AI Communications - Volume 29, issue 1

Article Type: Other

Citation: AI Communications, vol. 29, no. 1, pp. 1-1, 2016

Authors: Gratie, Cristian | Florea, Adina Magda | Meyer, John-Jules

Article Type: Research Article

Abstract: In abstract argumentation, the directionality principle conveys the intuition that, for an unattacked set, the choice of arguments that are part of an extension should only depend on the restriction of the framework to that set. Furthermore, having made such a choice, one should be able to select arguments from the rest of the framework so as to get an extension. In this paper we show how this idea can be generalized and used for formulating SCC-recursiveness as a stronger version of directionality. We argue that such properties characterize the information that is needed for computing the extensions of …an argumentation semantics. We provide a formal approach for describing and comparing directionality-like properties. Our model provides a clear distinction between SCC-recursive semantics that use defense information and those that do not use it. Show more

Keywords: Argumentation frameworks, argumentation semantics, SCC-recursiveness, directionality

DOI: 10.3233/AIC-140623

Citation: AI Communications, vol. 29, no. 1, pp. 3-16, 2016

Authors: Rebollo, M. | Palomares, A. | Carrascosa, C.

Article Type: Research Article

Abstract: If a set of agents tries to reach an agreement, there will be an Agreement Space that models the maximum valid space based on the individual constraints of each agent regarding the terms of the agreement. This paper presents a possible distributed solution using a consensus algorithm. This algorithm is applied to the MAS acquaintances network to build an Agreement Space, which allows us to determine whether or not an agreement can be achieved.

Keywords: Agreement, consensus, multi-agent systems

DOI: 10.3233/AIC-150667

Citation: AI Communications, vol. 29, no. 1, pp. 17-30, 2016

Authors: Gabbriellini, Simone | Torroni, Paolo

Article Type: Research Article

Abstract: Argumentative debates are a powerful tool for reaching agreements in open environments. However, in large scale settings, such as social networks, making sense of ongoing debates may be a compelling task, and debates risk to lose their effectiveness. We thus propose “microdebates” to help organizing and confronting users’ opinions in an automated way.

Keywords: Online debate, social networks, social media, Twitter, argumentation

DOI: 10.3233/AIC-150690

Citation: AI Communications, vol. 29, no. 1, pp. 31-51, 2016

Article Type: Other

Citation: AI Communications, vol. 29, no. 1, pp. 53-53, 2016

Authors: Musial, Katarzyna | Brodka, Piotr | Magnani, Matteo

Article Type: Introduction

DOI: 10.3233/AIC-150688

Citation: AI Communications, vol. 29, no. 1, pp. 55-56, 2016

Authors: Doran, Derek | Severin, Karl | Gokhale, Swapna | Dagnino, Aldo

Article Type: Research Article

Abstract: Smart city initiatives rely on real-time measurements and data collected by a large number of heterogenous physical sensors deployed throughout a city. Physical sensors, however, are fraught with interoperability, dependability, management and political challenges. Furthermore, these sensors are unable to sense the opinions and emotional reactions of citizens that invariably impact smart city initiatives. Yet everyday, millions of dwellers and visitors of a city share their observations, thoughts, feelings and experiences, or in other words, their perceptions about their city through social media updates. This paper reasons why “human sensors”, namely, citizens that share information about their surroundings via …social media can supplement, complement, or even replace the information measured by physical sensors. We present a methodology based on probabilistic language modeling to extract and visualize such perceptions that may be relevant to smart cities from social media updates. Using more than six million geo-tagged tweets collected over regions that feature widely varying geographical, social, cultural and political characteristics and tweet densities, we illustrate the potential of social media enabled human sensing to address diverse smart city challenges. Show more

Keywords: Social media, smart cities, language modeling, geo-locations

DOI: 10.3233/AIC-150683

Citation: AI Communications, vol. 29, no. 1, pp. 57-75, 2016

Authors: Ponieman, Nicolas B. | Sarraute, Carlos | Minnoni, Martin | Travizano, Matias | Rodriguez Zivic, Pablo | Salles, Alejo

Article Type: Research Article

Abstract: The massive amounts of geolocation data collected from mobile phone records have sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, providing some proof-of-principle examples. We illustrate in various ways how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user’s location. We further propose a method for the automatic detection of such events and discuss their relation to …the social fabric as derived from mobile phone communications. Show more

Keywords: Human mobility, human predictability, social phenomena

DOI: 10.3233/AIC-150687

Citation: AI Communications, vol. 29, no. 1, pp. 77-86, 2016

Authors: Elyashar, Aviad | Fire, Michael | Kagan, Dima | Elovici, Yuval

Article Type: Research Article

Abstract: A dimension of the Internet that has gained great popularity in recent years is the platform of online social networks (OSNs). Users all over the world write, share, and publish personal information about themselves, their friends, and their workplaces within this platform of communication. In this study we demonstrate the relative ease of creating malicious socialbots that act as social network “friends”, resulting in OSN users unknowingly exposing potentially harmful information about themselves and their places of employment. We present an algorithm for infiltrating specific OSN users who are employees of targeted organizations, using the topologies of organizational social networks …and utilizing socialbots to gain access to these networks. We focus on two well-known OSNs – Facebook and Xing – to evaluate our suggested method for infiltrating key-role employees in targeted organizations. The results obtained demonstrate how adversaries can infiltrate social networks to gain access to valuable, private information regarding employees and their organizations. Show more

Keywords: Socialbots, social networks security and privacy, organization mining, Facebook, Xing

DOI: 10.3233/AIC-140650

Citation: AI Communications, vol. 29, no. 1, pp. 87-106, 2016

Authors: Charles, Perez | Babiga, Birregah | Robert, Layton | Marc, Lemercier | Paul, Watters

Article Type: Research Article

Abstract: Social networking sites are increasingly subject to malicious activities such as self-propagating worms, confidence scams and drive-by-download malwares. The high number of users associated with the presence of sensitive data, such as personal or professional information, is certainly an unprecedented opportunity for attackers. These attackers are moving away from previous platforms of attack, such as emails, towards social networking websites. In this paper, we present a full stack methodology for the identification of campaigns of malicious profiles on social networking sites, composed of maliciousness classification, campaign discovery and attack profiling. The methodology named REPLOT, for REtrieving Profile Links On Twitter, …contains three major phases. First, profiles are analysed to determine whether they are more likely to be malicious or benign. Second, connections between suspected malicious profiles are retrieved using a late data fusion approach consisting of temporal and authorship analysis based models to discover campaigns. Third, the analysis of the discovered campaigns is performed to investigate the attacks. In this paper, we apply this methodology to a real world dataset, with a view to understanding the links between malicious profiles, their attack methods and their connections. Our analysis identifies a cluster of linked profiles focusing on propagating malicious links, as well as profiling two other major clusters of attacking campaigns. Show more

Keywords: Online social networks, suspicious profiles, clustering, malicious campaigns, authorship attribution, Twitter

DOI: 10.3233/AIC-150659

Citation: AI Communications, vol. 29, no. 1, pp. 107-122, 2016