G. Nie, Lingling Zhang, Yuejin Zhang, Wei Deng, Yong Shi
This paper discusses the second-order mining of the results of data mining. There is still gap between the knowledge which can direct the operation of company and the knowledge got from data mining. We take the knowledge from data mining as primary knowledge and the knowledge from second-order data mining as intelligent knowledge. We discuss the importance of intelligent knowledge and the way to find intelligent knowledge from primary knowledge via second-order mining process in this study. Three cases from China are used to demonstrate the methods to finish second mining. These cases are related to central bank credit scoring, credit card churn prediction, and email service fields respectively.
{"title":"Find Intelligent Knowledge by Second-Order Mining: Three Cases from China","authors":"G. Nie, Lingling Zhang, Yuejin Zhang, Wei Deng, Yong Shi","doi":"10.1109/ICDMW.2010.115","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.115","url":null,"abstract":"This paper discusses the second-order mining of the results of data mining. There is still gap between the knowledge which can direct the operation of company and the knowledge got from data mining. We take the knowledge from data mining as primary knowledge and the knowledge from second-order data mining as intelligent knowledge. We discuss the importance of intelligent knowledge and the way to find intelligent knowledge from primary knowledge via second-order mining process in this study. Three cases from China are used to demonstrate the methods to finish second mining. These cases are related to central bank credit scoring, credit card churn prediction, and email service fields respectively.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121234731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users¡¯ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Delicious website.
{"title":"Parallel User Profiling Based on Folksonomy for Large Scaled Recommender Systems: An Implimentation of Cascading MapReduce","authors":"Huizhi Liang, Jim Hogan, Yue Xu","doi":"10.1109/ICDMW.2010.161","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.161","url":null,"abstract":"The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users¡¯ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Delicious website.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127440143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we aim to resolve the recommendation problem by using the virtual ratings in online environments when user rating information is not available. As a matter of fact, in most of current websites especially the Chinese video-sharing ones, the traditional pure rating based collaborative filtering recommender methods are not fully qualified due to the sparsity of rating data. Motivated by our prior work on the investigation of user reviews that broadly appear in such sites, we hence propose a new recommender algorithm by fusing a self-supervised emoticon-integrated sentiment classification approach, by which the missing User-Item Rating Matrix can be substituted by the virtual ratings which are predicted by decomposing user reviews as given to the items. To test the algorithm’s practical value, we have first identified the self-supervised sentiment classification’s higher performance by comparing it with a supervised approach. Moreover, we conducted a statistic evaluation method to show the effectiveness of our recommender system on improving Chinese online video recommendations’ accuracy.
{"title":"Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification","authors":"Weishi Zhang, Guiguang Ding, Li Chen, Chunping Li","doi":"10.1109/ICDMW.2010.27","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.27","url":null,"abstract":"In this paper we aim to resolve the recommendation problem by using the virtual ratings in online environments when user rating information is not available. As a matter of fact, in most of current websites especially the Chinese video-sharing ones, the traditional pure rating based collaborative filtering recommender methods are not fully qualified due to the sparsity of rating data. Motivated by our prior work on the investigation of user reviews that broadly appear in such sites, we hence propose a new recommender algorithm by fusing a self-supervised emoticon-integrated sentiment classification approach, by which the missing User-Item Rating Matrix can be substituted by the virtual ratings which are predicted by decomposing user reviews as given to the items. To test the algorithm’s practical value, we have first identified the self-supervised sentiment classification’s higher performance by comparing it with a supervised approach. Moreover, we conducted a statistic evaluation method to show the effectiveness of our recommender system on improving Chinese online video recommendations’ accuracy.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126064237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Su, Sudheer Chelluboina, Michael Hahsler, M. Dunham
This paper proposes a new hurricane intensity prediction model, WFL-EMM, which is based on the data mining techniques of feature weight learning (WFL) and Extensible Markov Model (EMM). The data features used are those employed by one of the most popular intensity prediction models, SHIPS. In our algorithm, the weights of the features are learned by a genetic algorithm (GA) using historical hurricane data. As the GAs fitness function we use the error of the intensity prediction by an EMM learned using given feature weights. For fitness calculation we use a technique similar to k-fold cross validation on the training data. The best weights obtained by the genetic algorithm are used to build an EMM with all training data. This EMM is then applied to predict the hurricane intensities and compute prediction errors for the test data. Using historical data for the named Atlantic tropical cyclones from 1982 to 2003, experiments demonstrate that WFL-EMM provides significantly more accurate intensity predictions than SHIPS within 72 hours. Since we report here first results, we indicate how to improve WFL-EMM in the future.
{"title":"A New Data Mining Model for Hurricane Intensity Prediction","authors":"Y. Su, Sudheer Chelluboina, Michael Hahsler, M. Dunham","doi":"10.1109/ICDMW.2010.158","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.158","url":null,"abstract":"This paper proposes a new hurricane intensity prediction model, WFL-EMM, which is based on the data mining techniques of feature weight learning (WFL) and Extensible Markov Model (EMM). The data features used are those employed by one of the most popular intensity prediction models, SHIPS. In our algorithm, the weights of the features are learned by a genetic algorithm (GA) using historical hurricane data. As the GAs fitness function we use the error of the intensity prediction by an EMM learned using given feature weights. For fitness calculation we use a technique similar to k-fold cross validation on the training data. The best weights obtained by the genetic algorithm are used to build an EMM with all training data. This EMM is then applied to predict the hurricane intensities and compute prediction errors for the test data. Using historical data for the named Atlantic tropical cyclones from 1982 to 2003, experiments demonstrate that WFL-EMM provides significantly more accurate intensity predictions than SHIPS within 72 hours. Since we report here first results, we indicate how to improve WFL-EMM in the future.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115020229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stephan Günnemann, Hardy Kremer, Ines Färber, T. Seidl
Large amounts of data are ubiquitous today. Data mining methods like clustering were introduced to gain knowledge from these data. Recently, detection of multiple clusterings has become an active research area, where several alternative clustering solutions are generated for a single dataset. Each of the obtained clustering solutions is valid, of importance, and provides a different interpretation of the data. The key for knowledge extraction, however, is to learn how the different solutions are related to each other. This can be achieved by a comparison and analysis of the obtained clustering solutions. We introduce our demo MCExplorer, the first tool that allows for interactive exploration, browsing, and visualization of multiple clustering solutions on several granularities. MCExplorer is applicable to the output of both fullspace and subspace clustering approaches.
{"title":"MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions","authors":"Stephan Günnemann, Hardy Kremer, Ines Färber, T. Seidl","doi":"10.1109/ICDMW.2010.29","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.29","url":null,"abstract":"Large amounts of data are ubiquitous today. Data mining methods like clustering were introduced to gain knowledge from these data. Recently, detection of multiple clusterings has become an active research area, where several alternative clustering solutions are generated for a single dataset. Each of the obtained clustering solutions is valid, of importance, and provides a different interpretation of the data. The key for knowledge extraction, however, is to learn how the different solutions are related to each other. This can be achieved by a comparison and analysis of the obtained clustering solutions. We introduce our demo MCExplorer, the first tool that allows for interactive exploration, browsing, and visualization of multiple clustering solutions on several granularities. MCExplorer is applicable to the output of both fullspace and subspace clustering approaches.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117296257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Petko Bogdanov, Nicholas D. Larusso, Ambuj K. Singh
—We propose a framework for discovery of collaborative community structure in Wiki-based knowledge repositories based on raw-content generation analysis. We leverage topic modelling in order to capture agreement and opposition of contributors and analyze these multi-modal relations to map communities in the contributor base. The key steps of our approach include (i) modeling of pair wise variable-strength contributor interactions that can be both positive and negative, (ii) synthesis of a global network incorporating all pair wise interactions, and (iii) detection and analysis of community structure encoded in such networks. The global community discovery algorithm we propose outperforms existing alternatives in identifying coherent clusters according to objective optimality criteria. Analysis of the discovered community structure reveals coalitions of common interest editors who back each other in promoting some topics and collectively oppose other coalitions or single authors. We couple contributor interactions with content evolution and reveal the global picture of opposing themes within the self-regulated community base for both controversial and featured articles in Wikipedia.
{"title":"Towards Community Discovery in Signed Collaborative Interaction Networks","authors":"Petko Bogdanov, Nicholas D. Larusso, Ambuj K. Singh","doi":"10.1109/ICDMW.2010.174","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.174","url":null,"abstract":"—We propose a framework for discovery of collaborative community structure in Wiki-based knowledge repositories based on raw-content generation analysis. We leverage topic modelling in order to capture agreement and opposition of contributors and analyze these multi-modal relations to map communities in the contributor base. The key steps of our approach include (i) modeling of pair wise variable-strength contributor interactions that can be both positive and negative, (ii) synthesis of a global network incorporating all pair wise interactions, and (iii) detection and analysis of community structure encoded in such networks. The global community discovery algorithm we propose outperforms existing alternatives in identifying coherent clusters according to objective optimality criteria. Analysis of the discovered community structure reveals coalitions of common interest editors who back each other in promoting some topics and collectively oppose other coalitions or single authors. We couple contributor interactions with content evolution and reveal the global picture of opposing themes within the self-regulated community base for both controversial and featured articles in Wikipedia.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124572241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we put forward our approach for answering aggregated queries over imprecise data using domain specific taxonomies. A new concept we call the weighted hierarchical hyper graph has been introduced, which helps in answering aggregated queries when dealing with imprecise databases. We assume that the existence of a knowledge base is permanent and independent of the imprecision in the database. We use this concept to build a temporary database known as the extended database and use the extended database to build the marginal database, which efficiently answers aggregated queries over an imprecise data.
{"title":"Using Taxonomies to Perform Aggregated Querying over Imprecise Data","authors":"Atanu Roy, Chandrima Sarkar, R. Angryk","doi":"10.1109/ICDMW.2010.173","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.173","url":null,"abstract":"In this paper, we put forward our approach for answering aggregated queries over imprecise data using domain specific taxonomies. A new concept we call the weighted hierarchical hyper graph has been introduced, which helps in answering aggregated queries when dealing with imprecise databases. We assume that the existence of a knowledge base is permanent and independent of the imprecision in the database. We use this concept to build a temporary database known as the extended database and use the extended database to build the marginal database, which efficiently answers aggregated queries over an imprecise data.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhengzhang Chen, Kevin A. Wilson, Ye Jin, W. Hendrix, N. Samatova
Community structure or clustering is ubiquitous in many evolutionary networks including social networks, biological networks and financial market networks. Detecting and tracking community deviations in evolutionary networks can uncover important and interesting behaviors that are latent if we ignore the dynamic information. In biological networks, for example, a small variation in a gene community may indicate an event, such as gene fusion, gene fission, or gene decay. In contrast to the previous work on detecting communities in static graphs or tracking conserved communities in time-varying graphs, this paper first introduces the concept of community dynamics, and then shows that the baseline approach by enumerating all communities in each graph and comparing all pairs of communities between consecutive graphs is infeasible and impractical. We propose an efficient method for detecting and tracking community dynamics in evolutionary networks by introducing graph representatives and community representatives to avoid generating redundant communities and limit the search space. We measure the performance of the representative-based algorithm by comparison to the baseline algorithm on synthetic networks, and our experiments show that our algorithm achieves a runtime speedup of 11–46. The method has also been applied to two real-world evolutionary networks including Food Web and Enron Email. Significant and informative community dynamics have been detected in both cases.
{"title":"Detecting and Tracking Community Dynamics in Evolutionary Networks","authors":"Zhengzhang Chen, Kevin A. Wilson, Ye Jin, W. Hendrix, N. Samatova","doi":"10.1109/ICDMW.2010.32","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.32","url":null,"abstract":"Community structure or clustering is ubiquitous in many evolutionary networks including social networks, biological networks and financial market networks. Detecting and tracking community deviations in evolutionary networks can uncover important and interesting behaviors that are latent if we ignore the dynamic information. In biological networks, for example, a small variation in a gene community may indicate an event, such as gene fusion, gene fission, or gene decay. In contrast to the previous work on detecting communities in static graphs or tracking conserved communities in time-varying graphs, this paper first introduces the concept of community dynamics, and then shows that the baseline approach by enumerating all communities in each graph and comparing all pairs of communities between consecutive graphs is infeasible and impractical. We propose an efficient method for detecting and tracking community dynamics in evolutionary networks by introducing graph representatives and community representatives to avoid generating redundant communities and limit the search space. We measure the performance of the representative-based algorithm by comparison to the baseline algorithm on synthetic networks, and our experiments show that our algorithm achieves a runtime speedup of 11–46. The method has also been applied to two real-world evolutionary networks including Food Web and Enron Email. Significant and informative community dynamics have been detected in both cases.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123655428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, money laundering is becoming more and more sophisticated, it seems to have moved from the personal gain to the cliché of drug trafficking and financing terrorism. This criminal activity poses a serious threat not only to financial institutions but also to the nation. Today, most international financial institutions have been implementing anti-money laundering solutions but traditional investigative techniques consume numerous man-hours. Besides, most of the existing commercial solutions are based on statistics such as means and standard deviations and therefore are not efficient enough, especially for detecting suspicious cases in investment activities. In this paper, we present a case study of applying a knowledge-based solution that combines data mining and natural computing techniques to detect money laundering patterns. This solution is a part of a collaboration project between our research group and an international investment bank.
{"title":"Application of Data Mining for Anti-money Laundering Detection: A Case Study","authors":"Nhien-An Le-Khac, Mohand Tahar Kechadi","doi":"10.1109/ICDMW.2010.66","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.66","url":null,"abstract":"Recently, money laundering is becoming more and more sophisticated, it seems to have moved from the personal gain to the cliché of drug trafficking and financing terrorism. This criminal activity poses a serious threat not only to financial institutions but also to the nation. Today, most international financial institutions have been implementing anti-money laundering solutions but traditional investigative techniques consume numerous man-hours. Besides, most of the existing commercial solutions are based on statistics such as means and standard deviations and therefore are not efficient enough, especially for detecting suspicious cases in investment activities. In this paper, we present a case study of applying a knowledge-based solution that combines data mining and natural computing techniques to detect money laundering patterns. This solution is a part of a collaboration project between our research group and an international investment bank.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123728890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. S. Gowri, K. Shameer, C. C. S. Reddy, P. Shingate, R. Sowdhamini
Protein domains are the compact, evolutionarily conserved units of proteins that can be utilized for function association of the large number of gene products realised from whole genome sequencing projects. Homology, inferred by sequence similarity, is usually a reason for transfer of function annotation from pre-existing domain families to gene products. Sequence analysis protocols are directed by the reference sequence of families used for homology searches to reduce computational time in such large-scale data mining processes. As protein domain families are diverse in nature, it is an important task to identify a single best representative sequence member from a protein domain family using a well-defined, reproducible bioinformatics protocol. We report a new bioinformatics protocol that can be used to identify best representative sequence (BRS) from protein domain families. The method is based on “coverage analysis” score implemented using three different sequence search programs and the trends obtained in reporting best representative sequence are assessed. The highest average coverage for BRPs was 66% when searched using Hidden Markov Models. Further, it is crucial to select BRS specific for a sequence search method when searching in large sequence databases.
{"title":"A Sequence Data Mining Protocol to Identify Best Representative Sequence for Protein Domain Families","authors":"V. S. Gowri, K. Shameer, C. C. S. Reddy, P. Shingate, R. Sowdhamini","doi":"10.1109/ICDMW.2010.153","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.153","url":null,"abstract":"Protein domains are the compact, evolutionarily conserved units of proteins that can be utilized for function association of the large number of gene products realised from whole genome sequencing projects. Homology, inferred by sequence similarity, is usually a reason for transfer of function annotation from pre-existing domain families to gene products. Sequence analysis protocols are directed by the reference sequence of families used for homology searches to reduce computational time in such large-scale data mining processes. As protein domain families are diverse in nature, it is an important task to identify a single best representative sequence member from a protein domain family using a well-defined, reproducible bioinformatics protocol. We report a new bioinformatics protocol that can be used to identify best representative sequence (BRS) from protein domain families. The method is based on “coverage analysis” score implemented using three different sequence search programs and the trends obtained in reporting best representative sequence are assessed. The highest average coverage for BRPs was 66% when searched using Hidden Markov Models. Further, it is crucial to select BRS specific for a sequence search method when searching in large sequence databases.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115810086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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