Open source software (OSS) is a core part of virtually all software applications today. Due to the rapidly growing impact of OSS on society and the economy, the security aspect has attracted researchers' attention to investigate this distinctive phenomenon. Traditionally, research on OSS security has often focused on technical aspects of software development. We argue that these aspects are important, however, technical security practice considering different social aspects of OSS development will assure the effectiveness and efficiency of the implementation of the tool. To mitigate this research gap, in this empirical study, we explore the current security culture in the OSS development phenomenon using a survey instrument with six evaluation dimensions: attitude, behavior, competency, subjective norms, governance, and communication. By exploring the current security culture in OSS communities, we can start to understand the influence of security on participants' security behaviors and decision-making, so that we can make realistic and practical suggestions. In this paper, we present the measurements of security culture adopted in the study and discuss corresponding security issues that need to be addressed in OSS communities.
{"title":"An Empirical Study of Security Culture in Open Source Software Communities","authors":"Shao-Fang Wen, Mazaher Kianpour, S. Kowalski","doi":"10.1145/3341161.3343520","DOIUrl":"https://doi.org/10.1145/3341161.3343520","url":null,"abstract":"Open source software (OSS) is a core part of virtually all software applications today. Due to the rapidly growing impact of OSS on society and the economy, the security aspect has attracted researchers' attention to investigate this distinctive phenomenon. Traditionally, research on OSS security has often focused on technical aspects of software development. We argue that these aspects are important, however, technical security practice considering different social aspects of OSS development will assure the effectiveness and efficiency of the implementation of the tool. To mitigate this research gap, in this empirical study, we explore the current security culture in the OSS development phenomenon using a survey instrument with six evaluation dimensions: attitude, behavior, competency, subjective norms, governance, and communication. By exploring the current security culture in OSS communities, we can start to understand the influence of security on participants' security behaviors and decision-making, so that we can make realistic and practical suggestions. In this paper, we present the measurements of security culture adopted in the study and discuss corresponding security issues that need to be addressed in OSS communities.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131644583","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}
Thiago H. P. Silva, Alberto H. F. Laender, Pedro O. S. Vaz de Melo
Characterizing dynamic interactions is currently an important issue when analyzing complex social networks. In this paper, we reinforce the importance of social concepts as the strategic positioning of an actor in a social structure, thus bringing new insights to the analysis of complex networks. Specifically, we propose a new method to characterize relationships based on temporal node-attributes that captures how knowledge is transferred across the network. As a result, we unveil the differences of social relationships in different academic social networks and Q&A communities. We also validate our social definitions in terms of the importance of the edges as assessed by the betweenness centrality metric and compare our results with those of two existing methods. Finally, we apply our method to a ranking task in order to measure the academic importance of researchers.
{"title":"Characterizing Knowledge-Transfer Relationships in Dynamic Attributed Networks","authors":"Thiago H. P. Silva, Alberto H. F. Laender, Pedro O. S. Vaz de Melo","doi":"10.1145/3341161.3342883","DOIUrl":"https://doi.org/10.1145/3341161.3342883","url":null,"abstract":"Characterizing dynamic interactions is currently an important issue when analyzing complex social networks. In this paper, we reinforce the importance of social concepts as the strategic positioning of an actor in a social structure, thus bringing new insights to the analysis of complex networks. Specifically, we propose a new method to characterize relationships based on temporal node-attributes that captures how knowledge is transferred across the network. As a result, we unveil the differences of social relationships in different academic social networks and Q&A communities. We also validate our social definitions in terms of the importance of the edges as assessed by the betweenness centrality metric and compare our results with those of two existing methods. Finally, we apply our method to a ranking task in order to measure the academic importance of researchers.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115117602","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}
Indonesia's cities are growing faster than other Asian countries at a rate of 4.1% per year. Traditional java house in the Indonesia urban area have been undergoing dramatic physical changes since the beginning of the 20th century. From 1920 to 2018, the cities in Central Java Province have transformed becomes three types of housing: vernacular house, contemporary house, and modern house. This paper will focus on the process of house plan transformation and its impact of the user behavior to adapt with the surrounding environment. Three different Javanese houses in Java, Indonesia were analyzed by applying SNA (Social Network Analysis). The finding reveals that there is a socio-cultural factor from Javanese culture which still influence all architectural types of the Java housing.
{"title":"Transformation in Architecture and Spatial Organization at Javanese house","authors":"D. P. Sari, Y. Chiou","doi":"10.1145/3341161.3343684","DOIUrl":"https://doi.org/10.1145/3341161.3343684","url":null,"abstract":"Indonesia's cities are growing faster than other Asian countries at a rate of 4.1% per year. Traditional java house in the Indonesia urban area have been undergoing dramatic physical changes since the beginning of the 20th century. From 1920 to 2018, the cities in Central Java Province have transformed becomes three types of housing: vernacular house, contemporary house, and modern house. This paper will focus on the process of house plan transformation and its impact of the user behavior to adapt with the surrounding environment. Three different Javanese houses in Java, Indonesia were analyzed by applying SNA (Social Network Analysis). The finding reveals that there is a socio-cultural factor from Javanese culture which still influence all architectural types of the Java housing.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116253628","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}
Choukri Djellali, Mehdi Adda, Mohamed Tarik Moutacalli
Many models have been proposed to address deep learning problem. Most deep learning models are influenced by presentation order, complex shapes, architecture configuration and learning instability. This paper provides comparative study to deep learning for pattern recognition. Two types of supervised learning techniques were tested which are used for comparison purpose. They correspond to Batch Gradient Descent and Stochastic Gradient Descent. In order to obtain an accurate results with both methods, we used a re-sampling method based on k-fold cross-validation. Experimental Results show that Stochastic Gradient Descent gives good results in comparison to Batch Gradient Descent. The recognition accuracies are seen to improve significantly when Stochastic Gradient Descent is applied for intrusion detection.
{"title":"A Comparative Study to Deep Learning for Pattern Recognition, By using Online and Batch Learning; Taking Cybersecurity as a case","authors":"Choukri Djellali, Mehdi Adda, Mohamed Tarik Moutacalli","doi":"10.1145/3341161.3343533","DOIUrl":"https://doi.org/10.1145/3341161.3343533","url":null,"abstract":"Many models have been proposed to address deep learning problem. Most deep learning models are influenced by presentation order, complex shapes, architecture configuration and learning instability. This paper provides comparative study to deep learning for pattern recognition. Two types of supervised learning techniques were tested which are used for comparison purpose. They correspond to Batch Gradient Descent and Stochastic Gradient Descent. In order to obtain an accurate results with both methods, we used a re-sampling method based on k-fold cross-validation. Experimental Results show that Stochastic Gradient Descent gives good results in comparison to Batch Gradient Descent. The recognition accuracies are seen to improve significantly when Stochastic Gradient Descent is applied for intrusion detection.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121965490","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 offers a synthesis of a new analytical procedure based on the complementary use of a large number of methods and techniques for categorisation of objects, pattern recognition and for structural analysis. It represents an example of a functional clustering [1] and an extension to the ‘posteriori methods' for clusterisation [2]. We call this approach Multi-Stage Clustering (MSC), as it applies cluster analysis methods at three distinctive stages. We present the MSC and demonstrate its application to a business dataset of 275 multinational corporations (MNCs), aiming to address the inherent weaknesses of existing industrial classification tools designed to capture diversification of firms. We evaluate the outcomes from the MSC using a combination of complementary methods for structural analysis and data visualisation, such as multi-dimensional scaling (MDS), network mapping (NM) and multiple correspondence analysis (MCA). The MSC is designed for the analysis of diversification patterns of MNCs, which can enable the measurement of group competitiveness and performance across these patterns, known as industry segments, or strategic industry groups (SIGs).
{"title":"Multi Stage Clustering with Complementary Structural Analysis of 2-Mode Networks*","authors":"E. Todeva, D. Knoke, Donka Keskinova","doi":"10.1145/3341161.3344781","DOIUrl":"https://doi.org/10.1145/3341161.3344781","url":null,"abstract":"This paper offers a synthesis of a new analytical procedure based on the complementary use of a large number of methods and techniques for categorisation of objects, pattern recognition and for structural analysis. It represents an example of a functional clustering [1] and an extension to the ‘posteriori methods' for clusterisation [2]. We call this approach Multi-Stage Clustering (MSC), as it applies cluster analysis methods at three distinctive stages. We present the MSC and demonstrate its application to a business dataset of 275 multinational corporations (MNCs), aiming to address the inherent weaknesses of existing industrial classification tools designed to capture diversification of firms. We evaluate the outcomes from the MSC using a combination of complementary methods for structural analysis and data visualisation, such as multi-dimensional scaling (MDS), network mapping (NM) and multiple correspondence analysis (MCA). The MSC is designed for the analysis of diversification patterns of MNCs, which can enable the measurement of group competitiveness and performance across these patterns, known as industry segments, or strategic industry groups (SIGs).","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047311","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}
Civil wars are as frequent and debilitating now as ever. More often than not, their resolution consists of the negotiation of a peace accord that involves a number of provisions. Although previous work in political science indicates an underlying interdependence between provision implementation sequences, it is unclear how the structure and dynamics of this interdependence relate to the successful implementation of peace accords. To fill this gap, we systematically study peace process implementation activity from 34 peace accords containing 51 provisions negotiated between 1989 and 2015. We begin by constructing a bipartite network between peace accords and their provisions' implementation and explore statistical properties of the structural underpinnings of peace processes. Then, we examine motifs (i.e., significantly frequent patterns) in provision implementation activity and uncover higher order correlations between provisions. Finally, we identify provision implementation sequences (i.e., meta-groups) that are most strongly associated with successful peace processes. Our empirical findings provide new insights for the implementation of peace accords by revealing temporal sequences of peace process implementation that help build confidence, enhance security, and ultimately prevent negative cascading effects in different stages of the peacebuilding process.
{"title":"Network Perspective on the Efficiency of Peace Accords Implementation","authors":"H. Dambanemuya, M. Joshi, Emőke-Ágnes Horvát","doi":"10.1145/3341161.3342895","DOIUrl":"https://doi.org/10.1145/3341161.3342895","url":null,"abstract":"Civil wars are as frequent and debilitating now as ever. More often than not, their resolution consists of the negotiation of a peace accord that involves a number of provisions. Although previous work in political science indicates an underlying interdependence between provision implementation sequences, it is unclear how the structure and dynamics of this interdependence relate to the successful implementation of peace accords. To fill this gap, we systematically study peace process implementation activity from 34 peace accords containing 51 provisions negotiated between 1989 and 2015. We begin by constructing a bipartite network between peace accords and their provisions' implementation and explore statistical properties of the structural underpinnings of peace processes. Then, we examine motifs (i.e., significantly frequent patterns) in provision implementation activity and uncover higher order correlations between provisions. Finally, we identify provision implementation sequences (i.e., meta-groups) that are most strongly associated with successful peace processes. Our empirical findings provide new insights for the implementation of peace accords by revealing temporal sequences of peace process implementation that help build confidence, enhance security, and ultimately prevent negative cascading effects in different stages of the peacebuilding process.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128507551","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}
We demonstrate a machine learning method, namely lexical link analysis (LLA), which can be used to discover high-value information from financial data. LLA is an unsupervised learning method that does not require manually labeled training data. We also demonstrate how to form LLA in a game-theoretic framework. We show that with game theory: high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information and at the same time generates high social welfare, therefore containing higher intrinsic value. We show the results of LLA of two sets of financial data validating and correlating with the ground truth.
{"title":"Theory and Use Case of Game-theoretic Lexical Link Analysis","authors":"Ying Zhao, Charles C. Zhou, Sihui Huang","doi":"10.1145/3341161.3343706","DOIUrl":"https://doi.org/10.1145/3341161.3343706","url":null,"abstract":"We demonstrate a machine learning method, namely lexical link analysis (LLA), which can be used to discover high-value information from financial data. LLA is an unsupervised learning method that does not require manually labeled training data. We also demonstrate how to form LLA in a game-theoretic framework. We show that with game theory: high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information and at the same time generates high social welfare, therefore containing higher intrinsic value. We show the results of LLA of two sets of financial data validating and correlating with the ground truth.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127972004","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}
Curiosity is a natural trait of human behavior. When we take into account the time we spend consuming content online, it is expected that at least a fraction of that time was driven by curious behavior. Aiming at understanding how curiosity drives online information consumption, we here propose a model that captures user curiosity relying on several stimulus metrics. Our model relies on the well-established Wundt's curve from psychology and is based on metrics capturing Novelty, Complexity and Uncertainty as key stimuli driving one's curiosity. As a case study, we apply our model on a dataset of online music consumption from LastFM. We found that there are four main types of user behaviors in terms of how the curiosity stimulus metrics drive the user accesses to online music. These are characterized based on the diversity in the songs, artists and musical genres accessed.
{"title":"Analyzing and Modeling User Curiosity in Online Content Consumption: A LastFM Case Study","authors":"A. M. Sousa, J. Almeida, F. Figueiredo","doi":"10.1145/3341161.3342917","DOIUrl":"https://doi.org/10.1145/3341161.3342917","url":null,"abstract":"Curiosity is a natural trait of human behavior. When we take into account the time we spend consuming content online, it is expected that at least a fraction of that time was driven by curious behavior. Aiming at understanding how curiosity drives online information consumption, we here propose a model that captures user curiosity relying on several stimulus metrics. Our model relies on the well-established Wundt's curve from psychology and is based on metrics capturing Novelty, Complexity and Uncertainty as key stimuli driving one's curiosity. As a case study, we apply our model on a dataset of online music consumption from LastFM. We found that there are four main types of user behaviors in terms of how the curiosity stimulus metrics drive the user accesses to online music. These are characterized based on the diversity in the songs, artists and musical genres accessed.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263689","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}
Z. Borbora, Arpita Chandra, P. Kumaraguru, J. Srivastava
Massively Multiplayer Online Role-Playing Games (MMORPGs) are persistent virtual environments where millions of players interact in an online manner. We study the problem of player churn and social contagion using MMORPG game logs by analyzing the impact of a node's churn behavior on its immediate neighborhood or group. The two key research questions in this paper are - When an active node, ego, becomes dormant, what is the impact on the activity behavior of ego's immediate neighbor, alter, 1) based on ego's characteristics and ego's relationship with alter and 2) based on the activity behavior of alter's remaining neighbors. We use a supervised learning framework to study the impact of player churn and social contagion. Experimental results show that the classification models perform substantially better than random for both the research problems. Finally, we use a data-driven approach to propose a player typology based on degree of socialization and analyze churn behavior among these player types. Experimental results show that the loner player type is much more likely to churn than the socializer player types and as the degree of socialization decreases among socializers, the propensity to churn increases.
{"title":"On Churn and Social Contagion","authors":"Z. Borbora, Arpita Chandra, P. Kumaraguru, J. Srivastava","doi":"10.1145/3341161.3344378","DOIUrl":"https://doi.org/10.1145/3341161.3344378","url":null,"abstract":"Massively Multiplayer Online Role-Playing Games (MMORPGs) are persistent virtual environments where millions of players interact in an online manner. We study the problem of player churn and social contagion using MMORPG game logs by analyzing the impact of a node's churn behavior on its immediate neighborhood or group. The two key research questions in this paper are - When an active node, ego, becomes dormant, what is the impact on the activity behavior of ego's immediate neighbor, alter, 1) based on ego's characteristics and ego's relationship with alter and 2) based on the activity behavior of alter's remaining neighbors. We use a supervised learning framework to study the impact of player churn and social contagion. Experimental results show that the classification models perform substantially better than random for both the research problems. Finally, we use a data-driven approach to propose a player typology based on degree of socialization and analyze churn behavior among these player types. Experimental results show that the loner player type is much more likely to churn than the socializer player types and as the degree of socialization decreases among socializers, the propensity to churn increases.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126348585","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}
Krunal Dhiraj Patel, A. Heppner, Gautam Srivastava, Vijay K. Mago
Social Media platforms have become common venue for sharing experiences and knowledge about health-related topics. This research focuses on examining social media based communication patterns related to diabetes on the Twitter platform. Specifically, we apply an updated methodology to examine changes in the current use of hash-tags, trending hash-tags, and the frequency of diabetes-related tweets using a previous study as a baseline. Our results show significant growth in the diabetes community on Twitter over time and also evidence that this community is increasing in it's capacity to spread awareness around diabetes related health topics. Our methodological contributions include an improved framework for collecting, cleaning and analyzing Twitter data related to diabetes as well as the application of regular expressions to categorize subsets of Tweets.
{"title":"Analyzing Use of Twitter by Diabetes online Community","authors":"Krunal Dhiraj Patel, A. Heppner, Gautam Srivastava, Vijay K. Mago","doi":"10.1145/3341161.3343673","DOIUrl":"https://doi.org/10.1145/3341161.3343673","url":null,"abstract":"Social Media platforms have become common venue for sharing experiences and knowledge about health-related topics. This research focuses on examining social media based communication patterns related to diabetes on the Twitter platform. Specifically, we apply an updated methodology to examine changes in the current use of hash-tags, trending hash-tags, and the frequency of diabetes-related tweets using a previous study as a baseline. Our results show significant growth in the diabetes community on Twitter over time and also evidence that this community is increasing in it's capacity to spread awareness around diabetes related health topics. Our methodological contributions include an improved framework for collecting, cleaning and analyzing Twitter data related to diabetes as well as the application of regular expressions to categorize subsets of Tweets.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126784251","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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