"Advanced Pythonic Code",
"Basic Competency Files",
"Basic Python Code",
"Competent Code",
"Complex Code",
"Contributor Code Competency Visualization",
"Proficient Code",
"Py PI Contributors",
"Py PI Libraries",
"Py PI Projects",
"Visualization",
"Codes",
"Libraries",
"Python",
"Software Engineering",
"Code Competency",
"Pythonic Code",
"Software Visualization"
],
"authors": [
{
"affiliation": "Universitas Muhammadiyah Surakarta",
"fullName": "Indira Febriyanti",
"givenName": "Indira",
"surname": "Febriyanti",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nara Institute of Science and Technology",
"fullName": "Raula Gaikovina Kula",
"givenName": "Raula Gaikovina",
"surname": "Kula",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nara Institute of Science and Technology",
"fullName": "Ruksit Rojpaisarnkit",
"givenName": "Ruksit",
"surname": "Rojpaisarnkit",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nara Institute of Science and Technology",
"fullName": "Kanchanok Kannee",
"givenName": "Kanchanok",
"surname": "Kannee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Universitas Muhammadiyah Surakarta",
"fullName": "Yusuf Sulistyo Nugroho",
"givenName": "Yusuf Sulistyo",
"surname": "Nugroho",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nara Institute of Science and Technology",
"fullName": "Kenichi Matsumoto",
"givenName": "Kenichi",
"surname": "Matsumoto",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "apsec",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-12-01T00:00:00",
"pubType": "proceedings",
"pages": "472-476",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-5537-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "553700a467",
"articleId": "1KOvglZmSxG",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "553700a477",
"articleId": "1KOvfRtuoBG",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "trans/tg/2023/06/09693232",
"title": "Visualizing the Scripts of Data Wrangling With <sc>Somnus</sc>",
"doi": null,
"abstractUrl": "/journal/tg/2023/06/09693232/1As79CUmeZO",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpc/2022/9298/0/929800a173",
"title": "pycefr: Python Competency Level through Code Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icpc/2022/929800a173/1EpKFHMWn2o",
"parentPublication": {
"id": "proceedings/icpc/2022/9298/0",
"title": "2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpc/2022/9298/0/929800a575",
"title": "Does Coding in Pythonic Zen Peak Performance? Preliminary Experiments of Nine Pythonic Idioms at Scale",
"doi": null,
"abstractUrl": "/proceedings-article/icpc/2022/929800a575/1EpKJVfcLeM",
"parentPublication": {
"id": "proceedings/icpc/2022/9298/0",
"title": "2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2023/04/09832512",
"title": "Towards Better Dependency Management: A First Look at Dependency Smells in Python Projects",
"doi": null,
"abstractUrl": "/journal/ts/2023/04/09832512/1F6QgXLkDD2",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tc/2023/06/09961923",
"title": "PyTracer: Automatically Profiling Numerical Instabilities in Python",
"doi": null,
"abstractUrl": "/journal/tc/2023/06/09961923/1Ixw0hqCMVy",
"parentPublication": {
"id": "trans/tc",
"title": "IEEE Transactions on Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vissoft/2022/8092/0/809200a172",
"title": "VizAPI: Visualizing Interactions between Java Libraries and Clients",
"doi": null,
"abstractUrl": "/proceedings-article/vissoft/2022/809200a172/1JeEAFTTJ72",
"parentPublication": {
"id": "proceedings/vissoft/2022/8092/0",
"title": "2022 Working Conference on Software Visualization (VISSOFT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse/2019/0869/0/086900a328",
"title": "DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2019/086900a328/1cMFrmIXpde",
"parentPublication": {
"id": "proceedings/icse/2019/0869/0",
"title": "2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/euros&pw/2020/8597/0/859700a509",
"title": "Typosquatting and Combosquatting Attacks on the Python Ecosystem",
"doi": null,
"abstractUrl": "/proceedings-article/euros&pw/2020/859700a509/1o8qm9Tqkfe",
"parentPublication": {
"id": "proceedings/euros&pw/2020/8597/0",
"title": "2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse/2020/7121/0/712100a125",
"title": "Watchman: Monitoring Dependency Conflicts for Python Library Ecosystem",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2020/712100a125/1pK5hdbfDRS",
"parentPublication": {
"id": "proceedings/icse/2020/7121/0",
"title": "2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse-companion/2021/1219/0/121900a005",
"title": "APIScanner - Towards Automated Detection of Deprecated APIs in Python Libraries",
"doi": null,
"abstractUrl": "/proceedings-article/icse-companion/2021/121900a005/1sET5BJQTwk",
"parentPublication": {
"id": "proceedings/icse-companion/2021/1219/0/",
"title": "2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNro0Ib6",
"title": "2004 Southwest Symposium on Image Analysis and Interpretation",
"acronym": "iai",
"groupId": "1000345",
"volume": "0",
"displayVolume": "0",
"year": "2004",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNB7tUq4",
"doi": "10.1109/IAI.2004.1300934",
"title": "Imaging and rendering of oil paintings using a multi-band camera",
"normalizedTitle": "Imaging and rendering of oil paintings using a multi-band camera",
"abstract": "The paper proposes a method for the imaging and rendering of art paintings using a multi-band camera system. The surface shape of an art painting is considered as a rough plane rather than a three-dimensional curved surface. First. we present an algorithm for estimating the surface normal at each pixel point, based on a photometric stereo without using a rangefinder. Next, an algorithm is presented for estimating the spectral reflectance function from a set of pixel values acquired at different illumination directions. Then, the surface reflectance and normal data are used for estimating the light reflection properties. The Torrance-Sparrow model is used for model fitting and parameter estimation. Finally, an experiment using an oil painting is executed for demonstrating the feasibility of the proposed method.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The paper proposes a method for the imaging and rendering of art paintings using a multi-band camera system. The surface shape of an art painting is considered as a rough plane rather than a three-dimensional curved surface. First. we present an algorithm for estimating the surface normal at each pixel point, based on a photometric stereo without using a rangefinder. Next, an algorithm is presented for estimating the spectral reflectance function from a set of pixel values acquired at different illumination directions. Then, the surface reflectance and normal data are used for estimating the light reflection properties. The Torrance-Sparrow model is used for model fitting and parameter estimation. Finally, an experiment using an oil painting is executed for demonstrating the feasibility of the proposed method.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The paper proposes a method for the imaging and rendering of art paintings using a multi-band camera system. The surface shape of an art painting is considered as a rough plane rather than a three-dimensional curved surface. First. we present an algorithm for estimating the surface normal at each pixel point, based on a photometric stereo without using a rangefinder. Next, an algorithm is presented for estimating the spectral reflectance function from a set of pixel values acquired at different illumination directions. Then, the surface reflectance and normal data are used for estimating the light reflection properties. The Torrance-Sparrow model is used for model fitting and parameter estimation. Finally, an experiment using an oil painting is executed for demonstrating the feasibility of the proposed method.",
"fno": "01300934",
"keywords": [
"Art",
"Light Reflection",
"Reflectivity",
"Rendering Computer Graphics",
"Optical Images",
"Rough Surfaces",
"Parameter Estimation",
"Photometry",
"Stereo Image Processing",
"Art Painting Imaging",
"Art Painting Rendering",
"Multi Band Camera",
"Rough Plane",
"Surface Normal Estimation",
"Photometric Stereo",
"Spectral Reflectance Function",
"Surface Reflectance",
"Torrance Sparrow Model",
"Model Fitting",
"Parameter Estimation",
"Petroleum",
"Painting",
"Cameras",
"Rough Surfaces",
"Surface Roughness",
"Art",
"Reflectivity",
"Shape",
"Photometry",
"Lighting"
],
"authors": [
{
"affiliation": "Dept. of Eng. Informatics, Osaka Electro-Commun. Univ., Japan",
"fullName": "S. Tominaga",
"givenName": "S.",
"surname": "Tominaga",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dept. of Eng. Informatics, Osaka Electro-Commun. Univ., Japan",
"fullName": "N. Tanaka",
"givenName": "N.",
"surname": "Tanaka",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dept. of Eng. Informatics, Osaka Electro-Commun. Univ., Japan",
"fullName": "T. Komada",
"givenName": "T.",
"surname": "Komada",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iai",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2004-01-01T00:00:00",
"pubType": "proceedings",
"pages": "6-10",
"year": "2004",
"issn": null,
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "01300933",
"articleId": "12OmNCdk2Tn",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "01300935",
"articleId": "12OmNz5JBTu",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/1993/3880/0/00341164",
"title": "Roughness and shape of specular lobe surfaces using photometric sampling method",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1993/00341164/12OmNCdBDR7",
"parentPublication": {
"id": "proceedings/cvpr/1993/3880/0",
"title": "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sitis/2013/3211/0/3211a441",
"title": "An LED-Based Spectral Imaging System for Surface Reflectance and Normal Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/sitis/2013/3211a441/12OmNroijaF",
"parentPublication": {
"id": "proceedings/sitis/2013/3211/0",
"title": "2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2009/3992/0/05206498",
"title": "A unified model of specular and diffuse reflectance for rough, glossy surfaces",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2009/05206498/12OmNvAiShy",
"parentPublication": {
"id": "proceedings/cvpr/2009/3992/0",
"title": "2009 IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2004/2128/1/212810092",
"title": "Estimation of Surface Properties of Art Paintings Using a Multi-band Camera",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2004/212810092/12OmNwK7o73",
"parentPublication": {
"id": "proceedings/icpr/2004/2128/1",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2014/5209/0/5209c071",
"title": "Linear Unmixing in BRDF Reproduction and 3D Shape Recovery",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/5209c071/12OmNx7G5Xh",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cmvit/2017/4993/0/07878716",
"title": "Generation of Stereo Oil Paintings from RGBD Images",
"doi": null,
"abstractUrl": "/proceedings-article/cmvit/2017/07878716/12OmNxT56Bx",
"parentPublication": {
"id": "proceedings/cmvit/2017/4993/0",
"title": "2017 International Conference on Machine Vision and Information Technology (CMVIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2008/2153/0/04813385",
"title": "Recovering face shape and reflectance properties from single images",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2008/04813385/12OmNy5R3Fc",
"parentPublication": {
"id": "proceedings/fg/2008/2153/0",
"title": "2008 8th IEEE International Conference on Automatic Face & Gesture Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2009/4420/0/05459381",
"title": "Attached shadow coding: Estimating surface normals from shadows under unknown reflectance and lighting conditions",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2009/05459381/12OmNylsZWO",
"parentPublication": {
"id": "proceedings/iccv/2009/4420/0",
"title": "2009 IEEE 12th International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2001/1143/1/00937522",
"title": "New perspectives on geometric reflection theory from rough surfaces",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2001/00937522/12OmNzmclV4",
"parentPublication": {
"id": "proceedings/iccv/2001/1143/1",
"title": "Computer Vision, IEEE International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fones-aiot/2021/1091/0/109100a238",
"title": "The Art of Oil Paintings Creation with Computer Multimedia Technology",
"doi": null,
"abstractUrl": "/proceedings-article/fones-aiot/2021/109100a238/1CKQYQ1Dj9K",
"parentPublication": {
"id": "proceedings/fones-aiot/2021/1091/0",
"title": "2021 International Conference on Forthcoming Networks and Sustainability in AIoT Era (FoNeS-AIoT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNxG1yTG",
"title": "Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "1992",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNwoxSc1",
"doi": "10.1109/CVPR.1992.223149",
"title": "Extracting the shape and roughness of specular lobe objects using four light photometric stereo",
"normalizedTitle": "Extracting the shape and roughness of specular lobe objects using four light photometric stereo",
"abstract": "A noncontact method of measuring surface shape and surface roughness for part inspection is proposed. The method, called four-light photometric stereo, uses four lights that sequentially illuminate the object under inspection, and a video camera that takes images of the object. Conceptually, the problem has three parts: shape extraction, pixel segmentation, and roughness extraction. The shape information is produced directly by three-light and four-light photometric stereo methods. After shape information is obtained, statistical segmentation techniques can be applied to determine which pixels are specular and which are nonspecular. Then the specular pixels and shape information can be used, in conjunction with a simplified Torrance-Sparrow reflectance model, to determine the surface roughness. The method has successfully been applied to a number of synthetic and real objects.<>",
"abstracts": [
{
"abstractType": "Regular",
"content": "A noncontact method of measuring surface shape and surface roughness for part inspection is proposed. The method, called four-light photometric stereo, uses four lights that sequentially illuminate the object under inspection, and a video camera that takes images of the object. Conceptually, the problem has three parts: shape extraction, pixel segmentation, and roughness extraction. The shape information is produced directly by three-light and four-light photometric stereo methods. After shape information is obtained, statistical segmentation techniques can be applied to determine which pixels are specular and which are nonspecular. Then the specular pixels and shape information can be used, in conjunction with a simplified Torrance-Sparrow reflectance model, to determine the surface roughness. The method has successfully been applied to a number of synthetic and real objects.<>",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "A noncontact method of measuring surface shape and surface roughness for part inspection is proposed. The method, called four-light photometric stereo, uses four lights that sequentially illuminate the object under inspection, and a video camera that takes images of the object. Conceptually, the problem has three parts: shape extraction, pixel segmentation, and roughness extraction. The shape information is produced directly by three-light and four-light photometric stereo methods. After shape information is obtained, statistical segmentation techniques can be applied to determine which pixels are specular and which are nonspecular. Then the specular pixels and shape information can be used, in conjunction with a simplified Torrance-Sparrow reflectance model, to determine the surface roughness. The method has successfully been applied to a number of synthetic and real objects.",
"fno": "00223149",
"keywords": [
"Computer Vision",
"Image Recognition",
"Stereo Image Processing",
"Shape Extraction",
"Roughness Extraction",
"Specular Lobe Objects",
"Light Photometric Stereo",
"Noncontact Method",
"Part Inspection",
"Pixel Segmentation",
"Statistical Segmentation",
"Torrance Sparrow Reflectance Model",
"Photometry",
"Rough Surfaces",
"Surface Roughness",
"Shape Measurement",
"Stereo Vision",
"Inspection",
"Reflectivity",
"Data Mining",
"Manufacturing",
"Light Sources"
],
"authors": [
{
"affiliation": "Robot Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA",
"fullName": "F. Solomon",
"givenName": "F.",
"surname": "Solomon",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Robot Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA",
"fullName": "K. Ikeuchi",
"givenName": "K.",
"surname": "Ikeuchi",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "1992-01-01T00:00:00",
"pubType": "proceedings",
"pages": "466,467,468,469,470,471",
"year": "1992",
"issn": "1063-6919",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "00223148",
"articleId": "12OmNwlZu1a",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "00223150",
"articleId": "12OmNzwHvlo",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/pmcvg/1999/0271/0/02710039",
"title": "Generation of Diffuse and Specular Appearance from Photometric Images",
"doi": null,
"abstractUrl": "/proceedings-article/pmcvg/1999/02710039/12OmNA0vnOn",
"parentPublication": {
"id": "proceedings/pmcvg/1999/0271/0",
"title": "Photometric Modeling for Computer Vision and Graphics, IEEE Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iai/2004/8387/0/01300934",
"title": "Imaging and rendering of oil paintings using a multi-band camera",
"doi": null,
"abstractUrl": "/proceedings-article/iai/2004/01300934/12OmNB7tUq4",
"parentPublication": {
"id": "proceedings/iai/2004/8387/0",
"title": "2004 Southwest Symposium on Image Analysis and Interpretation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/1993/3880/0/00341164",
"title": "Roughness and shape of specular lobe surfaces using photometric sampling method",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1993/00341164/12OmNCdBDR7",
"parentPublication": {
"id": "proceedings/cvpr/1993/3880/0",
"title": "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2009/3992/0/05206498",
"title": "A unified model of specular and diffuse reflectance for rough, glossy surfaces",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2009/05206498/12OmNvAiShy",
"parentPublication": {
"id": "proceedings/cvpr/2009/3992/0",
"title": "2009 IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acv/1992/2840/0/00240318",
"title": "Shape recovery methods for visual inspection",
"doi": null,
"abstractUrl": "/proceedings-article/acv/1992/00240318/12OmNxRF6VK",
"parentPublication": {
"id": "proceedings/acv/1992/2840/0",
"title": "Proceedings IEEE Workshop on Applications of Computer Vision",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1992/2720/0/00220132",
"title": "Inspecting specular lobe objects using four light sources",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1992/00220132/12OmNzd7bWl",
"parentPublication": {
"id": "proceedings/robot/1992/2720/0",
"title": "Proceedings 1992 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/1994/6265/1/00576373",
"title": "Interreflections with rough surfaces",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1994/00576373/12OmNzw8jiH",
"parentPublication": {
"id": "proceedings/icpr/1994/6265/1",
"title": "Proceedings of 12th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/1996/04/i0449",
"title": "Extracting the Shape and Roughness of Specular Lobe Objects Using Four Light Photometric Stereo",
"doi": null,
"abstractUrl": "/journal/tp/1996/04/i0449/13rRUx0xPJA",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2011/02/tth2011020122",
"title": "Roughness Perception in Virtual Textures",
"doi": null,
"abstractUrl": "/journal/th/2011/02/tth2011020122/13rRUxYINfp",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/06/09689957",
"title": "Efficient Specular Glints Rendering With Differentiable Regularization",
"doi": null,
"abstractUrl": "/journal/tg/2023/06/09689957/1AlCfIlPhfy",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNwD1pTt",
"title": "2011 14th IEEE International Conference on Computational Science and Engineering",
"acronym": "cse",
"groupId": "1002115",
"volume": "0",
"displayVolume": "0",
"year": "2011",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNqJq4ny",
"doi": "10.1109/CSE.2011.38",
"title": "Detecting and Classifying Umblic Points from Polynomial Fitting Point Cloud",
"normalizedTitle": "Detecting and Classifying Umblic Points from Polynomial Fitting Point Cloud",
"abstract": "The topology of a tensor field is partially defined by its degenerate points, called umbilical points. We proposed a new approach for distinguishing them as trisector and wedge points on polynomial fitting surface of point-sampled models. Firstly, the polynomial fitting surface over neighborhoods of each point is obtained, and then the degenerate points are preliminary detected by building a Hessian matrix of the polynomial fitting. Computing the neighbor principal directions of the detected degenerate points, trisector and wedge points are finally classified according to the behavior of the nearby vector field.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The topology of a tensor field is partially defined by its degenerate points, called umbilical points. We proposed a new approach for distinguishing them as trisector and wedge points on polynomial fitting surface of point-sampled models. Firstly, the polynomial fitting surface over neighborhoods of each point is obtained, and then the degenerate points are preliminary detected by building a Hessian matrix of the polynomial fitting. Computing the neighbor principal directions of the detected degenerate points, trisector and wedge points are finally classified according to the behavior of the nearby vector field.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The topology of a tensor field is partially defined by its degenerate points, called umbilical points. We proposed a new approach for distinguishing them as trisector and wedge points on polynomial fitting surface of point-sampled models. Firstly, the polynomial fitting surface over neighborhoods of each point is obtained, and then the degenerate points are preliminary detected by building a Hessian matrix of the polynomial fitting. Computing the neighbor principal directions of the detected degenerate points, trisector and wedge points are finally classified according to the behavior of the nearby vector field.",
"fno": "4477a155",
"keywords": [
"Umbilical Point",
"Wedge",
"Trisector",
"Degenerate Point",
"Curvature Tensor"
],
"authors": [
{
"affiliation": null,
"fullName": "Xufang Pang",
"givenName": "Xufang",
"surname": "Pang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Zhan Song",
"givenName": "Zhan",
"surname": "Song",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cse",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2011-08-01T00:00:00",
"pubType": "proceedings",
"pages": "155-160",
"year": "2011",
"issn": null,
"isbn": "978-0-7695-4477-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4477a150",
"articleId": "12OmNwswg7m",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4477a161",
"articleId": "12OmNBuL1dX",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/wacv/2013/5053/0/06475013",
"title": "DIRSAC: A directed sampling and consensus approach to quasi-degenerate data fitting",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2013/06475013/12OmNAMtAOH",
"parentPublication": {
"id": "proceedings/wacv/2013/5053/0",
"title": "Applications of Computer Vision, IEEE Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/2004/8788/0/87880313",
"title": "Topological Lines in 3D Tensor Fields",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/2004/87880313/12OmNApLGKA",
"parentPublication": {
"id": "proceedings/ieee-vis/2004/8788/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/1996/7258/0/72580514",
"title": "New, faster, more controlled fitting of implicit polynomial 2D curves and 3D surfaces to data",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1996/72580514/12OmNBBzoiE",
"parentPublication": {
"id": "proceedings/cvpr/1996/7258/0",
"title": "Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmtma/2011/4296/1/4296a028",
"title": "A Curvature-based Automatic Registration Algorithm for the Scattered Points",
"doi": null,
"abstractUrl": "/proceedings-article/icmtma/2011/4296a028/12OmNC1oT4Y",
"parentPublication": {
"id": "proceedings/icmtma/2011/4296/1",
"title": "2011 Third International Conference on Measuring Technology and Mechatronics Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2010/4077/3/4077f028",
"title": "The Varying Rule on Internal Feature Points Strain and Stress of Pieces Rolled with Multi-wedges Cross Wedge Rolling",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2010/4077f028/12OmNrJRP0R",
"parentPublication": {
"id": "proceedings/icicta/2010/4077/3",
"title": "Intelligent Computation Technology and Automation, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iwcse/2009/3881/1/3881a538",
"title": "A Method for Filtering Noise Data by Blending Local Least Squares Fitting Curves",
"doi": null,
"abstractUrl": "/proceedings-article/iwcse/2009/3881a538/12OmNwCJOLL",
"parentPublication": {
"id": "proceedings/iwcse/2009/3881/1",
"title": "Computer Science and Engineering, International Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/2005/2766/0/27660070",
"title": "Topological Structures of 3D Tensor Fields",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/2005/27660070/12OmNxeusY2",
"parentPublication": {
"id": "proceedings/ieee-vis/2005/2766/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/1997/8262/0/82620059",
"title": "Singularities in nonuniform tensor fields",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/1997/82620059/12OmNzFMFiX",
"parentPublication": {
"id": "proceedings/ieee-vis/1997/8262/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/focs/2016/3933/0/3933a751",
"title": "Robust Fourier and Polynomial Curve Fitting",
"doi": null,
"abstractUrl": "/proceedings-article/focs/2016/3933a751/12OmNzayNu3",
"parentPublication": {
"id": "proceedings/focs/2016/3933/0",
"title": "2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2005/04/v0395",
"title": "Topological Lines in 3D Tensor Fields and Discriminant Hessian Factorization",
"doi": null,
"abstractUrl": "/journal/tg/2005/04/v0395/13rRUIM2VGT",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNrAdssG",
"title": "3D Digital Imaging and Modeling, International Conference on",
"acronym": "3dim",
"groupId": "1800494",
"volume": "0",
"displayVolume": "0",
"year": "1999",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxGja3m",
"doi": "10.1109/IM.1999.805360",
"title": "Generating Smooth Surfaces with Bicubic Splines over Triangular Meshes: Toward Automatic Model Building from Unorganized 3D Points",
"normalizedTitle": "Generating Smooth Surfaces with Bicubic Splines over Triangular Meshes: Toward Automatic Model Building from Unorganized 3D Points",
"abstract": "This paper presents a new algorithm for constructing tangent plane continuous (G 1 ) surfaces with piecewise polynomials over triangular meshes. The input mesh can be of arbitrary topological type, that is, any number of faces can meet at a mesh vertex. The mesh is first refined to one solely with quadrilateral cells. Rectangular B?zier patches are then assigned to each of the cells and control points are determined so that G 1 continuity across the patch boundaries is maintained. Since all the patches are rectangular, the resulting surface can be rendered efficiently by current commercial graphic hardware/software. In addition, by exploiting the fact that all the faces of the original mesh are triangular, the degree of each patch is optimized to three while more general method dealing with arbitrary irregular meshes requires biquartic patches. Several surface examples generated from real 3D data are shown.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper presents a new algorithm for constructing tangent plane continuous (G 1 ) surfaces with piecewise polynomials over triangular meshes. The input mesh can be of arbitrary topological type, that is, any number of faces can meet at a mesh vertex. The mesh is first refined to one solely with quadrilateral cells. Rectangular B?zier patches are then assigned to each of the cells and control points are determined so that G 1 continuity across the patch boundaries is maintained. Since all the patches are rectangular, the resulting surface can be rendered efficiently by current commercial graphic hardware/software. In addition, by exploiting the fact that all the faces of the original mesh are triangular, the degree of each patch is optimized to three while more general method dealing with arbitrary irregular meshes requires biquartic patches. Several surface examples generated from real 3D data are shown.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper presents a new algorithm for constructing tangent plane continuous (G 1 ) surfaces with piecewise polynomials over triangular meshes. The input mesh can be of arbitrary topological type, that is, any number of faces can meet at a mesh vertex. The mesh is first refined to one solely with quadrilateral cells. Rectangular B?zier patches are then assigned to each of the cells and control points are determined so that G 1 continuity across the patch boundaries is maintained. Since all the patches are rectangular, the resulting surface can be rendered efficiently by current commercial graphic hardware/software. In addition, by exploiting the fact that all the faces of the original mesh are triangular, the degree of each patch is optimized to three while more general method dealing with arbitrary irregular meshes requires biquartic patches. Several surface examples generated from real 3D data are shown.",
"fno": "00620302",
"keywords": [],
"authors": [
{
"affiliation": null,
"fullName": "Toshio Ueshiba",
"givenName": "Toshio",
"surname": "Ueshiba",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Gerhard Roth",
"givenName": "Gerhard",
"surname": "Roth",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "3dim",
"isOpenAccess": false,
"showRecommendedArticles": false,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "1999-10-01T00:00:00",
"pubType": "proceedings",
"pages": "0302",
"year": "1999",
"issn": null,
"isbn": "0-7695-0062-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "00620290",
"articleId": "12OmNBd9T4P",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "00620312",
"articleId": "12OmNqG0T0t",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNqG0SW5",
"title": "Computer Graphics and Applications, Pacific Conference on",
"acronym": "pg",
"groupId": "1000130",
"volume": "0",
"displayVolume": "0",
"year": "2001",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxvwp1G",
"doi": "10.1109/PCCGA.2001.962869",
"title": "Direct Reconstruction of Displaced Subdivision Surface from Unorganized Points",
"normalizedTitle": "Direct Reconstruction of Displaced Subdivision Surface from Unorganized Points",
"abstract": "In this paper we propose a new mesh reconstruction algorithm that produces a displaced subdivision mesh directly from unorganized points. The displaced subdivision surface is a new mesh representation that defines a detailed mesh with a displacement map over a smooth domain surface. This mesh representation has several benefits - compact mesh size, piecewise regular connectivity - to overcome limitations of irregular mesh produced by ordinary mesh reconstruction scheme, but the original displaced subdivision surface generation algorithm needs an explicit polygonal mesh to be converted. Our approach is producing displaced subdivision surface directly from input points during the mesh reconstruction process. The main ideas of our algorithm are building initial coarse control mesh by the shrink-wrapping like projection and sampling fine surface detail from unorganized points along the each limit vertex normal without any connectivity information of given points. We employ an existing subdivision surface fitting scheme to generate a parametric domain surface, and suggest a surface detail sampling scheme that determines a valid sampling triangle which can be made with combinations of input points. We show several reconstruction examples and applications to show the validity of suggested sampling technique and benefits of the result like multiresolution modeling.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper we propose a new mesh reconstruction algorithm that produces a displaced subdivision mesh directly from unorganized points. The displaced subdivision surface is a new mesh representation that defines a detailed mesh with a displacement map over a smooth domain surface. This mesh representation has several benefits - compact mesh size, piecewise regular connectivity - to overcome limitations of irregular mesh produced by ordinary mesh reconstruction scheme, but the original displaced subdivision surface generation algorithm needs an explicit polygonal mesh to be converted. Our approach is producing displaced subdivision surface directly from input points during the mesh reconstruction process. The main ideas of our algorithm are building initial coarse control mesh by the shrink-wrapping like projection and sampling fine surface detail from unorganized points along the each limit vertex normal without any connectivity information of given points. We employ an existing subdivision surface fitting scheme to generate a parametric domain surface, and suggest a surface detail sampling scheme that determines a valid sampling triangle which can be made with combinations of input points. We show several reconstruction examples and applications to show the validity of suggested sampling technique and benefits of the result like multiresolution modeling.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper we propose a new mesh reconstruction algorithm that produces a displaced subdivision mesh directly from unorganized points. The displaced subdivision surface is a new mesh representation that defines a detailed mesh with a displacement map over a smooth domain surface. This mesh representation has several benefits - compact mesh size, piecewise regular connectivity - to overcome limitations of irregular mesh produced by ordinary mesh reconstruction scheme, but the original displaced subdivision surface generation algorithm needs an explicit polygonal mesh to be converted. Our approach is producing displaced subdivision surface directly from input points during the mesh reconstruction process. The main ideas of our algorithm are building initial coarse control mesh by the shrink-wrapping like projection and sampling fine surface detail from unorganized points along the each limit vertex normal without any connectivity information of given points. We employ an existing subdivision surface fitting scheme to generate a parametric domain surface, and suggest a surface detail sampling scheme that determines a valid sampling triangle which can be made with combinations of input points. We show several reconstruction examples and applications to show the validity of suggested sampling technique and benefits of the result like multiresolution modeling.",
"fno": "12270160",
"keywords": [
"Mesh Reconstruction",
"Subdivision"
],
"authors": [
{
"affiliation": "Max-Planck-Institut fuer Informatik",
"fullName": "Won Ki Jeong",
"givenName": "Won Ki",
"surname": "Jeong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Korea University",
"fullName": "Chang Hun Kim",
"givenName": "Chang Hun",
"surname": "Kim",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "pg",
"isOpenAccess": false,
"showRecommendedArticles": false,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2001-10-01T00:00:00",
"pubType": "proceedings",
"pages": "0160",
"year": "2001",
"issn": null,
"isbn": "0-7695-1227-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "12270150",
"articleId": "12OmNvSbBqf",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "12270169",
"articleId": "12OmNBf94Yd",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNrAMEOd",
"title": "2016 International Conference on Computational Science and Computational Intelligence (CSCI)",
"acronym": "csci",
"groupId": "1803739",
"volume": "0",
"displayVolume": "0",
"year": "2016",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzXnNDD",
"doi": "10.1109/CSCI.2016.0132",
"title": "Automatic Detection of Characteristic Viscosity Points in Mineralogical Samples",
"normalizedTitle": "Automatic Detection of Characteristic Viscosity Points in Mineralogical Samples",
"abstract": "Hot stage microscopy (HSM) is a suitable technique for studying the behavior of mineralogical samples (such as basalt, rocks and glasses) viscosity in relation to temperature. HSM researches observe and analyze images of samples recorded during heating. This paper presents the development of a customized software, which uses digital image processing techniques to automatically detect characteristic viscosity points (CVP) based on a series of images that depict the evolution of the sample as it melts over time. This tool was developed to help determine the temperatures corresponding to CVP together with the HSM technique.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Hot stage microscopy (HSM) is a suitable technique for studying the behavior of mineralogical samples (such as basalt, rocks and glasses) viscosity in relation to temperature. HSM researches observe and analyze images of samples recorded during heating. This paper presents the development of a customized software, which uses digital image processing techniques to automatically detect characteristic viscosity points (CVP) based on a series of images that depict the evolution of the sample as it melts over time. This tool was developed to help determine the temperatures corresponding to CVP together with the HSM technique.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Hot stage microscopy (HSM) is a suitable technique for studying the behavior of mineralogical samples (such as basalt, rocks and glasses) viscosity in relation to temperature. HSM researches observe and analyze images of samples recorded during heating. This paper presents the development of a customized software, which uses digital image processing techniques to automatically detect characteristic viscosity points (CVP) based on a series of images that depict the evolution of the sample as it melts over time. This tool was developed to help determine the temperatures corresponding to CVP together with the HSM technique.",
"fno": "07881425",
"keywords": [
"Geophysical Image Processing",
"Microscopy",
"Minerals",
"Object Detection",
"Viscosity",
"Characteristic Viscosity Points Detection",
"Mineralogical Samples",
"Hot Stage Microscopy",
"HSM Technique",
"Image Analysis",
"Digital Image Processing Techniques",
"CVP Detection",
"Software",
"Microscopy",
"Softening",
"Image Segmentation",
"Glass",
"Heating Systems",
"Shape",
"Software Tools For Imaging",
"Image Analysis",
"Image Geometry",
"Hot Stage Microscopy"
],
"authors": [
{
"affiliation": "Dept. de Cienc. Computacionales, Univ. de Monterrey, San Pedro Garza Garcıa, Mexico",
"fullName": "Irving Alberto Cruz Matías",
"givenName": "Irving Alberto",
"surname": "Cruz Matías",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "csci",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2016-12-01T00:00:00",
"pubType": "proceedings",
"pages": "671-676",
"year": "2016",
"issn": null,
"isbn": "978-1-5090-5510-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "07881424",
"articleId": "12OmNvAAtyW",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07881426",
"articleId": "12OmNzd7blx",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNynsbx6",
"title": "Multi-Media Modeling Conference, International",
"acronym": "mmm",
"groupId": "1000481",
"volume": "0",
"displayVolume": "0",
"year": "2004",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzd7bUO",
"doi": "10.1109/MULMM.2004.1264985",
"title": "Decomposing Polygon Meshes by Means of Critical Points",
"normalizedTitle": "Decomposing Polygon Meshes by Means of Critical Points",
"abstract": "Polygon mesh is among the most common data structures used for representing objects in computer graphics. Unfortunately, a polygon mesh does not capture high-level structures, unlike a hierarchical model. In general, high-level abstractions are useful for managing data in applications. In this paper, we present a method for decomposing an object represented in polygon meshes into components by means of critical points. The method consists of steps to define the root vertex of the object, define a function on the polygon meshes, compute the geodesic tree and critical points, decide the decomposition order, and extract components using backwards flooding. We have implemented the method. The preliminary results show that it works effectively and efficiently. The decomposition results can be useful for applications such as 3D model retrieval and morphing.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Polygon mesh is among the most common data structures used for representing objects in computer graphics. Unfortunately, a polygon mesh does not capture high-level structures, unlike a hierarchical model. In general, high-level abstractions are useful for managing data in applications. In this paper, we present a method for decomposing an object represented in polygon meshes into components by means of critical points. The method consists of steps to define the root vertex of the object, define a function on the polygon meshes, compute the geodesic tree and critical points, decide the decomposition order, and extract components using backwards flooding. We have implemented the method. The preliminary results show that it works effectively and efficiently. The decomposition results can be useful for applications such as 3D model retrieval and morphing.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Polygon mesh is among the most common data structures used for representing objects in computer graphics. Unfortunately, a polygon mesh does not capture high-level structures, unlike a hierarchical model. In general, high-level abstractions are useful for managing data in applications. In this paper, we present a method for decomposing an object represented in polygon meshes into components by means of critical points. The method consists of steps to define the root vertex of the object, define a function on the polygon meshes, compute the geodesic tree and critical points, decide the decomposition order, and extract components using backwards flooding. We have implemented the method. The preliminary results show that it works effectively and efficiently. The decomposition results can be useful for applications such as 3D model retrieval and morphing.",
"fno": "20840187",
"keywords": [],
"authors": [
{
"affiliation": "National University of Singapore",
"fullName": "Yinan Zhou",
"givenName": "Yinan",
"surname": "Zhou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "National University of Singapore",
"fullName": "Zhiyong Huang",
"givenName": "Zhiyong",
"surname": "Huang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "mmm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2004-01-01T00:00:00",
"pubType": "proceedings",
"pages": "187",
"year": "2004",
"issn": null,
"isbn": "0-7695-2084-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "20840181",
"articleId": "12OmNzZmZv5",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "20840196",
"articleId": "12OmNrJAegS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/vv/1998/9180/0/91800119",
"title": "An Accurate Method for Voxelizing Polygon Meshes",
"doi": null,
"abstractUrl": "/proceedings-article/vv/1998/91800119/12OmNAYXWCF",
"parentPublication": {
"id": "proceedings/vv/1998/9180/0",
"title": "Volume Visualization and Graphics, IEEE Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2008/2570/0/04607747",
"title": "Hierarchical mesh decomposition and motion tracking for Time-Varying-Meshes",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2008/04607747/12OmNBO3K2L",
"parentPublication": {
"id": "proceedings/icme/2008/2570/0",
"title": "2008 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pg/2001/1227/0/12270004",
"title": "Compressing the Property Mapping of Polygon Meshes",
"doi": null,
"abstractUrl": "/proceedings-article/pg/2001/12270004/12OmNBeRtRc",
"parentPublication": {
"id": "proceedings/pg/2001/1227/0",
"title": "Computer Graphics and Applications, Pacific Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/gmp/2004/2078/0/20780143",
"title": "Segmentation and Parametrization of Arbitrary Polygon Meshes",
"doi": null,
"abstractUrl": "/proceedings-article/gmp/2004/20780143/12OmNBqv2h5",
"parentPublication": {
"id": "proceedings/gmp/2004/2078/0",
"title": "Geometric Modeling and Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmip/2017/5954/0/5954a315",
"title": "A Polygon Meshes Compression Algorithm Based on ASCII and Interval Coding",
"doi": null,
"abstractUrl": "/proceedings-article/icmip/2017/5954a315/12OmNCbU395",
"parentPublication": {
"id": "proceedings/icmip/2017/5954/0",
"title": "2017 2nd International Conference on Multimedia and Image Processing (ICMIP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/2002/7498/0/7498isenburg",
"title": "Compressing Polygon Mesh Geometry with Parallelogram Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/2002/7498isenburg/12OmNroijmR",
"parentPublication": {
"id": "proceedings/ieee-vis/2002/7498/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/smi/2003/1845/0/18450045",
"title": "New Results in Signal Processing and Compression of Polygon Meshes",
"doi": null,
"abstractUrl": "/proceedings-article/smi/2003/18450045/12OmNwwd2UD",
"parentPublication": {
"id": "proceedings/smi/2003/1845/0",
"title": "Shape Modeling and Applications, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/2005/2766/0/01532800",
"title": "Streaming meshes",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/2005/01532800/12OmNxRWIap",
"parentPublication": {
"id": "proceedings/ieee-vis/2005/2766/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/2005/2766/0/27660030",
"title": "Streaming Meshes",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/2005/27660030/12OmNxwENCm",
"parentPublication": {
"id": "proceedings/ieee-vis/2005/2766/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900a546",
"title": "Body Meshes as Points",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900a546/1yeHUobJhZK",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1LAyWN449uo",
"title": "2022 Tenth International Symposium on Computing and Networking Workshops (CANDARW)",
"acronym": "candarw",
"groupId": "10062380",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1LAz1N5rnva",
"doi": "10.1109/CANDARW57323.2022.00025",
"title": "3D Mesh Generation from a Defective Point Cloud using Style Transformation",
"normalizedTitle": "3D Mesh Generation from a Defective Point Cloud using Style Transformation",
"abstract": "In this paper, we propose a method to generate a 3D mesh by inpainting a point cloud having defective points. Our method learns to transfer the style of a defect-free point cloud for a particular object category to a mesh for that object. Then our method applies style transfer from an input point cloud with defective points to a defect-free mesh. Different from previous methods for point cloud inpainting, our method does not directly learn inpainting of defective parts by using set of defective and defect-free point clouds. In our method, we combine an autoencoder and style transformation. We build a 3D mesh generation model from a point cloud by incorporating an inpainting module that inpaints defective parts to the autoencoder. The inpainting module is designed to convert features of defective point clouds to features of defect-free point clouds. Experimental results show that our method can create a 3D model that completes the missing or defective parts of a point cloud.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we propose a method to generate a 3D mesh by inpainting a point cloud having defective points. Our method learns to transfer the style of a defect-free point cloud for a particular object category to a mesh for that object. Then our method applies style transfer from an input point cloud with defective points to a defect-free mesh. Different from previous methods for point cloud inpainting, our method does not directly learn inpainting of defective parts by using set of defective and defect-free point clouds. In our method, we combine an autoencoder and style transformation. We build a 3D mesh generation model from a point cloud by incorporating an inpainting module that inpaints defective parts to the autoencoder. The inpainting module is designed to convert features of defective point clouds to features of defect-free point clouds. Experimental results show that our method can create a 3D model that completes the missing or defective parts of a point cloud.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we propose a method to generate a 3D mesh by inpainting a point cloud having defective points. Our method learns to transfer the style of a defect-free point cloud for a particular object category to a mesh for that object. Then our method applies style transfer from an input point cloud with defective points to a defect-free mesh. Different from previous methods for point cloud inpainting, our method does not directly learn inpainting of defective parts by using set of defective and defect-free point clouds. In our method, we combine an autoencoder and style transformation. We build a 3D mesh generation model from a point cloud by incorporating an inpainting module that inpaints defective parts to the autoencoder. The inpainting module is designed to convert features of defective point clouds to features of defect-free point clouds. Experimental results show that our method can create a 3D model that completes the missing or defective parts of a point cloud.",
"fno": "753200a218",
"keywords": [
"Feature Extraction",
"Image Restoration",
"Mesh Generation",
"Solid Modelling",
"Defect Free Mesh",
"Defect Free Point Cloud",
"Defective Parts",
"Defective Point Cloud",
"Defective Points",
"Inpainting Module",
"Input Point Cloud",
"Missing Parts",
"Point Cloud Inpainting",
"Style Transformation",
"Point Cloud Compression",
"Training",
"Solid Modeling",
"Three Dimensional Displays",
"Conferences",
"Computational Modeling",
"Sensors",
"Mesh Generation",
"Point Cloud",
"Inpainting"
],
"authors": [
{
"affiliation": "Graduate School of Information Science and Technology Osaka University,Suita,Japan",
"fullName": "Kenshiro Tamata",
"givenName": "Kenshiro",
"surname": "Tamata",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Cybermedia Center Osaka University,Suita,Japan",
"fullName": "Tomohiro Mashita",
"givenName": "Tomohiro",
"surname": "Mashita",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "candarw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-11-01T00:00:00",
"pubType": "proceedings",
"pages": "218-223",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-7532-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "753200a212",
"articleId": "1LAz5LNHoRO",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "753200a224",
"articleId": "1LAz5RaESru",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "trans/tp/2023/02/09740525",
"title": "Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling",
"doi": null,
"abstractUrl": "/journal/tp/2023/02/09740525/1BYEd6sg5tC",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200k0458",
"title": "Point-set Distances for Learning Representations of 3D Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200k0458/1BmIlciDLLW",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200k0695",
"title": "Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200k0695/1BmJRYVvF6g",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200h588",
"title": "Robustness Certification for Point Cloud Models",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200h588/1BmJT3ZnWpi",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f479",
"title": "SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f479/1BmL45zCYda",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aiam/2021/1732/0/173200a237",
"title": "Path Planning of Arc Additive Remanufacturing for Defective Castings",
"doi": null,
"abstractUrl": "/proceedings-article/aiam/2021/173200a237/1BzTMK8pxoQ",
"parentPublication": {
"id": "proceedings/aiam/2021/1732/0",
"title": "2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600p5314",
"title": "Shape-invariant 3D Adversarial Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600p5314/1H1kgWOeU2Q",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600t9291",
"title": "Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600t9291/1H1mPsA11XW",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bdicn/2023/2136/0/213600a317",
"title": "3D reconstruction of PCB based on ICP point cloud optimization",
"doi": null,
"abstractUrl": "/proceedings-article/bdicn/2023/213600a317/1MIhrt8WBBC",
"parentPublication": {
"id": "proceedings/bdicn/2023/2136/0",
"title": "2023 2nd International Conference on Big Data, Information and Computer Network (BDICN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icis-fall/2021/7679/0/09627366",
"title": "Point Cloud Foot Model Extraction Algorithm for 3D Foot Model Scanner",
"doi": null,
"abstractUrl": "/proceedings-article/icis-fall/2021/09627366/1z7dN6BF5Zu",
"parentPublication": {
"id": "proceedings/icis-fall/2021/7679/0",
"title": "2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNAkWva5",
"title": "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "1",
"displayVolume": "1",
"year": "2006",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNwpoFKW",
"doi": "10.1109/CVPR.2006.233",
"title": "Quantitative Evaluation of Near Regular Texture Synthesis Algorithms",
"normalizedTitle": "Quantitative Evaluation of Near Regular Texture Synthesis Algorithms",
"abstract": "Near regular textures are pervasive in man-made and natural world. Their global regularity and local randomness pose new difficulties to the state of the art texture analysis and synthesis algorithms. We carry out a systematic comparison study on the performance of four texture synthesis algorithms on near-regular textures. Our results confirm that faithful near-regular texture synthesis remains a challenging problem for the state of the art general purpose texture synthesis algorithms. In addition, we provide comparison of human perception with computer evaluations on the quality of the texture synthesis results.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Near regular textures are pervasive in man-made and natural world. Their global regularity and local randomness pose new difficulties to the state of the art texture analysis and synthesis algorithms. We carry out a systematic comparison study on the performance of four texture synthesis algorithms on near-regular textures. Our results confirm that faithful near-regular texture synthesis remains a challenging problem for the state of the art general purpose texture synthesis algorithms. In addition, we provide comparison of human perception with computer evaluations on the quality of the texture synthesis results.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Near regular textures are pervasive in man-made and natural world. Their global regularity and local randomness pose new difficulties to the state of the art texture analysis and synthesis algorithms. We carry out a systematic comparison study on the performance of four texture synthesis algorithms on near-regular textures. Our results confirm that faithful near-regular texture synthesis remains a challenging problem for the state of the art general purpose texture synthesis algorithms. In addition, we provide comparison of human perception with computer evaluations on the quality of the texture synthesis results.",
"fno": "259710427",
"keywords": [],
"authors": [
{
"affiliation": "National Chiao-Tung University",
"fullName": "Wen-Chieh Lin",
"givenName": "Wen-Chieh",
"surname": "Lin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Carnegie Mellon University",
"fullName": "James Hays",
"givenName": "James",
"surname": "Hays",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Carnegie Mellon University",
"fullName": "Chenyu Wu",
"givenName": "Chenyu",
"surname": "Wu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Carnegie Mellon University",
"fullName": "Yanxi Liu",
"givenName": "Yanxi",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of North Carolina at Chapel Hill",
"fullName": "Vivek Kwatra",
"givenName": "Vivek",
"surname": "Kwatra",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2006-06-01T00:00:00",
"pubType": "proceedings",
"pages": "427-434",
"year": "2006",
"issn": "1063-6919",
"isbn": "0-7695-2597-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "259710419",
"articleId": "12OmNzTH0Hj",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "259710435",
"articleId": "12OmNzYeB3c",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iitsi/2010/4020/0/4020a483",
"title": "Three-Dimensional Multiscale Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/iitsi/2010/4020a483/12OmNBOll4X",
"parentPublication": {
"id": "proceedings/iitsi/2010/4020/0",
"title": "Intelligent Information Technology and Security Informatics, International Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2015/6964/0/07298817",
"title": "Texture representations for image and video synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2015/07298817/12OmNvoFjUu",
"parentPublication": {
"id": "proceedings/cvpr/2015/6964/0",
"title": "2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgiv/2009/3789/0/3789a018",
"title": "Skeletal Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cgiv/2009/3789a018/12OmNwIHopo",
"parentPublication": {
"id": "proceedings/cgiv/2009/3789/0",
"title": "2009 Sixth International Conference on Computer Graphics, Imaging and Visualization",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvrv/2014/6854/0/6854a018",
"title": "K-Coherence Terrain Texture Upscaling Using Gabor Wavelets",
"doi": null,
"abstractUrl": "/proceedings-article/icvrv/2014/6854a018/12OmNx6PiBV",
"parentPublication": {
"id": "proceedings/icvrv/2014/6854/0",
"title": "2014 International Conference on Virtual Reality and Visualization (ICVRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/imsccs/2006/2581/1/25810766",
"title": "Structural Pattern Analysis for Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/imsccs/2006/25810766/12OmNxvO086",
"parentPublication": {
"id": "proceedings/imsccs/2006/2581/1",
"title": "Computer and Computational Sciences, International Multi-Symposiums on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2008/2174/0/04761562",
"title": "Regular texture removal for video stabilization",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2008/04761562/12OmNy87QAu",
"parentPublication": {
"id": "proceedings/icpr/2008/2174/0",
"title": "ICPR 2008 19th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgiv/2013/5051/0/5051a050",
"title": "Texture Synthesis Approach Using Cooperative Features",
"doi": null,
"abstractUrl": "/proceedings-article/cgiv/2013/5051a050/12OmNyKJip8",
"parentPublication": {
"id": "proceedings/cgiv/2013/5051/0",
"title": "2013 10th International Conference Computer Graphics, Imaging and Visualization (CGIV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cmsp/2011/4356/1/4356a266",
"title": "Texel Based Regular and Near-Regular Texture Characterization",
"doi": null,
"abstractUrl": "/proceedings-article/cmsp/2011/4356a266/12OmNyuyadX",
"parentPublication": {
"id": "proceedings/cmsp/2011/4356/1",
"title": "Multimedia and Signal Processing, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2010/4109/0/4109c114",
"title": "Near-Regular BTF Texture Model",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2010/4109c114/12OmNzkuKBD",
"parentPublication": {
"id": "proceedings/icpr/2010/4109/0",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2010/04/ttp2010040733",
"title": "Texture Synthesis with Grouplets",
"doi": null,
"abstractUrl": "/journal/tp/2010/04/ttp2010040733/13rRUx0xQ0D",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNBzRNs8",
"title": "XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)",
"acronym": "sibgrapi",
"groupId": "1000131",
"volume": "0",
"displayVolume": "0",
"year": "2005",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNwtn3Df",
"doi": "10.1109/SIBGRAPI.2005.37",
"title": "Patch-Based Texture Synthesis Using Wavelets",
"normalizedTitle": "Patch-Based Texture Synthesis Using Wavelets",
"abstract": "Patch-based texture synthesis builds a texture by joining together blocks of pixels — patches — of the original sample. Usually the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel-to-pixel difference, disregarding relevant image structures — such as edges — that are relevant in the human visual system and therefore on synthesis of new textures. We present a wavelet-based approach for selecting patches for patch-based texture synthesis. For each possible patch we compute the wavelet coefficients for the boundary region and pick the patch with the smallest error computed from the wavelet coefficients. We show that the use of wavelets as metric for selection of the best patches improves texture synthesis for samples which previous work fails, mainly textures with prominent aligned features.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Patch-based texture synthesis builds a texture by joining together blocks of pixels — patches — of the original sample. Usually the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel-to-pixel difference, disregarding relevant image structures — such as edges — that are relevant in the human visual system and therefore on synthesis of new textures. We present a wavelet-based approach for selecting patches for patch-based texture synthesis. For each possible patch we compute the wavelet coefficients for the boundary region and pick the patch with the smallest error computed from the wavelet coefficients. We show that the use of wavelets as metric for selection of the best patches improves texture synthesis for samples which previous work fails, mainly textures with prominent aligned features.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Patch-based texture synthesis builds a texture by joining together blocks of pixels — patches — of the original sample. Usually the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel-to-pixel difference, disregarding relevant image structures — such as edges — that are relevant in the human visual system and therefore on synthesis of new textures. We present a wavelet-based approach for selecting patches for patch-based texture synthesis. For each possible patch we compute the wavelet coefficients for the boundary region and pick the patch with the smallest error computed from the wavelet coefficients. We show that the use of wavelets as metric for selection of the best patches improves texture synthesis for samples which previous work fails, mainly textures with prominent aligned features.",
"fno": "23890383",
"keywords": [],
"authors": [
{
"affiliation": "unisinos",
"fullName": "Leandro Tonietto",
"givenName": "Leandro",
"surname": "Tonietto",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "unisinos",
"fullName": "Marcelo Walter",
"givenName": "Marcelo",
"surname": "Walter",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "unisinos",
"fullName": "Cláudio Rosito Jung",
"givenName": "Cláudio Rosito",
"surname": "Jung",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "sibgrapi",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2005-10-01T00:00:00",
"pubType": "proceedings",
"pages": "383-389",
"year": "2005",
"issn": "1530-1834",
"isbn": "0-7695-2389-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "23890375",
"articleId": "12OmNyoAAcs",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "23890390",
"articleId": "12OmNALlcgy",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cad-cg/2005/2473/0/01604682",
"title": "Optimal sample patches selection for tile-based texture synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cad-cg/2005/01604682/12OmNAio72k",
"parentPublication": {
"id": "proceedings/cad-cg/2005/2473/0",
"title": "Ninth International Conference on Computer Aided Design and Computer Graphics (CAD-CG'05)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccsit/2009/4519/0/05234981",
"title": "Improved graph cuts for patch-based texture synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/iccsit/2009/05234981/12OmNBBQZtI",
"parentPublication": {
"id": "proceedings/iccsit/2009/4519/0",
"title": "Computer Science and Information Technology, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iitaw/2008/3505/0/3505a491",
"title": "Fast Texture Synthesis Using Wavelet Coefficient Fitting",
"doi": null,
"abstractUrl": "/proceedings-article/iitaw/2008/3505a491/12OmNBO3JWF",
"parentPublication": {
"id": "proceedings/iitaw/2008/3505/0",
"title": "2008 International Symposium on Intelligent Information Technology Application Workshops",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icccnt/2017/3038/0/08204010",
"title": "Patch-based texture synthesis using differential evolution",
"doi": null,
"abstractUrl": "/proceedings-article/icccnt/2017/08204010/12OmNBubOOd",
"parentPublication": {
"id": "proceedings/icccnt/2017/3038/0",
"title": "2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgiv/2009/3789/0/3789a018",
"title": "Skeletal Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cgiv/2009/3789a018/12OmNwIHopo",
"parentPublication": {
"id": "proceedings/cgiv/2009/3789/0",
"title": "2009 Sixth International Conference on Computer Graphics, Imaging and Visualization",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icis-comsar/2006/2613/0/26130333",
"title": "Irregular Patch for Image Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/icis-comsar/2006/26130333/12OmNx6g6ds",
"parentPublication": {
"id": "proceedings/icis-comsar/2006/2613/0",
"title": "Computer and Information Science, 5th IEEE/ACIS International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cisp/2008/3119/4/3119d441",
"title": "Triangular-Patch Based Texture Synthesis over Arbitrary Surfaces",
"doi": null,
"abstractUrl": "/proceedings-article/cisp/2008/3119d441/12OmNxcMSeN",
"parentPublication": {
"id": "proceedings/cisp/2008/3119/4",
"title": "Image and Signal Processing, Congress on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icassp/2004/8484/3/01326515",
"title": "Content based blurring coding artifact reduction using patch-based texture synthesis [image/video coding applications]",
"doi": null,
"abstractUrl": "/proceedings-article/icassp/2004/01326515/12OmNxdVgJE",
"parentPublication": {
"id": "proceedings/icassp/2004/8484/3",
"title": "2004 IEEE International Conference on Acoustics, Speech, and Signal Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgi/2004/2171/0/21710582",
"title": "Fast and High Quality Overlap Repair for Patch-Based Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cgi/2004/21710582/12OmNzE54Fm",
"parentPublication": {
"id": "proceedings/cgi/2004/2171/0",
"title": "Proceedings. Computer Graphics International",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2004/8603/1/01394272",
"title": "Constrained texture synthesis by scalable sub-patch algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2004/01394272/12OmNzvQI1j",
"parentPublication": {
"id": "proceedings/icme/2004/8603/1",
"title": "2004 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNqBtiEQ",
"title": "Proceedings ICIAP 2001. 11th International Conference on Image Analysis and Processing",
"acronym": "iciap",
"groupId": "1000346",
"volume": "0",
"displayVolume": "0",
"year": "2001",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxWLThP",
"doi": "10.1109/ICIAP.2001.957016",
"title": "Texture Synthesis Using Image Pyramids and Self-Organizing Maps",
"normalizedTitle": "Texture Synthesis Using Image Pyramids and Self-Organizing Maps",
"abstract": "Abstract: Markov Random Field is a very known model for the generation of textures. However, the estimation of its parameter results quite difficult in many cases. In this article a new algorithm for synthesis of textures is proposed, based on mage pyramids and self-organizing maps. This procedure avoids the explicit computation of its parameters. Preliminary results support the appropriateness of this new approach.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Abstract: Markov Random Field is a very known model for the generation of textures. However, the estimation of its parameter results quite difficult in many cases. In this article a new algorithm for synthesis of textures is proposed, based on mage pyramids and self-organizing maps. This procedure avoids the explicit computation of its parameters. Preliminary results support the appropriateness of this new approach.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Abstract: Markov Random Field is a very known model for the generation of textures. However, the estimation of its parameter results quite difficult in many cases. In this article a new algorithm for synthesis of textures is proposed, based on mage pyramids and self-organizing maps. This procedure avoids the explicit computation of its parameters. Preliminary results support the appropriateness of this new approach.",
"fno": "11830244",
"keywords": [],
"authors": [
{
"affiliation": "Universidad de Chile",
"fullName": "Patricio Parada",
"givenName": "Patricio",
"surname": "Parada",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Universidad de Chile",
"fullName": "Javier Ruiz-del-Solar",
"givenName": "Javier",
"surname": "Ruiz-del-Solar",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iciap",
"isOpenAccess": false,
"showRecommendedArticles": false,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2001-09-01T00:00:00",
"pubType": "proceedings",
"pages": "0244",
"year": "2001",
"issn": null,
"isbn": "0-7695-1183-X",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "11830236",
"articleId": "12OmNzC5SBD",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "11830250",
"articleId": "12OmNzvQHKL",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNyPQ4Eo",
"title": "Computer and Computational Sciences, International Multi-Symposiums on",
"acronym": "imsccs",
"groupId": "1001647",
"volume": "1",
"displayVolume": "1",
"year": "2006",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxvO086",
"doi": "10.1109/IMSCCS.2006.129",
"title": "Structural Pattern Analysis for Texture Synthesis",
"normalizedTitle": "Structural Pattern Analysis for Texture Synthesis",
"abstract": "Sample based texture synthesis allows the generation of arbitrarily sized textures based on a small sample image. One main drawback of the technique is that a new pixel or patch is selected only considering local information in the generated texture. Thus structural error can't assure to be avoided for structural textures. To accelerate texture synthesis process and to avoid structural mismatch in the output picture, we present in this paper a structural pattern analysis (SPA) algorithm and a texture synthesis method using the algorithm. The pattern analysis tool also quantizes the texture randomness in macro scope view. It can be used in other applications such as automatic texture classification and quality evaluation of synthesized texture. We illustrate our result with some examples.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Sample based texture synthesis allows the generation of arbitrarily sized textures based on a small sample image. One main drawback of the technique is that a new pixel or patch is selected only considering local information in the generated texture. Thus structural error can't assure to be avoided for structural textures. To accelerate texture synthesis process and to avoid structural mismatch in the output picture, we present in this paper a structural pattern analysis (SPA) algorithm and a texture synthesis method using the algorithm. The pattern analysis tool also quantizes the texture randomness in macro scope view. It can be used in other applications such as automatic texture classification and quality evaluation of synthesized texture. We illustrate our result with some examples.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Sample based texture synthesis allows the generation of arbitrarily sized textures based on a small sample image. One main drawback of the technique is that a new pixel or patch is selected only considering local information in the generated texture. Thus structural error can't assure to be avoided for structural textures. To accelerate texture synthesis process and to avoid structural mismatch in the output picture, we present in this paper a structural pattern analysis (SPA) algorithm and a texture synthesis method using the algorithm. The pattern analysis tool also quantizes the texture randomness in macro scope view. It can be used in other applications such as automatic texture classification and quality evaluation of synthesized texture. We illustrate our result with some examples.",
"fno": "25810766",
"keywords": [],
"authors": [
{
"affiliation": "University of Science and Technology of China",
"fullName": "Yizhe Li",
"givenName": "Yizhe",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Science and Technology of China",
"fullName": "Guoliang Chen",
"givenName": "Guoliang",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Science and Technology of China",
"fullName": "Qilong Zheng",
"givenName": "Qilong",
"surname": "Zheng",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "imsccs",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2006-06-01T00:00:00",
"pubType": "proceedings",
"pages": "766-771",
"year": "2006",
"issn": null,
"isbn": "0-7695-2581-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "25810759",
"articleId": "12OmNwDACfn",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "25810772",
"articleId": "12OmNC4O4Az",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/sibgrapi/2002/1846/0/18460252",
"title": "Towards Local Control for Image-Based Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/2002/18460252/12OmNB836Ko",
"parentPublication": {
"id": "proceedings/sibgrapi/2002/1846/0",
"title": "Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/1988/0878/0/00028168",
"title": "A unified structural-stochastic model for texture analysis and synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1988/00028168/12OmNCesr0k",
"parentPublication": {
"id": "proceedings/icpr/1988/0878/0",
"title": "9th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2015/6964/0/07298817",
"title": "Texture representations for image and video synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2015/07298817/12OmNvoFjUu",
"parentPublication": {
"id": "proceedings/cvpr/2015/6964/0",
"title": "2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgiv/2009/3789/0/3789a018",
"title": "Skeletal Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cgiv/2009/3789a018/12OmNwIHopo",
"parentPublication": {
"id": "proceedings/cgiv/2009/3789/0",
"title": "2009 Sixth International Conference on Computer Graphics, Imaging and Visualization",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2006/2597/1/259710427",
"title": "Quantitative Evaluation of Near Regular Texture Synthesis Algorithms",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2006/259710427/12OmNwpoFKW",
"parentPublication": {
"id": "proceedings/cvpr/2006/2597/2",
"title": "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sibgrapi/1999/0481/0/04810207",
"title": "Deterministic Texture Analysis and Synthesis Using Tree Structure Vector Quantization",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/1999/04810207/12OmNxHJ9qS",
"parentPublication": {
"id": "proceedings/sibgrapi/1999/0481/0",
"title": "XII Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00481)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icassp/1999/5041/3/00756238",
"title": "Color texture synthesis with 2-D moving average model",
"doi": null,
"abstractUrl": "/proceedings-article/icassp/1999/00756238/12OmNy5zsxB",
"parentPublication": {
"id": "proceedings/icassp/1999/5041/3",
"title": "Acoustics, Speech, and Signal Processing, IEEE International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgiv/2013/5051/0/5051a050",
"title": "Texture Synthesis Approach Using Cooperative Features",
"doi": null,
"abstractUrl": "/proceedings-article/cgiv/2013/5051a050/12OmNyKJip8",
"parentPublication": {
"id": "proceedings/cgiv/2013/5051/0",
"title": "2013 10th International Conference Computer Graphics, Imaging and Visualization (CGIV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2002/1695/1/169510239",
"title": "Multiresolution Block Sampling-Based Method for Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2002/169510239/12OmNz61cXZ",
"parentPublication": {
"id": "proceedings/icpr/2002/1695/1",
"title": "Proceedings of 16th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2010/04/ttp2010040733",
"title": "Texture Synthesis with Grouplets",
"doi": null,
"abstractUrl": "/journal/tp/2010/04/ttp2010040733/13rRUx0xQ0D",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNrIJqqu",
"title": "2013 10th International Conference Computer Graphics, Imaging and Visualization (CGIV)",
"acronym": "cgiv",
"groupId": "1001775",
"volume": "0",
"displayVolume": "0",
"year": "2013",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNyKJip8",
"doi": "10.1109/CGIV.2013.19",
"title": "Texture Synthesis Approach Using Cooperative Features",
"normalizedTitle": "Texture Synthesis Approach Using Cooperative Features",
"abstract": "In recent years, a lot of 2D textures have been synthesized from input 2D textures. However, the quality problems still exist for many textures. Further improvements are required to extract more reliable texture features. In this paper, we present a texture synthesis approach using cooperative color and grey-level features. For color feature extraction, we extract appearance vectors to replace RGB color values. For grey-level feature extraction, we extract the statistical features including entropy, contrast, and correlation based on the grey level co-occurrence probabilities (GLCPs). Moreover, we introduce cooperative color and GLCP features for neighborhood matching in the synthesis process. We assign different weights for color and grey-level features according to the characteristics of the input texture. The results show that the proposed approach performs well in terms of the synthesis quality.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In recent years, a lot of 2D textures have been synthesized from input 2D textures. However, the quality problems still exist for many textures. Further improvements are required to extract more reliable texture features. In this paper, we present a texture synthesis approach using cooperative color and grey-level features. For color feature extraction, we extract appearance vectors to replace RGB color values. For grey-level feature extraction, we extract the statistical features including entropy, contrast, and correlation based on the grey level co-occurrence probabilities (GLCPs). Moreover, we introduce cooperative color and GLCP features for neighborhood matching in the synthesis process. We assign different weights for color and grey-level features according to the characteristics of the input texture. The results show that the proposed approach performs well in terms of the synthesis quality.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In recent years, a lot of 2D textures have been synthesized from input 2D textures. However, the quality problems still exist for many textures. Further improvements are required to extract more reliable texture features. In this paper, we present a texture synthesis approach using cooperative color and grey-level features. For color feature extraction, we extract appearance vectors to replace RGB color values. For grey-level feature extraction, we extract the statistical features including entropy, contrast, and correlation based on the grey level co-occurrence probabilities (GLCPs). Moreover, we introduce cooperative color and GLCP features for neighborhood matching in the synthesis process. We assign different weights for color and grey-level features according to the characteristics of the input texture. The results show that the proposed approach performs well in terms of the synthesis quality.",
"fno": "5051a050",
"keywords": [
"Feature Extraction",
"Image Color Analysis",
"Visualization",
"Vectors",
"Three Dimensional Displays",
"Appearance Vector",
"2 D Texture",
"Texture Analysis",
"Texture Synthesis",
"Grey Level Co Occurrence Probabilities"
],
"authors": [
{
"affiliation": null,
"fullName": "Chin-Chen Chang",
"givenName": "Chin-Chen",
"surname": "Chang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Bing-Ruei Wu",
"givenName": "Bing-Ruei",
"surname": "Wu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Hao-Jen Hsu",
"givenName": "Hao-Jen",
"surname": "Hsu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Je-Wei Liang",
"givenName": "Je-Wei",
"surname": "Liang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Yuan-Ching Peng",
"givenName": "Yuan-Ching",
"surname": "Peng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Wen-Kai Tai",
"givenName": "Wen-Kai",
"surname": "Tai",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cgiv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2013-08-01T00:00:00",
"pubType": "proceedings",
"pages": "50-51",
"year": "2013",
"issn": null,
"isbn": "978-0-7695-5051-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "5051a046",
"articleId": "12OmNvA1hr8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "5051a052",
"articleId": "12OmNBf94Xm",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ism/2017/2937/0/2937a001",
"title": "Boundary-Preserving Depth Upsampling Without Texture Copying Artifacts and Holes",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2017/2937a001/12OmNCbCrSX",
"parentPublication": {
"id": "proceedings/ism/2017/2937/0",
"title": "2017 IEEE International Symposium on Multimedia (ISM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2011/0063/0/06130300",
"title": "Scale and rotation invariant color features for weakly-supervised object Learning in 3D space",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2011/06130300/12OmNCga1SZ",
"parentPublication": {
"id": "proceedings/iccvw/2011/0063/0",
"title": "2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmip/2017/5954/0/5954a136",
"title": "High Quality Texture Mapping for Multi-view Reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/icmip/2017/5954a136/12OmNz4BdpT",
"parentPublication": {
"id": "proceedings/icmip/2017/5954/0",
"title": "2017 2nd International Conference on Multimedia and Image Processing (ICMIP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/imvip/2011/0230/0/06167882",
"title": "Classification of Ordered Texture Images Using Regression Modelling and Granulometric Features",
"doi": null,
"abstractUrl": "/proceedings-article/imvip/2011/06167882/12OmNzt0IDJ",
"parentPublication": {
"id": "proceedings/imvip/2011/0230/0",
"title": "2011 Irish Machine Vision and Image Processing Conference (IMVIP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2018/3365/0/08446410",
"title": "Virtual Content Creation Using Dynamic Omnidirectional Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2018/08446410/13bd1gzWkRf",
"parentPublication": {
"id": "proceedings/vr/2018/3365/0",
"title": "2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000e645",
"title": "Texture Mapping for 3D Reconstruction with RGB-D Sensor",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000e645/17D45Wuc36V",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2018/7592/0/08699299",
"title": "Dynamic Omnidirectional Texture Synthesis for Photorealistic Virtual Content Creation",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2018/08699299/19F1MCTMB32",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2018/7592/0",
"title": "2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2019/3131/0/313100a700",
"title": "Adaptive Mesh Texture for Multi-View Appearance Modeling",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2019/313100a700/1ezRCcyrt7i",
"parentPublication": {
"id": "proceedings/3dv/2019/3131/0",
"title": "2019 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412450",
"title": "Surface IR Reflectance Estimation and Material Recognition using ToF Camera",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412450/1tmhVxRZjQk",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/03/09645189",
"title": "Seamless Texture Optimization for RGB-D Reconstruction",
"doi": null,
"abstractUrl": "/journal/tg/2023/03/09645189/1zc6CdFskcU",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNAfy7Gp",
"title": "Proceedings of 16th International Conference on Pattern Recognition",
"acronym": "icpr",
"groupId": "1000545",
"volume": "1",
"displayVolume": "1",
"year": "2002",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNz61cXZ",
"doi": "10.1109/ICPR.2002.1044666",
"title": "Multiresolution Block Sampling-Based Method for Texture Synthesis",
"normalizedTitle": "Multiresolution Block Sampling-Based Method for Texture Synthesis",
"abstract": "We present an efficient multiresolution block sampling-based texture synthesis algorithm that works well for a wide variety of challenging textures. We consider the texture synthesis as a procedure that first estimates and then resamples a probability function. To achieve the effect of good and fast estimation of the probabaility function for a given texture as well as to retain the structural information during synthesis, we propose a concept of block sampling and a corresponding novel scheme of texture synthesis based on multiresolution block sampling (MBS). The computational complexity of the proposed algorithm is much lower than that of existing algorithms. Experimental results have shown that this novel texture synthesis algorithm produces superior results for many challenging textures.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present an efficient multiresolution block sampling-based texture synthesis algorithm that works well for a wide variety of challenging textures. We consider the texture synthesis as a procedure that first estimates and then resamples a probability function. To achieve the effect of good and fast estimation of the probabaility function for a given texture as well as to retain the structural information during synthesis, we propose a concept of block sampling and a corresponding novel scheme of texture synthesis based on multiresolution block sampling (MBS). The computational complexity of the proposed algorithm is much lower than that of existing algorithms. Experimental results have shown that this novel texture synthesis algorithm produces superior results for many challenging textures.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present an efficient multiresolution block sampling-based texture synthesis algorithm that works well for a wide variety of challenging textures. We consider the texture synthesis as a procedure that first estimates and then resamples a probability function. To achieve the effect of good and fast estimation of the probabaility function for a given texture as well as to retain the structural information during synthesis, we propose a concept of block sampling and a corresponding novel scheme of texture synthesis based on multiresolution block sampling (MBS). The computational complexity of the proposed algorithm is much lower than that of existing algorithms. Experimental results have shown that this novel texture synthesis algorithm produces superior results for many challenging textures.",
"fno": "169510239",
"keywords": [],
"authors": [
{
"affiliation": "Motorola Broadband",
"fullName": "Yue Yu",
"givenName": "Yue",
"surname": "Yu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Eastman Kodak Company",
"fullName": "Jiebo Luo",
"givenName": "Jiebo",
"surname": "Luo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Sarnoff Corporation",
"fullName": "Chang Wen Chen",
"givenName": "Chang Wen",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2002-08-01T00:00:00",
"pubType": "proceedings",
"pages": "10239",
"year": "2002",
"issn": "1051-4651",
"isbn": "0-7695-1695-X",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "169510235",
"articleId": "12OmNzaQoKD",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "169510243",
"articleId": "12OmNzZEAxG",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icip/1998/8821/1/882110049",
"title": "Texture Analysis Using Multiresolution Moments",
"doi": null,
"abstractUrl": "/proceedings-article/icip/1998/882110049/12OmNBTawuS",
"parentPublication": {
"id": "proceedings/icip/1998/8821/1",
"title": "Proceedings of IPCIP'98 International Conference on Image Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dcabes/2011/4415/0/4415a170",
"title": "Optimization-Based Multi-scale Texture Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/dcabes/2011/4415a170/12OmNCfjeDK",
"parentPublication": {
"id": "proceedings/dcabes/2011/4415/0",
"title": "2011 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2003/7965/1/7965113",
"title": "Constrained texture synthesis for image post processing",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2003/7965113/12OmNvAiSiO",
"parentPublication": {
"id": "proceedings/icme/2003/7965/1",
"title": "2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dcabes/2010/4110/0/4110a552",
"title": "Fast Texture Synthesis Based on Correlation by Block Tiling",
"doi": null,
"abstractUrl": "/proceedings-article/dcabes/2010/4110a552/12OmNwp74In",
"parentPublication": {
"id": "proceedings/dcabes/2010/4110/0",
"title": "2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vsmm/2001/1402/0/14020780",
"title": "Multiresolution Model Generation with Geometry and Texture",
"doi": null,
"abstractUrl": "/proceedings-article/vsmm/2001/14020780/12OmNx0A7Gv",
"parentPublication": {
"id": "proceedings/vsmm/2001/1402/0",
"title": "Proceedings Seventh International Conference on Virtual Systems and MultiMedia Enhanced Realities: Augmented and Unplugged",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgi/2003/1946/0/19460120",
"title": "Image Restoration using Multiresolution Texture Synthesis and Image Inpainting",
"doi": null,
"abstractUrl": "/proceedings-article/cgi/2003/19460120/12OmNxA3Z5z",
"parentPublication": {
"id": "proceedings/cgi/2003/1946/0",
"title": "Computer Graphics International Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icic/2009/3634/3/3634c015",
"title": "A Texture Synthesis Algorithm Based on Block Matching",
"doi": null,
"abstractUrl": "/proceedings-article/icic/2009/3634c015/12OmNyz5JOU",
"parentPublication": {
"id": "proceedings/icic/2009/3634/2",
"title": "2009 Second International Conference on Information and Computing Science",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/1999/0164/2/01641033",
"title": "Texture Synthesis by Non-Parametric Sampling",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/1999/01641033/12OmNz4BdvL",
"parentPublication": {
"id": "proceedings/iccv/1999/0164/2",
"title": "Proceedings of the Seventh IEEE International Conference on Computer Vision",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmtma/2015/7143/0/7143a549",
"title": "Image Synthesis Algorithms Based on Texture Feature",
"doi": null,
"abstractUrl": "/proceedings-article/icmtma/2015/7143a549/12OmNz6iOpY",
"parentPublication": {
"id": "proceedings/icmtma/2015/7143/0",
"title": "2015 Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iceee/2019/3910/0/391000a340",
"title": "Comparison of Region Filling Algorithms Using Texture Synthesis Methodologies",
"doi": null,
"abstractUrl": "/proceedings-article/iceee/2019/391000a340/1cpqEuZlRfy",
"parentPublication": {
"id": "proceedings/iceee/2019/3910/0",
"title": "2019 6th International Conference on Electrical and Electronics Engineering (ICEEE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1H1gVMlkl32",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1H1jiWtcBOw",
"doi": "10.1109/CVPR52688.2022.00152",
"title": "AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis",
"normalizedTitle": "AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis",
"abstract": "In this paper, we address the problem of texture representation for 3D shapes for the challenging and under-explored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large distortions, or use continuous texture fields that yield smooth outputs lacking details. We argue that the traditional way of representing textures with images and linking them to a 3D mesh via UV mapping is more desirable, since synthesizing 2D images is a well-studied problem. We propose AUV-Net which learns to embed 3D surfaces into a 2D aligned UV space, by mapping the corresponding semantic parts of different 3D shapes to the same location in the UV space. As a result, textures are aligned across objects, and can thus be easily synthesized by generative models of images. Texture alignment is learned in an unsupervised manner by a simple yet effective texture alignment module, taking inspiration from traditional works on linear subspace learning. The learned UV mapping and aligned texture representations enable a variety of applications including texture transfer, texture synthesis, and textured single view 3D reconstruction. We conduct experiments on multiple datasets to demonstrate the effectiveness of our method.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we address the problem of texture representation for 3D shapes for the challenging and under-explored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large distortions, or use continuous texture fields that yield smooth outputs lacking details. We argue that the traditional way of representing textures with images and linking them to a 3D mesh via UV mapping is more desirable, since synthesizing 2D images is a well-studied problem. We propose AUV-Net which learns to embed 3D surfaces into a 2D aligned UV space, by mapping the corresponding semantic parts of different 3D shapes to the same location in the UV space. As a result, textures are aligned across objects, and can thus be easily synthesized by generative models of images. Texture alignment is learned in an unsupervised manner by a simple yet effective texture alignment module, taking inspiration from traditional works on linear subspace learning. The learned UV mapping and aligned texture representations enable a variety of applications including texture transfer, texture synthesis, and textured single view 3D reconstruction. We conduct experiments on multiple datasets to demonstrate the effectiveness of our method.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we address the problem of texture representation for 3D shapes for the challenging and under-explored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large distortions, or use continuous texture fields that yield smooth outputs lacking details. We argue that the traditional way of representing textures with images and linking them to a 3D mesh via UV mapping is more desirable, since synthesizing 2D images is a well-studied problem. We propose AUV-Net which learns to embed 3D surfaces into a 2D aligned UV space, by mapping the corresponding semantic parts of different 3D shapes to the same location in the UV space. As a result, textures are aligned across objects, and can thus be easily synthesized by generative models of images. Texture alignment is learned in an unsupervised manner by a simple yet effective texture alignment module, taking inspiration from traditional works on linear subspace learning. The learned UV mapping and aligned texture representations enable a variety of applications including texture transfer, texture synthesis, and textured single view 3D reconstruction. We conduct experiments on multiple datasets to demonstrate the effectiveness of our method.",
"fno": "694600b455",
"keywords": [
"Image Reconstruction",
"Image Representation",
"Image Texture",
"Learning Artificial Intelligence",
"Solid Modelling",
"Different 3 D Shapes",
"Simple Yet Effective Texture Alignment Module",
"Linear Subspace Learning",
"Learned UV Mapping",
"Aligned Texture Representations",
"Texture Transfer",
"Texture Synthesis",
"Single View 3 D Reconstruction",
"AUV Net",
"Aligned UV Maps",
"Texture Representation",
"Challenging Tasks",
"Under Explored Tasks",
"Spherical Texture Maps",
"Use Continuous Texture Fields",
"AUV Net",
"2 D Aligned UV Space",
"Computer Vision",
"Three Dimensional Displays",
"Shape",
"Semantics",
"Distortion",
"Pattern Recognition",
"Task Analysis",
"Segmentation",
"Grouping And Shape Analysis 3 D From Single Images Vision Graphics"
],
"authors": [
{
"affiliation": "NVIDIA",
"fullName": "Zhiqin Chen",
"givenName": "Zhiqin",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "NVIDIA",
"fullName": "Kangxue Yin",
"givenName": "Kangxue",
"surname": "Yin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "NVIDIA",
"fullName": "Sanja Fidler",
"givenName": "Sanja",
"surname": "Fidler",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-06-01T00:00:00",
"pubType": "proceedings",
"pages": "1455-1464",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6946-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "694600b446",
"articleId": "1H1k0VMzxfi",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "694600b465",
"articleId": "1H1kkMKDubS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icdar/2013/4999/0/06628669",
"title": "Semi-synthetic Document Image Generation Using Texture Mapping on Scanned 3D Document Shapes",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2013/06628669/12OmNAmmuTr",
"parentPublication": {
"id": "proceedings/icdar/2013/4999/0",
"title": "2013 12th International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000h093",
"title": "UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000h093/17D45WWzW3C",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2022/0915/0/091500b411",
"title": "Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2022/091500b411/1B12ZvyMfQI",
"parentPublication": {
"id": "proceedings/wacv/2022/0915/0",
"title": "2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2023/4544/0/10042545",
"title": "Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2023/10042545/1KOv4OD5vwY",
"parentPublication": {
"id": "proceedings/fg/2023/4544/0",
"title": "2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h052",
"title": "3D Human Mesh Regression With Dense Correspondence",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h052/1m3o9diJQAw",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h021",
"title": "Learning to Transfer Texture From Clothing Images to 3D Humans",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h021/1m3odfHuXF6",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900l1698",
"title": "UV-Net: Learning from Boundary Representations",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900l1698/1yeI6u95kY0",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900e637",
"title": "Neural Surface Maps",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900e637/1yeKbNb6law",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900h624",
"title": "OSTeC: One-Shot Texture Completion",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900h624/1yeLcIOqn9C",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900h115",
"title": "NeuTex: Neural Texture Mapping for Volumetric Neural Rendering",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900h115/1yeLdyIKnV6",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNArbG4m",
"title": "2012 IEEE International Conference on Services Computing (SCC)",
"acronym": "scc",
"groupId": "1001209",
"volume": "0",
"displayVolume": "0",
"year": "2012",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNBCqbCi",
"doi": "10.1109/SCC.2012.60",
"title": "Analysis of Scientific Workflow Provenance Access Control Policies",
"normalizedTitle": "Analysis of Scientific Workflow Provenance Access Control Policies",
"abstract": "Provenance has become an important concept for services computing in general, and for scientific workflows in particular. Provenance often contains confidential data and dependencies whose access needs to be protected. Provenance access control policies control who can access which provenance information. Correct specification of provenance access control policies is critical to ensure system security. However, due to the sheer size of provenance, it is often difficult to comprehend the full effects of an access control policy by manual inspection alone due to complex multi-step dependencies and their interactions. In this paper, we present automated analysis algorithms and complexity results for three provenance analysis problems. We have also developed incremental strategies for these algorithms for evolving provenance and access control policies.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Provenance has become an important concept for services computing in general, and for scientific workflows in particular. Provenance often contains confidential data and dependencies whose access needs to be protected. Provenance access control policies control who can access which provenance information. Correct specification of provenance access control policies is critical to ensure system security. However, due to the sheer size of provenance, it is often difficult to comprehend the full effects of an access control policy by manual inspection alone due to complex multi-step dependencies and their interactions. In this paper, we present automated analysis algorithms and complexity results for three provenance analysis problems. We have also developed incremental strategies for these algorithms for evolving provenance and access control policies.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Provenance has become an important concept for services computing in general, and for scientific workflows in particular. Provenance often contains confidential data and dependencies whose access needs to be protected. Provenance access control policies control who can access which provenance information. Correct specification of provenance access control policies is critical to ensure system security. However, due to the sheer size of provenance, it is often difficult to comprehend the full effects of an access control policy by manual inspection alone due to complex multi-step dependencies and their interactions. In this paper, we present automated analysis algorithms and complexity results for three provenance analysis problems. We have also developed incremental strategies for these algorithms for evolving provenance and access control policies.",
"fno": "06274153",
"keywords": [
"Authorisation",
"Scientific Workflow Provenance Access Control Policies Analysis",
"Services Computing",
"Confidential Data",
"Security System",
"Manual Inspection",
"Complex Multistep Dependencies",
"Automated Analysis Algorithms",
"Access Control",
"Algorithm Design And Analysis",
"Polynomials",
"Complexity Theory",
"DNA",
"Genomics",
"Inspection",
"Scientific Workflow Provenance",
"Policy Analysis"
],
"authors": [
{
"affiliation": null,
"fullName": "Ruiqi Luo",
"givenName": "Ruiqi",
"surname": "Luo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Ping Yang",
"givenName": "Ping",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Shiyong Lu",
"givenName": "Shiyong",
"surname": "Lu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Mikhail Gofman",
"givenName": "Mikhail",
"surname": "Gofman",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "scc",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2012-06-01T00:00:00",
"pubType": "proceedings",
"pages": "266-273",
"year": "2012",
"issn": null,
"isbn": "978-1-4673-3049-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06274152",
"articleId": "12OmNym2c5f",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06274154",
"articleId": "12OmNCf1Dsz",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/e-science/2012/4467/0/06404419",
"title": "Scientific workflow rewriting while preserving provenance",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2012/06404419/12OmNBqMDot",
"parentPublication": {
"id": "proceedings/e-science/2012/4467/0",
"title": "2012 IEEE 8th International Conference on E-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/waim/2008/3185/0/3185a349",
"title": "Scientific Workflow Provenance Querying with Security Views",
"doi": null,
"abstractUrl": "/proceedings-article/waim/2008/3185a349/12OmNBtl1Cr",
"parentPublication": {
"id": "proceedings/waim/2008/3185/0",
"title": "Web-Age Information Management, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/services/2017/2002/0/2002a094",
"title": "Provenance-Based Analytics Services for Access Control Policies",
"doi": null,
"abstractUrl": "/proceedings-article/services/2017/2002a094/12OmNrMHOhp",
"parentPublication": {
"id": "proceedings/services/2017/2002/0",
"title": "2017 IEEE World Congress on Services (SERVICES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pst/2015/7828/0/07232963",
"title": "An access control model for protecting provenance graphs",
"doi": null,
"abstractUrl": "/proceedings-article/pst/2015/07232963/12OmNvnOwyh",
"parentPublication": {
"id": "proceedings/pst/2015/7828/0",
"title": "2015 13th Annual Conference on Privacy, Security and Trust (PST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/trustcom/2014/6513/0/6513a769",
"title": "Towards Policy Retrieval for Provenance Based Access Control Model",
"doi": null,
"abstractUrl": "/proceedings-article/trustcom/2014/6513a769/12OmNx3Zjn0",
"parentPublication": {
"id": "proceedings/trustcom/2014/6513/0",
"title": "2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2010/5445/0/05447741",
"title": "Provenance browser: Displaying and querying scientific workflow provenance graphs",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2010/05447741/12OmNzaQos7",
"parentPublication": {
"id": "proceedings/icde/2010/5445/0",
"title": "2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/2016/04/07056545",
"title": "A Provenance-Aware Access Control Framework with Typed Provenance",
"doi": null,
"abstractUrl": "/journal/tq/2016/04/07056545/13rRUxYrbNK",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/scc/2018/7250/0/725001a261",
"title": "Quality Analysis for Scientific Workflow Provenance Access Control Policies",
"doi": null,
"abstractUrl": "/proceedings-article/scc/2018/725001a261/13rRUxbTMw0",
"parentPublication": {
"id": "proceedings/scc/2018/7250/0",
"title": "2018 IEEE International Conference on Services Computing (SCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/sc/2010/04/tsc2010040322",
"title": "Secure Abstraction Views for Scientific Workflow Provenance Querying",
"doi": null,
"abstractUrl": "/journal/sc/2010/04/tsc2010040322/13rRUyYSWpR",
"parentPublication": {
"id": "trans/sc",
"title": "IEEE Transactions on Services Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/sc/2022/01/08738910",
"title": "A Security Framework for Scientific Workflow Provenance Access Control Policies",
"doi": null,
"abstractUrl": "/journal/sc/2022/01/08738910/1aXLseHPJD2",
"parentPublication": {
"id": "trans/sc",
"title": "IEEE Transactions on Services Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNCeaPZK",
"title": "2011 IEEE 7th International Conference on E-Science (e-Science)",
"acronym": "e-science",
"groupId": "1001511",
"volume": "0",
"displayVolume": "0",
"year": "2011",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNvqW6TS",
"doi": "10.1109/eScience.2011.41",
"title": "In Situ Data Provenance Capture in Spreadsheets",
"normalizedTitle": "In Situ Data Provenance Capture in Spreadsheets",
"abstract": "The capture of data provenance is a fundamentally important task in eScience. While provenance can be captured using techniques such as scientific workflows, typically these techniques do not trace internal data manipulations that occur within off-the-shelf analysis tools. Yet it is still essential to capture data provenance within such environments. This paper discusses an in situ provenance approach for spreadsheet data in MS Excel, a commonly used analysis environment among scientists. We describe the design and implementation of an Excel tool that captures provenance unobtrusively in the background, allows for user annotations, provides undo/redo functionality at various levels of task granularity, and presents the captured provenance in an accessible format to support a range of provenance queries for analysis. We also present several motivating use case scenarios and a user evaluation which suggests that our approach is both efficient and useful to scientists.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The capture of data provenance is a fundamentally important task in eScience. While provenance can be captured using techniques such as scientific workflows, typically these techniques do not trace internal data manipulations that occur within off-the-shelf analysis tools. Yet it is still essential to capture data provenance within such environments. This paper discusses an in situ provenance approach for spreadsheet data in MS Excel, a commonly used analysis environment among scientists. We describe the design and implementation of an Excel tool that captures provenance unobtrusively in the background, allows for user annotations, provides undo/redo functionality at various levels of task granularity, and presents the captured provenance in an accessible format to support a range of provenance queries for analysis. We also present several motivating use case scenarios and a user evaluation which suggests that our approach is both efficient and useful to scientists.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The capture of data provenance is a fundamentally important task in eScience. While provenance can be captured using techniques such as scientific workflows, typically these techniques do not trace internal data manipulations that occur within off-the-shelf analysis tools. Yet it is still essential to capture data provenance within such environments. This paper discusses an in situ provenance approach for spreadsheet data in MS Excel, a commonly used analysis environment among scientists. We describe the design and implementation of an Excel tool that captures provenance unobtrusively in the background, allows for user annotations, provides undo/redo functionality at various levels of task granularity, and presents the captured provenance in an accessible format to support a range of provenance queries for analysis. We also present several motivating use case scenarios and a user evaluation which suggests that our approach is both efficient and useful to scientists.",
"fno": "06123284",
"keywords": [
"Natural Sciences Computing",
"Spreadsheet Programs",
"In Situ Data Provenance",
"Spreadsheets",
"Escience",
"Scientific Workflows",
"Off The Shelf Analysis Tools",
"MS Excel",
"User Annotations",
"Data Analysis",
"Semantics",
"Filtering",
"Noise",
"Data Mining",
"Context",
"Data Provenance",
"In Situ Capture",
"Spreadsheets"
],
"authors": [
{
"affiliation": null,
"fullName": "Hazeline U. Asuncion",
"givenName": "Hazeline U.",
"surname": "Asuncion",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "e-science",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2011-12-01T00:00:00",
"pubType": "proceedings",
"pages": "240-247",
"year": "2011",
"issn": null,
"isbn": "978-1-4577-2163-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06123283",
"articleId": "12OmNxGALbf",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06123285",
"articleId": "12OmNynJMT5",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icws/2015/7272/0/7272a089",
"title": "Climate Analytics Workflow Recommendation as a Service - Provenance-Driven Automatic Workflow Mashup",
"doi": null,
"abstractUrl": "/proceedings-article/icws/2015/7272a089/12OmNAR1aWR",
"parentPublication": {
"id": "proceedings/icws/2015/7272/0",
"title": "2015 IEEE International Conference on Web Services (ICWS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2012/4467/0/06404467",
"title": "From scripts towards provenance inference",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2012/06404467/12OmNAWpypA",
"parentPublication": {
"id": "proceedings/e-science/2012/4467/0",
"title": "2012 IEEE 8th International Conference on E-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ccgrid/2015/8006/0/8006a797",
"title": "Big Data Provenance Analysis and Visualization",
"doi": null,
"abstractUrl": "/proceedings-article/ccgrid/2015/8006a797/12OmNCm7BK6",
"parentPublication": {
"id": "proceedings/ccgrid/2015/8006/0",
"title": "2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2014/4288/1/06972267",
"title": "Study in Usefulness of Middleware-Only Provenance",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2014/06972267/12OmNwErpCM",
"parentPublication": {
"id": "proceedings/e-science/2014/4288/1",
"title": "2014 IEEE 10th International Conference on e-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2014/4288/2/06972106",
"title": "Towards Planning Scientific Experiments through Declarative Model Discovery in Provenance Data",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2014/06972106/12OmNzZWbRU",
"parentPublication": {
"id": "proceedings/e-science/2014/4288/2",
"title": "2014 IEEE 10th International Conference on e-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2013/12/ttg2013122476",
"title": "Evaluation of Filesystem Provenance Visualization Tools",
"doi": null,
"abstractUrl": "/journal/tg/2013/12/ttg2013122476/13rRUytWF9j",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/works/2018/0196/0/019600a031",
"title": "A Practical Roadmap for Provenance Capture and Data Analysis in Spark-Based Scientific Workflows",
"doi": null,
"abstractUrl": "/proceedings-article/works/2018/019600a031/18jX6oaW3Wo",
"parentPublication": {
"id": "proceedings/works/2018/0196/0",
"title": "2018 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2021/3902/0/09671867",
"title": "Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2021/09671867/1A8hgPBVq3S",
"parentPublication": {
"id": "proceedings/big-data/2021/3902/0",
"title": "2021 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2019/7474/0/747400a184",
"title": "Fine-Grained Provenance for Matching & ETL",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2019/747400a184/1aDSQZV7gFq",
"parentPublication": {
"id": "proceedings/icde/2019/7474/0",
"title": "2019 IEEE 35th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vis/2020/8014/0/801400a116",
"title": "Trrack: A Library for Provenance-Tracking in Web-Based Visualizations",
"doi": null,
"abstractUrl": "/proceedings-article/vis/2020/801400a116/1qRO3wCZWAE",
"parentPublication": {
"id": "proceedings/vis/2020/8014/0",
"title": "2020 IEEE Visualization Conference (VIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "17D45VtKirC",
"title": "2017 IEEE Conference on Visual Analytics Science and Technology (VAST)",
"acronym": "vast",
"groupId": "1001630",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "17D45VTRovn",
"doi": "10.1109/VAST.2017.8585615",
"title": "MC2 — Spatio-Temporal Provenance Data Aggregation for Visual Analysis",
"normalizedTitle": "MC2 — Spatio-Temporal Provenance Data Aggregation for Visual Analysis",
"abstract": "We describe our approach to the analysis of 2017 VAST Challenge Mini-Challenge 2 data. The challenge deals with readings from air sampler stations. To answer the main question, the provenance of the chemicals measured at the sampler stations, we extend the provided data set by aggregated spatio-temporal provenance data. This data is generated from the provided meteorological data and locations map by using it as input for a particle tracer which calculates the provenance of the particles arriving from the emitters (factories) at the collectors (the locations of sampler stations). We use ComVis [3], a coordinated multiple views (CMV) system, to analyze the whole data set (the provided and generated data) by applying a sensor centric data model.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We describe our approach to the analysis of 2017 VAST Challenge Mini-Challenge 2 data. The challenge deals with readings from air sampler stations. To answer the main question, the provenance of the chemicals measured at the sampler stations, we extend the provided data set by aggregated spatio-temporal provenance data. This data is generated from the provided meteorological data and locations map by using it as input for a particle tracer which calculates the provenance of the particles arriving from the emitters (factories) at the collectors (the locations of sampler stations). We use ComVis [3], a coordinated multiple views (CMV) system, to analyze the whole data set (the provided and generated data) by applying a sensor centric data model.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We describe our approach to the analysis of 2017 VAST Challenge Mini-Challenge 2 data. The challenge deals with readings from air sampler stations. To answer the main question, the provenance of the chemicals measured at the sampler stations, we extend the provided data set by aggregated spatio-temporal provenance data. This data is generated from the provided meteorological data and locations map by using it as input for a particle tracer which calculates the provenance of the particles arriving from the emitters (factories) at the collectors (the locations of sampler stations). We use ComVis [3], a coordinated multiple views (CMV) system, to analyze the whole data set (the provided and generated data) by applying a sensor centric data model.",
"fno": "08585615",
"keywords": [
"Data Aggregation",
"Data Analysis",
"Data Visualisation",
"Time Series",
"Data Set",
"Sensor Centric Data Model",
"Spatio Temporal Provenance Data Aggregation",
"Visual Analysis",
"Air Sampler Stations",
"Meteorological Data",
"Coordinated Multiple Views",
"CMV System",
"Particle Tracer",
"Locations Map",
"Production Facilities",
"Brushes",
"Time Series Analysis",
"Chemicals",
"Data Models",
"Visualization",
"Birds",
"I 3 8 Computing Methodologies Computer Graphics X 2014 Applications"
],
"authors": [
{
"affiliation": "VRVis Research Center, Vienna, Austria",
"fullName": "Rainer Splechtna",
"givenName": "Rainer",
"surname": "Splechtna",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "VRVis Research Center, Vienna, Austria",
"fullName": "Silvana Podaras",
"givenName": "Silvana",
"surname": "Podaras",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "VRVis Research Center, Vienna, Austria",
"fullName": "Michael Beham",
"givenName": "Michael",
"surname": "Beham",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Virginia Tech, Blacksburg, VA, USA",
"fullName": "Denis Gračanin",
"givenName": "Denis",
"surname": "Gračanin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "VRVis Research Center, Vienna, Austria",
"fullName": "Krešimir Matković",
"givenName": "Krešimir",
"surname": "Matković",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "vast",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-10-01T00:00:00",
"pubType": "proceedings",
"pages": "229-230",
"year": "2017",
"issn": null,
"isbn": "978-1-5386-3163-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "08585513",
"articleId": "17D45W1Oa2l",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08585476",
"articleId": "17D45Vw15wl",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/e-science/2012/4467/0/06404477",
"title": "Temporal representation for scientific data provenance",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2012/06404477/12OmNAY79ma",
"parentPublication": {
"id": "proceedings/e-science/2012/4467/0",
"title": "2012 IEEE 8th International Conference on E-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2012/4467/0/06404480",
"title": "Provenance analysis: Towards quality provenance",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2012/06404480/12OmNAoUTiq",
"parentPublication": {
"id": "proceedings/e-science/2012/4467/0",
"title": "2012 IEEE 8th International Conference on E-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pact/2017/6764/0/6764a117",
"title": "Lightweight Provenance Service for High-Performance Computing",
"doi": null,
"abstractUrl": "/proceedings-article/pact/2017/6764a117/12OmNyUFfLZ",
"parentPublication": {
"id": "proceedings/pact/2017/6764/0",
"title": "2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2014/4288/2/06972100",
"title": "A Multi-level Funneling Approach to Data Provenance Reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2014/06972100/12OmNyen1pZ",
"parentPublication": {
"id": "proceedings/e-science/2014/4288/2",
"title": "2014 IEEE 10th International Conference on e-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/saint/2009/3700/0/3700a263",
"title": "Measurement Analysis of the Live E! Sensor Network: Spatial-Temporal Correlations and Data Aggregation",
"doi": null,
"abstractUrl": "/proceedings-article/saint/2009/3700a263/12OmNzV70kn",
"parentPublication": {
"id": "proceedings/saint/2009/3700/0",
"title": "2009 Ninth Annual International Symposium on Applications and the Internet",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ds-rt/2018/5048/0/08600928",
"title": "Keynote Speaker: Credible Simulation Models — Provenance beyond Reproducibility",
"doi": null,
"abstractUrl": "/proceedings-article/ds-rt/2018/08600928/17D45VsBTVD",
"parentPublication": {
"id": "proceedings/ds-rt/2018/5048/0",
"title": "2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vast/2017/3163/0/08585631",
"title": "WindNebula: Vectorial-Temporal Analysis for Environmental Assessment : VAST Challenge MC2 Award: Multi-challenge Award for Aesthetic Design",
"doi": null,
"abstractUrl": "/proceedings-article/vast/2017/08585631/17D45WYQJ6K",
"parentPublication": {
"id": "proceedings/vast/2017/3163/0",
"title": "2017 IEEE Conference on Visual Analytics Science and Technology (VAST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vast/2017/3163/0/08585678",
"title": "Visual Analytic Design for Characterizing Air-Sampling Sensor Performance and Operation",
"doi": null,
"abstractUrl": "/proceedings-article/vast/2017/08585678/17D45We0UEI",
"parentPublication": {
"id": "proceedings/vast/2017/3163/0",
"title": "2017 IEEE Conference on Visual Analytics Science and Technology (VAST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2022/0883/0/088300c277",
"title": "Provenance in Temporal Interaction Networks",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2022/088300c277/1FwBGdMVjFK",
"parentPublication": {
"id": "proceedings/icde/2022/0883/0",
"title": "2022 IEEE 38th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09652041",
"title": "Provectories: Embedding-based Analysis of Interaction Provenance Data",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09652041/1zmuReh8VZ6",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNx6g6nT",
"title": "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNBOllj0",
"doi": "10.1109/BIBM.2017.8217632",
"title": "HIV1-human protein-protein interaction prediction based on interface architecture similarity",
"normalizedTitle": "HIV1-human protein-protein interaction prediction based on interface architecture similarity",
"abstract": "In this paper, we computationally predicted the interactions between HIV-1 and human proteins, based on the hypothesis that proteins with similar interface architecture share similar interaction partners. Evolution – aware protein structural alignment method UniAlign was used to calculate the similarity between two protein interface architectures. Using experimentally verified HIV-1, human protein-protein interactions data, we first selected 12 features, including geometric similarity, conversion similarity etc.; then trained a support vector machine (SVM) with Gaussian kernel for the binary classification problem: whether a given protein pairs ‘interact’ or ‘no interact’. We used the trained and tuned SVM classifier to discover potential novel HIV-1 interacting partners for human proteins. Many predicted interactions had significant literature support, and we modeled the novel 3D interacting complex for HIV-1 envelope gp120 and gp41 proteins. We provided the first structural evidence for those interactions.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we computationally predicted the interactions between HIV-1 and human proteins, based on the hypothesis that proteins with similar interface architecture share similar interaction partners. Evolution – aware protein structural alignment method UniAlign was used to calculate the similarity between two protein interface architectures. Using experimentally verified HIV-1, human protein-protein interactions data, we first selected 12 features, including geometric similarity, conversion similarity etc.; then trained a support vector machine (SVM) with Gaussian kernel for the binary classification problem: whether a given protein pairs ‘interact’ or ‘no interact’. We used the trained and tuned SVM classifier to discover potential novel HIV-1 interacting partners for human proteins. Many predicted interactions had significant literature support, and we modeled the novel 3D interacting complex for HIV-1 envelope gp120 and gp41 proteins. We provided the first structural evidence for those interactions.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we computationally predicted the interactions between HIV-1 and human proteins, based on the hypothesis that proteins with similar interface architecture share similar interaction partners. Evolution – aware protein structural alignment method UniAlign was used to calculate the similarity between two protein interface architectures. Using experimentally verified HIV-1, human protein-protein interactions data, we first selected 12 features, including geometric similarity, conversion similarity etc.; then trained a support vector machine (SVM) with Gaussian kernel for the binary classification problem: whether a given protein pairs ‘interact’ or ‘no interact’. We used the trained and tuned SVM classifier to discover potential novel HIV-1 interacting partners for human proteins. Many predicted interactions had significant literature support, and we modeled the novel 3D interacting complex for HIV-1 envelope gp120 and gp41 proteins. We provided the first structural evidence for those interactions.",
"fno": "08217632",
"keywords": [
"Proteins",
"Support Vector Machines",
"Computer Architecture",
"Kernel",
"Benchmark Testing",
"Feature Extraction",
"Protein Engineering",
"Protein Protein Interaction",
"Protein Structure Alignment",
"Protein Interface Architecture",
"HIV 1"
],
"authors": [
{
"affiliation": "PennCHOP Microbiome Program, Children's Hospital of Philadelphia, Philadelphia, US",
"fullName": "Chunyu Zhao",
"givenName": "Chunyu",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Computing and Informatics, Drexel University, Philadelphia, US",
"fullName": "Yizhou Zang",
"givenName": "Yizhou",
"surname": "Zang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Computing and Informatics, Drexel University, Philadelphia, US",
"fullName": "Wei Quan",
"givenName": "Wei",
"surname": "Quan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Computing and Informatics, Drexel University, Philadelphia, US",
"fullName": "Xiaohua Hu",
"givenName": "Xiaohua",
"surname": "Hu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Biomedical, Engineering, Science and Health Systems, Drexel University, Philadelphia, US",
"fullName": "Ahmet Sacan",
"givenName": "Ahmet",
"surname": "Sacan",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-11-01T00:00:00",
"pubType": "proceedings",
"pages": "97-100",
"year": "2017",
"issn": null,
"isbn": "978-1-5090-3050-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "08217631",
"articleId": "12OmNroij0v",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08217633",
"articleId": "12OmNvw2TeC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2008/3452/0/3452a417",
"title": "Evaluating Protein Sequence Signatures Inferred from Protein-Protein Interaction Data by Gene Ontology Annotations",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2008/3452a417/12OmNASraJK",
"parentPublication": {
"id": "proceedings/bibm/2008/3452/0",
"title": "2008 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisip/2005/9588/0/01619418",
"title": "Predict Protein-Protein Interaction using Heuristic Approaches",
"doi": null,
"abstractUrl": "/proceedings-article/icisip/2005/01619418/12OmNBbsiiH",
"parentPublication": {
"id": "proceedings/icisip/2005/9588/0",
"title": "Proceedings. Third International Conference on Intelligent Sensing and Information Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdew/2005/2657/0/22851156",
"title": "Prediction of Protein Functions Based on Protein-Protein Interaction Networks: A Min-Cut Approach",
"doi": null,
"abstractUrl": "/proceedings-article/icdew/2005/22851156/12OmNClQ0Ai",
"parentPublication": {
"id": "proceedings/icdew/2005/2657/0",
"title": "21st International Conference on Data Engineering Workshops (ICDEW'05)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999204",
"title": "Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999204/12OmNrY3Lxn",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2017/3050/0/08217927",
"title": "Topological properties of protein interaction network and phylogenetic age of proteins",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2017/08217927/12OmNxIzWOf",
"parentPublication": {
"id": "proceedings/bibm/2017/3050/0",
"title": "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2010/8306/0/05706635",
"title": "Ranking SVM for multiple kernels output combination in protein-protein interaction extraction from biomedical literature",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2010/05706635/12OmNy7Qfug",
"parentPublication": {
"id": "proceedings/bibm/2010/8306/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2016/3834/0/3834a007",
"title": "Host-Pathogen Protein Interaction Prediction Based on Local Topology Structures of a Protein Interaction Network",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2016/3834a007/12OmNz5JBPC",
"parentPublication": {
"id": "proceedings/bibe/2016/3834/0",
"title": "2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccabs/2014/5786/0/06863923",
"title": "Identification of protein interaction methods from biomedical literature",
"doi": null,
"abstractUrl": "/proceedings-article/iccabs/2014/06863923/12OmNzXFoDk",
"parentPublication": {
"id": "proceedings/iccabs/2014/5786/0",
"title": "2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2013/02/ttb2013020423",
"title": "Mining Quasi-Bicliques from HIV-1-Human Protein Interaction Network: A Multiobjective Biclustering Approach",
"doi": null,
"abstractUrl": "/journal/tb/2013/02/ttb2013020423/13rRUEgarmr",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2018/04/07276997",
"title": "IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks",
"doi": null,
"abstractUrl": "/journal/tb/2018/04/07276997/13rRUx0geox",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNxaw592",
"title": "21st International Conference on Data Engineering Workshops (ICDEW'05)",
"acronym": "icdew",
"groupId": "1001384",
"volume": "0",
"displayVolume": "0",
"year": "2005",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNClQ0Ai",
"doi": "10.1109/ICDE.2005.260",
"title": "Prediction of Protein Functions Based on Protein-Protein Interaction Networks: A Min-Cut Approach",
"normalizedTitle": "Prediction of Protein Functions Based on Protein-Protein Interaction Networks: A Min-Cut Approach",
"abstract": "This paper presents an approach for prediction of protein functions based on protein-protein interaction networks. For the purpose of prediction, we not only consider the interactions of functionunknown proteins with function-known proteins but also the interactions of function-unknown proteins with function-unknown proteins. Prediction is performed in the context of the entire network. This is a min-cut approach because we try to assign function-unknown proteins to different functional groups in such a way so that the number of intergroup interactions becomes the minimum in the whole network. Also, we show some evaluation results by applying the proposed method to yeast Saccharomyces cerevisiae protein-protein interaction network. However, this is a general method and can be applied to other organisms alike.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper presents an approach for prediction of protein functions based on protein-protein interaction networks. For the purpose of prediction, we not only consider the interactions of functionunknown proteins with function-known proteins but also the interactions of function-unknown proteins with function-unknown proteins. Prediction is performed in the context of the entire network. This is a min-cut approach because we try to assign function-unknown proteins to different functional groups in such a way so that the number of intergroup interactions becomes the minimum in the whole network. Also, we show some evaluation results by applying the proposed method to yeast Saccharomyces cerevisiae protein-protein interaction network. However, this is a general method and can be applied to other organisms alike.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper presents an approach for prediction of protein functions based on protein-protein interaction networks. For the purpose of prediction, we not only consider the interactions of functionunknown proteins with function-known proteins but also the interactions of function-unknown proteins with function-unknown proteins. Prediction is performed in the context of the entire network. This is a min-cut approach because we try to assign function-unknown proteins to different functional groups in such a way so that the number of intergroup interactions becomes the minimum in the whole network. Also, we show some evaluation results by applying the proposed method to yeast Saccharomyces cerevisiae protein-protein interaction network. However, this is a general method and can be applied to other organisms alike.",
"fno": "22851156",
"keywords": [
"Protein Function Prediction",
"Protein Protein Interaction Network",
"Min Cut",
"Heuristic Method"
],
"authors": [
{
"affiliation": "Nara Institute of Science and Technology (NAIST), Japan",
"fullName": "Md. Altaf-Ul-Amin",
"givenName": "Md.",
"surname": "Altaf-Ul-Amin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nara Institute of Science and Technology (NAIST), Japan",
"fullName": "Toshihiro Koma",
"givenName": "Toshihiro",
"surname": "Koma",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nara Institute of Science and Technology (NAIST), Japan",
"fullName": "Ken Kurokawa",
"givenName": "Ken",
"surname": "Kurokawa",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nara Institute of Science and Technology (NAIST), Japan",
"fullName": "Shigehiko Kanaya",
"givenName": "Shigehiko",
"surname": "Kanaya",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icdew",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2005-04-01T00:00:00",
"pubType": "proceedings",
"pages": "1156",
"year": "2005",
"issn": null,
"isbn": "0-7695-2657-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "22851155",
"articleId": "12OmNyv7m07",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "22851157",
"articleId": "12OmNBDyA8c",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/csb/2005/2344/0/23440268",
"title": "A Topological Measurement for Weighted Protein Interaction Network",
"doi": null,
"abstractUrl": "/proceedings-article/csb/2005/23440268/12OmNA0dMHx",
"parentPublication": {
"id": "proceedings/csb/2005/2344/0",
"title": "Proceedings. 2005 IEEE Computational Systems Bioinformatics Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2008/3452/0/3452a417",
"title": "Evaluating Protein Sequence Signatures Inferred from Protein-Protein Interaction Data by Gene Ontology Annotations",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2008/3452a417/12OmNASraJK",
"parentPublication": {
"id": "proceedings/bibm/2008/3452/0",
"title": "2008 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisip/2005/9588/0/01619418",
"title": "Predict Protein-Protein Interaction using Heuristic Approaches",
"doi": null,
"abstractUrl": "/proceedings-article/icisip/2005/01619418/12OmNBbsiiH",
"parentPublication": {
"id": "proceedings/icisip/2005/9588/0",
"title": "Proceedings. Third International Conference on Intelligent Sensing and Information Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2012/2559/0/06392693",
"title": "A random walk based approach for improving protein-protein interaction network and protein complex prediction",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2012/06392693/12OmNBpmDK9",
"parentPublication": {
"id": "proceedings/bibm/2012/2559/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2009/5121/0/05332118",
"title": "Computational testing of protein-protein interactions",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2009/05332118/12OmNC2OSMq",
"parentPublication": {
"id": "proceedings/bibmw/2009/5121/0",
"title": "2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2010/4083/0/4083a144",
"title": "Functional Flow Simulation Based Analysis of Protein Interaction Network",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2010/4083a144/12OmNxWuiej",
"parentPublication": {
"id": "proceedings/bibe/2010/4083/0",
"title": "2010 IEEE International Conference on Bioinformatics and Bioengineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/csse/2008/3336/1/3336a324",
"title": "Prediction of Protein-Protein Interaction Sites Using Granularity Computing of Quotient Space Theory",
"doi": null,
"abstractUrl": "/proceedings-article/csse/2008/3336a324/12OmNxeM46p",
"parentPublication": {
"id": "proceedings/csse/2008/3336/1",
"title": "Computer Science and Software Engineering, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iacsit-sc/2009/3653/0/3653a538",
"title": "Combined Database System for Binary Protein Interaction and Co-complex Association",
"doi": null,
"abstractUrl": "/proceedings-article/iacsit-sc/2009/3653a538/12OmNyYm2zt",
"parentPublication": {
"id": "proceedings/iacsit-sc/2009/3653/0",
"title": "Computer Science and Information Technology, International Association of",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2016/3834/0/3834a007",
"title": "Host-Pathogen Protein Interaction Prediction Based on Local Topology Structures of a Protein Interaction Network",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2016/3834a007/12OmNz5JBPC",
"parentPublication": {
"id": "proceedings/bibe/2016/3834/0",
"title": "2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2009/3885/0/3885a111",
"title": "High Functional Coherence in k-Partite Protein Cliques of Protein Interaction Networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2009/3885a111/12OmNznkK4I",
"parentPublication": {
"id": "proceedings/bibm/2009/3885/0",
"title": "2009 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNzcxZq8",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNrY3Lxn",
"doi": "10.1109/BIBM.2014.6999204",
"title": "Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights",
"normalizedTitle": "Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights",
"abstract": "Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW), to innovatively predict proteins' essentiality based on essential protein-protein interactions. The experimental results on all three organisms: Saccharomyces cere-visiae (Yeast), Escherichia coli (E. coli), and Caenorhabditis ele-gans (C. elegans) show that IEW achieves better performance than the state-of-the-art methods in terms of precision-recall. Furthermore, we have demonstrated that the highly-ranked protein-protein interactions predicted by our approach tend to be biologically significant in Yeast, E. coli, and C. elegans proteinprotein interaction (PPI) networks.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW), to innovatively predict proteins' essentiality based on essential protein-protein interactions. The experimental results on all three organisms: Saccharomyces cere-visiae (Yeast), Escherichia coli (E. coli), and Caenorhabditis ele-gans (C. elegans) show that IEW achieves better performance than the state-of-the-art methods in terms of precision-recall. Furthermore, we have demonstrated that the highly-ranked protein-protein interactions predicted by our approach tend to be biologically significant in Yeast, E. coli, and C. elegans proteinprotein interaction (PPI) networks.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW), to innovatively predict proteins' essentiality based on essential protein-protein interactions. The experimental results on all three organisms: Saccharomyces cere-visiae (Yeast), Escherichia coli (E. coli), and Caenorhabditis ele-gans (C. elegans) show that IEW achieves better performance than the state-of-the-art methods in terms of precision-recall. Furthermore, we have demonstrated that the highly-ranked protein-protein interactions predicted by our approach tend to be biologically significant in Yeast, E. coli, and C. elegans proteinprotein interaction (PPI) networks.",
"fno": "06999204",
"keywords": [
"Proteins",
"Protein Engineering",
"Gene Expression",
"Organisms",
"Databases",
"Vectors",
"Integrated Edge Weights",
"Essential Protein",
"Essential Protein Protein Interaction"
],
"authors": [
{
"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun, China",
"fullName": "Yuexu Jiang",
"givenName": "Yuexu",
"surname": "Jiang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun, China",
"fullName": "Yan Wang",
"givenName": "Yan",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun, China",
"fullName": "Wei Pang",
"givenName": "Wei",
"surname": "Pang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun, China",
"fullName": "Liang Chen",
"givenName": "Liang",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun, China",
"fullName": "Huiyan Sun",
"givenName": "Huiyan",
"surname": "Sun",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun, China",
"fullName": "Yanchun Liang",
"givenName": "Yanchun",
"surname": "Liang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Department of Information Engineering and Computer Science, University of Trento, Povo, Italy",
"fullName": "Enrico Blanzieri",
"givenName": "Enrico",
"surname": "Blanzieri",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-11-01T00:00:00",
"pubType": "proceedings",
"pages": "480-483",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-5669-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06999203",
"articleId": "12OmNCf1DrE",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06999205",
"articleId": "12OmNxiKs6g",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibmw/2010/8303/0/05703806",
"title": "Analyzing genetic factors involved in recombinant protein expression enhancement",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703806/12OmNrAMEOK",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2012/2559/0/06392716",
"title": "Identifying essential proteins via integration of protein interaction and gene expression data",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2012/06392716/12OmNvq5jD7",
"parentPublication": {
"id": "proceedings/bibm/2012/2559/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999291",
"title": "Identification of protein complexes and functional modules in integrated PPI networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999291/12OmNynsbzG",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2009/5121/0/05332102",
"title": "A topology-sharing based method for protein function prediction via analysis of protein functional association networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2009/05332102/12OmNyvoXe2",
"parentPublication": {
"id": "proceedings/bibmw/2009/5121/0",
"title": "2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2011/1799/0/06120405",
"title": "Active Protein Interaction Network and Its Application on Protein Complex Detection",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2011/06120405/12OmNzBOhxt",
"parentPublication": {
"id": "proceedings/bibm/2011/1799/0",
"title": "2011 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2014/02/06687204",
"title": "Predicting Essential Proteins Based on Weighted Degree Centrality",
"doi": null,
"abstractUrl": "/journal/tb/2014/02/06687204/13rRUxAASRF",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/02/07927488",
"title": "Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks",
"doi": null,
"abstractUrl": "/journal/tb/2019/02/07927488/13rRUxASuRj",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2014/04/06744575",
"title": "From Function to Interaction: A New Paradigm for Accurately Predicting Protein Complexes Based on Protein-to-Protein Interaction Networks",
"doi": null,
"abstractUrl": "/journal/tb/2014/04/06744575/13rRUxDItfM",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/04/07845558",
"title": "Construction of Refined Protein Interaction Network for Predicting Essential Proteins",
"doi": null,
"abstractUrl": "/journal/tb/2019/04/07845558/13rRUyfbwpj",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669606",
"title": "Improving human essential protein prediction using only protein sequences via ensemble learning",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669606/1A9W69hcizK",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNzVGcJp",
"title": "2011 IEEE International Conference on Bioinformatics and Biomedicine",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2011",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNs0kyt4",
"doi": "10.1109/BIBM.2011.36",
"title": "Multi-label Learning for Protein Subcellular Location Prediction",
"normalizedTitle": "Multi-label Learning for Protein Subcellular Location Prediction",
"abstract": "Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.",
"fno": "06120452",
"keywords": [
"Biology Computing",
"Learning Artificial Intelligence",
"Molecular Biophysics",
"Proteins",
"Multilabel Learning",
"Protein Subcellular Location Prediction",
"Protein Function",
"Drug Target Identification",
"Single Location Protein",
"Multiplex Protein",
"Protein Data Sets",
"Label Specific Features",
"Proteins",
"Correlation",
"Protein Engineering",
"Multiplexing",
"Machine Learning",
"Humans",
"Learning Systems",
"Protein Subcellular Localization",
"Multi Label Learning"
],
"authors": [
{
"affiliation": null,
"fullName": "Xiao Wang",
"givenName": "Xiao",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Guo-Zheng Li",
"givenName": "Guo-Zheng",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jia-Ming Liu",
"givenName": "Jia-Ming",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Rui-Wei Zhao",
"givenName": "Rui-Wei",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2011-11-01T00:00:00",
"pubType": "proceedings",
"pages": "282-285",
"year": "2011",
"issn": null,
"isbn": "978-1-4577-1799-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06120451",
"articleId": "12OmNvqmUHj",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06120453",
"articleId": "12OmNxuo0jo",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2014/5209/0/5209a512",
"title": "Dual Fuzzy Hypergraph Regularized Multi-label Learning for Protein Subcellular Location Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/5209a512/12OmNAgGwf5",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999133",
"title": "MultiP-SChlo: Multi-label protein subchloroplast localization prediction",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999133/12OmNwFzO2H",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2013/1293/0/06691734",
"title": "A novel integrated method for human multiplex protein subcellular localization prediction",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2013/06691734/12OmNweBUOM",
"parentPublication": {
"id": "proceedings/big-data/2013/1293/0",
"title": "2013 IEEE International Conference on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isda/2008/3382/1/3382a165",
"title": "Prediction of Protein Subcellular Localizations",
"doi": null,
"abstractUrl": "/proceedings-article/isda/2008/3382a165/12OmNxR5UIK",
"parentPublication": {
"id": "proceedings/isda/2008/3382/1",
"title": "Intelligent Systems Design and Applications, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2013/02/ttb2013020436",
"title": "Multilabel Learning via Random Label Selection for Protein Subcellular Multilocations Prediction",
"doi": null,
"abstractUrl": "/journal/tb/2013/02/ttb2013020436/13rRUILtJkg",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2011/03/ttb2011030748",
"title": "Multitask Learning for Protein Subcellular Location Prediction",
"doi": null,
"abstractUrl": "/journal/tb/2011/03/ttb2011030748/13rRUxYINdG",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/05/08634877",
"title": "Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields",
"doi": null,
"abstractUrl": "/journal/tb/2019/05/08634877/17D45VsBU6Z",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdacai/2022/5470/0/547000a085",
"title": "Random Forest Algorithm in Prediction of Protein Subcellular Localization",
"doi": null,
"abstractUrl": "/proceedings-article/icdacai/2022/547000a085/1J7WSG9pLtS",
"parentPublication": {
"id": "proceedings/icdacai/2022/5470/0",
"title": "2022 International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2022/6819/0/09995180",
"title": "Protein Subcellular Localization Prediction by Combining ProtBert and BiGRU",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2022/09995180/1JC2HR6dSta",
"parentPublication": {
"id": "proceedings/bibm/2022/6819/0",
"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2022/02/09431661",
"title": "Identifying Protein Subcellular Locations With Embeddings-Based node2loc",
"doi": null,
"abstractUrl": "/journal/tb/2022/02/09431661/1tB9aeLWyti",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNzcxZq8",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNvTTc7P",
"doi": "10.1109/BIBM.2014.6999122",
"title": "Minimum dominating sets in cell cycle specific protein interaction networks",
"normalizedTitle": "Minimum dominating sets in cell cycle specific protein interaction networks",
"abstract": "Recently, scientists start to examine the dynamics of biological networks from a control theory perspective. Based on the determination of minimum dominating sets (MDSets), this paper investigated the properties associated with MDSet proteins in the context of the analysis of protein interaction networks specific to the yeast cell cycle. Statistically significant differences between MDSet and non-MDSet proteins were observed in terms of topological features, Gene Ontology-driven semantic similarities, and the number of protein domains associated with each protein. However, unlike previous studies, MDSet proteins were found to be enriched with essential genes. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the distinction between MDSet and non-MDSet proteins is far more complex than that observed in yeast. The system used to determine a minimum dominating set in a protein interaction network was implemented as a user-friendly Java-based plugin for Cytoscape.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recently, scientists start to examine the dynamics of biological networks from a control theory perspective. Based on the determination of minimum dominating sets (MDSets), this paper investigated the properties associated with MDSet proteins in the context of the analysis of protein interaction networks specific to the yeast cell cycle. Statistically significant differences between MDSet and non-MDSet proteins were observed in terms of topological features, Gene Ontology-driven semantic similarities, and the number of protein domains associated with each protein. However, unlike previous studies, MDSet proteins were found to be enriched with essential genes. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the distinction between MDSet and non-MDSet proteins is far more complex than that observed in yeast. The system used to determine a minimum dominating set in a protein interaction network was implemented as a user-friendly Java-based plugin for Cytoscape.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Recently, scientists start to examine the dynamics of biological networks from a control theory perspective. Based on the determination of minimum dominating sets (MDSets), this paper investigated the properties associated with MDSet proteins in the context of the analysis of protein interaction networks specific to the yeast cell cycle. Statistically significant differences between MDSet and non-MDSet proteins were observed in terms of topological features, Gene Ontology-driven semantic similarities, and the number of protein domains associated with each protein. However, unlike previous studies, MDSet proteins were found to be enriched with essential genes. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the distinction between MDSet and non-MDSet proteins is far more complex than that observed in yeast. The system used to determine a minimum dominating set in a protein interaction network was implemented as a user-friendly Java-based plugin for Cytoscape.",
"fno": "06999122",
"keywords": [
"Proteins",
"Protein Engineering",
"Semantics",
"Bioinformatics",
"Ontologies",
"Cancer",
"Gene Ontology",
"Protein Interaction Network",
"Cell Cycle",
"Minimum Dominating Set"
],
"authors": [
{
"affiliation": "School of Computing and Mathematics, Computer Science, Research Institute, University of Ulster, Co. Antrim, UK",
"fullName": "Haiying Wang",
"givenName": "Haiying",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computing and Mathematics, Computer Science, Research Institute, University of Ulster, Co. Antrim, UK",
"fullName": "Huiru Zheng",
"givenName": "Huiru",
"surname": "Zheng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computing and Mathematics, Computer Science, Research Institute, University of Ulster, Co. Antrim, UK",
"fullName": "Fiona Browne",
"givenName": "Fiona",
"surname": "Browne",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK",
"fullName": "Chaoyang Wang",
"givenName": "Chaoyang",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-11-01T00:00:00",
"pubType": "proceedings",
"pages": "25-30",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-5669-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06999121",
"articleId": "12OmNvlg8pU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06999123",
"articleId": "12OmNwIpNno",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibmw/2009/5121/0/05332118",
"title": "Computational testing of protein-protein interactions",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2009/05332118/12OmNC2OSMq",
"parentPublication": {
"id": "proceedings/bibmw/2009/5121/0",
"title": "2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdew/2005/2657/0/22851156",
"title": "Prediction of Protein Functions Based on Protein-Protein Interaction Networks: A Min-Cut Approach",
"doi": null,
"abstractUrl": "/proceedings-article/icdew/2005/22851156/12OmNClQ0Ai",
"parentPublication": {
"id": "proceedings/icdew/2005/2657/0",
"title": "21st International Conference on Data Engineering Workshops (ICDEW'05)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999204",
"title": "Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999204/12OmNrY3Lxn",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2010/8303/0/05703936",
"title": "Early stage evolution in metabolic related protein interaction network",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703936/12OmNwekjzM",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2017/3050/0/08217927",
"title": "Topological properties of protein interaction network and phylogenetic age of proteins",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2017/08217927/12OmNxIzWOf",
"parentPublication": {
"id": "proceedings/bibm/2017/3050/0",
"title": "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2011/1799/0/06120500",
"title": "ProKinO: A Framework for Protein Kinase Ontology",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2011/06120500/12OmNxWcH44",
"parentPublication": {
"id": "proceedings/bibm/2011/1799/0",
"title": "2011 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2017/02/07164288",
"title": "Analysis of Organization of the Interactome Using Dominating Sets: A Case Study on Cell Cycle Interaction Networks",
"doi": null,
"abstractUrl": "/journal/tb/2017/02/07164288/13rRUNvyarS",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2014/03/06718040",
"title": "Detecting Protein Complexes Based on Uncertain Graph Model",
"doi": null,
"abstractUrl": "/journal/tb/2014/03/06718040/13rRUwh80t5",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2011/03/ttb2011030775",
"title": "Prediction of Protein Functions with Gene Ontology and Interspecies Protein Homology Data",
"doi": null,
"abstractUrl": "/journal/tb/2011/03/ttb2011030775/13rRUxOdDbf",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2014/06/06804637",
"title": "Organized Modularity in the Interactome: Evidence from the Analysis of Dynamic Organization in the Cell Cycle",
"doi": null,
"abstractUrl": "/journal/tb/2014/06/06804637/13rRUxOdDbi",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNx6g6nT",
"title": "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxIzWOf",
"doi": "10.1109/BIBM.2017.8217927",
"title": "Topological properties of protein interaction network and phylogenetic age of proteins",
"normalizedTitle": "Topological properties of protein interaction network and phylogenetic age of proteins",
"abstract": "Proteins interact with each other to regulate their functionality and localization. The accumulated protein interaction evidences are represented by protein interaction network using a graph abstraction. Topological properties of protein interaction networks have been explored to characterize proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. While one group of researchers have made efforts to simulate current protein interaction starting from hypothetical infant state of protein interaction networks through suggested evolutionary models, another group of researchers have made efforts to estimate the phylogenetic age of proteins from evolutionary relationship. Recently, these efforts gave rise to the database of phylogenetic age of proteins and this allows many researchers to estimate phylogenetic age of proteins of their interest easily. As seen by their terms, the evolutionary model of protein interaction networks and phylogenetic age of proteins are closely related, thus topological properties of protein interactions, which is important in studies of the evolutionary models, can be linked to the phylogenetic age of proteins. In this paper, we construct a weighted human protein interaction network from a human protein interaction network, which is provided by BioGRID database. The weight of an edge is defined as the number of triangles which contains this edge in the protein interaction network and we call this weight as the triangle score. From the weighted protein interaction network, we extract proteins that are incident to an edge that has a high triangle score. We obtain phylogenetic age of proteins and measure various statistical values to observe correlation between phylogenetic age and the triangle score. As a result, we show that the proteins, that participate in interactions with high triangle score, are old in terms of phylogenetic age. Also we show that for interactions within a same phylogenetic age category tend to have higher triangle scores than the interactions with only one of its participant protein contained in the given age category.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Proteins interact with each other to regulate their functionality and localization. The accumulated protein interaction evidences are represented by protein interaction network using a graph abstraction. Topological properties of protein interaction networks have been explored to characterize proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. While one group of researchers have made efforts to simulate current protein interaction starting from hypothetical infant state of protein interaction networks through suggested evolutionary models, another group of researchers have made efforts to estimate the phylogenetic age of proteins from evolutionary relationship. Recently, these efforts gave rise to the database of phylogenetic age of proteins and this allows many researchers to estimate phylogenetic age of proteins of their interest easily. As seen by their terms, the evolutionary model of protein interaction networks and phylogenetic age of proteins are closely related, thus topological properties of protein interactions, which is important in studies of the evolutionary models, can be linked to the phylogenetic age of proteins. In this paper, we construct a weighted human protein interaction network from a human protein interaction network, which is provided by BioGRID database. The weight of an edge is defined as the number of triangles which contains this edge in the protein interaction network and we call this weight as the triangle score. From the weighted protein interaction network, we extract proteins that are incident to an edge that has a high triangle score. We obtain phylogenetic age of proteins and measure various statistical values to observe correlation between phylogenetic age and the triangle score. As a result, we show that the proteins, that participate in interactions with high triangle score, are old in terms of phylogenetic age. Also we show that for interactions within a same phylogenetic age category tend to have higher triangle scores than the interactions with only one of its participant protein contained in the given age category.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Proteins interact with each other to regulate their functionality and localization. The accumulated protein interaction evidences are represented by protein interaction network using a graph abstraction. Topological properties of protein interaction networks have been explored to characterize proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. While one group of researchers have made efforts to simulate current protein interaction starting from hypothetical infant state of protein interaction networks through suggested evolutionary models, another group of researchers have made efforts to estimate the phylogenetic age of proteins from evolutionary relationship. Recently, these efforts gave rise to the database of phylogenetic age of proteins and this allows many researchers to estimate phylogenetic age of proteins of their interest easily. As seen by their terms, the evolutionary model of protein interaction networks and phylogenetic age of proteins are closely related, thus topological properties of protein interactions, which is important in studies of the evolutionary models, can be linked to the phylogenetic age of proteins. In this paper, we construct a weighted human protein interaction network from a human protein interaction network, which is provided by BioGRID database. The weight of an edge is defined as the number of triangles which contains this edge in the protein interaction network and we call this weight as the triangle score. From the weighted protein interaction network, we extract proteins that are incident to an edge that has a high triangle score. We obtain phylogenetic age of proteins and measure various statistical values to observe correlation between phylogenetic age and the triangle score. As a result, we show that the proteins, that participate in interactions with high triangle score, are old in terms of phylogenetic age. Also we show that for interactions within a same phylogenetic age category tend to have higher triangle scores than the interactions with only one of its participant protein contained in the given age category.",
"fno": "08217927",
"keywords": [
"Proteins",
"Phylogeny",
"Protein Engineering",
"Databases",
"History",
"Vegetation",
"Bioinformatics",
"Protein Interaction Network",
"Phylogenetic Age",
"Subfunctionalizationcomponent"
],
"authors": [
{
"affiliation": "Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea",
"fullName": "Hyeonseong Jeon",
"givenName": "Hyeonseong",
"surname": "Jeon",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dpt of Mathematics Education, Seoul National University, Seoul, South Korea",
"fullName": "Suh-Ryung Kim",
"givenName": "Suh-Ryung",
"surname": "Kim",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dpt of Mathematics Education, Seoul National University, Seoul, South Korea",
"fullName": "Yun Joo Yoo",
"givenName": "Yun Joo",
"surname": "Yoo",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-11-01T00:00:00",
"pubType": "proceedings",
"pages": "1761-1768",
"year": "2017",
"issn": null,
"isbn": "978-1-5090-3050-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "08217926",
"articleId": "12OmNBV9Iej",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08217928",
"articleId": "12OmNCd2rpB",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibmw/2010/8303/0/05703806",
"title": "Analyzing genetic factors involved in recombinant protein expression enhancement",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703806/12OmNrAMEOK",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999122",
"title": "Minimum dominating sets in cell cycle specific protein interaction networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999122/12OmNvTTc7P",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2009/3656/0/3656a374",
"title": "Phylogenetic Analysis Using Nuclear-Encoded Mitochondrial Proteins",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2009/3656a374/12OmNyeWdPr",
"parentPublication": {
"id": "proceedings/bibe/2009/3656/0",
"title": "2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2010/4083/0/4083a099",
"title": "Detecting Coevolution of Functionally Related Proteins for Automated Protein Annotation",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2010/4083a099/12OmNzTH0JI",
"parentPublication": {
"id": "proceedings/bibe/2010/4083/0",
"title": "2010 IEEE International Conference on Bioinformatics and Bioengineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2012/04/06081844",
"title": "Identification of Essential Proteins Based on Edge Clustering Coefficient",
"doi": null,
"abstractUrl": "/journal/tb/2012/04/06081844/13rRUwcS1Bn",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2017/01/07390043",
"title": "Extracting Coevolutionary Features from Protein Sequences for Predicting Protein-Protein Interactions",
"doi": null,
"abstractUrl": "/journal/tb/2017/01/07390043/13rRUx0xPtV",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2015/02/06853342",
"title": "UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks",
"doi": null,
"abstractUrl": "/journal/tb/2015/02/06853342/13rRUxlgxMW",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2018/04/07348677",
"title": "Towards Extracting Supporting Information About Predicted Protein-Protein Interactions",
"doi": null,
"abstractUrl": "/journal/tb/2018/04/07348677/13rRUyfbwpi",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/04/07845558",
"title": "Construction of Refined Protein Interaction Network for Predicting Essential Proteins",
"doi": null,
"abstractUrl": "/journal/tb/2019/04/07845558/13rRUyfbwpj",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpcc-smartcity-dss/2018/6614/0/661400a632",
"title": "GPU Parallelism of Phylogenetic Likelihood Estimates for Protein Data",
"doi": null,
"abstractUrl": "/proceedings-article/hpcc-smartcity-dss/2018/661400a632/183rAfFN7WP",
"parentPublication": {
"id": "proceedings/hpcc-smartcity-dss/2018/6614/0",
"title": "2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "12OmNyoSbiA",
"title": "2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)",
"acronym": "bibe",
"groupId": "1000075",
"volume": "0",
"displayVolume": "0",
"year": "2016",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNz5JBPC",
"doi": "10.1109/BIBE.2016.26",
"title": "Host-Pathogen Protein Interaction Prediction Based on Local Topology Structures of a Protein Interaction Network",
"normalizedTitle": "Host-Pathogen Protein Interaction Prediction Based on Local Topology Structures of a Protein Interaction Network",
"abstract": "Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's proteins with similar function. In addition, conservation between a protein's function and its local topological structure in a protein-protein interaction network (PIN) has been previously characterized. This leads to the hypothesis that pathogens target the host's proteins with a similar local topological structure in a PIN. In this work, this hypothesis is examined by adding a graphlet degree vector of a protein in the human PIN as a feature in the prediction model and using that model to predict the protein-protein interaction between human and four pathogens. The results show that this graphlet degree vector increases the performance significantly for all pathogens. This suggests that the intraspecies protein-protein interactions should be taken into consideration when developing prediction methods for host-pathogen protein interaction. The results also support the hypothesis that there exists a relationship between a protein's function and the local topology of the PIN.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's proteins with similar function. In addition, conservation between a protein's function and its local topological structure in a protein-protein interaction network (PIN) has been previously characterized. This leads to the hypothesis that pathogens target the host's proteins with a similar local topological structure in a PIN. In this work, this hypothesis is examined by adding a graphlet degree vector of a protein in the human PIN as a feature in the prediction model and using that model to predict the protein-protein interaction between human and four pathogens. The results show that this graphlet degree vector increases the performance significantly for all pathogens. This suggests that the intraspecies protein-protein interactions should be taken into consideration when developing prediction methods for host-pathogen protein interaction. The results also support the hypothesis that there exists a relationship between a protein's function and the local topology of the PIN.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's proteins with similar function. In addition, conservation between a protein's function and its local topological structure in a protein-protein interaction network (PIN) has been previously characterized. This leads to the hypothesis that pathogens target the host's proteins with a similar local topological structure in a PIN. In this work, this hypothesis is examined by adding a graphlet degree vector of a protein in the human PIN as a feature in the prediction model and using that model to predict the protein-protein interaction between human and four pathogens. The results show that this graphlet degree vector increases the performance significantly for all pathogens. This suggests that the intraspecies protein-protein interactions should be taken into consideration when developing prediction methods for host-pathogen protein interaction. The results also support the hypothesis that there exists a relationship between a protein's function and the local topology of the PIN.",
"fno": "3834a007",
"keywords": [
"Proteins",
"Orbits",
"Protein Engineering",
"Pathogens",
"Pins",
"Predictive Models",
"Topology",
"Protein Protein Interaction Network",
"Graphlet Degree Vector",
"Host Pathogen Interactions",
"Local Topology Structure",
"Protein Interaction Prediction"
],
"authors": [
{
"affiliation": null,
"fullName": "Jira Jindalertudomdee",
"givenName": "Jira",
"surname": "Jindalertudomdee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Morihiro Hayashida",
"givenName": "Morihiro",
"surname": "Hayashida",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jiangning Song",
"givenName": "Jiangning",
"surname": "Song",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Tatsuya Akutsu",
"givenName": "Tatsuya",
"surname": "Akutsu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibe",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2016-10-01T00:00:00",
"pubType": "proceedings",
"pages": "7-12",
"year": "2016",
"issn": "2471-7819",
"isbn": "978-1-5090-3834-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "3834a001",
"articleId": "12OmNyugyS7",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "3834a013",
"articleId": "12OmNBhHtgy",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibmw/2010/8303/0/05703768",
"title": "Identification of core-attachment complexes based on maximal frequent patterns in protein-protein interaction networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703768/12OmNB7LvJl",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fbit/2007/2999/0/29990294",
"title": "Molecular Mimicry between SARS Coronavirus Spike Protein and Human Protein",
"doi": null,
"abstractUrl": "/proceedings-article/fbit/2007/29990294/12OmNBA9oAM",
"parentPublication": {
"id": "proceedings/fbit/2007/2999/0",
"title": "2007 Frontiers in the Convergence of Bioscience and Information Technologies (FBIT '07)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2008/3452/0/3452a461",
"title": "Document Classification for Mining Host Pathogen Protein-Protein Interactions",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2008/3452a461/12OmNrJiCTz",
"parentPublication": {
"id": "proceedings/bibm/2008/3452/0",
"title": "2008 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999204",
"title": "Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999204/12OmNrY3Lxn",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2016/1611/0/07822592",
"title": "Construction of the spatial and temporal active protein interaction network for identifying protein complexes",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2016/07822592/12OmNvD8RDM",
"parentPublication": {
"id": "proceedings/bibm/2016/1611/0",
"title": "2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigdata-congress/2016/2622/0/07584966",
"title": "Towards Data Analytics of Pathogen-Host Protein-Protein Interaction: A Survey",
"doi": null,
"abstractUrl": "/proceedings-article/bigdata-congress/2016/07584966/12OmNya72wR",
"parentPublication": {
"id": "proceedings/bigdata-congress/2016/2622/0",
"title": "2016 IEEE International Congress on Big Data (BigData Congress)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2020/03/08027089",
"title": "Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network",
"doi": null,
"abstractUrl": "/journal/tb/2020/03/08027089/13rRUy0qnEM",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/04/07845558",
"title": "Construction of Refined Protein Interaction Network for Predicting Essential Proteins",
"doi": null,
"abstractUrl": "/journal/tb/2019/04/07845558/13rRUyfbwpj",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigdata-congress/2017/1996/0/08029324",
"title": "Towards Elucidating the Structural Principles of Host-Pathogen Protein-Protein Interaction Networks: A Bioinformatics Survey",
"doi": null,
"abstractUrl": "/proceedings-article/bigdata-congress/2017/08029324/17D45XDIXRR",
"parentPublication": {
"id": "proceedings/bigdata-congress/2017/1996/0",
"title": "2017 IEEE International Congress on Big Data (BigData Congress)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2021/02/08737727",
"title": "Colored Network Motif Analysis by Dynamic Programming Approach: An Application in Host Pathogen Interaction Network",
"doi": null,
"abstractUrl": "/journal/tb/2021/02/08737727/1bmELtYoBSU",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1A9VchbY4Mw",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1A9W69hcizK",
"doi": "10.1109/BIBM52615.2021.9669606",
"title": "Improving human essential protein prediction using only protein sequences via ensemble learning",
"normalizedTitle": "Improving human essential protein prediction using only protein sequences via ensemble learning",
"abstract": "Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-GBDT.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-GBDT.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-GBDT.",
"fno": "09669606",
"keywords": [
"Biology Computing",
"Decision Trees",
"Gradient Methods",
"Learning Artificial Intelligence",
"Molecular Biophysics",
"Pattern Classification",
"Proteins",
"EP GBDT Outperforms State Of The Art Sequence Based Methods",
"Improving Human Essential Protein Prediction",
"Protein Sequences",
"Ensemble Learning",
"Essential Proteins",
"Computational Methods",
"Constructed Protein Protein Interaction Networks",
"Biological Data",
"High Quality PPI Networks",
"Fast Prediction",
"Effective Prediction",
"EPGBDT",
"Multiple Gradient Boosting Decision Tree Base Classifiers",
"Proteins",
"Protein Engineering",
"Costs",
"Codes",
"Conferences",
"Predictive Models",
"Biomedical Measurement",
"Essential Protein Prediction",
"Machine Learning",
"Ensemble Learning",
"Sequence Based Method"
],
"authors": [
{
"affiliation": "Central South University,Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering,Changsha,Hunan,China,410083",
"fullName": "Min Zeng",
"givenName": "Min",
"surname": "Zeng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Central South University,Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering,Changsha,Hunan,China,410083",
"fullName": "Nian Wang",
"givenName": "Nian",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Central South University,Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering,Changsha,Hunan,China,410083",
"fullName": "Yifan Wu",
"givenName": "Yifan",
"surname": "Wu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Central South University,Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering,Changsha,Hunan,China,410083",
"fullName": "Yiming Li",
"givenName": "Yiming",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Saskatchewan,Division of Biomedical Engineering,Department of Computer Science, Department of Mechanical Engineering,Saskatoon,Canada,SK S7N 5A9",
"fullName": "Fang-Xiang Wu",
"givenName": "Fang-Xiang",
"surname": "Wu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Central South University,Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering,Changsha,Hunan,China,410083",
"fullName": "Min Li",
"givenName": "Min",
"surname": "Li",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-12-01T00:00:00",
"pubType": "proceedings",
"pages": "98-103",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-0126-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09669781",
"articleId": "1A9VnIQzfIQ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09669306",
"articleId": "1A9WaG2KJqg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibmw/2009/5121/0/05332069",
"title": "Mining impact of protein modifications on protein-protein interactions from literature",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2009/05332069/12OmNCmGNNk",
"parentPublication": {
"id": "proceedings/bibmw/2009/5121/0",
"title": "2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999204",
"title": "Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999204/12OmNrY3Lxn",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2008/3440/2/3440b066",
"title": "A Unified Scoring Scheme for Detecting Essential Proteins in Protein Interaction Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2008/3440b066/12OmNvAiSl3",
"parentPublication": {
"id": "proceedings/ictai/2008/3440/2",
"title": "2008 20th IEEE International Conference on Tools with Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2012/2559/0/06392716",
"title": "Identifying essential proteins via integration of protein interaction and gene expression data",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2012/06392716/12OmNvq5jD7",
"parentPublication": {
"id": "proceedings/bibm/2012/2559/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2011/4584/0/4584a063",
"title": "Exploring Centrality-Lethality Rule from Evolution Constraint on Essential Hub and Nonessential Hub in Yeast Protein-Protein Interaction Network",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2011/4584a063/12OmNzYeAQD",
"parentPublication": {
"id": "proceedings/cis/2011/4584/0",
"title": "2011 Seventh International Conference on Computational Intelligence and Security",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/02/07927488",
"title": "Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks",
"doi": null,
"abstractUrl": "/journal/tb/2019/02/07927488/13rRUxASuRj",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2015/02/06853342",
"title": "UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks",
"doi": null,
"abstractUrl": "/journal/tb/2015/02/06853342/13rRUxlgxMW",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2018/04/07348677",
"title": "Towards Extracting Supporting Information About Predicted Protein-Protein Interactions",
"doi": null,
"abstractUrl": "/journal/tb/2018/04/07348677/13rRUyfbwpi",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2023/01/09730023",
"title": "Analyzing Effect of Multi-Modality in Predicting Protein-Protein Interactions",
"doi": null,
"abstractUrl": "/journal/tb/2023/01/09730023/1BzORTQ41Lq",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2022/06/09585636",
"title": "Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning",
"doi": null,
"abstractUrl": "/journal/tb/2022/06/09585636/1y10pQWgDao",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |
{
"proceeding": {
"id": "1JC1F8KcINO",
"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "9994793",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1JC2QzXaDCM",
"doi": "10.1109/BIBM55620.2022.9995224",
"title": "Protein-Protein Interaction Network Analysis Reveals Distinct Patterns of Antibiotic Resistance Genes",
"normalizedTitle": "Protein-Protein Interaction Network Analysis Reveals Distinct Patterns of Antibiotic Resistance Genes",
"abstract": "Antibiotic resistance genes (ARGs) are responsible for an increasing number of bacterial infections worldwide. ARGs are challenging to track within bacterial genomes as they are often subject to horizontal gene transfer via mobile genetic elements (MGEs). Complex protein-protein networks can reveal proteins contributing to the spread and persistence. Here, we developed a pipeline that facilitates this process by analyzing features of a Protein-Protein Interaction Network (PPIN). This pipeline uses a random forest model to distinguish ARGs from non-ARGs and explores associations between ARGs and proteins with which they functionally interact. We tested the approach using the PPINs of Escherichia coli and Acinetobacter baumannii, two deadly organisms known to carry ARGs harbored by MGEs, and achieved a macro average accuracy of 85% in ARG identification. The approach also revealed that ARGs are disproportionately associated with MGEs and the neighbors (genes connected with only one edge to the ARGs) are likely to be less mobile.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Antibiotic resistance genes (ARGs) are responsible for an increasing number of bacterial infections worldwide. ARGs are challenging to track within bacterial genomes as they are often subject to horizontal gene transfer via mobile genetic elements (MGEs). Complex protein-protein networks can reveal proteins contributing to the spread and persistence. Here, we developed a pipeline that facilitates this process by analyzing features of a Protein-Protein Interaction Network (PPIN). This pipeline uses a random forest model to distinguish ARGs from non-ARGs and explores associations between ARGs and proteins with which they functionally interact. We tested the approach using the PPINs of Escherichia coli and Acinetobacter baumannii, two deadly organisms known to carry ARGs harbored by MGEs, and achieved a macro average accuracy of 85% in ARG identification. The approach also revealed that ARGs are disproportionately associated with MGEs and the neighbors (genes connected with only one edge to the ARGs) are likely to be less mobile.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Antibiotic resistance genes (ARGs) are responsible for an increasing number of bacterial infections worldwide. ARGs are challenging to track within bacterial genomes as they are often subject to horizontal gene transfer via mobile genetic elements (MGEs). Complex protein-protein networks can reveal proteins contributing to the spread and persistence. Here, we developed a pipeline that facilitates this process by analyzing features of a Protein-Protein Interaction Network (PPIN). This pipeline uses a random forest model to distinguish ARGs from non-ARGs and explores associations between ARGs and proteins with which they functionally interact. We tested the approach using the PPINs of Escherichia coli and Acinetobacter baumannii, two deadly organisms known to carry ARGs harbored by MGEs, and achieved a macro average accuracy of 85% in ARG identification. The approach also revealed that ARGs are disproportionately associated with MGEs and the neighbors (genes connected with only one edge to the ARGs) are likely to be less mobile.",
"fno": "09995224",
"keywords": [
"Bioinformatics",
"Biology Computing",
"Cellular Biophysics",
"Diseases",
"Genetics",
"Genomics",
"Microorganisms",
"Molecular Biophysics",
"Molecular Configurations",
"Proteins",
"Antibiotic Resistance Genes",
"ARG Identification",
"AR Gs",
"Complex Protein Protein Networks",
"Horizontal Gene Transfer",
"MG Es",
"Non AR Gs",
"Protein Protein Interaction Network Analysis Reveals Distinct Patterns",
"Proteins",
"Protein Engineering",
"Microorganisms",
"Antibiotics",
"Pipelines",
"Genomics",
"Network Analyzers",
"ARG",
"PPI",
"PPIN",
"Protein",
"Resistance"
],
"authors": [
{
"affiliation": "Virginia Polytechnic Institute and State University,Department of Computer Science,Blacksburg,Virginia",
"fullName": "Nazifa Ahmed Moumi",
"givenName": "Nazifa Ahmed",
"surname": "Moumi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Virginia Polytechnic Institute and State University,Genetics, Bioinformatics, and Computational Biology,Blacksburg,Virginia",
"fullName": "Connor L. Brown",
"givenName": "Connor L.",
"surname": "Brown",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Virginia Polytechnic Institute and State University,Department of Civil & Environmental Engineering,Blacksburg,Virginia",
"fullName": "Peter J. Vikesland",
"givenName": "Peter J.",
"surname": "Vikesland",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Virginia Polytechnic Institute and State University,Department of Civil & Environmental Engineering,Blacksburg,Virginia",
"fullName": "Amy Pruden",
"givenName": "Amy",
"surname": "Pruden",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Virginia Polytechnic Institute and State University,Department of Computer Science,Blacksburg,Virginia",
"fullName": "Liqing Zhang",
"givenName": "Liqing",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-12-01T00:00:00",
"pubType": "proceedings",
"pages": "73-76",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6819-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09995256",
"articleId": "1JC2VSmuW0E",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09995592",
"articleId": "1JC33b2rpXG",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibmw/2010/8303/0/05703806",
"title": "Analyzing genetic factors involved in recombinant protein expression enhancement",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703806/12OmNrAMEOK",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2014/7502/0/7502a163",
"title": "New Feature Weighting Technique for Predicting Protein Subcellular Localization",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2014/7502a163/12OmNx2QULG",
"parentPublication": {
"id": "proceedings/bibe/2014/7502/0",
"title": "2014 IEEE International Conference on Bioinformatics and Bioengineering (BIBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bmei/2008/3118/1/3118a437",
"title": "Schizophrenia Genes: Characteristics of Function and Protein Interaction Networks",
"doi": null,
"abstractUrl": "/proceedings-article/bmei/2008/3118a437/12OmNxcMSjt",
"parentPublication": {
"id": "proceedings/bmei/2008/3118/1",
"title": "2008 International Conference on Biomedical Engineering and Informatics (BMEI 2008)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2012/4925/0/4925a094",
"title": "Discovering Aging-Genes by Topological Features in Drosophila melanogaster Protein-Protein Interaction Network",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2012/4925a094/12OmNy2agJn",
"parentPublication": {
"id": "proceedings/icdmw/2012/4925/0",
"title": "2012 IEEE 12th International Conference on Data Mining Workshops",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2017/3050/0/08217626",
"title": "Co-expression networks between protein encoding mitochondrial genes and all the remaining genes in human tissues",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2017/08217626/12OmNyp9Mii",
"parentPublication": {
"id": "proceedings/bibm/2017/3050/0",
"title": "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/04/07845607",
"title": "HMMCAS: A Web Tool for the Identification and Domain Annotations of CAS Proteins",
"doi": null,
"abstractUrl": "/journal/tb/2019/04/07845607/13rRUILtJkr",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/04/07857030",
"title": "Identifying Bacterial Essential Genes Based on a Feature-Integrated Method",
"doi": null,
"abstractUrl": "/journal/tb/2019/04/07857030/13rRUwh80Bs",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669712",
"title": "Integration of text mining and biological network analysis to access essential genes in Desulfovibrio alaskensis G20",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669712/1A9VmXJrg8U",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669320",
"title": "Triple-view Learning for Predicting Antibiotic Resistance Genes",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669320/1A9WxGziUiA",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2022/6819/0/09995492",
"title": "LM-ARG: Identification & classification of antibiotic resistance genes leveraging pre-trained protein language models",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2022/09995492/1JC22vVg7hm",
"parentPublication": {
"id": "proceedings/bibm/2022/6819/0",
"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
} |