M. Hoogendoorn, S. W. Jaffry, P. V. Maanen, Jan Treur
When considering intelligent agents that interact with humans, having an idea of the trust levels of the human, for example in other agents or services, can be of great importance. Most models of human trust that exist, are based on some rationality assumption, and biased behavior is not represented, whereas a vast literature in Cognitive and Social Sciences indicates that humans often exhibit non-rational, biased behavior with respect to trust. This paper reports how some variations of biased human trust models have been designed, analyzed and validated against empirical data. The results show that such biased trust models are able to predict human trust significantly better.
{"title":"Modeling and Validation of Biased Human Trust","authors":"M. Hoogendoorn, S. W. Jaffry, P. V. Maanen, Jan Treur","doi":"10.1109/WI-IAT.2011.198","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.198","url":null,"abstract":"When considering intelligent agents that interact with humans, having an idea of the trust levels of the human, for example in other agents or services, can be of great importance. Most models of human trust that exist, are based on some rationality assumption, and biased behavior is not represented, whereas a vast literature in Cognitive and Social Sciences indicates that humans often exhibit non-rational, biased behavior with respect to trust. This paper reports how some variations of biased human trust models have been designed, analyzed and validated against empirical data. The results show that such biased trust models are able to predict human trust significantly better.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133789868","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}
Björn-Oliver Hartmann, Klemens Böhm, Christian Hütter
Social search platforms like Aardvark or Yahoo Answers have attracted a lot of attention lately. In principle, participants have two strategic dimensions in social search systems: (1) Interaction selection, i.e., forwarding/processing incoming requests (or not), and (2) contact selection, i.e., adding or dropping contacts. In systems with these strategic dimensions, it is unclear whether nodes cooperate, and if they form efficient network structures. To shed light on this fundamental question, we have conducted a study to investigate human behavior in interaction selection and to investigate the ability of humans to form efficient networks. In order to limit the degree of problem understanding necessary by the study participants, we have introduced the problem as an online game. 193 subjects joined the study that was online for 67 days. One result is that subjects choose contacts strategically and that they use strategies that lead to cooperative and almost efficient systems. Surprisingly, subjects tend to overestimate the value of cooperative contacts and keep cooperative but costly contacts. This observation is important: Assisting agents that help subjects to avoid this behavior might yield more efficiency.
{"title":"Strategic Behavior in Interaction Selection and Contact Selection","authors":"Björn-Oliver Hartmann, Klemens Böhm, Christian Hütter","doi":"10.1109/WI-IAT.2011.23","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.23","url":null,"abstract":"Social search platforms like Aardvark or Yahoo Answers have attracted a lot of attention lately. In principle, participants have two strategic dimensions in social search systems: (1) Interaction selection, i.e., forwarding/processing incoming requests (or not), and (2) contact selection, i.e., adding or dropping contacts. In systems with these strategic dimensions, it is unclear whether nodes cooperate, and if they form efficient network structures. To shed light on this fundamental question, we have conducted a study to investigate human behavior in interaction selection and to investigate the ability of humans to form efficient networks. In order to limit the degree of problem understanding necessary by the study participants, we have introduced the problem as an online game. 193 subjects joined the study that was online for 67 days. One result is that subjects choose contacts strategically and that they use strategies that lead to cooperative and almost efficient systems. Surprisingly, subjects tend to overestimate the value of cooperative contacts and keep cooperative but costly contacts. This observation is important: Assisting agents that help subjects to avoid this behavior might yield more efficiency.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100145","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}
Róbert Móro, Ivan Srba, Maros Uncík, M. Bieliková, Marián Simko
In recent years we have witnessed expansion of Web 2.0. Its main feature is allowing users' collaboration in content creation using various means, e.g. annotations, discussions, wikis, blogs or tags. This approach has influenced also web-based learning, for which the term "Learning 2.0" has been introduced. In this paper we explore using tags in such systems. Tags can be used for improving of searching, categorization of web-documents, creating folksonomies and ontologies or enhancing the user-model. Another aspect of tags is that they act as a bridge between resources and users to create a social network. We integrated tags in a learning framework ALEF and experimentally evaluated their usage in education process.
{"title":"Towards Collaborative Metadata Enrichment for Adaptive Web-Based Learning","authors":"Róbert Móro, Ivan Srba, Maros Uncík, M. Bieliková, Marián Simko","doi":"10.1109/WI-IAT.2011.220","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.220","url":null,"abstract":"In recent years we have witnessed expansion of Web 2.0. Its main feature is allowing users' collaboration in content creation using various means, e.g. annotations, discussions, wikis, blogs or tags. This approach has influenced also web-based learning, for which the term \"Learning 2.0\" has been introduced. In this paper we explore using tags in such systems. Tags can be used for improving of searching, categorization of web-documents, creating folksonomies and ontologies or enhancing the user-model. Another aspect of tags is that they act as a bridge between resources and users to create a social network. We integrated tags in a learning framework ALEF and experimentally evaluated their usage in education process.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321450","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 present a collective classification approach for identifying untrustworthy individuals in multi-agent communities from a combination of observable features and network connections. Under the assumption that data are organized as independent and identically distributed (i.i.d.)samples, traditional classification is typically performed on each object independently, without considering the underlying network connecting the instances. In collective classification, a set of relational features, based on the connections between instances, is used to augment the feature vector used in classification. This approach can perform particularly well when the underlying data exhibits homophily, a propensity for similar items to be connected. We suggest that in many cases human communities exhibit homophily in trust levels since shared attitudes toward trust can facilitate the formation and maintenance of bonds, in the same way that other types of shared beliefs and value systems do. Hence, knowledge of an agent's connections provides a valuable cue that can assist in the identification of untrustworthy individuals who are misrepresenting themselves by modifying their observable information. This paper presents results that demonstrate that our proposed trust evaluation method is robust in cases where a large percentage of the individuals present misleading information.
{"title":"Leveraging Network Properties for Trust Evaluation in Multi-agent Systems","authors":"Xi Wang, M. Maghami, G. Sukthankar","doi":"10.1109/WI-IAT.2011.217","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.217","url":null,"abstract":"In this paper, we present a collective classification approach for identifying untrustworthy individuals in multi-agent communities from a combination of observable features and network connections. Under the assumption that data are organized as independent and identically distributed (i.i.d.)samples, traditional classification is typically performed on each object independently, without considering the underlying network connecting the instances. In collective classification, a set of relational features, based on the connections between instances, is used to augment the feature vector used in classification. This approach can perform particularly well when the underlying data exhibits homophily, a propensity for similar items to be connected. We suggest that in many cases human communities exhibit homophily in trust levels since shared attitudes toward trust can facilitate the formation and maintenance of bonds, in the same way that other types of shared beliefs and value systems do. Hence, knowledge of an agent's connections provides a valuable cue that can assist in the identification of untrustworthy individuals who are misrepresenting themselves by modifying their observable information. This paper presents results that demonstrate that our proposed trust evaluation method is robust in cases where a large percentage of the individuals present misleading information.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115785377","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}
Most traditional information retrieval systems are based on single terms indexing. However, it is admitted that semantic content of a document (or a query) cannot be accurately captured by a simple set of independent keywords. Although, several works have incorporated phrases or other syntactic information in IR, such attempts have shown slight benefit, at best. Particularly in language modeling approaches this is achieved through the use of the big ram or n-gram models. However, in these models all big rams/n-grams are considered and weighted uniformly. In this paper we introduce a new approach to weight and consider only certain types of N-grams "compound terms". Experimental results on three test collections showed an improvement.
{"title":"A New Language Model Combining Single and Compound Terms","authors":"Arezki Hammache, R. Ahmed-Ouamer, M. Boughanem","doi":"10.1109/WI-IAT.2011.52","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.52","url":null,"abstract":"Most traditional information retrieval systems are based on single terms indexing. However, it is admitted that semantic content of a document (or a query) cannot be accurately captured by a simple set of independent keywords. Although, several works have incorporated phrases or other syntactic information in IR, such attempts have shown slight benefit, at best. Particularly in language modeling approaches this is achieved through the use of the big ram or n-gram models. However, in these models all big rams/n-grams are considered and weighted uniformly. In this paper we introduce a new approach to weight and consider only certain types of N-grams \"compound terms\". Experimental results on three test collections showed an improvement.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122180998","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}
Improving web search solely based on algorithmic refinements has reached a plateau. The emerging generation of searching techniques tries to harness the ``wisdom of crowds'', using inputs from users in the spirit of Web 2.0. In this paper, we introduce a framework facilitating friends augmented search techniques (FAST). To that end, we present a browser add-on as front end for collaborative browsing and searching, supporting synchronous and asynchronous collaboration between users. We then describe the back end, a distributed key-value store for efficient information retrieval in the presence of an evolving knowledge base. The mechanisms we explore in supporting efficient query processing for FAST are applicable for many other recent Web 2.0 applications that rely on similar key-value stores. The specific collaborative search tool we present is expected to be an useful utility in its own right and spur further research on friends augmented search techniques, while the data-management techniques we developed are of general interest and applicability.
{"title":"FAST: Friends Augmented Search Techniques - System Design & Data-Management Issues","authors":"C. Weth, Anwitaman Datta","doi":"10.1109/WI-IAT.2011.239","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.239","url":null,"abstract":"Improving web search solely based on algorithmic refinements has reached a plateau. The emerging generation of searching techniques tries to harness the ``wisdom of crowds'', using inputs from users in the spirit of Web 2.0. In this paper, we introduce a framework facilitating friends augmented search techniques (FAST). To that end, we present a browser add-on as front end for collaborative browsing and searching, supporting synchronous and asynchronous collaboration between users. We then describe the back end, a distributed key-value store for efficient information retrieval in the presence of an evolving knowledge base. The mechanisms we explore in supporting efficient query processing for FAST are applicable for many other recent Web 2.0 applications that rely on similar key-value stores. The specific collaborative search tool we present is expected to be an useful utility in its own right and spur further research on friends augmented search techniques, while the data-management techniques we developed are of general interest and applicability.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125505242","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}
G. Dias, Sebastião Pais, K. Wegrzyn-Wolska, R. Mahl
In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope, we introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific web snippet towards a more general web snippet. The subjacent idea is to find the best web snippet, which summarizes and subsumes all the other web snippets within an ephemeral cluster. To reach this objective, we first propose a new informative asymmetric similarity measure called the Simplified Asymmetric InfoSimba(AISs), which can be combined with different asymmetric association measures. In particular, the AISs proposes an unsupervised language-independent solution to infer Textual Entailment by Generality and as such can help to encounter the web snippet with maximum semantic coverage. This new methodology is tested against the first Recognizing Textual Entailment data set (RTE-1)1 for an exhaustive number of asymmetric association measures with and without the identification of Multiword Units. The comparative experiments with existing state-of-the-art methodologies show promising results.
{"title":"Recognizing Textual Entailment by Generality Using Informative Asymmetric Measures and Multiword Unit Identification to Summarize Ephemeral Clusters","authors":"G. Dias, Sebastião Pais, K. Wegrzyn-Wolska, R. Mahl","doi":"10.1109/WI-IAT.2011.122","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.122","url":null,"abstract":"In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope, we introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific web snippet towards a more general web snippet. The subjacent idea is to find the best web snippet, which summarizes and subsumes all the other web snippets within an ephemeral cluster. To reach this objective, we first propose a new informative asymmetric similarity measure called the Simplified Asymmetric InfoSimba(AISs), which can be combined with different asymmetric association measures. In particular, the AISs proposes an unsupervised language-independent solution to infer Textual Entailment by Generality and as such can help to encounter the web snippet with maximum semantic coverage. This new methodology is tested against the first Recognizing Textual Entailment data set (RTE-1)1 for an exhaustive number of asymmetric association measures with and without the identification of Multiword Units. The comparative experiments with existing state-of-the-art methodologies show promising results.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132542547","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}
Assuming a binomial distribution for word occurrence, we propose computing a standardized Z score to define the specific vocabulary of a subset compared to that of the entire corpus. This approach is applied to weight terms characterizing a document (or a sample of texts). We then show how these Z score values can be used to derive an efficient categorization scheme. To evaluate this proposition we categorize speeches given by B. Obama as either electoral or presidential. The results tend to show that the suggested classification scheme performs better than a Support Vector Machine scheme, and a Naive Bayes classifier (10-fold cross validation).
{"title":"Classification Based on Specific Vocabulary","authors":"J. Savoy, Olena Zubaryeva","doi":"10.1109/WI-IAT.2011.19","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.19","url":null,"abstract":"Assuming a binomial distribution for word occurrence, we propose computing a standardized Z score to define the specific vocabulary of a subset compared to that of the entire corpus. This approach is applied to weight terms characterizing a document (or a sample of texts). We then show how these Z score values can be used to derive an efficient categorization scheme. To evaluate this proposition we categorize speeches given by B. Obama as either electoral or presidential. The results tend to show that the suggested classification scheme performs better than a Support Vector Machine scheme, and a Naive Bayes classifier (10-fold cross validation).","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115903044","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}
A. Kopliku, Firas Damak, K. Pinel-Sauvagnat, M. Boughanem
Major search engines perform what is known as Aggregated Search (AS). They integrate results coming from different vertical search engines (images, videos, news, etc.) with typical Web search results. Aggregated search is relatively new and its advantages need to be evaluated. Some existing works have already tried to evaluate the interest (usefulness) of aggregated search as well as the effectiveness of the existing approaches. However, most of evaluation methodologies were based (i) on what we call relevance by intent (i.e. search results were not shown to real users), and (ii) short text queries. In this paper, we conducted a user study which was designed to revisit and compare the interest of aggregated search, by exploiting both relevance by intent and content, and using both short text and fixed need queries. This user study allowed us to analyze the distribution of relevant results across different verticals, and to show that AS helps to identify complementary relevant sources for the same information need. Comparison between relevance by intent and relevance by content showed that relevance by intent introduces a bias in evaluation. Discussion about the results also allowed us to identify some useful thoughts concerning the evaluation of AS approaches.
{"title":"Interest and Evaluation of Aggregated Search","authors":"A. Kopliku, Firas Damak, K. Pinel-Sauvagnat, M. Boughanem","doi":"10.1109/WI-IAT.2011.99","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.99","url":null,"abstract":"Major search engines perform what is known as Aggregated Search (AS). They integrate results coming from different vertical search engines (images, videos, news, etc.) with typical Web search results. Aggregated search is relatively new and its advantages need to be evaluated. Some existing works have already tried to evaluate the interest (usefulness) of aggregated search as well as the effectiveness of the existing approaches. However, most of evaluation methodologies were based (i) on what we call relevance by intent (i.e. search results were not shown to real users), and (ii) short text queries. In this paper, we conducted a user study which was designed to revisit and compare the interest of aggregated search, by exploiting both relevance by intent and content, and using both short text and fixed need queries. This user study allowed us to analyze the distribution of relevant results across different verticals, and to show that AS helps to identify complementary relevant sources for the same information need. Comparison between relevance by intent and relevance by content showed that relevance by intent introduces a bias in evaluation. Discussion about the results also allowed us to identify some useful thoughts concerning the evaluation of AS approaches.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132256717","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}
Knowledge-based data mining and classification algorithms require of systems that are able to extract textual attributes contained in raw text documents, and map them to structured knowledge sources (e.g. ontologies) so that they can be semantically analyzed. The system presented in this paper performs this tasks in an automatic way, relying on a predefined ontology which states the concepts in this the posterior data analysis will be focused. As features, our system focuses on extracting relevant Named Entities from textual resources describing a particular entity. Those are evaluated by means of linguistic and Web-based co-occurrence analyses to map them to ontological concepts, thereby discovering relevant features of the object. The system has been preliminary tested with tourist destinations and Wikipedia textual resources, showing promising results.
{"title":"Ontology-Based Feature Extraction","authors":"C. Vicient, D. Sánchez, Antonio Moreno","doi":"10.1109/WI-IAT.2011.199","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.199","url":null,"abstract":"Knowledge-based data mining and classification algorithms require of systems that are able to extract textual attributes contained in raw text documents, and map them to structured knowledge sources (e.g. ontologies) so that they can be semantically analyzed. The system presented in this paper performs this tasks in an automatic way, relying on a predefined ontology which states the concepts in this the posterior data analysis will be focused. As features, our system focuses on extracting relevant Named Entities from textual resources describing a particular entity. Those are evaluated by means of linguistic and Web-based co-occurrence analyses to map them to ontological concepts, thereby discovering relevant features of the object. The system has been preliminary tested with tourist destinations and Wikipedia textual resources, showing promising results.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133761226","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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