Context-aware recommender system (CARS) can provide more accurate rating predictions and more relevant recommendations by taking into account the contextual in-formation. Yet the state-of-the-art context-aware matrix factorization approaches only consider the influence of con-textual information on item bias. Tensor factorization based Multiverse Recommendation deals with the contextual in-formation by incorporating user-item-context interaction into recommendation model. However, all of these approaches cannot fully capture the influence of contextual information on the rating. In this paper, we propose two improved context-aware matrix factorization approaches to fully capture the influence of contextual information on the rating. Both of the baseline predictors (user bias and item bias) and user-item-context interaction are fully concerned. Experimental results on three semi-synthetic datasets and one real world dataset show that the two proposed approaches outperform Multiverse Recommendation and the state-of-the-art context-aware matrix factorization methods in prediction performance.
{"title":"ICAMF: Improved Context-Aware Matrix Factorization for Collaborative Filtering","authors":"Jiyun Li, Pengcheng Feng, Juntao Lv","doi":"10.1109/ICTAI.2013.20","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.20","url":null,"abstract":"Context-aware recommender system (CARS) can provide more accurate rating predictions and more relevant recommendations by taking into account the contextual in-formation. Yet the state-of-the-art context-aware matrix factorization approaches only consider the influence of con-textual information on item bias. Tensor factorization based Multiverse Recommendation deals with the contextual in-formation by incorporating user-item-context interaction into recommendation model. However, all of these approaches cannot fully capture the influence of contextual information on the rating. In this paper, we propose two improved context-aware matrix factorization approaches to fully capture the influence of contextual information on the rating. Both of the baseline predictors (user bias and item bias) and user-item-context interaction are fully concerned. Experimental results on three semi-synthetic datasets and one real world dataset show that the two proposed approaches outperform Multiverse Recommendation and the state-of-the-art context-aware matrix factorization methods in prediction performance.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114991747","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}
Identifying outlier objects against main community evolution trends is not only meaningful itself for the purpose of finding novel evolution behaviors, but also helpful for better understanding the mainstream of community evolution. With the definition of community belongingness matrix of data objects, we constructed the transition matrix to least square optimize the pattern of evolutionary quantity between two consecutive belongingness snapshots. A set of properties about the transition matrix is discussed, which reveals its close relation to the step by step community membership change. The transition matrix is further optimized using robust regression methods by minimizing the disturbance incurred by the outliers, and the outlier factor of the anomalous object was defined. Being aware that large proportion of trivial but nomadic objects may exist in large datasets. This paper focus only on the influential community evolutionary outliers which both show remarkable difference from the main body of their community and sharp changes of their membership role within the communities. An algorithm on detection such kind of outliers are purposed in the paper. Experimental results on both synthetic and real world datasets show that the proposed approach is highly effective and efficient in discovering reasonable influential evolutionary community outliers.
{"title":"An Algorithm for Mining Top K Influential Community Based Evolutionary Outliers in Temporal Dataset","authors":"Yun Hu, Junyuan Xie, Chong-Jun Wang, Zuojian Zhou","doi":"10.1109/ICTAI.2013.84","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.84","url":null,"abstract":"Identifying outlier objects against main community evolution trends is not only meaningful itself for the purpose of finding novel evolution behaviors, but also helpful for better understanding the mainstream of community evolution. With the definition of community belongingness matrix of data objects, we constructed the transition matrix to least square optimize the pattern of evolutionary quantity between two consecutive belongingness snapshots. A set of properties about the transition matrix is discussed, which reveals its close relation to the step by step community membership change. The transition matrix is further optimized using robust regression methods by minimizing the disturbance incurred by the outliers, and the outlier factor of the anomalous object was defined. Being aware that large proportion of trivial but nomadic objects may exist in large datasets. This paper focus only on the influential community evolutionary outliers which both show remarkable difference from the main body of their community and sharp changes of their membership role within the communities. An algorithm on detection such kind of outliers are purposed in the paper. Experimental results on both synthetic and real world datasets show that the proposed approach is highly effective and efficient in discovering reasonable influential evolutionary community outliers.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114656287","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 problem of real-time occupancy forecasting for single person offices is critical for energy efficient buildings which use predictive control techniques. Due to the highly uncertain nature of occupancy dynamics, the modeling and prediction of occupancy is a challenging problem. This paper proposes an algorithm for learning and predicting single occupant presence in office buildings, by considering the occupant behaviour as an ensemble of multiple Markov models at different time lags. This model has been tested using real occupancy data collected from PIR sensors installed in three different buildings and compared with state of the art methods, reducing the error rate by on average 5% over the best comparator method.
{"title":"Learning Occupancy in Single Person Offices with Mixtures of Multi-lag Markov Chains","authors":"C. Manna, D. Fay, Kenneth N. Brown, Nic Wilson","doi":"10.1109/ICTAI.2013.32","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.32","url":null,"abstract":"The problem of real-time occupancy forecasting for single person offices is critical for energy efficient buildings which use predictive control techniques. Due to the highly uncertain nature of occupancy dynamics, the modeling and prediction of occupancy is a challenging problem. This paper proposes an algorithm for learning and predicting single occupant presence in office buildings, by considering the occupant behaviour as an ensemble of multiple Markov models at different time lags. This model has been tested using real occupancy data collected from PIR sensors installed in three different buildings and compared with state of the art methods, reducing the error rate by on average 5% over the best comparator method.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116880907","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}
Constraint Satisfaction Problems (CSPs) have the big advantage of a succinct, declarative and easy to understand representation form. Unfortunately, solving CSPs is NP-complete in the general case. In order to cope with this, common CSP frameworks offer the possibility to use different built-in heuristics. However, the provided built-in heuristics are often not suitable to significantly boost solution calculation. Also the facilities for expressing domain-specific heuristics in a declarative manner within the CSP framework are typically very limited (e.g. by defining a static variable selection order)and thus are often not applicable. As a consequence such domain-specific heuristics are often implemented by means of custom propagators or custom constraints (e.g. a special constraint for bin packing problems) forcing domain experts and knowledge engineers to leave the declarative world and implement the heuristics in a procedural manner. In this paper we propose a new declarative language for expressing domain specific heuristics for CSPs which can be easily integrated in every CSP framework. We also describe a prototype implementation within a state-of-the-art CSP solver and present proof of concept results on real world configuration problem instances.
{"title":"Declarative Heuristics in Constraint Satisfaction","authors":"E. Teppan, G. Friedrich","doi":"10.1109/ICTAI.2013.150","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.150","url":null,"abstract":"Constraint Satisfaction Problems (CSPs) have the big advantage of a succinct, declarative and easy to understand representation form. Unfortunately, solving CSPs is NP-complete in the general case. In order to cope with this, common CSP frameworks offer the possibility to use different built-in heuristics. However, the provided built-in heuristics are often not suitable to significantly boost solution calculation. Also the facilities for expressing domain-specific heuristics in a declarative manner within the CSP framework are typically very limited (e.g. by defining a static variable selection order)and thus are often not applicable. As a consequence such domain-specific heuristics are often implemented by means of custom propagators or custom constraints (e.g. a special constraint for bin packing problems) forcing domain experts and knowledge engineers to leave the declarative world and implement the heuristics in a procedural manner. In this paper we propose a new declarative language for expressing domain specific heuristics for CSPs which can be easily integrated in every CSP framework. We also describe a prototype implementation within a state-of-the-art CSP solver and present proof of concept results on real world configuration problem instances.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124842420","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}
Rinaldo Lima, B. Espinasse, Hilário Oliveira, Laura Pentagrossa, F. Freitas
Relevant information extraction from text and web pages in particular is an intensive and time-consuming task that needs important semantic resources. Thus, to be efficient, automatic information extraction systems have to exploit semantic resources (or ontologies) and employ machine-learning techniques to make them more adaptive. This paper presents an Ontology-based Information Extraction method using Inductive Logic Programming that allows inducing symbolic predicates expressed in Horn clausal logic that subsume information extraction rules. Such rules allow the system to extract class and relation instances from English corpora for ontology population purposes. Several experiments were conducted and preliminary experimental results are promising, showing that the proposed approach improves previous work over extracting instances of classes and relations, either separately or altogether.
{"title":"Information Extraction from the Web: An Ontology-Based Method Using Inductive Logic Programming","authors":"Rinaldo Lima, B. Espinasse, Hilário Oliveira, Laura Pentagrossa, F. Freitas","doi":"10.1109/ICTAI.2013.114","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.114","url":null,"abstract":"Relevant information extraction from text and web pages in particular is an intensive and time-consuming task that needs important semantic resources. Thus, to be efficient, automatic information extraction systems have to exploit semantic resources (or ontologies) and employ machine-learning techniques to make them more adaptive. This paper presents an Ontology-based Information Extraction method using Inductive Logic Programming that allows inducing symbolic predicates expressed in Horn clausal logic that subsume information extraction rules. Such rules allow the system to extract class and relation instances from English corpora for ontology population purposes. Several experiments were conducted and preliminary experimental results are promising, showing that the proposed approach improves previous work over extracting instances of classes and relations, either separately or altogether.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125035980","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 we attempt to study and discover the principles behind one of the most succesfulvariable selection heuristics in branch-and-prune interval-based solvers: the Smear-based heuristics. Why these heuristics work? Which is their objective?Can we do any better?Based on the principles of the Smear functionand the well-known first-fail principle: "To succeed, try first where you are most likely to fail" we propose several variable selection heuristics. The heuristics are tested and compared to the Smear-based oneson solving twenty nonlinear systems of equations. We report our first results and conclusions.
{"title":"More Smear-Based Variable Selection Heuristics for NCSPs","authors":"Ignacio Araya, Víctor Reyes, Cristian Oreallana","doi":"10.1109/ICTAI.2013.151","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.151","url":null,"abstract":"In this work we attempt to study and discover the principles behind one of the most succesfulvariable selection heuristics in branch-and-prune interval-based solvers: the Smear-based heuristics. Why these heuristics work? Which is their objective?Can we do any better?Based on the principles of the Smear functionand the well-known first-fail principle: \"To succeed, try first where you are most likely to fail\" we propose several variable selection heuristics. The heuristics are tested and compared to the Smear-based oneson solving twenty nonlinear systems of equations. We report our first results and conclusions.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116095198","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}
R. Interdonato, Salvatore Romeo, Andrea Tagarelli, G. Karypis
An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. The problem is challenging due to a variety of critical aspects, including context-based and user-provided constraints for the items constituting a package, but also the high sparsity and limited accessibility of the primary data used to solve the problem. Most existing works on the topic have focused on a specific application domain (e.g., travel package recommendation), thus often providing ad-hoc solutions that cannot be adapted to other domains. By contrast, in this paper we propose a versatile package recommendation approach that is substantially independent of the peculiarities of a particular application domain. A key aspect in our framework is the exploitation of prior knowledge on the content type models of the packages being generated that express what the users expect from the recommendation task. Packages are learned for each package model, while the recommendation stage is accomplished by performing a PageRank-style method personalized w.r.t. the target user's preferences, possibly including a limited budget. Our developed method has been tested on a TripAdvisor dataset and compared with a recently proposed method for learning composite recommendations.
{"title":"A Versatile Graph-Based Approach to Package Recommendation","authors":"R. Interdonato, Salvatore Romeo, Andrea Tagarelli, G. Karypis","doi":"10.1109/ICTAI.2013.130","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.130","url":null,"abstract":"An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. The problem is challenging due to a variety of critical aspects, including context-based and user-provided constraints for the items constituting a package, but also the high sparsity and limited accessibility of the primary data used to solve the problem. Most existing works on the topic have focused on a specific application domain (e.g., travel package recommendation), thus often providing ad-hoc solutions that cannot be adapted to other domains. By contrast, in this paper we propose a versatile package recommendation approach that is substantially independent of the peculiarities of a particular application domain. A key aspect in our framework is the exploitation of prior knowledge on the content type models of the packages being generated that express what the users expect from the recommendation task. Packages are learned for each package model, while the recommendation stage is accomplished by performing a PageRank-style method personalized w.r.t. the target user's preferences, possibly including a limited budget. Our developed method has been tested on a TripAdvisor dataset and compared with a recently proposed method for learning composite recommendations.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122453692","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}
This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-MNN Controller (Environment Observation Method based on Multiple Neural Networks Controller). Traffic congestion leads to problems like delays and higher fuel consumption. Consequently, alleviating congested situation is not only good to economy but also to environment. The problem of traffic light control is very challenging. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, modern artificial intelligent ways have gained more and more attentions. In this work, EOM is a very interesting mathematical method for determining traffic lights timing that was developed by Ejzenberg [4]. However, this method has some implications in which multiple neural networks were proposed to improve such problems. The solution was compared with the conventional method through scenario of simulation in microscopic traffic simulation software.
{"title":"Optimization of Traffic Lights Timing Based on Multiple Neural Networks","authors":"Michel B. W. De Oliveira, A. A. Neto","doi":"10.1109/ICTAI.2013.126","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.126","url":null,"abstract":"This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-MNN Controller (Environment Observation Method based on Multiple Neural Networks Controller). Traffic congestion leads to problems like delays and higher fuel consumption. Consequently, alleviating congested situation is not only good to economy but also to environment. The problem of traffic light control is very challenging. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, modern artificial intelligent ways have gained more and more attentions. In this work, EOM is a very interesting mathematical method for determining traffic lights timing that was developed by Ejzenberg [4]. However, this method has some implications in which multiple neural networks were proposed to improve such problems. The solution was compared with the conventional method through scenario of simulation in microscopic traffic simulation software.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129569546","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}
Everyone having used Constraint Programming (CP) to solve hard combinatorial optimization problems with a standard exhaustive Branch & Bound Depth First Search (B&B DFS) has probably experienced scalability issues. In the 2011 Panel of the Future of CP, one of the identified challenges was the need to handle large-scale problems. In this paper, we address the scalability issues of CP when minimizing a sum objective function. We suggest extending the Large Neighborhood Search (LNS) framework enabling it with the possibility of changing dynamically the objective function along the restarts. The motivation for this extended framework - called the Variable Objective Large Neighborhood Search (VO-LNS) - is solving efficiently a real-life over-constrained timetabling application. Our experiments show that this simple approach has two main benefits on solving this problem: 1) a better pruning, boosting the speed of LNS to reach high quality solutions, 2) a better control to balance or weight the terms composing the sum objective function, especially in over-constrained problems.
{"title":"Variable Objective Large Neighborhood Search: A Practical Approach to Solve Over-Constrained Problems","authors":"P. Schaus","doi":"10.1109/ICTAI.2013.147","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.147","url":null,"abstract":"Everyone having used Constraint Programming (CP) to solve hard combinatorial optimization problems with a standard exhaustive Branch & Bound Depth First Search (B&B DFS) has probably experienced scalability issues. In the 2011 Panel of the Future of CP, one of the identified challenges was the need to handle large-scale problems. In this paper, we address the scalability issues of CP when minimizing a sum objective function. We suggest extending the Large Neighborhood Search (LNS) framework enabling it with the possibility of changing dynamically the objective function along the restarts. The motivation for this extended framework - called the Variable Objective Large Neighborhood Search (VO-LNS) - is solving efficiently a real-life over-constrained timetabling application. Our experiments show that this simple approach has two main benefits on solving this problem: 1) a better pruning, boosting the speed of LNS to reach high quality solutions, 2) a better control to balance or weight the terms composing the sum objective function, especially in over-constrained problems.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434578","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}
Pub Date : 2013-11-04DOI: 10.1142/S0218213014600318
L. Sánchez, J. A. Recio-García, B. Díaz-Agudo
Social recommender systems exploit the social knowledge available in social networks to provide accurate recommendations. However, their instantiation is not straightforward due to its complexity. To alleviate this development complexity, we propose a methodology based on templates that conceptualize the behavior of such applications and can be reused to create several social recommender applications in social networks. This development methodology comprises not only templates but also a generic architecture named ARISE and a collection of software components that provide the required functionality. We prove that our social templates speed up and facilitate the development process, and demonstrate the viability of our generic architecture in two different case studies.
{"title":"A Reusable Methodology for the Instantiation of Social Recommender Systems","authors":"L. Sánchez, J. A. Recio-García, B. Díaz-Agudo","doi":"10.1142/S0218213014600318","DOIUrl":"https://doi.org/10.1142/S0218213014600318","url":null,"abstract":"Social recommender systems exploit the social knowledge available in social networks to provide accurate recommendations. However, their instantiation is not straightforward due to its complexity. To alleviate this development complexity, we propose a methodology based on templates that conceptualize the behavior of such applications and can be reused to create several social recommender applications in social networks. This development methodology comprises not only templates but also a generic architecture named ARISE and a collection of software components that provide the required functionality. We prove that our social templates speed up and facilitate the development process, and demonstrate the viability of our generic architecture in two different case studies.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129799888","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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