Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.167
Yu-Hsuan Cheng, C. Wu, Tsun Ku, Gwo-Dong Chen
TV audience rating is an important indicator regarding the popularity of programs and it is also a factor to influence the revenue of broadcast stations via advertisements. Presently, the only way for assessing audience rating is the Nielsen TV rating, which depends on a small number of randomly selected representative groups, because of practical considerations such as cost and survey time. The way to obtain audience rating is using 'People-meter' which is a device installed in user's house and regularly records the rating surveys. However, we are not able to know the audience rating immediately since sometimes we have to make a marketing decision and lack of indicator. Currently, the present media environments are drastically changing our media consumption patterns. We can watch TV programs on Youtube regardless location and timing. And Nielsen TV audience rating does not take the social networking site into account. In this paper, we develop a model for predicting TV audience rating. We accumulate the broadcasted TV programs' word-of-mouse on Facebook and apply the Back-propagation Network to predict the latest program audience rating. We also present the audience rating trend analysis on demo system which is used to describe the relation between predictive audience rating and Nielsen TV rating.
{"title":"A Predicting Model of TV Audience Rating Based on the Facebook","authors":"Yu-Hsuan Cheng, C. Wu, Tsun Ku, Gwo-Dong Chen","doi":"10.1109/SocialCom.2013.167","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.167","url":null,"abstract":"TV audience rating is an important indicator regarding the popularity of programs and it is also a factor to influence the revenue of broadcast stations via advertisements. Presently, the only way for assessing audience rating is the Nielsen TV rating, which depends on a small number of randomly selected representative groups, because of practical considerations such as cost and survey time. The way to obtain audience rating is using 'People-meter' which is a device installed in user's house and regularly records the rating surveys. However, we are not able to know the audience rating immediately since sometimes we have to make a marketing decision and lack of indicator. Currently, the present media environments are drastically changing our media consumption patterns. We can watch TV programs on Youtube regardless location and timing. And Nielsen TV audience rating does not take the social networking site into account. In this paper, we develop a model for predicting TV audience rating. We accumulate the broadcasted TV programs' word-of-mouse on Facebook and apply the Back-propagation Network to predict the latest program audience rating. We also present the audience rating trend analysis on demo system which is used to describe the relation between predictive audience rating and Nielsen TV rating.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133890867","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.124
Shaoda Yu, Peng Li, H. Lin, E. Rohani, G. Choi, B. Shao, Qian Wang
Drowsiness presents major safety concerns for tasks that require long periods of focus and alertness. While there is a body of work on drowsiness detection using EEG signals in neuroscience and engineering, there exist unanswered questions pertaining to the best mechanisms to use for detecting drowsiness. Targeting a range of practical safety-awareness applications, this study adopts a machine learning based approach to build support vector machine (SVM) classifiers to distinguish between awake and drowsy states. While broadband alpha, beta, delta, and theta waves are often used as features in the existing work, lack of widely agreed precise definitions of such broadband signals and difficulty in accounting for interpersonal variability has led to poor classification performance as demonstrated in this study. Furthermore, the transition from wakefulness to drowsiness and deeper sleep stages is a complex multifaceted process. The richness of this process calls for inclusion of sub-band features for more accurate drowsiness detection. To shed light on the effectiveness of sub-banding, we quantitatively compare the performances of a large set of SVM classifiers trained upon a varying number of 1Hz sub band features. More importantly, we identify a compact set of neuroscientifcally motivated EEG features and demonstrate that the resulting classifier not only outperforms traditional broadband based classifiers but also is on a par with or superior than the best sub-band classifiers found by thorough search in a large space of 1Hz sub band features.
{"title":"Support Vector Machine Based Detection of Drowsiness Using Minimum EEG Features","authors":"Shaoda Yu, Peng Li, H. Lin, E. Rohani, G. Choi, B. Shao, Qian Wang","doi":"10.1109/SocialCom.2013.124","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.124","url":null,"abstract":"Drowsiness presents major safety concerns for tasks that require long periods of focus and alertness. While there is a body of work on drowsiness detection using EEG signals in neuroscience and engineering, there exist unanswered questions pertaining to the best mechanisms to use for detecting drowsiness. Targeting a range of practical safety-awareness applications, this study adopts a machine learning based approach to build support vector machine (SVM) classifiers to distinguish between awake and drowsy states. While broadband alpha, beta, delta, and theta waves are often used as features in the existing work, lack of widely agreed precise definitions of such broadband signals and difficulty in accounting for interpersonal variability has led to poor classification performance as demonstrated in this study. Furthermore, the transition from wakefulness to drowsiness and deeper sleep stages is a complex multifaceted process. The richness of this process calls for inclusion of sub-band features for more accurate drowsiness detection. To shed light on the effectiveness of sub-banding, we quantitatively compare the performances of a large set of SVM classifiers trained upon a varying number of 1Hz sub band features. More importantly, we identify a compact set of neuroscientifcally motivated EEG features and demonstrate that the resulting classifier not only outperforms traditional broadband based classifiers but also is on a par with or superior than the best sub-band classifiers found by thorough search in a large space of 1Hz sub band features.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133487674","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 : 2013-09-08DOI: 10.1109/SOCIALCOM.2013.62
Marcello Tomasini, F. Zambonelli, Angelo Brayner, R. Menezes
Sensor Networks are becoming ubiquitous in our society due to their broad applicability to data intensive tasks such as keeping air population to safe levels, efficient communication in military applications, to mention but a few. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, phones, and even being attached to living beings such as dolphins, birds and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static and mobile nodes. When mobility is a variable one should define which mobility model is best for the infrastructure given their differences. This paper evaluates which mobility pattern is best suited to be used in a Social Network of Sensors (SNoS). We evaluate several mobility models and measure the efficiency of information flow in a SNoS if mobile sensors follow these mobility patterns. The paper provides us with a greater understanding of the benefits of mobility in realistic scenarios.
{"title":"Evaluating the Performance of Social Networks of Sensors under Different Mobility Models","authors":"Marcello Tomasini, F. Zambonelli, Angelo Brayner, R. Menezes","doi":"10.1109/SOCIALCOM.2013.62","DOIUrl":"https://doi.org/10.1109/SOCIALCOM.2013.62","url":null,"abstract":"Sensor Networks are becoming ubiquitous in our society due to their broad applicability to data intensive tasks such as keeping air population to safe levels, efficient communication in military applications, to mention but a few. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, phones, and even being attached to living beings such as dolphins, birds and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static and mobile nodes. When mobility is a variable one should define which mobility model is best for the infrastructure given their differences. This paper evaluates which mobility pattern is best suited to be used in a Social Network of Sensors (SNoS). We evaluate several mobility models and measure the efficiency of information flow in a SNoS if mobile sensors follow these mobility patterns. The paper provides us with a greater understanding of the benefits of mobility in realistic scenarios.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133583446","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.101
Marco Guerini, Jacopo Staiano, Davide Albanese
Reactions to posts in an online social network show different dynamics depending on several textual features of the corresponding content. Do similar dynamics exist when images are posted? Exploiting a novel dataset of posts, gathered from the most popular Google+ users, we try to give an answer to such a question. We describe several virality phenomena that emerge when taking into account visual characteristics of images (such as orientation, mean saturation, etc.). We also provide hypotheses and potential explanations for the dynamics behind them, and include cases for which common-sense expectations do not hold true in our experiments.
{"title":"Exploring Image Virality in Google Plus","authors":"Marco Guerini, Jacopo Staiano, Davide Albanese","doi":"10.1109/SocialCom.2013.101","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.101","url":null,"abstract":"Reactions to posts in an online social network show different dynamics depending on several textual features of the corresponding content. Do similar dynamics exist when images are posted? Exploiting a novel dataset of posts, gathered from the most popular Google+ users, we try to give an answer to such a question. We describe several virality phenomena that emerge when taking into account visual characteristics of images (such as orientation, mean saturation, etc.). We also provide hypotheses and potential explanations for the dynamics behind them, and include cases for which common-sense expectations do not hold true in our experiments.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130188460","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.155
Radu Machedon, W. Rand, Yogesh V. Joshi
As the volume of social media communications grow, many different stakeholders have sought to apply tools and methods for automatic identification of sentiment and topic in social network communications. In the domain of social media marketing it would be useful to automatically classify social media messaging into the classic framework of informative, persuasive and transformative advertising. In this paper we develop and present the construction and evaluation of supervised machine-learning classifiers for these concepts, drawing upon established procedures from the domains of sentiment analysis and crowd sourced text classification. We demonstrate that a reasonably effective classifier can be created to identify the informative nature of Tweets based on crowd sourced training data, we also present results for identifying persuasive and transformative content. We finish by summarizing our findings regarding applying these methods and by discussing recommendations for future work in the area of classifying the marketing content of Tweets.
{"title":"Automatic Crowdsourcing-Based Classification of Marketing Messaging on Twitter","authors":"Radu Machedon, W. Rand, Yogesh V. Joshi","doi":"10.1109/SocialCom.2013.155","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.155","url":null,"abstract":"As the volume of social media communications grow, many different stakeholders have sought to apply tools and methods for automatic identification of sentiment and topic in social network communications. In the domain of social media marketing it would be useful to automatically classify social media messaging into the classic framework of informative, persuasive and transformative advertising. In this paper we develop and present the construction and evaluation of supervised machine-learning classifiers for these concepts, drawing upon established procedures from the domains of sentiment analysis and crowd sourced text classification. We demonstrate that a reasonably effective classifier can be created to identify the informative nature of Tweets based on crowd sourced training data, we also present results for identifying persuasive and transformative content. We finish by summarizing our findings regarding applying these methods and by discussing recommendations for future work in the area of classifying the marketing content of Tweets.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114648455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last two decades, there have been studies claiming that science is becoming ever more interdisciplinary. However, the evidence has been anecdotal or partial. Here for the first time, we investigate a large size citation network of computer science domain with the intention to develop an automated unsupervised classification model that can efficiently distinguish the core and the interdisciplinary research fields. For this purpose, we propose four indicative features, three of these are directly related to the topological structure of the citation network, while the fourth is an external indicator based on the attractiveness of a field for the in-coming researchers. The significance of each of these features in characterizing interdisciplinary is measured independently and then systematically accumulated to build an unsupervised classification model. The result of the classification model shows two distinctive clusters that clearly distinguish core and interdisciplinary fields of computer science domain. Based on this classification, we further study the evolution dynamics at a microscopic level to show how interdisciplinarity emerges through cross-fertilization of ideas between the fields that otherwise have little overlap as they are mostly studied independently. Finally, to understand the overall impact of interdisciplinary research on the entire domain, we analyze selective citation based measurements of core and interdisciplinary fields, paper submission and acceptance statistics at top-tier conferences and the core-periphery structure of citation network, and observe an increasing impact of the interdisciplinary fields along with their steady integration with the computer science core in recent times.
{"title":"Automatic Classification and Analysis of Interdisciplinary Fields in Computer Sciences","authors":"Tanmoy Chakraborty, Srijan Kumar, M. Reddy, Suhansanu Kumar, Niloy Ganguly, Animesh Mukherjee","doi":"10.1109/SocialCom.2013.34","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.34","url":null,"abstract":"In the last two decades, there have been studies claiming that science is becoming ever more interdisciplinary. However, the evidence has been anecdotal or partial. Here for the first time, we investigate a large size citation network of computer science domain with the intention to develop an automated unsupervised classification model that can efficiently distinguish the core and the interdisciplinary research fields. For this purpose, we propose four indicative features, three of these are directly related to the topological structure of the citation network, while the fourth is an external indicator based on the attractiveness of a field for the in-coming researchers. The significance of each of these features in characterizing interdisciplinary is measured independently and then systematically accumulated to build an unsupervised classification model. The result of the classification model shows two distinctive clusters that clearly distinguish core and interdisciplinary fields of computer science domain. Based on this classification, we further study the evolution dynamics at a microscopic level to show how interdisciplinarity emerges through cross-fertilization of ideas between the fields that otherwise have little overlap as they are mostly studied independently. Finally, to understand the overall impact of interdisciplinary research on the entire domain, we analyze selective citation based measurements of core and interdisciplinary fields, paper submission and acceptance statistics at top-tier conferences and the core-periphery structure of citation network, and observe an increasing impact of the interdisciplinary fields along with their steady integration with the computer science core in recent times.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"605 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131881984","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.152
H. Dong, M. Halem, Shujia Zhou
Social media websites are an integral part of many people's lives in delivering news and other emergency information. This is especially true during natural disasters. Furthermore, the role of social media websites is becoming more important due to the cost of recent natural disasters. These online platforms are usually the first to deliver emergency news to a wide variety of people due to the significantly large number of users registered. During disasters, extracting useful information from this pool of social media data can be useful in understanding the sentiment of the public, this information can then be used to improve decision making. In this paper, we developed a prototype that automates the process of collecting and analyzing social media data from Twitter. Furthermore, we explore a variety of visualizations that can be generated by the tool in order to understand the public sentiment. We demonstrate an example of utilizing this tool on the Hurricane Sandy disaster between October 26, 2012 to October 30, 2012. Finally, we perform a statistical analysis to explore the causality correlation between an approaching hurricane and the sentiment of the public.
{"title":"Social Media Data Analytics Applied to Hurricane Sandy","authors":"H. Dong, M. Halem, Shujia Zhou","doi":"10.1109/SocialCom.2013.152","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.152","url":null,"abstract":"Social media websites are an integral part of many people's lives in delivering news and other emergency information. This is especially true during natural disasters. Furthermore, the role of social media websites is becoming more important due to the cost of recent natural disasters. These online platforms are usually the first to deliver emergency news to a wide variety of people due to the significantly large number of users registered. During disasters, extracting useful information from this pool of social media data can be useful in understanding the sentiment of the public, this information can then be used to improve decision making. In this paper, we developed a prototype that automates the process of collecting and analyzing social media data from Twitter. Furthermore, we explore a variety of visualizations that can be generated by the tool in order to understand the public sentiment. We demonstrate an example of utilizing this tool on the Hurricane Sandy disaster between October 26, 2012 to October 30, 2012. Finally, we perform a statistical analysis to explore the causality correlation between an approaching hurricane and the sentiment of the public.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128630608","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.104
Vito Giovanni Castellana, Antonino Tumeo, Oreste Villa, D. Haglin, J. Feo
The emergence of petascale triple stores have motivated the investigation of alternates to traditional table-based relational methods. Since triple stores represent data as structured tuples, graphs are a natural data structure for encoding their information. The use of graph data structures, rather than tables, requires us to rethink the methods used to process queries on the store. We are developing a scalable, in-memory SPARQL graph engine that scales to hundreds of nodes while maintaining constant query throughput. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods, and a custom multithreaded runtime layer for multinode commodity systems. Rather than transforming SPARQL queries into a series of select and join operations on tables, our front end compiles the queries into data parallel C code with calls to graph methods that walk internal data structures, constructing answers in their wake. In this paper, we describe the compilation process and give examples of the generated C code parallelized with OpenMP. We present performance numbers for the SP2Bench SPARQL benchmark queries on a 48-core shared-memory system. With respect to conventional relational database systems such as Virtuoso, our approach uses less memory and provides higher performance.
{"title":"Composing Data Parallel Code for a SPARQL Graph Engine","authors":"Vito Giovanni Castellana, Antonino Tumeo, Oreste Villa, D. Haglin, J. Feo","doi":"10.1109/SocialCom.2013.104","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.104","url":null,"abstract":"The emergence of petascale triple stores have motivated the investigation of alternates to traditional table-based relational methods. Since triple stores represent data as structured tuples, graphs are a natural data structure for encoding their information. The use of graph data structures, rather than tables, requires us to rethink the methods used to process queries on the store. We are developing a scalable, in-memory SPARQL graph engine that scales to hundreds of nodes while maintaining constant query throughput. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods, and a custom multithreaded runtime layer for multinode commodity systems. Rather than transforming SPARQL queries into a series of select and join operations on tables, our front end compiles the queries into data parallel C code with calls to graph methods that walk internal data structures, constructing answers in their wake. In this paper, we describe the compilation process and give examples of the generated C code parallelized with OpenMP. We present performance numbers for the SP2Bench SPARQL benchmark queries on a 48-core shared-memory system. With respect to conventional relational database systems such as Virtuoso, our approach uses less memory and provides higher performance.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133287353","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.143
Nour Salama, S. Aly, Ahmed Rafea
Web access, especially through search, is by far one of the most popular operations performed on mobile devices. It is very interesting how the Mobile and Social worlds have significantly converged in many interesting ways, the least of which is the ability to simply access social networks on the move. However, much literature indicates how search results, and in specific mobile search is far from satisfactory in terms of meeting user intent and need. This has ultimately led to the introduction of context-aware web search to obtain more adequate results. In this research, we focus on using social context obtained from user social networks to refine search queries. Our initial target is to propose a system that will ultimately demonstrate the effectiveness of integrating this type of context when conducting mobile search. We also describe how we will utilize this system to classify user queries issued on mobile devices to determine the degree by which social context used, to then reformulate the queries by augmenting relevant context, and then finally ranking the results to match user needs.
{"title":"The Use of Social Context to Enhance Mobile Web Search Experience","authors":"Nour Salama, S. Aly, Ahmed Rafea","doi":"10.1109/SocialCom.2013.143","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.143","url":null,"abstract":"Web access, especially through search, is by far one of the most popular operations performed on mobile devices. It is very interesting how the Mobile and Social worlds have significantly converged in many interesting ways, the least of which is the ability to simply access social networks on the move. However, much literature indicates how search results, and in specific mobile search is far from satisfactory in terms of meeting user intent and need. This has ultimately led to the introduction of context-aware web search to obtain more adequate results. In this research, we focus on using social context obtained from user social networks to refine search queries. Our initial target is to propose a system that will ultimately demonstrate the effectiveness of integrating this type of context when conducting mobile search. We also describe how we will utilize this system to classify user queries issued on mobile devices to determine the degree by which social context used, to then reformulate the queries by augmenting relevant context, and then finally ranking the results to match user needs.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"86 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716351","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.89
M. Shin, Swetha Dorbala, Dongsoo Jang
This paper describes an approach to modeling security threats to applications and to deriving security failure-tolerant requirements from the threats. This paper assumes that unbreakable core security services for applications, such as authentication, access control, cryptosystem, or digital signature, are broken all the time in a real-world setting. The UML use case model for application requirements is analyzed to model security threats to the system in terms of threat points at which each threat is described using a structured template. This paper also derives security failure-tolerant requirements from the threats at threat points, and the requirements are modeled by means of security failure-tolerant use cases separately from application use cases in the use case model. A security failure-tolerant use case is extended from an application use case at a security point. The Internet banking application is used to illustrate the proposed approach.
{"title":"Threat Modeling for Security Failure-Tolerant Requirements","authors":"M. Shin, Swetha Dorbala, Dongsoo Jang","doi":"10.1109/SocialCom.2013.89","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.89","url":null,"abstract":"This paper describes an approach to modeling security threats to applications and to deriving security failure-tolerant requirements from the threats. This paper assumes that unbreakable core security services for applications, such as authentication, access control, cryptosystem, or digital signature, are broken all the time in a real-world setting. The UML use case model for application requirements is analyzed to model security threats to the system in terms of threat points at which each threat is described using a structured template. This paper also derives security failure-tolerant requirements from the threats at threat points, and the requirements are modeled by means of security failure-tolerant use cases separately from application use cases in the use case model. A security failure-tolerant use case is extended from an application use case at a security point. The Internet banking application is used to illustrate the proposed approach.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127361080","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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