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Torra V.,IIIA CSIC
Studies in Fuzziness and Soft Computing | Year: 2010

This chapter reviews the use of aggregation functions and operators in the field of decision making. We first present an overview of main decision making problems, and, then, we show that aggregation operators are in common use for solving them. Once having presented their interest, we describe the major aggregation operators, their properties and their major differences. © 2010 Springer-Verlag Berlin Heidelberg.


Bras-Amoros M.,Rovira i Virgili University | Domingo-Ferrer J.,Rovira i Virgili University | Torra V.,IIIA CSIC
Journal of Informetrics | Year: 2011

The popular h-index used to measure scientific output can be described in terms of a pool of evaluated objects (the papers), a quality function on the evaluated objects (the number of citations received by each paper) and a sentencing line crossing the origin, whose intersection with the graph of the quality function yields the index value (in the h-index this is a line with slope 1). Based on this abstraction, we present a new index, the c-index, in which the evaluated objects are the citations received by an author, a group of authors, a journal, etc., the quality function of a citation is the collaboration distance between the authors of the cited and the citing papers, and the sentencing line can take slopes between 0 and ∞. As a result, the new index counts only those citations which are significant enough, where significance is proportional to collaboration distance. Several advantages of the new c-index with respect to previous proposals are discussed. © 2010 Elsevier Ltd.


Serra J.,Telefonica | Arcos J.L.,IIIA CSIC
Knowledge-Based Systems | Year: 2016

Efficiently finding similar segments or motifs in time series data is a fundamental task that, due to the ubiquity of these data, is present in a wide range of domains and situations. Because of this, countless solutions have been devised but, to date, none of them seems to be fully satisfactory and flexible. In this article, we propose an innovative standpoint and present a solution coming from it: an anytime multimodal optimization algorithm for time series motif discovery based on particle swarms. By considering data from a variety of domains, we show that this solution is extremely competitive when compared to the state-of-the-art, obtaining comparable motifs in considerably less time using minimal memory. In addition, we show that it is robust to different implementation choices and see that it offers an unprecedented degree of flexibility with regard to the task. All these qualities make the presented solution stand out as one of the most prominent candidates for motif discovery in long time series streams. Besides, we believe the proposed standpoint can be exploited in further time series analysis and mining tasks, widening the scope of research and potentially yielding novel effective solutions. © 2015 Elsevier B.V. All rights reserved.


Villatoro D.,IIIA CSIC | Sabater-Mir J.,IIIA CSIC | Sen S.,University of Tulsa
IJCAI International Joint Conference on Artificial Intelligence | Year: 2011

We present the notion of Social Instruments as mechanisms that facilitate the emergence of conventions from repeated interactions between members of a society. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions when effectively addressing coordination and learning problems, paying special attention to dissolving metastable subconventions. Our initial experiments throw some light on how Self-Reinforcing Substructures (SRS) in the network prevent full convergence to society-wide conventions, resulting in reduced convergence rates. The use of an effective composed social instrument, observation + rewiring, allow agents to achieve convergence by eliminating the subconventions that otherwise remained meta-stable.


Ansotegui C.,University of Lleida | Giraldez-Cru J.,IIIA CSIC | Levy J.,IIIA CSIC
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

The research community on complex networks has developed techniques of analysis and algorithms that can be used by the SAT community to improve our knowledge about the structure of industrial SAT instances. It is often argued that modern SAT solvers are able to exploit this hidden structure, without a precise definition of this notion. In this paper, we show that most industrial SAT instances have a high modularity that is not present in random instances. We also show that successful techniques, like learning, (indirectly) take into account this community structure. Our experimental study reveal that most learnt clauses are local on one of those modules or communities. © 2012 Springer-Verlag.

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