Pub Date : 2012-06-11DOI: 10.1109/ISI.2012.6282258
Hsien-Ming Chou, Lina Zhou
Many computer-based communication media offer visual anonymity. As a result, detecting online deception tends to be more difficult relative to traditional non-mediated communication. The state of the art research on online deception has focused on using linear statistical approaches to identifying behavioral differences between deceivers and truth-tellers. However, deception behaviors are not linear because deceivers may adopt dynamic strategies when they are motivated to succeed, and deceivers could disguise themselves to maximize their payoffs. Given such backdrop, this research is aimed to address deception strategies with a game theory approach. The results of an empirical study with a multi-stage game show that deceivers tend to select different strategies from truth-tellers and deceivers may adjust their strategies to avoid detection. These findings provide significant implications for explaining online deception in the full rationality paradigm.
{"title":"A game theory approach to deception strategy in computer mediated communication","authors":"Hsien-Ming Chou, Lina Zhou","doi":"10.1109/ISI.2012.6282258","DOIUrl":"https://doi.org/10.1109/ISI.2012.6282258","url":null,"abstract":"Many computer-based communication media offer visual anonymity. As a result, detecting online deception tends to be more difficult relative to traditional non-mediated communication. The state of the art research on online deception has focused on using linear statistical approaches to identifying behavioral differences between deceivers and truth-tellers. However, deception behaviors are not linear because deceivers may adopt dynamic strategies when they are motivated to succeed, and deceivers could disguise themselves to maximize their payoffs. Given such backdrop, this research is aimed to address deception strategies with a game theory approach. The results of an empirical study with a multi-stage game show that deceivers tend to select different strategies from truth-tellers and deceivers may adjust their strategies to avoid detection. These findings provide significant implications for explaining online deception in the full rationality paradigm.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"243 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122473533","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284090
Yang Song, Xiaolin Zhang
Rescue robots possessing human-like active binocular systems would allow high quality remote control by 3D viewing and stable robot vision. However, this type of system has not been researched thoroughly because it is difficult to control, and there are few accurate integrated eye motion control models. In this paper, we propose an integrated eye motion control system for a rescue robot, which integrates smooth pursuit, saccade, Vestibulo-ocular reflex (VOR) and Optokinetic response (OKR) into a binocular model. To simplify this system, we also include aspects of the human visual system, in which only the saccade command is externally applied, whereas the smooth pursuit, VOR and OKR commands are internally auto-implemented.
{"title":"An active binocular integrated system for intelligent robot vision","authors":"Yang Song, Xiaolin Zhang","doi":"10.1109/ISI.2012.6284090","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284090","url":null,"abstract":"Rescue robots possessing human-like active binocular systems would allow high quality remote control by 3D viewing and stable robot vision. However, this type of system has not been researched thoroughly because it is difficult to control, and there are few accurate integrated eye motion control models. In this paper, we propose an integrated eye motion control system for a rescue robot, which integrates smooth pursuit, saccade, Vestibulo-ocular reflex (VOR) and Optokinetic response (OKR) into a binocular model. To simplify this system, we also include aspects of the human visual system, in which only the saccade command is externally applied, whereas the smooth pursuit, VOR and OKR commands are internally auto-implemented.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121644585","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284102
Zhangwen Tan, W. Mao, D. Zeng, Xiaochen Li, Xiuguo Bao
Food safety events are typical public security events that draw great public concern. In food safety events, millions of netizens pay close attention to the event, express their opinions online and thus influence the decisions of government or food producers. Modeling netizen groups, especially the dynamics of their opinions in these events, can help us understand the mechanism and evolvement of such events and provide valuable insights for social management. However, conventional computational models, such as agent-based models, are usually constructed manually. In this paper, we propose an approach to acquiring netizen group's opinions from online comments to facilitate the modeling of food safety events. We conduct experimental study on typical events happened in China and empirically evaluate the performance of our proposed approach. The results verify the effectiveness of the approach.
{"title":"Acquiring netizen group's opinions for modeling food safety events","authors":"Zhangwen Tan, W. Mao, D. Zeng, Xiaochen Li, Xiuguo Bao","doi":"10.1109/ISI.2012.6284102","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284102","url":null,"abstract":"Food safety events are typical public security events that draw great public concern. In food safety events, millions of netizens pay close attention to the event, express their opinions online and thus influence the decisions of government or food producers. Modeling netizen groups, especially the dynamics of their opinions in these events, can help us understand the mechanism and evolvement of such events and provide valuable insights for social management. However, conventional computational models, such as agent-based models, are usually constructed manually. In this paper, we propose an approach to acquiring netizen group's opinions from online comments to facilitate the modeling of food safety events. We conduct experimental study on typical events happened in China and empirically evaluate the performance of our proposed approach. The results verify the effectiveness of the approach.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133346059","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 : 2012-06-11DOI: 10.1109/ISI.2012.6283222
R. Colbaugh, K. Glass
Adaptive adversaries are a primary concern in several domains, including cyber defense, border security, counterterrorism, and fraud prevention, and consequently there is great interest in developing defenses that maintain their effectiveness in the presence of evolving adversary strategies and tactics. This paper leverages the coevolutionary relationship between attackers and defenders to derive two new approaches to predictive defense, in which future attack techniques are anticipated and these insights are incorporated into defense designs. The first method combines game theory with machine learning to model and predict future adversary actions in the learner's “feature space”; these predictions form the basis for synthesizing robust defenses. The second approach to predictive defense involves extrapolating the evolution of defense configurations forward in time, in the space of defense parameterizations, as a way of generating defenses which work well against evolving threats. Case studies with a large cyber security dataset assembled for this investigation demonstrate that each method provides effective, scalable defense against current and future attacks, outperforming gold-standard techniques. Additionally, preliminary tests indicate that a simple variant of the proposed design methodology yields defenses which are difficult for adversaries to reverse-engineer.
{"title":"Predictive defense against evolving adversaries","authors":"R. Colbaugh, K. Glass","doi":"10.1109/ISI.2012.6283222","DOIUrl":"https://doi.org/10.1109/ISI.2012.6283222","url":null,"abstract":"Adaptive adversaries are a primary concern in several domains, including cyber defense, border security, counterterrorism, and fraud prevention, and consequently there is great interest in developing defenses that maintain their effectiveness in the presence of evolving adversary strategies and tactics. This paper leverages the coevolutionary relationship between attackers and defenders to derive two new approaches to predictive defense, in which future attack techniques are anticipated and these insights are incorporated into defense designs. The first method combines game theory with machine learning to model and predict future adversary actions in the learner's “feature space”; these predictions form the basis for synthesizing robust defenses. The second approach to predictive defense involves extrapolating the evolution of defense configurations forward in time, in the space of defense parameterizations, as a way of generating defenses which work well against evolving threats. Case studies with a large cyber security dataset assembled for this investigation demonstrate that each method provides effective, scalable defense against current and future attacks, outperforming gold-standard techniques. Additionally, preliminary tests indicate that a simple variant of the proposed design methodology yields defenses which are difficult for adversaries to reverse-engineer.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133871754","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284278
Fatih Özgül, Murat Gök, Z. Erdem, Yakup Ozal
Criminal networks have been an area of interest for Public Safety and Intelligence Community as well as social network analysis and data mining community. Existing literature shows that offender demographics and crime features are not taken into account to identify their possible links to find out criminal networks. Four crime data specific proprietary group detection models (GDM, OGDM, SoDM, and ComDM) have been developed based on these crime data features. These specific criminal network detection models are compared more common baseline SNA group detection algorithms. It is intended to find out, whether these four crime data specific group detection models can perform better than widely used k-cores and n-clique algorithms. Two datasets which contain various real criminal networks are used as experimental testbeds.
{"title":"Detecting criminal networks: SNA models are compared to proprietary models","authors":"Fatih Özgül, Murat Gök, Z. Erdem, Yakup Ozal","doi":"10.1109/ISI.2012.6284278","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284278","url":null,"abstract":"Criminal networks have been an area of interest for Public Safety and Intelligence Community as well as social network analysis and data mining community. Existing literature shows that offender demographics and crime features are not taken into account to identify their possible links to find out criminal networks. Four crime data specific proprietary group detection models (GDM, OGDM, SoDM, and ComDM) have been developed based on these crime data features. These specific criminal network detection models are compared more common baseline SNA group detection algorithms. It is intended to find out, whether these four crime data specific group detection models can perform better than widely used k-cores and n-clique algorithms. Two datasets which contain various real criminal networks are used as experimental testbeds.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"52 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113970241","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284089
D. Skillicorn
We describe a technique that calculates the expected relationships among attributes from training data, and uses this to generate anomaly scores reflecting the intuition that a record with anomalous values for related attributes is more anomalous than one with anomalous values for unrelated attributes. The expected relations among attributes are calculated in two ways: using a data-dependent projection via singular value decomposition, and using the maximal information coefficient. Sufficiently anomalous records are displayed on a sensor dashboard, making it possible for an analyst to judge why each record has been classified as anomalous. The technique is illustrated for an intrusion detection dataset, and a set of contract descriptors.
{"title":"Outlier detection using semantic sensors","authors":"D. Skillicorn","doi":"10.1109/ISI.2012.6284089","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284089","url":null,"abstract":"We describe a technique that calculates the expected relationships among attributes from training data, and uses this to generate anomaly scores reflecting the intuition that a record with anomalous values for related attributes is more anomalous than one with anomalous values for unrelated attributes. The expected relations among attributes are calculated in two ways: using a data-dependent projection via singular value decomposition, and using the maximal information coefficient. Sufficiently anomalous records are displayed on a sensor dashboard, making it possible for an analyst to judge why each record has been classified as anomalous. The technique is illustrated for an intrusion detection dataset, and a set of contract descriptors.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125141149","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284101
Jiyoung Woo, Hsinchun Chen
Social media is being increasingly used as a communication channel. Among social media, web forums, where people in online communities disseminate and receive information by interaction, provide a good environment to examine information diffusion. In this research, we aim to understand the mechanisms and properties of the information diffusion in the web forum. For that, we model topic-level information diffusion in web forums using the baseline epidemic model, the SIR(Susceptible, Infective, and Recovered) model, frequently used in previous research to analyze disease outbreaks and knowledge diffusion. In addition, we propose an event-driven SIR model that reflects the event effect on information diffusion in the web forum. The proposed model incorporates the effect of news postings on the web forum. We evaluate two models using a large longitudinal dataset from the web forum of a major company. The event-SIR model outperforms the SIR model in fitting on major spikey topics that have peaks of author participation.
{"title":"An event-driven SIR model for topic diffusion in web forums","authors":"Jiyoung Woo, Hsinchun Chen","doi":"10.1109/ISI.2012.6284101","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284101","url":null,"abstract":"Social media is being increasingly used as a communication channel. Among social media, web forums, where people in online communities disseminate and receive information by interaction, provide a good environment to examine information diffusion. In this research, we aim to understand the mechanisms and properties of the information diffusion in the web forum. For that, we model topic-level information diffusion in web forums using the baseline epidemic model, the SIR(Susceptible, Infective, and Recovered) model, frequently used in previous research to analyze disease outbreaks and knowledge diffusion. In addition, we propose an event-driven SIR model that reflects the event effect on information diffusion in the web forum. The proposed model incorporates the effect of news postings on the web forum. We evaluate two models using a large longitudinal dataset from the web forum of a major company. The event-SIR model outperforms the SIR model in fitting on major spikey topics that have peaks of author participation.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125979762","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284276
M. Goldberg, J. Greenman, B. Gutting, M. Magdon-Ismail, J. Schwartz, W. Wallace
We present novel indexing and searching schemes for semantic graphs based on the notion of the i.degrees of a node. The i.degrees allow searches performed on the graph to use “type” and connection information, rather than textual labels, to identify nodes. We aim to identify a network graph (fragment) within a large semantic graph (database). A fragment may represent incomplete information that a researcher has collected on a sub-network of interest. While textual labels might be available, they are highly unreliable, and cannot be used for identification of hidden networks. Since this problem comes from the classically NP-hard problem of identifying isomorphic subgraphs, our algorithms are heuristic.
{"title":"Graph search beyond text: Relational searches in semantic hyperlinked data","authors":"M. Goldberg, J. Greenman, B. Gutting, M. Magdon-Ismail, J. Schwartz, W. Wallace","doi":"10.1109/ISI.2012.6284276","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284276","url":null,"abstract":"We present novel indexing and searching schemes for semantic graphs based on the notion of the i.degrees of a node. The i.degrees allow searches performed on the graph to use “type” and connection information, rather than textual labels, to identify nodes. We aim to identify a network graph (fragment) within a large semantic graph (database). A fragment may represent incomplete information that a researcher has collected on a sub-network of interest. While textual labels might be available, they are highly unreliable, and cannot be used for identification of hidden networks. Since this problem comes from the classically NP-hard problem of identifying isomorphic subgraphs, our algorithms are heuristic.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126612299","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284094
R. Colbaugh, K. Glass
There is considerable interest in developing techniques for predicting human behavior, and a promising approach to this problem is to collect phenomenon-relevant empirical data and then apply machine learning methods to these data to form predictions. This two-part paper shows that the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In this paper, the second of the two parts, we demonstrate that a sociologically-grounded learning algorithm outperforms a gold-standard method for the task of predicting whether nascent social diffusion events will “go viral”. Significantly, the proposed algorithm performs well even when there is only limited time series data available for analysis.
{"title":"Leveraging sociological models for prediction II: Early warning for complex contagions","authors":"R. Colbaugh, K. Glass","doi":"10.1109/ISI.2012.6284094","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284094","url":null,"abstract":"There is considerable interest in developing techniques for predicting human behavior, and a promising approach to this problem is to collect phenomenon-relevant empirical data and then apply machine learning methods to these data to form predictions. This two-part paper shows that the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In this paper, the second of the two parts, we demonstrate that a sociologically-grounded learning algorithm outperforms a gold-standard method for the task of predicting whether nascent social diffusion events will “go viral”. Significantly, the proposed algorithm performs well even when there is only limited time series data available for analysis.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127850672","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 : 2012-06-11DOI: 10.1109/ISI.2012.6284144
J. Koepke, S. Kaza, A. Abbasi
Users on the web are unknowingly becoming more susceptible to scams from cyber deviants and malicious websites. There has been much work in the identification of malicious websites using application layer features based on content (HTML, images, links, etc.) and a plethora of classification techniques. However, there has been little work on using features from the other layers in the Open Systems Interconnection (OSI) network stack. Capturing features from the transport and internet layers of the network stack based on responses to various Hypertext Transfer Protocol (HTTP) requests may allow for increased classification accuracy. In this paper, we use learning techniques (Winnow, Logit Regression, Naïve Bayes, J48, and Bayesian) utilizing these new features to identify fake pharmacy websites. The results show that using transport and Internet layer features yields an accuracy of 80% to 95% for detecting fake websites using standard machine learning algorithms. The results suggest that many organizations may be hosting multiple websites using shared code and hosting services to enable them to produce the maximum number of fraudulent websites.
网络用户在不知不觉中变得更容易受到网络变态和恶意网站的欺骗。在使用基于内容(HTML、图像、链接等)的应用层特征和大量分类技术来识别恶意网站方面已经做了很多工作。然而，在使用开放系统互连(OSI)网络堆栈中其他层的特性方面，很少有工作。基于对各种超文本传输协议(Hypertext Transfer Protocol, HTTP)请求的响应，从网络堆栈的传输层和互联网层捕获特性，可以提高分类的准确性。在本文中，我们使用学习技术(Winnow, Logit Regression, Naïve贝叶斯，J48和贝叶斯)利用这些新特征来识别假冒药店网站。结果表明，使用传输和互联网层特征，使用标准机器学习算法检测虚假网站的准确率为80%至95%。结果表明，许多组织可能使用共享代码和托管服务托管多个网站，使他们能够产生最大数量的欺诈性网站。
{"title":"Exploratory experiments to identify fake websites by using features from the network stack","authors":"J. Koepke, S. Kaza, A. Abbasi","doi":"10.1109/ISI.2012.6284144","DOIUrl":"https://doi.org/10.1109/ISI.2012.6284144","url":null,"abstract":"Users on the web are unknowingly becoming more susceptible to scams from cyber deviants and malicious websites. There has been much work in the identification of malicious websites using application layer features based on content (HTML, images, links, etc.) and a plethora of classification techniques. However, there has been little work on using features from the other layers in the Open Systems Interconnection (OSI) network stack. Capturing features from the transport and internet layers of the network stack based on responses to various Hypertext Transfer Protocol (HTTP) requests may allow for increased classification accuracy. In this paper, we use learning techniques (Winnow, Logit Regression, Naïve Bayes, J48, and Bayesian) utilizing these new features to identify fake pharmacy websites. The results show that using transport and Internet layer features yields an accuracy of 80% to 95% for detecting fake websites using standard machine learning algorithms. The results suggest that many organizations may be hosting multiple websites using shared code and hosting services to enable them to produce the maximum number of fraudulent websites.","PeriodicalId":199734,"journal":{"name":"2012 IEEE International Conference on Intelligence and Security Informatics","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131818694","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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