Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing

In this contribution, we will analyse the importance of the fuzzy partition granularity for the linguistic variables in the design of fuzzy rule-based systems (FRBSs). In order to put this into effect, we will study the FRBS behaviour considering uniform fuzzy partitions with the same number of labels for all the linguistic variables, and considering uniform fuzzy partitions with any number of labels for each linguistic variable. We will present a method based on Simulated Annealing (SA) in order to obtain a good uniform fuzzy partition granularity that improves the FRBS behaviour. It is an efficient granularity search method for finding a good number of labels per variable.CICYT projects TIC96-0778 and PB98-131

Modeling Genetic Networks: Comparison of Static and Dynamic Models

Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. The interest shown over network models and systems biology is rapidly raising. Genetic networks arise as an essential task to mine these data since they explain the function of genes in terms of how they influence other genes. Many modeling approaches have been proposed for building genetic networks up. However, it is not clear what the advantages and disadvantages of each model are. There are several ways to discriminate network building models, being one of the most important whether the data being mined presents a static or dynamic fashion. In this work we compare static and dynamic models over a problem related to the inflammation and the host response to injury. We show how both models provide complementary information and cross-validate the obtained results

A proposal on reasoning methods in fuzzy rule-based classification systems

Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism to the set of rules. Finally, to show the increase of the system generalization capability provided by the proposed FRMs, we point out some results obtained by their integration in a fuzzy rule generation process.CICYT TIC96-077

Special issue on soft computing applications to intelligent information retrieval on the Internet

This special issue encompasses eleven papers devoted to the recent developments in the applications of soft computing (SC) techniques to information retrieval (IR), both in the text and Web retrieval areas. The seed of the current issue were some of the presentations made in two special sessions organized by the guest editors in two different conferences: the First Spanish Conference on Evolutionary and Bioinspired Algorithms (AEB’02), that was held in M erida, Spain, February 2002, and the Seventh International ISKO Conference (ISKO’02), held in Granada, Spain, July 2002. The scope of both special sessions was pretty related. In the former conference, the session topic was ‘‘Applications of Evolutionary Computation to Information Retrieval’’ while in the latter the session was entitled ‘‘Artificial Intelligence Applications to Information Retrieval’’

Mapping the Situation of Educational Technologies in the Spanish University System Using Social Network Analysis and Visualization

Educational Technologies (EdTech) are based on the use of Information and Communication Technologies (ICT) to improve the quality of teaching and learning. EdTech is experiencing great development at different educational levels worldwide, especially since the appearance of Covid-19. The recent publication of a study by the ICT Sectorial of CRUE Universidades Españolas, the Spanish University Association, is the first report on the implementation of such technologies within Spain´s University System. This paper presents two different maps based on the data from that report. Together, they illustrate the penetration of different types of EdTech in our university system and shed light on the strategic interest behind their adoption. Our goal is to produce self-explanatory maps that can be easily and directly interpreted. The first map reflects wide granularity in terms of the global importance of technologies, while the second points to relevant conclusions given the spatial position of Spain´s universities, and the size of the nodes that represent them (directly related with their strategic interests on EdTech), as well as with the local relationships existing among them (identifying similarities on those strategic interests).Working Group in Online Training and Educational Technologies (FOLTE), of the ICT Sectorial of the Commission of Rectors of Spanish Universities (the Spanish University AssociationCRUE Universidades Españolas). Moreover, this work was supported by the Spanish Ministry of Science, Innovation and UniversitiesERDF under grant EXASOCO (PGC2018-101216-B-I00

Analyzing the extremization of opinions in a general framework of bounded confidence and repulsion

In the bounded confidence framework, agents’ opinions evolve as a result of interactions with other agents having similar opinions. Thus, consensus or fragmentation of opinions can be reached, but not extremization (the evolution of opinions towards an extreme value). In contrast, when repulsion mechanisms are at work, agents with distant opinions interact and repel each other, leading to extremization. This work proposes a general opinion dynamics framework of bounded confidence and repulsion, which includes social network interactions and agent-independent time-varying rationality. We extensively analyze the performance of our model to show that the degree of extremization among a population can be controlled by the repulsion rule, and social networks promote extreme opinions. Agent-based rationality and time-varying adaptation also bear a strong impact on opinion dynamics. The high accuracy of our model is determined in a real-world social network well referenced in the literature, the Zachary Karate Club (with a known ground truth). Finally, we use our model to analyze the extremization of opinions in a real-world scenario, in Spain: a marketing action for the Netflix series “Narcos”

Custom Structure Preservation in Face Aging

In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style and content representations of the input image while conditioning the output on the target age. We go beyond existing aging methods allowing users to adjust the degree of structure preservation in the input image during inference. To this purpose, we introduce a masking mechanism, the CUstom Structure Preservation module, that distinguishes relevant regions in the input image from those that should be discarded. CUSP requires no additional supervision. Finally, our quantitative and qualitative analysis which include a user study, show that our method outperforms prior art and demonstrates the effectiveness of our strategy regarding image editing and adjustable structure preservation. Code and pretrained models are available at https://github.com/guillermogotre/CUSP.Comment: 36 pages, 21 figure

Evolutionary multiobjective optimization for automatic agent-based model calibration: A comparative study

This work was supported by the Spanish Agencia Estatal de Investigacion, the Andalusian Government, the University of Granada, and European Regional Development Funds (ERDF) under Grants EXASOCO (PGC2018-101216-B-I00), SIMARK (P18-TP-4475), and AIMAR (A-TIC-284-UGR18). Manuel Chica was also supported by the Ramon y Cajal program (RYC-2016-19800).The authors would like to thank the ``Centro de Servicios de Informática y Redes de Comunicaciones'' (CSIRC), University of Granada, for providing the computing resources (Alhambra supercomputer).Complex problems can be analyzed by using model simulation but its use is not straight-forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. We propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent-based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e., the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.Spanish Agencia Estatal de InvestigacionAndalusian GovernmentUniversity of GranadaEuropean Commission PGC2018-101216-B-I00 P18-TP-4475 A-TIC-284-UGR18Spanish Government RYC-2016-1980

An Integrative Decision-Making Mechanism for Consumers’ Brand Selection using 2-Tuple Fuzzy Linguistic Perceptions and Decision Heuristics

Consumers perform decision-making (DM) processes to select their preferred brands during their entire consumer journeys. These DM processes are based on the multiple perceptions they have about the products available in the market they are aware of. These consumers usually perform different DM strategies and employ diverse heuristics depending on the nature of the purchase, ranging from more pure optimal choices to faster decisions. Therefore, the design of realistic DM approaches for modeling these consumer behaviors requires a good representation of consumer perceptions and a reliable process for integrating their corresponding heuristics. In this work, we use fuzzy linguistic information to represent consumer perceptions and propose four consumer DM heuristics to model the qualitative linguistic information for the consumer buying decision. In particular, we use 2-tuple fuzzy linguistic variables, which is a substantially more natural and realistic representation without falling in a loss of information. The set of selected heuristics differ in the degree of involvement the consumers give to their decisions. Additionally, we propose a heuristic selection mechanism to integrate the four heuristics in a single DM procedure by using a regulation parameter. Our experimental analysis shows that the combination of these heuristics in a portfolio manner improves the performance of our model with a realistic representation of consumer perceptions. The model’s outcome matches the expected behavior of the consumers in several real market scenarios

Optimal Selection of Microarray Analysis Methods Using a Conceptual Clustering Algorithm

The rapid development of methods that select over/under expressed genes from microarray experiments have not yet matched the need for tools that identify informational profiles that differentiate between experimental condi tions such as time, treatment and phenotype. Uncertainty arises when methods devoted to identify significantly expressed genes are evaluated: do all microar ray analysis methods yield similar results from the same input dataset? do dif ferent microarray datasets require distinct analysis methods?. We performed a detailed evaluation of several microarray analysis methods, finding that none of these methods alone identifies all observable differential profiles, nor subsumes the results obtained by the other methods. Consequently, we propose a proce dure that, given certain user-defined preferences, generates an optimal suite of statistical methods. These solutions are optimal in the sense that they constitute partial ordered subsets of all possible method-associations bounded by both, the most specific and the most sensitive available solution