An explainable prediction method based on Fuzzy Rough Sets, TOPSIS and hexagons of opposition: Applications to the analysis of Information Disorder

This paper presents a novel approach for predicting and explaining instances of Information Disorder. The paper reports two significant findings: i) the use of structures of opposition to describe relationships between instances of Information Disorder, and ii) the development of an explainable prediction method that combines Fuzzy Rough Sets and TOPSIS with these structures. The findings have the potential to assist analysts and decision-makers in gaining a deeper understanding of the phenomenon of Information Disorder. The results are based on real data and demonstrate promising applications for future research

Fine-Grained Context-aware Ad Targeting on Social Media Platforms

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordOne of the most important sources of revenue for social media platforms, like, Twitter, Facebook, Reddit, etc., is advertising. An effective social media advertising plan moves people from awareness and interest in desire and action. Despite the potentiality, campaigns and marketing strategies should be improved. One of the challenges is to identify the right target audience at the right time, considering both communities of interests and locations and the development of these conditions along the timeline. This is crucial to create the right communication strategy and the right advertising message. This paper proposes a context-aware ad-targeting methodology using time, locations, and inferring users' interests by analyzing published content. The method relies on a fuzzy extension of Triadic Formal Concept Analysis for identifying Location-based and Content-based communities of users. Then, a task of community fusion takes place, named Join, for matching a target audience. The matching may be tuned for identifying a wide or narrow community and implementing a fine-grained ad targeting. Experimental results are given.European Union Horizon 202

Promoting Cooperation in Service-Oriented MAS through Social Plasticity and Incentives

[EN] In distributed environments where entities only have a partial view of the system, cooperation plays a key issue. In the case of decentralized service discovery in open service-oriented multi-agent systems, agents only know about the services they provide and their direct neighbors. Therefore, they need the cooperation of their neighbors in order to locate the required services. However, cooperation is not always present in open and distributed systems. Non-cooperative agents pursuing their own goals could refuse to forward queries from other agents to avoid the cost of this action; therefore, the efficiency of the decentralized service discovery could be seriously damaged. In this paper, we propose the combination of local structural changes and incentives in order to promote cooperation in the service discovery process. The results show that, even in scenarios where the predominant behavior is not collaborative the cooperation emerges.Work partially supported by the Spanish Ministry of Science and Innovation through grants TIN2009-13839-C03-01, CSD2007-0022 (CONSOLIDER-INGENIO 2010), FPU grant AP-2008-00601 awarded to E. del Val.Del Val Noguera, E.; Rebollo Pedruelo, M.; Botti, V. (2013). Promoting Cooperation in Service-Oriented MAS through Social Plasticity and Incentives. Journal of Systems and Software. 86(2):520-537. https://doi.org/10.1016/j.jss.2012.09.031S52053786

Distributed information and control in a concurrent hypermedia-oriented architecture

The market for parallel and distributed computing systems keeps growing. Technological advances in processor power, networking, telecommunication and multimedia are stimulating the development of applications requiring parallel and distributed computing. An important research problem in this area is the need to find a robust bridge between the decentralization of knowledge sources in information-based systems and the distribution of computational power. Consequently, the attention of the research community has been directed towards high-level, concurrent, distributed programming. This work proposes a new hypermedia framework based on the metaphor of the actor model. The storage and run-time layers are represented entirely as communities of independent actors that cooperate in order to accomplish common goals, such as version management or user adaptivity. These goals involve fundamental and complex hypermedia issues, which, thanks to the distribution of tasks, are treated in an efficient and simple way

A dynamical cognitive multi-agent system for enhancing ambient intelligence scenarios

Ambient Intelligence (AmI) is born as a computer paradigm that deals with a new world where computing devices are spread everywhere in order to make wider the interaction between human beings and information technology and put together a dynamic computational-ecosystem capable of satisfying the users requirements. However, the AmI systems are more than a simple integration among computer technologies, indeed, their design can strongly depend upon psychology and social sciences aspects able to describe and analyze the human being status during the system's decision making. Consequently, from a computational point of view, an AmI system can be considered as a distributed cognitive framework composed by a collection of intelligent entities capable of modifying their behaviours by taking into account the user's cognitive status in a given time. This paper introduces a novel methodology of AmI systems' design that exploits multi-agent paradigm and a novel extension of Fuzzy Cognitive Maps theory benefiting on the theory of Timed Automata in order to create a collection of dynamical intelligent agents that use cognitive computing to define actions' patterns able to maximize environmental parameters as, for instance, user's comfort or energy saving. ©2009 IEEE