Guía Técnica Práctica: Diseño de Sistemas Software + Implementación e Implantación de Sistemas Software

Este documento se plantea como una guía técnica para la realización de las prácticas y el proyecto de las asignaturas de la especialidad de Ingeniería del Software en el Grado en Ingeniería Informática y en el segundo ciclo de Ingeniería Informática. Esta guía es válida para, entre otras, las siguientes asignaturas: • Diseño de Sistemas Software (DSS) • Ingeniería Web (IW) • Implementación e Implantación de Sistemas Software (IISS)Guía técnica para la realización de las prácticas y el proyecto de las asignaturas de la especialidad de Ingeniería del Software en el Grado en Ingeniería Informática y en el segundo ciclo de Ingeniería Informátic

Desarrollo De Herramientas Web Interactivas para el Aprendizaje de Algoritmos en Metaheurísticas

Presentación del Proyecto de Innovación Docente titulado "Desarrollo De Herramientas Web Interactivas para el Aprendizaje de Algoritmos en Metaheurísticas" del 2023 con código 22-15

Heteroevaluación de trabajos colaborativos en wikis

Este documento es un informe de estudio asociado al Proyecto de Innovación Docente La heteroevaluación como apoyo a la sostenibilidad en evaluaciones complejas de trabajos colaborativos en wikis, que supone el desarrollo de una aplicación, AssessMediaWiki. Esta aplicación supone un complemento cualitativo a StatMediaWiki, que hace un análisis puramente estadístico

Memetic Algorithms with Local Search Chains in R: The Rmalschains Package

Global optimization is an important field of research both in mathematics and computer sciences. It has applications in nearly all fields of modern science and engineering. Memetic algorithms are powerful problem solvers in the domain of continuous optimization, as they offer a trade-off between exploration of the search space using an evolutionary algorithm scheme, and focused exploitation of promising regions with a local search algorithm. In particular, we describe the memetic algorithms with local search chains (MA-LS-Chains) paradigm, and the R package Rmalschains, which implements them. MA-LS-Chains has proven to be effective compared to other algorithms, especially in high-dimensional problem solving. In an experimental study, we demonstrate the advantages of using Rmalschains for high-dimension optimization problems in comparison to other optimization methods already available in R.This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) under Project TIN-2009-14575. The work was performed while C. Bergmeir held a scholarship from the Spanish Ministry of Education (MEC) of the “Programa de Formación del Profesorado Universitario (FPU)”

Strange meson production at high density and temperature

The properties of strange mesons ($K$, $\bar K$ and $\bar K^*$) in dense matter are studied using a unitary approach in coupled channels for meson-baryon scattering. The kaon-nucleon interaction incorporates $s$- and $p$-wave contributions within a chiral model whereas the interaction of $\bar K^*$ with nucleons is obtained in the framework of the local hidden gauge formalism. The in-medium solution for the scattering amplitude accounts for Pauli blocking effects, mean-field binding on baryons, and meson self-energies. We obtain the $K$, $\bar K$ and $\bar K^*$ (off-shell) spectral functions in the nuclear medium and study their behaviour at finite density, temperature and momentum. We also analyze the energy weighted sum rules of the kaon propagator as a quality test of model calculations. We finally estimate the transparency ratio of the $\gamma A \to K^+ K^{*-} A^\prime$ reaction, which we propose as a feasible scenario at present facilities to detect in-medium modifications of the $\bar K^*$ meson.Comment: 6 pages, 5 figures, invited seminar in New Frontiers in QCD 2010 -Exotic Hadron Systems and Dense Matter- (NFQCD2010), Kyoto, January 18-March 19, 201

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.Basque GovernmentConsolidated Research Group MATHMODE - Department of Education of the Basque Government IT1294-19Spanish GovernmentEuropean Commission TIN2017-89517-PBBVA Foundation through its Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018 call (DeepSCOP project)European Commission 82561

Optimización inspirada en la naturaleza y en la biología: Lo bueno, lo malo, lo feo y lo esperanzador

Nowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine-tuning system designs considering one or more performance factors. Unfortunately, for many complex problems there is no optimization technique that can achieve the optimum solution in a reasonable computation time. As a result, the optimization process is often done manually. In recent years a myriad of optimization techniques have appeared, all inspired by phenomena observed in nature, such as behavioral patterns in animals (such as the exploration and search for food, moving, hunting, …), physical and chemical processes [1]. These techniques, often referred to as nature- or bio-inspired optimization algorithms, allow users to optimize a problem without requiring special knowledge about it: they only need to be informed about the fitness function to be optimized, and the mechanisms by which new candidate solutions can be produced. Each algorithm defines how existing solutions can be combined and modified to create new ones in an intelligent way to search for the best solution. Although they cannot guarantee that the optimum solution will be eventually achieved, they can automatically yield good solutions in reasonable computation times. These features make bio-inspired optimization proposals a promising research area and a great alternative to optimize complex processes, as has been already showcased in many real-world problems. In this work we present nature- and bio-inspired optimization from a global perspective. We describe techniques falling in this area, their evolution, how they operate, and why they bridge an important gap not covered by previous optimization techniques. On a critical note, we also give a clear view of the current situation in the area, indicating the positive aspects and issues that should be urgently improved. Considering this critical view, we suggest promising trends that we believe will lead us to a brighter future in nature- and bio-inspired optimization, plenty of successful examples of their application to real-world engineering problems. The manuscript is structured as follows: Section 2 describes bio-inspired optimization and exposes the reasons and advantages that make this area interesting from the scientific and practical points of view (focusing on introducing what they are and why they are useful). In Section 3 we examine the exciting panorama of recent applications in which nature- and bio-inspired optimization has become a central technology (the good), the upsurge of novel metaphors for the design of new proposals that do not lead to innovative solutions (the bad), and poor methodological practices that draw misleading conclusions that must be avoided in this field (the ugly). Finally, Section 4 summarizes the paper and highlights what is next to be done in the area of bio-inspired optimization (the hopeful), especially for engineering applications

Strange and charm mesons at fair

We study the properties of strange and charm mesons in hot and dense matter within a self-consistent coupled-channel approach for the experimental conditions of density and temperature expected for the CBM experiment at FAIR/GSI. The in-medium solution at finite temperature accounts for Pauli blocking effects, mean-field binding of all the baryons involved, and meson self-energies. We analyse the behaviour in this hot and dense environment of dynamically-generated baryonic resonances together with the evolution with density and temperature of the strange and open-charm meson spectral functions. We test the spectral functions for strange mesons using energy-weighted sum rules and finally discuss the implications of the properties of charm mesons on the Ds0(2317) and the predicted X(3700) scalar resonances.Molina Peralta, Raquel, [email protected] ; Nieves Pamplona, Juan Miguel, [email protected] ; Oset Báguena, Eulogio, [email protected]