Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165952
Zhipeng Jin, Qiudan Li, D. Zeng, Lei Wang
Weibo has become an important information sharing platform in our daily life in China. Many applications utilize Weibo data to analyze hot topic and opinion evolution patterns to gain insights into user behavior. However, various spam messages degrade the performance of these applications and thus are essential to be filtered. In this paper, we propose a unified spam detection approach, which utilizes external knowledge sources to expand keywords features and applies an ensemble under-sampling based strategy to handle the class-imbalance problem. The experimental results show the effectiveness and robustness of our approach in Weibo data.
{"title":"Filtering spam in Weibo using ensemble imbalanced classification and knowledge expansion","authors":"Zhipeng Jin, Qiudan Li, D. Zeng, Lei Wang","doi":"10.1109/ISI.2015.7165952","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165952","url":null,"abstract":"Weibo has become an important information sharing platform in our daily life in China. Many applications utilize Weibo data to analyze hot topic and opinion evolution patterns to gain insights into user behavior. However, various spam messages degrade the performance of these applications and thus are essential to be filtered. In this paper, we propose a unified spam detection approach, which utilizes external knowledge sources to expand keywords features and applies an ensemble under-sampling based strategy to handle the class-imbalance problem. The experimental results show the effectiveness and robustness of our approach in Weibo data.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132942653","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 : 2015-05-27DOI: 10.1109/ISI.2015.7165937
Royal A. Elmore, W. Charlton
Decision making on weapons of mass effect (WME) proliferation and counter-proliferation is information driven. However, the large data requirements, along with associated knowledge gaps and intelligence uncertainties, impedes optimal strategy selection. Combining Bayesian analysis, agent based modeling (ABM), and information theory within a security informatics context can aid understanding of dynamic WME proliferation and counter-proliferation pathways and possibilities. The Bayesian ABM Nonproliferation Enterprise (BANE) was developed to incorporate large databases and information sets. There are three broad BANE agent classes: 1) proliferator, 2) defensive, and 3) neutral. Within each agent class exists significant flexibility for them pursuing different objectives. Bayesian analysis cover the technical linkages realistically tying proliferation pathway process steps together. In BANE, Bayesian networks using the Netica software program provide a wide array of scientific and engineering pathway options. Information theory, especially entropy reduction and mutual information, in a Bayesian security informatics arrangement help identify optimal technical areas to master or disrupt. Concurrently, interlocking factors such as available resources, technical sophistication, time horizons, detection risks, and agent affinities impact agents' ability to achieve their goals. Actions taken by one BANE agent on the proliferation or counter-proliferation front affect its future opportunities and those of potential partner or adversarial agents. An explanation of the BANE framework and several key security informatics aspects crucial to WME proliferation and counter-proliferation analysis are provided.
{"title":"Nonproliferation informatics: Employing Bayesian analysis, agent based modeling, and information theory for dynamic proliferation pathway studies","authors":"Royal A. Elmore, W. Charlton","doi":"10.1109/ISI.2015.7165937","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165937","url":null,"abstract":"Decision making on weapons of mass effect (WME) proliferation and counter-proliferation is information driven. However, the large data requirements, along with associated knowledge gaps and intelligence uncertainties, impedes optimal strategy selection. Combining Bayesian analysis, agent based modeling (ABM), and information theory within a security informatics context can aid understanding of dynamic WME proliferation and counter-proliferation pathways and possibilities. The Bayesian ABM Nonproliferation Enterprise (BANE) was developed to incorporate large databases and information sets. There are three broad BANE agent classes: 1) proliferator, 2) defensive, and 3) neutral. Within each agent class exists significant flexibility for them pursuing different objectives. Bayesian analysis cover the technical linkages realistically tying proliferation pathway process steps together. In BANE, Bayesian networks using the Netica software program provide a wide array of scientific and engineering pathway options. Information theory, especially entropy reduction and mutual information, in a Bayesian security informatics arrangement help identify optimal technical areas to master or disrupt. Concurrently, interlocking factors such as available resources, technical sophistication, time horizons, detection risks, and agent affinities impact agents' ability to achieve their goals. Actions taken by one BANE agent on the proliferation or counter-proliferation front affect its future opportunities and those of potential partner or adversarial agents. An explanation of the BANE framework and several key security informatics aspects crucial to WME proliferation and counter-proliferation analysis are provided.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131015030","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 : 2015-05-27DOI: 10.1109/ISI.2015.7165968
Cynthia L. Claiborne, C. Ncube, R. Dantu
With the increasing ability to accurately classify activities of mobile users from what was once viewed as innocuous mobile sensor data, the risk of users compromising their privacy has risen exponentially. Currently, mobile owners cannot control how various applications handle the privacy of their sensor data, or even determine if a service provider is adversarial or trustworthy. To address these privacy concerns, third party applications have been designed to allow mobile users to have control over the data that is sent to service providers. However, these applications require users to set flags and parameters that place restrictions on the anonymized or real sensor data that is sent to the requestor. Therefore, in this paper, we introduce a new framework, RANDSOM, that moves the decision-making from the application level to the operating system level.
{"title":"Random anonymization of mobile sensor data: Modified Android framework","authors":"Cynthia L. Claiborne, C. Ncube, R. Dantu","doi":"10.1109/ISI.2015.7165968","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165968","url":null,"abstract":"With the increasing ability to accurately classify activities of mobile users from what was once viewed as innocuous mobile sensor data, the risk of users compromising their privacy has risen exponentially. Currently, mobile owners cannot control how various applications handle the privacy of their sensor data, or even determine if a service provider is adversarial or trustworthy. To address these privacy concerns, third party applications have been designed to allow mobile users to have control over the data that is sent to service providers. However, these applications require users to set flags and parameters that place restrictions on the anonymized or real sensor data that is sent to the requestor. Therefore, in this paper, we introduce a new framework, RANDSOM, that moves the decision-making from the application level to the operating system level.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134360573","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 : 2015-05-27DOI: 10.1109/ISI.2015.7165957
Joshua J. Harrison, Eric Bell, Courtney Corley, Chase P. Dowling, A. Cowell
This study presents an assessment of multiple approaches to determine the home and/or other important locations to a Twitter user. In this study, we present a unique approach to the problem of geotagged data sparsity in social media when performing geoinferencing tasks. Given the sparsity of explicitly geotagged Twitter data, the ability to perform accurate and reliable user geolocation from a limited number of geotagged posts has proven to be quite useful. In our survey, we have achieved accuracy rates of over 86% in matching Twitter user profile locations with their inferred home locations derived from geotagged posts.
{"title":"Assessment of user home location geoinference methods","authors":"Joshua J. Harrison, Eric Bell, Courtney Corley, Chase P. Dowling, A. Cowell","doi":"10.1109/ISI.2015.7165957","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165957","url":null,"abstract":"This study presents an assessment of multiple approaches to determine the home and/or other important locations to a Twitter user. In this study, we present a unique approach to the problem of geotagged data sparsity in social media when performing geoinferencing tasks. Given the sparsity of explicitly geotagged Twitter data, the ability to perform accurate and reliable user geolocation from a limited number of geotagged posts has proven to be quite useful. In our survey, we have achieved accuracy rates of over 86% in matching Twitter user profile locations with their inferred home locations derived from geotagged posts.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133711274","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 : 2015-05-27DOI: 10.1109/ISI.2015.7165958
Junjie Lin, W. Mao
In recent years, microblog has become one of the most widely used social media for people to exchange ideas and express emotions. As information propagates fast in social network, it's crucial for governments and public agencies to effectively monitor public sentiment implied in user-generated content. Most previous work of public sentiment analysis takes tweets of different users as a whole without considering the diverse word use of people. Thus, some sentiment words may be neglected in the process of analysis because they are only used by people of specific groups. Inspired by previous psychological findings that personality influences the ways people write and talk, we propose a personality based sentiment classification method. In order to capture more useful but not widely used sentiment words, our approach extracts textual features for people of different personality traits based on the Big Five model. Moreover, we adopt an ensemble learning strategy to utilize both personality related and commonly used textual features. Experimental study shows the effectiveness of our method.
{"title":"Personality based public sentiment classification in microblog","authors":"Junjie Lin, W. Mao","doi":"10.1109/ISI.2015.7165958","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165958","url":null,"abstract":"In recent years, microblog has become one of the most widely used social media for people to exchange ideas and express emotions. As information propagates fast in social network, it's crucial for governments and public agencies to effectively monitor public sentiment implied in user-generated content. Most previous work of public sentiment analysis takes tweets of different users as a whole without considering the diverse word use of people. Thus, some sentiment words may be neglected in the process of analysis because they are only used by people of specific groups. Inspired by previous psychological findings that personality influences the ways people write and talk, we propose a personality based sentiment classification method. In order to capture more useful but not widely used sentiment words, our approach extracts textual features for people of different personality traits based on the Big Five model. Moreover, we adopt an ensemble learning strategy to utilize both personality related and commonly used textual features. Experimental study shows the effectiveness of our method.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121235012","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 : 2015-05-27DOI: 10.1109/ISI.2015.7165933
Ian A. Andrews, Srijan Kumar, Francesca Spezzano, V. S. Subrahmanian
The best known analyses to date of nuclear proliferation networks are qualitative analyses of networks consisting of just hundreds of nodes and edges. We propose SPINN - a computational framework that performs the following tasks. Starting from existing lists of sanctioned entities, SPINN automatically builds a highly augmented network by scraping connections between individuals, companies, and government organizations from sources like LinkedIN and public company data from Bloomberg. By analyzing this open source information alone, we have built up a network of over 74K nodes and 1.09M edges, containing a smaller whitelist and a blacklist. We develop numerous “features” of nodes in such networks that take both intrinsic node properties and network properties into account, and based on these, we develop methods to classify previously unclassified nodes as suspicious or unsuspicious. On 10-fold cross validation on ground truth data, we obtain a Matthews Correlation Coefficient for our best classifier of just over 0.9. We show that of the 10 most relevant features for distinguishing between suspicious and non-suspicious nodes, the top 8 are network related measures including a novel notion of suspicion rank.
{"title":"SPINN: Suspicion prediction in nuclear networks","authors":"Ian A. Andrews, Srijan Kumar, Francesca Spezzano, V. S. Subrahmanian","doi":"10.1109/ISI.2015.7165933","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165933","url":null,"abstract":"The best known analyses to date of nuclear proliferation networks are qualitative analyses of networks consisting of just hundreds of nodes and edges. We propose SPINN - a computational framework that performs the following tasks. Starting from existing lists of sanctioned entities, SPINN automatically builds a highly augmented network by scraping connections between individuals, companies, and government organizations from sources like LinkedIN and public company data from Bloomberg. By analyzing this open source information alone, we have built up a network of over 74K nodes and 1.09M edges, containing a smaller whitelist and a blacklist. We develop numerous “features” of nodes in such networks that take both intrinsic node properties and network properties into account, and based on these, we develop methods to classify previously unclassified nodes as suspicious or unsuspicious. On 10-fold cross validation on ground truth data, we obtain a Matthews Correlation Coefficient for our best classifier of just over 0.9. We show that of the 10 most relevant features for distinguishing between suspicious and non-suspicious nodes, the top 8 are network related measures including a novel notion of suspicion rank.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131542069","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 : 2015-05-27DOI: 10.1109/ISI.2015.7165945
Sanjaya Wijeratne, Derek Doran, A. Sheth, Jack L. Dustin
Gangs utilize social media as a way to maintain threatening virtual presences, to communicate about their activities, and to intimidate others. Such usage has gained the attention of many justice service agencies that wish to create better crime prevention and judicial services. However, these agencies use analysis methods that are labor intensive and only lead to basic, qualitative data interpretations. This paper presents the architecture of a modern platform to discover the structure, function, and operation of gangs through the lens of social media. Preliminary analysis of social media posts shared in the greater Chicago, IL region demonstrate the platform's capability to understand gang members' social media usage patterns.
{"title":"Analyzing the social media footprint of street gangs","authors":"Sanjaya Wijeratne, Derek Doran, A. Sheth, Jack L. Dustin","doi":"10.1109/ISI.2015.7165945","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165945","url":null,"abstract":"Gangs utilize social media as a way to maintain threatening virtual presences, to communicate about their activities, and to intimidate others. Such usage has gained the attention of many justice service agencies that wish to create better crime prevention and judicial services. However, these agencies use analysis methods that are labor intensive and only lead to basic, qualitative data interpretations. This paper presents the architecture of a modern platform to discover the structure, function, and operation of gangs through the lens of social media. Preliminary analysis of social media posts shared in the greater Chicago, IL region demonstrate the platform's capability to understand gang members' social media usage patterns.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132450517","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 : 2015-05-27DOI: 10.1109/ISI.2015.7165942
Bryan Monk, Russell Allsup, Richard Frank
The advent of the internet has unfortunately increased the scale and complexity of child exploitation material (CEM) with content increasingly moving online, forming online CEM networks through a series of websites that are hyperlinked to each other and lead consumers from one website to another. Extending on prior research focusing on examining network structure and network disruption strategies it was prudent to expand avenues to increase attack strategies. Geolocation and Whois data were utilized to map the prevalence of CEM globally. Differences in the Geolocation and Whois data were observed, suggesting both are critical pieces of information in generating accurate geo-mapping of CEM. These maps show how multi-jurisdictional attack strategies may be employed to attack these networks and remove this content.
{"title":"LECENing places to hide: Geo-mapping child exploitation material","authors":"Bryan Monk, Russell Allsup, Richard Frank","doi":"10.1109/ISI.2015.7165942","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165942","url":null,"abstract":"The advent of the internet has unfortunately increased the scale and complexity of child exploitation material (CEM) with content increasingly moving online, forming online CEM networks through a series of websites that are hyperlinked to each other and lead consumers from one website to another. Extending on prior research focusing on examining network structure and network disruption strategies it was prudent to expand avenues to increase attack strategies. Geolocation and Whois data were utilized to map the prevalence of CEM globally. Differences in the Geolocation and Whois data were observed, suggesting both are critical pieces of information in generating accurate geo-mapping of CEM. These maps show how multi-jurisdictional attack strategies may be employed to attack these networks and remove this content.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926709","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 : 2015-05-01DOI: 10.1109/ISI.2015.7165950
Julius Adebayo, Lalana Kagal
We present a transformation procedure for large scale individual level data that produces output data in which no linear combinations of the resulting attributes can yield the original sensitive attributes from the transformed data. In doing this, our procedure eliminates all linear information regarding a sensitive attribute from the input data. The algorithm combines principal components analysis of the data set with orthogonal projection onto the subspace containing the sensitive attribute(s). The algorithm presented is motivated by applications where there is a need to drastically `sanitize' a data set of all information relating to sensitive attribute(s) before analysis of the data using a data mining algorithm. Sensitive attribute removal (sanitization) is often needed to prevent disparate impact and discrimination on the basis of race, gender, and sexual orientation in high stakes contexts such as determination of access to loans, credit, employment, and insurance. We show through experiments that our proposed algorithm outperforms other privacy preserving techniques by more than 20 percent in lowering the ability to reconstruct sensitive attributes from large scale data.
{"title":"A privacy protection procedure for large scale individual level data","authors":"Julius Adebayo, Lalana Kagal","doi":"10.1109/ISI.2015.7165950","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165950","url":null,"abstract":"We present a transformation procedure for large scale individual level data that produces output data in which no linear combinations of the resulting attributes can yield the original sensitive attributes from the transformed data. In doing this, our procedure eliminates all linear information regarding a sensitive attribute from the input data. The algorithm combines principal components analysis of the data set with orthogonal projection onto the subspace containing the sensitive attribute(s). The algorithm presented is motivated by applications where there is a need to drastically `sanitize' a data set of all information relating to sensitive attribute(s) before analysis of the data using a data mining algorithm. Sensitive attribute removal (sanitization) is often needed to prevent disparate impact and discrimination on the basis of race, gender, and sexual orientation in high stakes contexts such as determination of access to loans, credit, employment, and insurance. We show through experiments that our proposed algorithm outperforms other privacy preserving techniques by more than 20 percent in lowering the ability to reconstruct sensitive attributes from large scale data.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130162259","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 : 2015-01-07DOI: 10.1109/ISI.2015.7165940
D. Skillicorn
The jihadist groups AQAP, ISIS, and the Taliban have all produced glossy English magazines designed to influence Western sympathizers. We examine these magazines empirically with respect to models of the intensity of informative, imaginative, deceptive, jihadist, and gamification language. This allows their success to be estimated and their similarities and differences to be exposed. We also develop and validate an empirical model of propaganda; according to this model Dabiq, ISIS's magazine ranks highest of the three.
{"title":"Empirical assessment of al qaeda, ISIS, and taliban propaganda","authors":"D. Skillicorn","doi":"10.1109/ISI.2015.7165940","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165940","url":null,"abstract":"The jihadist groups AQAP, ISIS, and the Taliban have all produced glossy English magazines designed to influence Western sympathizers. We examine these magazines empirically with respect to models of the intensity of informative, imaginative, deceptive, jihadist, and gamification language. This allows their success to be estimated and their similarities and differences to be exposed. We also develop and validate an empirical model of propaganda; according to this model Dabiq, ISIS's magazine ranks highest of the three.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130154443","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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