2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing最新文献

{"title":"User Generated Human Computation Applications","authors":"Nadin Kökciyan, Suzan Üsküdarli, T. Dinesh","doi":"10.1109/SocialCom-PASSAT.2012.118","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.118","url":null,"abstract":"Social Web applications have successfully transformed content consuming users into content producers. Aside of socializing, these applications are frequently used to disseminate information and coordinate purposeful activities, such as disaster response, political action, and neighborhood organizations. These activities are carried out via human interpreted messages. In many cases, dedicated information processing spaces would better serve such needs. Unfortunately, a typical Web user is not able to create even very simple applications, as they require significant technical know-how. This work proposes an approach towards creating simple collaborative applications. The approach, called We Flow, proposes a collaborative application specification, an application generator, and an execution engine. The We Flow framework is presented with focus on the implementation issues. A case study, where users report and track accessibility violations, is presented for demonstration purposes.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128036711","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}

{"title":"Automatic Detection of Social Signals in Digital Playgrounds","authors":"Alejandro Moreno","doi":"10.1109/SocialCom-PASSAT.2012.17","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.17","url":null,"abstract":"Play is a vital activity in which children observe the world, learn new concepts and experiment with them. Even though the social aspect of play is very important, the computer science community has struggled to address it. Digital playgrounds have been built in which children can play in technologically enhanced installations, but the detailed study of the social component of play within these installations has been overlooked. The research proposed here aims to fill in the gap between the human sciences and computer sciences in this context. The emerging field of research called Social Signal Processing (SSP) tasks itself with a goal similar to ours, design systems that are able to detect, interpret and/or reproduce social signals. We plan on using concepts and practices from both the SSP and the Computer Vision fields to analyze the social behavior of children during play in digital playgrounds. We aim to design playgrounds that are able to automatically interpret social interactions and change their dynamics depending on the behavior being exhibited by the children within.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126664391","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}

{"title":"Robotic Social Therapy on Children with Autism: Preliminary Evaluation through Multi-parametric Analysis","authors":"D. Mazzei, A. Greco, N. Lazzeri, A. Zaraki, A. Lanatà, R. Igliozzi, Alice Mancini, Francesca Stoppa, E. Scilingo, F. Muratori, D. Rossi","doi":"10.1109/SocialCom-PASSAT.2012.101","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.101","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a neural development disorder characterized by specific patterns of behavioral and social difficulties. Beyond these core symptoms, additional problems such as absence of gender differences identification, interactional distortions of environmental and family responses are often present. Taking into account these emotional and behavioral problems researchers and clinicians are focusing on the design of innovative therapeutic approaches aimed to improve social capabilities of subjects with ASD. Thanks to the technological and scientific progresses of the last years, nowadays it is possible to create human-like robots with social and emotional capabilities. Furthermore it is also possible to analyze physiological signals inferring subjects' psycho-physiological state which can be compared with a behavioral analysis in order to obtain a deeper understanding of subjects reactions to treatments. In this work a preliminary evaluation of an innovative social robot-based treatment for subjects with ASD is described. The treatment consists in a complex stimulation and acquisition platform composed of a social robot, a multi-parametric acquisition system and a therapeutic protocol. During the preliminary tests of the treatment the subject's physiological signals and behavioral parameters have been recorded and used together with the therapists' annotations to infer the subjects' induced reactions. Physiological signals were analyzed and statistically evaluated demonstrating the possibility to correctly discern the two groups (ASD and normally developing subjects) with a classification percentage higher than 92%. Statistical analysis also highlighted the treatment capability to induce different affective states in subjects with ASDs more than in control subjects, demonstrating that the treatment is well designed and tuned on ASDs deficits and behavioral lacks.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126788628","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}

{"title":"Quality of WordPress Plug-Ins: An Overview of Security and User Ratings","authors":"Teemu Koskinen, Petri Ihantola, V. Karavirta","doi":"10.1109/SOCIALCOM-PASSAT.2012.31","DOIUrl":"https://doi.org/10.1109/SOCIALCOM-PASSAT.2012.31","url":null,"abstract":"We have applied static analysis to find out how vulnerable the plugins available at the official Word Press plug in directory are to well known security exploits. We have compared the amount of potential vulnerabilities and vulnerability density to the user ratings, to determine if user ratings can be used for finding secure plugins. We conclude that the quality of the plugins varies and there is no clear correlation between the ratings of plugins and the number of vulnerabilities detected in them. Indeed, an additional manual review exposed a simple but severe SQL injection vulnerability in a plug in, which has both good user ratings and a high download count. We recommend plugins to be individually inspected for typical vulnerabilities before using them in any Word Press powered site.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123697480","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}

{"title":"A Decentralized Online Social Network with Efficient User-Driven Replication","authors":"Rammohan Narendula, Thanasis G. Papaioannou, K. Aberer","doi":"10.1109/SocialCom-PASSAT.2012.127","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.127","url":null,"abstract":"Unprecedented growth of online social networks (OSNs) increasingly makes privacy advocates and government agencies worrisome alike. In this paper, we propose My3, a privacy-friendly decentralized alternative for online social networking. The My3 system exploits well-known interesting properties of the current online social networks in its novel design namely, locality of access, predictable access times, geolocalization of friends, unique access requirements of the social content, and implicit trust among friends. It allows users to exercise finer granular access control on the content, thus making My3 extremely privacy-preserving. Moreover, we propose different replication strategies that users may independently choose for meeting their personalized performance objectives. A detailed performance study evaluates the system regarding profile availability, access delay, freshness and storage load. By using real-world data traces, we prove that My3 offers high availability even with low average online time of users in the network.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122746874","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}

{"title":"Harnessing Twitter \"Big Data\" for Automatic Emotion Identification","authors":"Wenbo Wang, Lu Chen, K. Thirunarayan, A. Sheth","doi":"10.1109/SocialCom-PASSAT.2012.119","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.119","url":null,"abstract":"User generated content on Twitter (produced at an enormous rate of 340 million tweets per day) provides a rich source for gleaning people's emotions, which is necessary for deeper understanding of people's behaviors and actions. Extant studies on emotion identification lack comprehensive coverage of \"emotional situations\" because they use relatively small training datasets. To overcome this bottleneck, we have automatically created a large emotion-labeled dataset (of about 2.5 million tweets) by harnessing emotion-related hash tags available in the tweets. We have applied two different machine learning algorithms for emotion identification, to study the effectiveness of various feature combinations as well as the effect of the size of the training data on the emotion identification task. Our experiments demonstrate that a combination of unigrams, big rams, sentiment/emotion-bearing words, and parts-of-speech information is most effective for gleaning emotions. The highest accuracy (65.57%) is achieved with a training data containing about 2 million tweets.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121575298","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}

{"title":"A Draw-and-Guess Game to Segment Images","authors":"Luca Galli, P. Fraternali, D. Martinenghi, M. Tagliasacchi, J. Novak","doi":"10.1109/SocialCom-PASSAT.2012.139","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.139","url":null,"abstract":"Human Computation is defined as the integration of human tasks and automated algorithms to achieve superior quality in complex tasks like multimedia content analysis. This paper discusses a scenario in which human computation is used to segment time stamped fashion images for mining trends based on visual features of garments (e.g., color and texture) and attributes of portrayed subjects (e.g., gender and age). State-of-the-art algorithms for body part detection and feature extraction can produce low quality results when parts of the body are occluded and when dealing with complex human poses. In such cases, these algorithms could benefit from the assistance of human agents. In order to jointly leverage the potential of crowds and image analysis algorithms, a game with a purpose (GWAP) is proposed, whereby players can help segment images for which specialized algorithms have failed, so as to improve the extraction of color and texture features of garments and their association with the features of the subject wearing them.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130558689","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}

{"title":"Parallelizing Preferential Attachment Models for Generating Large-Scale Social Networks that Cannot Fit into Memory","authors":"Yi-Chen Lo, Cheng-te Li, Shou-de Lin","doi":"10.1109/SocialCom-PASSAT.2012.28","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.28","url":null,"abstract":"Social network generation is an important problem in social network analysis. The goal is to produce artificial networks that preserve some real world properties of social networks. As one of most popular social network generation algorithms, the Barabási -- Albert (BA) model is a method that can generate random social networks with power-law degree distribution. This paper discusses the situation of generating large-sized social network that cannot fit into the memory. We design a parallel framework to tackle this problem. The challenge lies in the fact that the preferential attachment mechanism used in the BA model has direct conflict with the concept of parallelism. To achieve the preferential attachment, during the generation processes the degree information of nodes needs to be known, which prohibits the parallelism that allows nodes to generate edges independently. To handle this issue, this paper proposes a method to generate the expected accumulated degree of vertices for the parallel BA model. We further propose several novel techniques to reduce the complexity of generating N vertices with P processes to O(NlogN/P). We implement the model using MapReduce and the experiment results show that our model can produce billion-sized scale-free networks in minutes.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115036972","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}

{"title":"Reconstructing Profiles from Information Disseminated on the Internet","authors":"Esma Aïmeur, G. Brassard, Paul Molins","doi":"10.1109/SocialCom-PASSAT.2012.38","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.38","url":null,"abstract":"In this paper, we warn social network users about the threat that their profiles can easily be rebuilt from information disseminated on the Internet. The micro-blogging site Twitter is supposed to retain less personal information than sites like Facebook. Despite this, is it possible to reconstruct Twitter user profiles solely from publicly available information? We propose a new system based on a method of re-identification, which consists in two phases. Starting from a given Twitter user profile (first database), we try in the first phase to find his or her information scattered on other websites, such as blogs or social networks, from which we obtain a second database. The second phase of re-identification consists in forming a link between the two databases in order to reconstruct the digital identity of the user. We based our experiment on 250 randomly selected Twitter profiles on which we attempted to identify their owners. We believe that we have managed to recognize 41.6% of our sample. We conclude that the digital identity of users can be easily reconstructed solely from publicly available data that they or others have freely made available on the Internet.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"65 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120976452","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}

{"title":"Better the Tweeter You Know: Social Signals on Twitter","authors":"M. J. Chorley, Gualtiero Colombo, S. M. Allen, R. Whitaker","doi":"10.1109/SocialCom-PASSAT.2012.27","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.27","url":null,"abstract":"We present results from a web-based experiment conducted to assess the effect of Twitter metadata on decision making in content consumption. Participants were presented with information concerning two tweets and asked which they would prefer to read. Analysis of the results shows that recognition of the author as being within the readers local network is highly influential in the decision to read a tweet. This has analogies with results from cognitive psychology on decision making processes such as the recognition heuristic. The role of more detailed quantitative metadata has also been assessed. Surprisingly, metadata describing the popularity of tweet authors in terms of the number of followers or the number of tweets written has no significant impact on decision making, while metadata describing the tweet content (the number of retweets) has a significant impact, with a large proportion of users preferring to read content that has been retweeted a larger number of times. When friendship information and quantitative values are combined the impact of the friendship information is reduced, but a larger proportion of users still prefer to choose based on this information, while the impact of the retweet value is reduced.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131162367","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}