Electronic word-of-mouth (eWOM) is one important information source that influences consumers' product evaluations. This paper presents(1) hypotheses for the potency of eWOM messages with a focus on subjective rank expressions, which refer to linguistic representations related to the level of subjective evaluations and the strength of recommendations, and (2) the results of hypothesis testing on the dataset collected from a questionnaire survey administered to one hundred and fifty two undergraduate students. Two expression types of subjective rank expressions - comparison and degree - we reexamined. A two-way ANOVA was performed to test the effects of two independent variables "evaluation skill" (SKILL) and "expressiontype" (TYPE) on the dependent variable "degree of positive change in the evaluations" (POTENCY). The results provide some pieces of evidence in support of the hypotheses. The findings obtained through the research are discussed from a viewpoint of developing accurate methods for the potency prediction of eWOM messages.
{"title":"An Investigation of Potency of eWOM Messages with a Focus on Subjective Rank Expressions","authors":"K. Fujimoto","doi":"10.1109/WI-IAT.2010.240","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.240","url":null,"abstract":"Electronic word-of-mouth (eWOM) is one important information source that influences consumers' product evaluations. This paper presents(1) hypotheses for the potency of eWOM messages with a focus on subjective rank expressions, which refer to linguistic representations related to the level of subjective evaluations and the strength of recommendations, and (2) the results of hypothesis testing on the dataset collected from a questionnaire survey administered to one hundred and fifty two undergraduate students. Two expression types of subjective rank expressions - comparison and degree - we reexamined. A two-way ANOVA was performed to test the effects of two independent variables \"evaluation skill\" (SKILL) and \"expressiontype\" (TYPE) on the dependent variable \"degree of positive change in the evaluations\" (POTENCY). The results provide some pieces of evidence in support of the hypotheses. The findings obtained through the research are discussed from a viewpoint of developing accurate methods for the potency prediction of eWOM messages.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129753839","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 introduce how one can validate an event-centric trading simulation platform that is built with multi-agent technology. The issue of validation is extremely important for agent-based simulations, but unfortunately, so far there is no one universal method that would work in all domains. The primary contribution of this paper is a novel combination of event-centric simulation design and event study approach for market dynamics generation and validation. In our event-centric design, the simulation is progressed by announcing news events that affect market prices. Upon receiving these events, event-aware software agents would adjust their views on the market and act accordingly. Their actions would be based on their roles and also their private information, and collectively the market dynamics will be shaped. The generated market dynamics can then be validated by a variant of the event study approach. We demonstrate how the methodology works with several numerical experiments and conclude by highlighting the practical significance of such simulation platform.
{"title":"Event Study Approach for Validating Agent-Based Trading Simulations","authors":"Shih-Fen Cheng","doi":"10.1109/WI-IAT.2010.212","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.212","url":null,"abstract":"In this paper, we introduce how one can validate an event-centric trading simulation platform that is built with multi-agent technology. The issue of validation is extremely important for agent-based simulations, but unfortunately, so far there is no one universal method that would work in all domains. The primary contribution of this paper is a novel combination of event-centric simulation design and event study approach for market dynamics generation and validation. In our event-centric design, the simulation is progressed by announcing news events that affect market prices. Upon receiving these events, event-aware software agents would adjust their views on the market and act accordingly. Their actions would be based on their roles and also their private information, and collectively the market dynamics will be shaped. The generated market dynamics can then be validated by a variant of the event study approach. We demonstrate how the methodology works with several numerical experiments and conclude by highlighting the practical significance of such simulation platform.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128450727","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 propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a ``stakeholder'' is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of Relationship WordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource Senti WordNet. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents a user study to validate the proposed methods.
{"title":"Stakeholder Mining and Its Application to News Comparison","authors":"Tatsuya Ogawa, Qiang Ma, Masatoshi Yoshikawa","doi":"10.1109/WI-IAT.2010.156","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.156","url":null,"abstract":"In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a ``stakeholder'' is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of Relationship WordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource Senti WordNet. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents a user study to validate the proposed methods.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128265294","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}
Our goal in this paper was to investigate the impact that the addition of a Heterogeneous layered Social network will have on problem solving ability of a Cultural system. The synergisms of the emergent swarms in the population and belief spaces are affected by training the social network on a dynamic but recurring pattern weaved by our social influence function. The cultural system has adjusted its knowledge sources and their interactions in order to produce swarms of agents that can predict changes in parameters. Improved knowledge of the situation produced tighter formations because of increased concurrence on the planned trajectory among the active knowledge sources through the use of history knowledge which was illustrated through the interaction and overlapping of the bounding boxes.
{"title":"Enhancing Cultural Learning under Environmental Variability Using Layered Heterogeneous Sociometry-Based Networks","authors":"Mostafa Z. Ali, R. Reynolds, Rose Ali","doi":"10.1109/WI-IAT.2010.215","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.215","url":null,"abstract":"Our goal in this paper was to investigate the impact that the addition of a Heterogeneous layered Social network will have on problem solving ability of a Cultural system. The synergisms of the emergent swarms in the population and belief spaces are affected by training the social network on a dynamic but recurring pattern weaved by our social influence function. The cultural system has adjusted its knowledge sources and their interactions in order to produce swarms of agents that can predict changes in parameters. Improved knowledge of the situation produced tighter formations because of increased concurrence on the planned trajectory among the active knowledge sources through the use of history knowledge which was illustrated through the interaction and overlapping of the bounding boxes.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127138324","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}
An infrastructure-as-a-service cloud system provides computational capacities to remote users. Parallel processing in the cloud system can shorten the execution of jobs. Parallel processing requires a mechanism to scheduling the executions order as well as resource allocation. Furthermore, a preemptable scheduling mechanism can improve the utilization of resources in clouds. In this paper, we present a preemptable job scheduling mechanism in cloud system. We propose two feedback dynamic scheduling algorithms for this scheduling mechanism. We compare these two scheduling algorithms in simulations. The results show that the feedback procedure in our algorithms works well in the situation where resource contentions are fierce.
{"title":"Feedback Dynamic Algorithms for Preemptable Job Scheduling in Cloud Systems","authors":"Jiayin Li, Meikang Qiu, Jianwei Niu, Wenzhong Gao, Ziliang Zong, Xiao Qin","doi":"10.1109/WI-IAT.2010.30","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.30","url":null,"abstract":"An infrastructure-as-a-service cloud system provides computational capacities to remote users. Parallel processing in the cloud system can shorten the execution of jobs. Parallel processing requires a mechanism to scheduling the executions order as well as resource allocation. Furthermore, a preemptable scheduling mechanism can improve the utilization of resources in clouds. In this paper, we present a preemptable job scheduling mechanism in cloud system. We propose two feedback dynamic scheduling algorithms for this scheduling mechanism. We compare these two scheduling algorithms in simulations. The results show that the feedback procedure in our algorithms works well in the situation where resource contentions are fierce.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127168535","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}
It is widely acknowledged that providing explanations is an important capability of intelligent systems. Explanation capabilities are useful, for example, in scenario-based training systems with intelligent virtual agents. Trainees learn more from scenario-based training when they understand why the virtual agents act the way they do. In this paper, we present a model for explainable BDI agents which enables the explanation of BDI agent behavior in terms of underlying beliefs and goals. Different explanation algorithms can be specified in the model, generating different types of explanations. In a user study (n=20), we compare four explanation algorithms by asking trainees which explanations they consider most useful. Based on the results, we discuss which explanation types should be given under what conditions.
{"title":"Design and Evaluation of Explainable BDI Agents","authors":"M. Harbers, K. Bosch, J. Meyer","doi":"10.1109/WI-IAT.2010.115","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.115","url":null,"abstract":"It is widely acknowledged that providing explanations is an important capability of intelligent systems. Explanation capabilities are useful, for example, in scenario-based training systems with intelligent virtual agents. Trainees learn more from scenario-based training when they understand why the virtual agents act the way they do. In this paper, we present a model for explainable BDI agents which enables the explanation of BDI agent behavior in terms of underlying beliefs and goals. Different explanation algorithms can be specified in the model, generating different types of explanations. In a user study (n=20), we compare four explanation algorithms by asking trainees which explanations they consider most useful. Based on the results, we discuss which explanation types should be given under what conditions.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127364166","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}
Agents in networks have two strategic choices: They can forward/process incoming service requests – or not, and they can establish additional contacts and maintain or terminate existing ones. In other words, an agent can choose both an action-selection and a link-selection strategy. So far, it is unclear which equilibria exist in such settings. We show that there are the following equilibria: First, an inefficient one where agents leave the network. Second, an equilibrium where agents process requests on behalf of others, i.e., they cooperate. In this second equilibrium, agents distribute their contacts uniformly, which is not efficient. We show that a strategy, we propose in this paper, yields an equilibrium that is optimal, i.e., that yields the highest sum of payoffs over all equilibria. If agents base their link-selection decisions on the processing times of their requests, optimal system states can be equilibria.
{"title":"Towards Efficient Equilibria of Combinations of Network-Formation and Interaction Strategies","authors":"Björn-Oliver Hartmann, Klemens Böhm","doi":"10.1109/WI-IAT.2010.50","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.50","url":null,"abstract":"Agents in networks have two strategic choices: They can forward/process incoming service requests – or not, and they can establish additional contacts and maintain or terminate existing ones. In other words, an agent can choose both an action-selection and a link-selection strategy. So far, it is unclear which equilibria exist in such settings. We show that there are the following equilibria: First, an inefficient one where agents leave the network. Second, an equilibrium where agents process requests on behalf of others, i.e., they cooperate. In this second equilibrium, agents distribute their contacts uniformly, which is not efficient. We show that a strategy, we propose in this paper, yields an equilibrium that is optimal, i.e., that yields the highest sum of payoffs over all equilibria. If agents base their link-selection decisions on the processing times of their requests, optimal system states can be equilibria.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122344012","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 propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that divides the distribution function of the observable popularity metric into two components: a) one that can be explained by the given set of explanatory factors (called risk factors) and b) a baseline distribution function that integrates all the factors not taken into account. To validate our proposed approach, we use data sets from two different online discussion forums: dpreview.com, one of the largest online discussion groups providing news and discussion forums about all kinds of digital cameras, and myspace.com, one of the representative online social networking services. On these two data sets we model two different popularity metrics, the lifetime of threads and the number of comments, and show that our approach can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5~6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2~3 days.
{"title":"An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors","authors":"Jong Gun Lee, S. Moon, Kave Salamatian","doi":"10.1109/WI-IAT.2010.209","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.209","url":null,"abstract":"In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that divides the distribution function of the observable popularity metric into two components: a) one that can be explained by the given set of explanatory factors (called risk factors) and b) a baseline distribution function that integrates all the factors not taken into account. To validate our proposed approach, we use data sets from two different online discussion forums: dpreview.com, one of the largest online discussion groups providing news and discussion forums about all kinds of digital cameras, and myspace.com, one of the representative online social networking services. On these two data sets we model two different popularity metrics, the lifetime of threads and the number of comments, and show that our approach can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5~6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2~3 days.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132322766","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 significant proportion of e-Learning services are delivered through Cloud Computing. These use Web services as an interface integrator to support communication across heterogeneous platforms over internet protocols. Similarly, a high level of task collaboration is needed to form an e-Learning community. Therefore, we propose an e-Learning Computational Cloud (eLC2) based on the Model-View-Controller design patterns paradigm. The reusable task objects collaborate in a Model that is wrapped inside the reconfigurable Controller which transforms the request/response parameters of the end user View to that of the Model. The eLC2 offers software development platform for e-Learning Task Management. The main deliverable of eLC2 is a Task as a Service which is decoupled from View as well as user session maintenance. It is directly exposed to external the e-Learning Cloud for scalability.
{"title":"E-Learning Computational Cloud (eLC2): Web Services Platform to Enhance Task Collaboration","authors":"S. Rajam, Ruth Cortez, A. Vazhenin, S. Bhalla","doi":"10.1109/WI-IAT.2010.294","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.294","url":null,"abstract":"A significant proportion of e-Learning services are delivered through Cloud Computing. These use Web services as an interface integrator to support communication across heterogeneous platforms over internet protocols. Similarly, a high level of task collaboration is needed to form an e-Learning community. Therefore, we propose an e-Learning Computational Cloud (eLC2) based on the Model-View-Controller design patterns paradigm. The reusable task objects collaborate in a Model that is wrapped inside the reconfigurable Controller which transforms the request/response parameters of the end user View to that of the Model. The eLC2 offers software development platform for e-Learning Task Management. The main deliverable of eLC2 is a Task as a Service which is decoupled from View as well as user session maintenance. It is directly exposed to external the e-Learning Cloud for scalability.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130949144","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 widespread use of ontologies to associate semantics with data has resulted in a growing interest in the problem of learning predictive models from data sources that use different ontologies to model the same underlying domain (world of interest). Learning from such emph{semantically disparate} data sources involves the use of a mapping to resolve semantic disparity among the ontologies used. Often, in practice, the mapping used to resolve the disparity may contain errors and as such the learning algorithms used in such a setting must be robust in presence of mapping errors. We reduce the problem of learning from semantically disparate data sources in the presence of mapping errors to a variant of the problem of learning in the presence of nasty classification noise. This reduction allows us to transfer theoretical results and algorithms from the latter to the former.
{"title":"Learning in Presence of Ontology Mapping Errors","authors":"Neeraj Koul, V. Honavar","doi":"10.1109/WI-IAT.2010.138","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.138","url":null,"abstract":"The widespread use of ontologies to associate semantics with data has resulted in a growing interest in the problem of learning predictive models from data sources that use different ontologies to model the same underlying domain (world of interest). Learning from such emph{semantically disparate} data sources involves the use of a mapping to resolve semantic disparity among the ontologies used. Often, in practice, the mapping used to resolve the disparity may contain errors and as such the learning algorithms used in such a setting must be robust in presence of mapping errors. We reduce the problem of learning from semantically disparate data sources in the presence of mapping errors to a variant of the problem of learning in the presence of nasty classification noise. This reduction allows us to transfer theoretical results and algorithms from the latter to the former.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125510293","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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