Cyber threat such as malware and exploit have causes significant losses to the economy and has become a lucrative form of illicit business by leveraging the darkweb as a communication channel. To understand more about the emerging cyber threats of attacking tools and its actors, a threat intelligence collecting mechanism is proposed for identifying the emerging threat. With crowdsourcing intelligence and public threat intelligence such as NVD and CERT, it is able to leverage multiple sources of information and provide domain-specific security intelligence. In addition, we propose a network-based darkweb cyberthreat alert model, which can well represent and visualize actors' similarity and thus uncover the vulnerable vendor (organization) exposed in the underground markets.
{"title":"A multi-channel cybersecurity news and threat intelligent engine - SecBuzzer","authors":"Shin-Ying Huang, Yennun Huang, Ching-Hao Mao","doi":"10.1145/3341161.3345309","DOIUrl":"https://doi.org/10.1145/3341161.3345309","url":null,"abstract":"Cyber threat such as malware and exploit have causes significant losses to the economy and has become a lucrative form of illicit business by leveraging the darkweb as a communication channel. To understand more about the emerging cyber threats of attacking tools and its actors, a threat intelligence collecting mechanism is proposed for identifying the emerging threat. With crowdsourcing intelligence and public threat intelligence such as NVD and CERT, it is able to leverage multiple sources of information and provide domain-specific security intelligence. In addition, we propose a network-based darkweb cyberthreat alert model, which can well represent and visualize actors' similarity and thus uncover the vulnerable vendor (organization) exposed in the underground markets.","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":"124895151","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}
D. Spiliotopoulos, C. Vassilakis, Dionisis Margaris
Terrorism is a key risk for prospective visitors of tourist destinations. This work reports on the analysis of past terrorist attack data, focusing on tourist-related attacks and attack types in Mediterranean EU area and the development of algorithms to predict terrorist attack risk levels. Data on attacks in 10 countries have been analyzed to quantify the threat level of tourism-related terrorism based on the data from 2000 to 2017 and formulate predictions for subsequent periods. Results show that predictions on potential target types can be derived with adequate accuracy. Such results are useful for initiating, shifting and validating active terrorism surveillance based on predicted attack and target types per country from real past data.
{"title":"Data-driven Country Safety Monitoring Terrorist Attack Prediction","authors":"D. Spiliotopoulos, C. Vassilakis, Dionisis Margaris","doi":"10.1145/3341161.3343527","DOIUrl":"https://doi.org/10.1145/3341161.3343527","url":null,"abstract":"Terrorism is a key risk for prospective visitors of tourist destinations. This work reports on the analysis of past terrorist attack data, focusing on tourist-related attacks and attack types in Mediterranean EU area and the development of algorithms to predict terrorist attack risk levels. Data on attacks in 10 countries have been analyzed to quantify the threat level of tourism-related terrorism based on the data from 2000 to 2017 and formulate predictions for subsequent periods. Results show that predictions on potential target types can be derived with adequate accuracy. Such results are useful for initiating, shifting and validating active terrorism surveillance based on predicted attack and target types per country from real past data.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"21 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":"125881605","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}
On line social networks (e.g., Facebook, Twitter) allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of tweets manifest the spatial-temporal movements of people in real life. The paper presents an approach to recommend travel routes to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. Travel routes recommendation is formulated as a ranking problem aiming at minimg the top interesting locations and travel sequences among them, and exploit such information to recommend the most suitable travel routes to a target user. A ranking function that exploits users' similarity in visiting locations and in travelling along mobility paths is used to predict places the user could like. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending travel routes achieving remarkable precision and recall rates.
{"title":"Travel Routes Recommendations via Online Social Networks","authors":"C. Comito","doi":"10.1145/3341161.3345619","DOIUrl":"https://doi.org/10.1145/3341161.3345619","url":null,"abstract":"On line social networks (e.g., Facebook, Twitter) allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of tweets manifest the spatial-temporal movements of people in real life. The paper presents an approach to recommend travel routes to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. Travel routes recommendation is formulated as a ranking problem aiming at minimg the top interesting locations and travel sequences among them, and exploit such information to recommend the most suitable travel routes to a target user. A ranking function that exploits users' similarity in visiting locations and in travelling along mobility paths is used to predict places the user could like. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending travel routes achieving remarkable precision and recall rates.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"59 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":"117038449","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}
Customer satisfaction surveys, which have been the most common way of gauging customer feedback, involve high costs, require customer active participation, and typically involve low response rates. The tremendous growth of social media platforms such as Twitter provides businesses an opportunity to continuously gather and analyze customer feedback, with the goal of identifying and rectifying issues. This paper examines the alternative of replacing traditional customer satisfaction surveys with social media data. To evaluate this approach the following steps were taken, using customer feedback data extracted from Twitter: 1) Applying sentiment to each Tweet to compare the overall sentiment across different products and/or services. 2) Constructing a hashtag co-occurrence network to further optimize the customer feedback query process from Twitter. 3) Comparing customer feedback from survey responses with social media feedback, while considering content and added value. We find that social media provides advantages over traditional surveys.
{"title":"Social Media as a Main Source of Customer Feedback - Alternative to Customer Satisfaction Surveys","authors":"S. Hasson, J. Piorkowski, I. McCulloh","doi":"10.1145/3341161.3345642","DOIUrl":"https://doi.org/10.1145/3341161.3345642","url":null,"abstract":"Customer satisfaction surveys, which have been the most common way of gauging customer feedback, involve high costs, require customer active participation, and typically involve low response rates. The tremendous growth of social media platforms such as Twitter provides businesses an opportunity to continuously gather and analyze customer feedback, with the goal of identifying and rectifying issues. This paper examines the alternative of replacing traditional customer satisfaction surveys with social media data. To evaluate this approach the following steps were taken, using customer feedback data extracted from Twitter: 1) Applying sentiment to each Tweet to compare the overall sentiment across different products and/or services. 2) Constructing a hashtag co-occurrence network to further optimize the customer feedback query process from Twitter. 3) Comparing customer feedback from survey responses with social media feedback, while considering content and added value. We find that social media provides advantages over traditional surveys.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 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":"122459343","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}
Mohammad Raihanul Islam, S. Muthiah, Naren Ramakrishnan
The penetration of social media has had deep and far-reaching consequences in information production and consumption. Widespread use of social media platforms has engendered malicious users and attention seekers to spread rumors and fake news. This trend is particularly evident in various microblogging platforms where news becomes viral in a matter of hours and can lead to mass panic and confusion. One intriguing fact regarding rumors and fake news is that very often rumor stories prompt users to adopt different stances about the rumor posts. Understanding user stances in rumor posts is thus very important to identify the veracity of the underlying content. While rumor veracity and stance detection have been viewed as disjoint tasks we demonstrate here how jointly learning both of them can be fruitful. In this paper, we propose RumorSleuth, a multitask deep learning model which can leverage both the textual information and user profile information to jointly identify the veracity of a rumor along with users' stances. Tests on two publicly available rumor datasets demonstrate that RumorSleuth outperforms current state-of-the-art models and achieves up to 14% performance gain in rumor veracity classification and around 6% improvement in user stance classification.
{"title":"RumorSleuth: Joint Detection of Rumor Veracity and User Stance","authors":"Mohammad Raihanul Islam, S. Muthiah, Naren Ramakrishnan","doi":"10.1145/3341161.3342916","DOIUrl":"https://doi.org/10.1145/3341161.3342916","url":null,"abstract":"The penetration of social media has had deep and far-reaching consequences in information production and consumption. Widespread use of social media platforms has engendered malicious users and attention seekers to spread rumors and fake news. This trend is particularly evident in various microblogging platforms where news becomes viral in a matter of hours and can lead to mass panic and confusion. One intriguing fact regarding rumors and fake news is that very often rumor stories prompt users to adopt different stances about the rumor posts. Understanding user stances in rumor posts is thus very important to identify the veracity of the underlying content. While rumor veracity and stance detection have been viewed as disjoint tasks we demonstrate here how jointly learning both of them can be fruitful. In this paper, we propose RumorSleuth, a multitask deep learning model which can leverage both the textual information and user profile information to jointly identify the veracity of a rumor along with users' stances. Tests on two publicly available rumor datasets demonstrate that RumorSleuth outperforms current state-of-the-art models and achieves up to 14% performance gain in rumor veracity classification and around 6% improvement in user stance classification.","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":"123946996","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}
Logan Praznik, Gautam Srivastava, Chetan Mendhe, Vijay K. Mago
Communications on the popular social networking platform, Twitter, can be mapped in terms of a hashtag graph, where vertices correspond to hashtags, and edges correspond to co-occurrences of hashtags within the same distinct tweet. Furthermore, a vertex in hashtag graphs can be weighted with the number of tweets a hashtag has occurred in, and edges can be weighted with the number of tweets both hashtags have co-occurred in. In this paper, we describe additions to some well-known link prediction methods that allow the weights of both vertices and edges in a weighted hashtag graph to be taken into account. We base our novel predictive additions on the assumption that more popular hashtags have a higher probability to appear with other hashtags in the future. We then apply these improved methods to 3 sets of Twitter data with the intent of predicting hashtags co-occurences in the future. Experimental results on real-life data sets consisting of over 3, 000, 000 combined unique Tweets and over 250, 000 unique hashtags show the effectiveness of the proposed models and algorithms on weighted hashtag graphs.
{"title":"Vertex-Weighted Measures for Link Prediction in Hashtag Graphs","authors":"Logan Praznik, Gautam Srivastava, Chetan Mendhe, Vijay K. Mago","doi":"10.1145/3341161.3344828","DOIUrl":"https://doi.org/10.1145/3341161.3344828","url":null,"abstract":"Communications on the popular social networking platform, Twitter, can be mapped in terms of a hashtag graph, where vertices correspond to hashtags, and edges correspond to co-occurrences of hashtags within the same distinct tweet. Furthermore, a vertex in hashtag graphs can be weighted with the number of tweets a hashtag has occurred in, and edges can be weighted with the number of tweets both hashtags have co-occurred in. In this paper, we describe additions to some well-known link prediction methods that allow the weights of both vertices and edges in a weighted hashtag graph to be taken into account. We base our novel predictive additions on the assumption that more popular hashtags have a higher probability to appear with other hashtags in the future. We then apply these improved methods to 3 sets of Twitter data with the intent of predicting hashtags co-occurences in the future. Experimental results on real-life data sets consisting of over 3, 000, 000 combined unique Tweets and over 250, 000 unique hashtags show the effectiveness of the proposed models and algorithms on weighted hashtag graphs.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"31 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":"130981635","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 proposes a novel approach to efficiently compute the exact closeness centrality values of all nodes in dynamically evolving directed and weighted networks. Closeness centrality is one of the most frequently used centrality measures in the field of social network analysis. It uses the total distance to all other nodes to determine node centrality. Previous work has addressed the problem of dynamically updating closeness centrality values for either undirected networks or only for the top-$k$ nodes in terms of closeness centrality. Here, we propose a fast approach for exactly computing all closeness centrality values at each timestamp of directed and weighted evolving networks. Such networks are prevalent in many real-world situations. The main ingredients of our approach are a combination of work filtering methods and efficient incremental updates that avoid unnecessary recomputation. We tested the approach on several real-world datasets of dynamic small-world networks and found that we have mean speed-ups of about 33 times. In addition, the method is highly parallelizable.
{"title":"Fast Incremental Computation of Harmonic Closeness Centrality in Directed Weighted Networks","authors":"K. Putman, Hanjo D. Boekhout, Frank W. Takes","doi":"10.1145/3341161.3344829","DOIUrl":"https://doi.org/10.1145/3341161.3344829","url":null,"abstract":"This paper proposes a novel approach to efficiently compute the exact closeness centrality values of all nodes in dynamically evolving directed and weighted networks. Closeness centrality is one of the most frequently used centrality measures in the field of social network analysis. It uses the total distance to all other nodes to determine node centrality. Previous work has addressed the problem of dynamically updating closeness centrality values for either undirected networks or only for the top-$k$ nodes in terms of closeness centrality. Here, we propose a fast approach for exactly computing all closeness centrality values at each timestamp of directed and weighted evolving networks. Such networks are prevalent in many real-world situations. The main ingredients of our approach are a combination of work filtering methods and efficient incremental updates that avoid unnecessary recomputation. We tested the approach on several real-world datasets of dynamic small-world networks and found that we have mean speed-ups of about 33 times. In addition, the method is highly parallelizable.","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":"125307799","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}
Complex networks have attracted a great deal of research interest in the last two decades since Watts & Strogatz, Barabási & Albert and Girvan & Newman published their highly-cited seminal papers on small-world networks, on scale-free networks and on the community structure of complex networks, respectively. These fundamental papers initiated a new era of research establishing an interdisciplinary field called network science. Due to the multidisciplinary nature of the field, a diverse but not divided network science community has emerged in the past 20 years. This paper honors the contributions of network science by exploring the evolution of this community as seen through the growing co-authorship network of network scientists (here the notion refers to a scholar with at least one paper citing at least one of the three aforementioned milestone papers). After investigating various characteristics of 29,528 network science papers, we construct the co-authorship network of 52,406 network scientists and we analyze its topology and dynamics. We shed light on the collaboration patterns of the last 20 years of network science by investigating numerous structural properties of the co-authorship network and by using enhanced data visualization techniques. We also identify the most central authors, the largest communities, investigate the spatiotemporal changes, and compare the properties of the network to scientometric indicators.
{"title":"Two Decades of Network Science: as seen through the co-authorship network of network scientists","authors":"Roland Molontay, Marcell Nagy","doi":"10.1145/3341161.3343685","DOIUrl":"https://doi.org/10.1145/3341161.3343685","url":null,"abstract":"Complex networks have attracted a great deal of research interest in the last two decades since Watts & Strogatz, Barabási & Albert and Girvan & Newman published their highly-cited seminal papers on small-world networks, on scale-free networks and on the community structure of complex networks, respectively. These fundamental papers initiated a new era of research establishing an interdisciplinary field called network science. Due to the multidisciplinary nature of the field, a diverse but not divided network science community has emerged in the past 20 years. This paper honors the contributions of network science by exploring the evolution of this community as seen through the growing co-authorship network of network scientists (here the notion refers to a scholar with at least one paper citing at least one of the three aforementioned milestone papers). After investigating various characteristics of 29,528 network science papers, we construct the co-authorship network of 52,406 network scientists and we analyze its topology and dynamics. We shed light on the collaboration patterns of the last 20 years of network science by investigating numerous structural properties of the co-authorship network and by using enhanced data visualization techniques. We also identify the most central authors, the largest communities, investigate the spatiotemporal changes, and compare the properties of the network to scientometric indicators.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"9 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":"114444137","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}
Social trading platforms provide a forum for investors to share their analysis and opinions. Posts on these platforms are characterized by narrative styles which are much different from posts on general social platforms, for instance tweets. As a result, recommendation systems for social trading platforms should leverage tailor-made latent features. This paper presents a representation for these latent features in both textual data and market information. A real-world dataset is adopted to conduct experiments involving a novel task called next cashtag prediction. We propose a joint learning model with an attentive capsule network. Experimental results show positive results with the proposed methods and the corresponding auxiliary tasks.
{"title":"Next Cashtag Prediction on Social Trading Platforms with Auxiliary Tasks","authors":"Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen","doi":"10.1145/3341161.3342945","DOIUrl":"https://doi.org/10.1145/3341161.3342945","url":null,"abstract":"Social trading platforms provide a forum for investors to share their analysis and opinions. Posts on these platforms are characterized by narrative styles which are much different from posts on general social platforms, for instance tweets. As a result, recommendation systems for social trading platforms should leverage tailor-made latent features. This paper presents a representation for these latent features in both textual data and market information. A real-world dataset is adopted to conduct experiments involving a novel task called next cashtag prediction. We propose a joint learning model with an attentive capsule network. Experimental results show positive results with the proposed methods and the corresponding auxiliary tasks.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"21 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":"123499670","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 relationship extraction and fusion of networks are the hotspots of current research in social network mining. Most previous work is based on single-source data. However, the relationships portrayed by single-source data are not sufficient to characterize the relationships of the real world. To solve this problem, a Semi-supervised Fusion framework for Multiple Network (SFMN), using gradient boosting decision tree algorithm (GBDT) to fuse the information of multi-source networks into a single network, is proposed in this paper. Our framework aims to take advantage of multi-source networks fusion to enhance the accuracy of the network construction. The experiment shows that our method optimizes the structural and community accuracy of social networks which makes our framework outperforms several state-of-the-art methods.
{"title":"Strengthening Social Networks Analysis by Networks Fusion","authors":"Feiyu Long, Nianwen Ning, Chenguang Song, Bin Wu","doi":"10.1145/3341161.3342939","DOIUrl":"https://doi.org/10.1145/3341161.3342939","url":null,"abstract":"The relationship extraction and fusion of networks are the hotspots of current research in social network mining. Most previous work is based on single-source data. However, the relationships portrayed by single-source data are not sufficient to characterize the relationships of the real world. To solve this problem, a Semi-supervised Fusion framework for Multiple Network (SFMN), using gradient boosting decision tree algorithm (GBDT) to fuse the information of multi-source networks into a single network, is proposed in this paper. Our framework aims to take advantage of multi-source networks fusion to enhance the accuracy of the network construction. The experiment shows that our method optimizes the structural and community accuracy of social networks which makes our framework outperforms several state-of-the-art methods.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"74 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":"122616578","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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