Repositorio Institucional
El repositorio institucional recoge la producción científica del personal docente e investigador de la Universidad de Deusto. Su propósito es reunir, archivar, preservar y aumentar la visibilidad en acceso abierto de los resultados de investigación.
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Connecting roma communities in COVID-19 times: the first roma women students’ gathering held online
(MDPI, 2022-05-02) Aiello, Emilia; Khalfaoui Larrañaga, Andrea; Torrens Llambrich, Xavier; Flecha García, José Ramón
COVID-19 has exacerbated the vulnerability of the Roma communities in Europe. However, these communities have a strong sense of resilience, and the role of Roma women must be highlighted since they have historically nurtured solidarity networks even in the most challenging situations. Aim: A particular action organized by a Roma Association of Women is analyzed: the Roma Women Students’ Gathering (RWSG, or gathering). In its 19th edition, this democratic space aimed at tackling the challenges the pandemic has raised and its impact on the Roma communities. Method: The 19th RWSG, which was the first one held online, was inductively analyzed to gain a deeper understanding of the key aspects that the Roma women highlight when they organize themselves. Results: RWSG generates optimal conditions where Roma women identify the challenges affecting their community and, drawing on the dialogues shared, agree on strategies to contest them. RWSG also enhanced solidarity interactions that enabled the conquering of the virtual space, transforming it into an additional space where the Roma could help each other and thus better navigate the uncertainties unleashed by COVID-19. Key features of the Roma culture emerged in these spaces of solidarity, such as protecting the elderly and prioritizing community wellbeing rather than only the individual’s preferences. Conclusion: Roma women play a key role in weaving an organized response to the uncertainty derived from COVID-19, and connecting them to the public sphere, potentially achieving social and political impacts.
Adaptability and efficiency in population management: a multi-population CMA-ES strategy for high-dimensional optimization
(Elsevier B.V., 2024) Morales Castañeda, Bernardo; Rodríguez Esparza, Erick; Oliva, Diego; Navarro, Mario A.; Aranguren, Itzel; Casas Ordaz, Ángel; Beltran, Luis A.; Zapotecas Martínez, Saúl
In the context of evolutionary algorithms, having the ability to adapt to any search space within an optimization problem is an essential task. Appropriately adapting the population can lead to better solutions and more efficient use of function call resources. This article presents a renewed approach to population management inspired by modifying the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The proposed strategy aims to improve the algorithm's population adaptability to the search space and optimize function evaluations. Statistically evaluated experimental test outcomes demonstrate significantly better performance on high-dimensional problems in comparison to the original CMA-ES and seven other known evolutionary algorithms in the literature.
Psychological impact of COVID-19 on primary education teachers in the Basque Country
(Frontiers Media S.A., 2022-07-08) Arruti, Arantza; Korres, Oihane
; Paños Castro, Jessica
COVID-19 has greatly challenged many areas, including those affecting educational systems. Teachers have had to cope with tremendous pressure, stress, and anxiety. The objective of this study is to analyze how primary education teachers in the Autonomous Region of the Basque Country (Spain) perceive the impact that moods and the ensuing consequences of COVID-19 have on their personal and professional spheres. This study used a quantitative methodology based on a dedicated questionnaire. A total of 849 teachers answered the questionnaire and reported that they had felt nervous ( (Formula presented.) : 8.77) and tense ( (Formula presented.) : 8.57), and that they had been shocked by the excessive length of the lockdown ( (Formula presented.) : 7.70) and the restrictions in sports and leisure activities ( (Formula presented.) : 7.59). Significant differences were found according to gender, type of school, socioeconomic environment, age, and years of experience. The study highlights the need to educate both teachers and students so that they can manage and regulate their emotions in unexpected situations. An additional need was identified to enhance teachers’ digital skills to better enable them to face the challenges of the Information and Knowledge Society.
On the improvement of generalization and stability of forward-only learning via neural polarization
(IOS Press BV, 2024-10-16) Terres Escudero, Erik B.; Ser Lorente, Javier del; García Bringas, Pablo
Forward-only learning algorithms have recently gained attention as alternatives to gradient backpropagation, replacing the backward step of this latter solver with an additional contrastive forward pass. Among these approaches, the so-called Forward-Forward Algorithm (FFA) has been shown to achieve competitive levels of performance in terms of generalization and complexity. Networks trained using FFA learn to contrastively maximize a layer-wise defined goodness score when presented with real data (denoted as positive samples) and to minimize it when processing synthetic data (corr. negative samples). However, this algorithm still faces weaknesses that negatively affect the model accuracy and training stability, primarily due to a gradient imbalance between positive and negative samples. To overcome this issue, in this work we propose a novel implementation of the FFA algorithm, denoted as Polar-FFA, which extends the original formulation by introducing a neural division (polarization) between positive and negative instances. Neurons in each of these groups aim to maximize their goodness when presented with their respective data type, thereby creating a symmetric gradient behavior. To empirically gauge the improved learning capabilities of our proposed Polar-FFA, we perform several systematic experiments using different activation and goodness functions over image classification datasets. Our results demonstrate that Polar-FFA outperforms FFA in terms of accuracy and convergence speed. Furthermore, its lower reliance on hyperparameters reduces the need for hyperparameter tuning to guarantee optimal generalization capabilities, thereby allowing for a broader range of neural network configurations.
La inteligencia emocional y su influencia en el autoliderazgo en estudiantes de máster
(Dykinson, 2024) Quevedo Torrientes, Elena; Díez Ruiz, Fernand; Igoa Iraola, Elene