Belief change in Probabilistic Graphical Models in general, and Bayesian Networks in particular, is often thought of as change in the model parameters when data consistent with the graphical model is observed. The assumption is the network structure for the graphical model is a true representation of the knowledge about the domain and therefore it does not change. In dynamic environments, this assumption is not always true. The network structure is bound to change in response to changes in the domain or correction of mistaken propositions. In such domains, the true Bayesian Network structure at any given point in time, and the events that provides an impetus for change in the network structure are unobservable and are not known with certainty. This paper presents, the Unified Belief Change Operator for Bayesian Networks (UBCOBaN). The UBCOBaN effects both belief revision and update on a given Bayesian network structure based on the data emitted from the domain modelled by the Bayesian Network. We present the conceptualization and implementation of the operator, and its evaluation based on synthetic data simulated from the Alarm Network. The operator was found to be more rational, with respect to the principle minimal change, than the classical search-and-score algorithm. The operator was also found to be faster in adapting to necessary changes than the classical search-and-score algorithm.
{"title":"Using Belief Change Principles for Evolving Bayesian Network Structures in Probabilistic Knowledge Representations","authors":"E. Jembere, S. S. Xulu","doi":"10.1109/WI.2016.0013","DOIUrl":"https://doi.org/10.1109/WI.2016.0013","url":null,"abstract":"Belief change in Probabilistic Graphical Models in general, and Bayesian Networks in particular, is often thought of as change in the model parameters when data consistent with the graphical model is observed. The assumption is the network structure for the graphical model is a true representation of the knowledge about the domain and therefore it does not change. In dynamic environments, this assumption is not always true. The network structure is bound to change in response to changes in the domain or correction of mistaken propositions. In such domains, the true Bayesian Network structure at any given point in time, and the events that provides an impetus for change in the network structure are unobservable and are not known with certainty. This paper presents, the Unified Belief Change Operator for Bayesian Networks (UBCOBaN). The UBCOBaN effects both belief revision and update on a given Bayesian network structure based on the data emitted from the domain modelled by the Bayesian Network. We present the conceptualization and implementation of the operator, and its evaluation based on synthetic data simulated from the Alarm Network. The operator was found to be more rational, with respect to the principle minimal change, than the classical search-and-score algorithm. The operator was also found to be faster in adapting to necessary changes than the classical search-and-score algorithm.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"27 1","pages":"9-17"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81000322","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}
Given a graph G = (V, E), a node is called perfect (with respect to a set S ⊆ V) if its closed neighborhood contains exactly one node in set S, a node is called nearly perfect if it is not perfect but is adjacent to a perfect node. S is called a perfect neighborhood set if each node is either perfect or nearly perfect. We present the first self-stabilizing algorithm for computing a perfect neighborhood set in an arbitrary graph. This anonymous, constant space algorithm terminates in O(n2) steps using an unfair central daemon, where n is the number of nodes in the graph.
{"title":"Self-Stabilizing Computation of Perfect Neighborhood Set in Large Network Graphs","authors":"Yihua Ding, J. Wang, P. Srimani","doi":"10.1109/WI.2016.0069","DOIUrl":"https://doi.org/10.1109/WI.2016.0069","url":null,"abstract":"Given a graph G = (V, E), a node is called perfect (with respect to a set S ⊆ V) if its closed neighborhood contains exactly one node in set S, a node is called nearly perfect if it is not perfect but is adjacent to a perfect node. S is called a perfect neighborhood set if each node is either perfect or nearly perfect. We present the first self-stabilizing algorithm for computing a perfect neighborhood set in an arbitrary graph. This anonymous, constant space algorithm terminates in O(n2) steps using an unfair central daemon, where n is the number of nodes in the graph.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"2 1","pages":"435-438"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89987220","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}
With the incredibly growing amount of multimedia data uploaded and shared via the social media web sites, recommender systems have become an important necessity to ease users'burden on the information overload. In such a scenario, extensive amount of content information, such as tags, image content and user to item preferences are also available and extremely valuable for making effective recommendations. In this paper, we explore a novel topic model for image recommendation that jointly considers the problem of image content analysis with the users' preference on the basis of sparse representation. Our model is based on the classical probabilistic matrix factorization and can be easily extended to incorporate other useful information such as the social relationship. We evaluate our approach with a newly collected large scale social image data set from Flickr. The experimental results demonstrate that sparse topic modeling of the image content leads to more effective recommendations.
{"title":"A Sparse Image Recommendation Model Using Content and User Preference Information","authors":"Lei Liu","doi":"10.1109/WI.2016.0041","DOIUrl":"https://doi.org/10.1109/WI.2016.0041","url":null,"abstract":"With the incredibly growing amount of multimedia data uploaded and shared via the social media web sites, recommender systems have become an important necessity to ease users'burden on the information overload. In such a scenario, extensive amount of content information, such as tags, image content and user to item preferences are also available and extremely valuable for making effective recommendations. In this paper, we explore a novel topic model for image recommendation that jointly considers the problem of image content analysis with the users' preference on the basis of sparse representation. Our model is based on the classical probabilistic matrix factorization and can be easily extended to incorporate other useful information such as the social relationship. We evaluate our approach with a newly collected large scale social image data set from Flickr. The experimental results demonstrate that sparse topic modeling of the image content leads to more effective recommendations.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"5 1","pages":"232-239"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86766689","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}
Akira Kubota, T. Tominaga, Y. Hijikata, Nobuchika Sakata
A picture lifelog is a type of lifelog that consists of pictures, mainly taken by the user. Recently, users have been able to easily create picture lifelogs because many portable devices such as smart phones have a camera. When a user sees a picture in their picture lifelog, it is sometimes difficult to recall the events related to the picture. Therefore, we proposed to combine search queries on a picture lifelog in order to support memory retrieval. Search queries are input into a web search engine to satisfy a user's need for information. Recently, because of the prevalence of smart phones, the opportunity to input search queries has increased to anytime and anywhere. Search queries are stored in a cloud user database such as Google search history. In addition, those search queries imply what the user was thinking at the time. We investigated whether search queries enable a user to recall their thoughts regarding picture lifelogs. Thus, we conducted an experiment to ascertain whether search queries reminded a user of past events. As a result, we reveal that displaying a picture with search queries performed around the time it was taken tends to improve users' memories better than its time, location, or emails sent during that time.
{"title":"Adding Search Queries to Picture Lifelogs for Memory Retrieval","authors":"Akira Kubota, T. Tominaga, Y. Hijikata, Nobuchika Sakata","doi":"10.1109/WI.2016.0123","DOIUrl":"https://doi.org/10.1109/WI.2016.0123","url":null,"abstract":"A picture lifelog is a type of lifelog that consists of pictures, mainly taken by the user. Recently, users have been able to easily create picture lifelogs because many portable devices such as smart phones have a camera. When a user sees a picture in their picture lifelog, it is sometimes difficult to recall the events related to the picture. Therefore, we proposed to combine search queries on a picture lifelog in order to support memory retrieval. Search queries are input into a web search engine to satisfy a user's need for information. Recently, because of the prevalence of smart phones, the opportunity to input search queries has increased to anytime and anywhere. Search queries are stored in a cloud user database such as Google search history. In addition, those search queries imply what the user was thinking at the time. We investigated whether search queries enable a user to recall their thoughts regarding picture lifelogs. Thus, we conducted an experiment to ascertain whether search queries reminded a user of past events. As a result, we reveal that displaying a picture with search queries performed around the time it was taken tends to improve users' memories better than its time, location, or emails sent during that time.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"88 1","pages":"689-694"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85899459","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}
Fabien L. Gandon, R. Boyer, O. Corby, Alexandre Monnin
DBpedia is a huge dataset essentially extracted from the content and structure of Wikipedia. We present a new extraction producing a linked data representation of the editing history of Wikipedia pages. This supports custom querying and combining with other data providing new indicators and insights. We explain the architecture, representation and an immediate application to monitoring events.
{"title":"Wikipedia Editing History in DBpedia: Extracting and Publishing the Encyclopedia Editing Activity as Linked Data","authors":"Fabien L. Gandon, R. Boyer, O. Corby, Alexandre Monnin","doi":"10.1109/WI.2016.0079","DOIUrl":"https://doi.org/10.1109/WI.2016.0079","url":null,"abstract":"DBpedia is a huge dataset essentially extracted from the content and structure of Wikipedia. We present a new extraction producing a linked data representation of the editing history of Wikipedia pages. This supports custom querying and combining with other data providing new indicators and insights. We explain the architecture, representation and an immediate application to monitoring events.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"146 1","pages":"479-482"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86645739","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 work, a new approach for analysing the Web user behavior is introduced, consisting of a physiological-based click intention assessment, based on pupil dilation and electroencephalogram (EEG) responses evaluation. For this, an empirical study was conducted, where the mentioned responses of 21 subjects were recorded while performing diverse information foraging tasks from five real web sites. We found a statistical difference between click and not-click pupil dilation curves, more precisely, fixations corresponding to clicks had greater pupil size than fixations without clicks. In addition, seven classification models were applied, using 15 out 789 pupil dilation and EEG features obtained from a Random Lasso feature selection process. Results showed good performance for Accuracy (71,09% using Logistic Regression), whereas for Precision, Recall and F-Measure remained low, which indicates the behavior we were studying was not well classified. Despite the quality of these results, it is possible to mention that the reviewed responses could be used from a Web Intelligence perspective as a proxy of Web user behavior, for example, to generate an online recommender to improve websites structure or content. However, we concluded that better quality instruments are necessary to achieve higher results.
{"title":"Predicting Web User Click Intention Using Pupil Dilation and Electroencephalogram Analysis","authors":"Gino Slanzi, Jorge A. Balazs, J. D. Velásquez","doi":"10.1109/WI.2016.0065","DOIUrl":"https://doi.org/10.1109/WI.2016.0065","url":null,"abstract":"In this work, a new approach for analysing the Web user behavior is introduced, consisting of a physiological-based click intention assessment, based on pupil dilation and electroencephalogram (EEG) responses evaluation. For this, an empirical study was conducted, where the mentioned responses of 21 subjects were recorded while performing diverse information foraging tasks from five real web sites. We found a statistical difference between click and not-click pupil dilation curves, more precisely, fixations corresponding to clicks had greater pupil size than fixations without clicks. In addition, seven classification models were applied, using 15 out 789 pupil dilation and EEG features obtained from a Random Lasso feature selection process. Results showed good performance for Accuracy (71,09% using Logistic Regression), whereas for Precision, Recall and F-Measure remained low, which indicates the behavior we were studying was not well classified. Despite the quality of these results, it is possible to mention that the reviewed responses could be used from a Web Intelligence perspective as a proxy of Web user behavior, for example, to generate an online recommender to improve websites structure or content. However, we concluded that better quality instruments are necessary to achieve higher results.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"33 1","pages":"417-420"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87530092","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}
Julien Savaux, Julien Vion, S. Piechowiak, R. Mandiau, T. Matsui, K. Hirayama, M. Yokoo, Shakre Elmane, M. Silaghi
Much of the Distributed Constraint Satisfaction Problem (DisCSP) solving research has addressed cooperating agents, and privacy was frequently mentioned as a significant motivation of the decentralization. While privacy may have a role for cooperating agents, it is easier understood in the context of self-interested utility-based agents, and this is the situation considered here. With utility-based agents, the DisCSP framework can be extended to model privacy and satisfaction under the concept of utility. We introduce Utilitarian Distributed Constraint Satisfaction Problems (UDisCSP), an extension of the DisCSP that exploits the rewards for finding a solution and the costs for losing privacy as guidance for the utility-based agents. A parallel can be drawn between Partially Observable Markov Decision Processes (POMDPs) and the problems solved by individual agents for UDisCSPs. Common DisCSP solvers are extended to take into account the utility function. In these extensions we assume that the planning problem is further restricting the set of communication actions to only the ones available in the corresponding solver protocols. The solvers obtained propose the action to be performed in each situation, defining thereby the policy of the agents.
{"title":"DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents","authors":"Julien Savaux, Julien Vion, S. Piechowiak, R. Mandiau, T. Matsui, K. Hirayama, M. Yokoo, Shakre Elmane, M. Silaghi","doi":"10.1109/WI.2016.0057","DOIUrl":"https://doi.org/10.1109/WI.2016.0057","url":null,"abstract":"Much of the Distributed Constraint Satisfaction Problem (DisCSP) solving research has addressed cooperating agents, and privacy was frequently mentioned as a significant motivation of the decentralization. While privacy may have a role for cooperating agents, it is easier understood in the context of self-interested utility-based agents, and this is the situation considered here. With utility-based agents, the DisCSP framework can be extended to model privacy and satisfaction under the concept of utility. We introduce Utilitarian Distributed Constraint Satisfaction Problems (UDisCSP), an extension of the DisCSP that exploits the rewards for finding a solution and the costs for losing privacy as guidance for the utility-based agents. A parallel can be drawn between Partially Observable Markov Decision Processes (POMDPs) and the problems solved by individual agents for UDisCSPs. Common DisCSP solvers are extended to take into account the utility function. In these extensions we assume that the planning problem is further restricting the set of communication actions to only the ones available in the corresponding solver protocols. The solvers obtained propose the action to be performed in each situation, defining thereby the policy of the agents.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"29 1","pages":"359-366"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86150314","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}
Just-In-Time Recommender Systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. In this paper, we propose a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for users to initiate any interaction.
{"title":"Just-In-Time Recommendation Approach within a Mobile Context","authors":"Imen Akermi, M. Boughanem, R. Faiz","doi":"10.1109/WI.2016.0112","DOIUrl":"https://doi.org/10.1109/WI.2016.0112","url":null,"abstract":"Just-In-Time Recommender Systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. In this paper, we propose a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for users to initiate any interaction.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"58 1","pages":"636-639"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91338741","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}
Visual analytics on frequent web usage patterns aims to help users to (i) analyze the data so as to discover implicit, previously unknown and potentially useful information in the form of collections of frequently visited web pages in a single session and to (ii) visually represent the discovered knowledge so as to gain insight about the data. In this paper, we propose an interactive visual analytics tool (iVAT) for frequent pattern mining. It uses an orientation free, circular layout to show frequent patterns. Moreover, we provide users with interactive feature to explicitly show connections between superset and subsets of sets of visited web pages. Experimental results show the effectiveness of our iVAT for visual analytics of frequent patterns about web data.
{"title":"An Interactive Circular Visual Analytic Tool for Visualization of Web Data","authors":"P. Dubois, Zhao Han, Fan Jiang, C. Leung","doi":"10.1109/WI.2016.0127","DOIUrl":"https://doi.org/10.1109/WI.2016.0127","url":null,"abstract":"Visual analytics on frequent web usage patterns aims to help users to (i) analyze the data so as to discover implicit, previously unknown and potentially useful information in the form of collections of frequently visited web pages in a single session and to (ii) visually represent the discovered knowledge so as to gain insight about the data. In this paper, we propose an interactive visual analytics tool (iVAT) for frequent pattern mining. It uses an orientation free, circular layout to show frequent patterns. Moreover, we provide users with interactive feature to explicitly show connections between superset and subsets of sets of visited web pages. Experimental results show the effectiveness of our iVAT for visual analytics of frequent patterns about web data.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"1 1","pages":"709-712"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90620252","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 day-to-day behavior of the individuals reveal their personality traits. With the emergence of the social media platforms, some aspects of this behavior are being recorded in their online profiles. This provides necessary input to develop algorithms that can predict personality traits of individuals. However, these algorithms need to exploit the semantics of the data in order to reveal the personality traits. Current studies on this topic mainly exploited the syntactic features of the language used by individuals to predict their personality traits. In this work we demonstrate the value of exploiting semantics of the messages conveyed in social media posts for predicting personality traits. In other words, we present a study that attempts to simulate the cognitive ability of the human brain, which allows to identify the important implicit information in social media posts for understanding the personality traits of an individual. Our approach shows the value of publicly available knowledge bases in eliciting implicit information in the user generated content and their impact on predicting the personality traits of an individual. We evaluated our approach using well-known 'myPersonality' dataset and showed that it outperforms the state-of-the-art algorithms that mainly depend on syntactic features.
{"title":"Knowledge-Driven Approach to Predict Personality Traits by Leveraging Social Media Data","authors":"M. Thilakaratne, R. Weerasinghe, Sujan Perera","doi":"10.1109/WI.2016.0048","DOIUrl":"https://doi.org/10.1109/WI.2016.0048","url":null,"abstract":"The day-to-day behavior of the individuals reveal their personality traits. With the emergence of the social media platforms, some aspects of this behavior are being recorded in their online profiles. This provides necessary input to develop algorithms that can predict personality traits of individuals. However, these algorithms need to exploit the semantics of the data in order to reveal the personality traits. Current studies on this topic mainly exploited the syntactic features of the language used by individuals to predict their personality traits. In this work we demonstrate the value of exploiting semantics of the messages conveyed in social media posts for predicting personality traits. In other words, we present a study that attempts to simulate the cognitive ability of the human brain, which allows to identify the important implicit information in social media posts for understanding the personality traits of an individual. Our approach shows the value of publicly available knowledge bases in eliciting implicit information in the user generated content and their impact on predicting the personality traits of an individual. We evaluated our approach using well-known 'myPersonality' dataset and showed that it outperforms the state-of-the-art algorithms that mainly depend on syntactic features.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"33 1","pages":"288-295"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89303884","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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