Comunicacions a congressos (Enginyeria Informàtica i Disseny Digital)
Permanent URI for this collection
Browse
Recent Submissions
- ItemOpen AccessPhysics Informed Neural Networks for a Gasification Reactor(IOS Press, 2025) Fornieles, Aleix; Etxegarai, Maddi; Gibert, Daniel; Planes Cid, JordiHydrogen arises as an alternative to decarbonize heavy industries, yet its production remains either expensive or environmentally harmful, prompting the exploration of waste-to-hydrogen gasification as a viable solution. This study addresses the challenges in accurately modeling the gasification reactor, a critical step in waste-to-hydrogen conversion, by confronting issues such as the inherent diversity of biomass and the complex interactions among its constituent properties using Artificial Intelligence. To address these challenges, we propose a methodology that integrates Physics-Informed Neural Networks, merging experimental data with physical principles to develop robust and adaptive models to predict syngas composition. Preliminary results indicate improvements in model accuracy over other models, achieving high predictive accuracies with an R2 value of 0.96 for the syngas composition, as well as greater scalability compared to existing approaches in this field.
- ItemOpen AccessUnpacking Computational Empowerment in Participatory Design with Children: Opportunities, Tensions, and Emerging Questions(ACM, 2026) Durall Gazulla, Eva; Sharma, Sumita; Iivari, Netta; Kinnula, Marianne; Schaper, Marie-Monique; Antle, Alissa N.; Veldhuis, Annemiek; Kafai, Yasmin; Kayali, Fares; Göbl, Barbara; Weixelbraun, Petra Francesca; Satavlekar, Spruha; Venkatachalam, Harshini; Iversen, Ole SejerThis paper advances a collective research manifesto for Computational Empowerment (CE) in Child–Computer Interaction (CCI), grounded in diverse practices across global contexts. Drawing on five cases from Asia, Europe, and North America, we examine how CE commitments materialize in real world settings, revealing both their transformative potential and the tensions they introduce. Our cases illustrate that CE is inherently context dependent, socially situated, and shaped by surrounding structures. We identify key directions for extending CE: embedding critical and ethical perspectives, foregrounding situatedness and relationality, challenging convenient choices, and questioning design assumptions. This involves strengthening children’s agency in shaping computational systems and widening CE’s reach through more inclusive, sustainable practices. We offer this work as intentionally incomplete—an invitation to ongoing dialogue and collective action toward empowering children in computing.
- ItemOpen AccessBridging Performative Arts, Feminist Pedagogy and Technology: A Framework for Inclusive Learning Strategies to engage Young Women from Marginalized Communities in Computing Education(ACM, 2026) Schaper, Marie-Monique; Gil Iranzo, Rosa María; Teixidó Cairol, Mercè; Segura-Nebot, Rocío; Bivol, Miruna; Sáinz, MilagrosGender disparities persist in computing education and disproportionately affect girls and young women from marginalized communities. Addressing these inequities requires reconsidering not only the content of education, but also the ways it is taught and the perspectives that are given priority. This paper introduces a novel interdisciplinary framework that bridges theatre education, feminist pedagogy, and computing education to inform critical, creative, and socially responsive approaches to non-formal and formal learning contexts. Drawing on a narrative review across these domains, we identify 12 educational principles that foreground equity and cultural relevance in computing education. We illustrate the framework through a case study within the #ACTFEM program aimed at motivating young women from marginalized communities to participate in computing activities. For the IDC community, this work advances discussions on feminist HCI, theatre-inspired practices, and computational empowerment, positioning it as a space where social innovation, such as inclusion, community impact, and transformative change play a crucial role in advancing the field.
- ItemOpen AccessChecking Robustness of Neural Network Models for the Classification of Malware(IOS Press, 2024) Alsinet, Teresa; Argelich Romà, Josep; Béjar Torres, Ramón; Morgado, Antonio; Planes Cid, JordiIn the recent years, there has been a strong concern over the robustness of machine learning systems, specially when working in critical systems. One of such critical domains is cybersecurity, and a particular example is malware detection. This works aims to provide a formal technique to check the robustness of neural networks applied to the detection of malware. The technique is based on the automatic translation of the neural network to an equivalent set of equations that can be subsequently rigorously analyzed with respect to certain conditions for its input and output. That is, given a particular input for the neural network, check if there exist slight variations of such an input that can modify the output of the neural network. As a case study, we present preliminary results of a robustness analysis for a neural network that detects Windows PE malware. The results of the robustness analysis can be used to certify the robustness of the classifier or for improving such a classifier by fixing the flaws detected.
- ItemOpen AccessLocally-Minimal Probabilistic Explanations(IOS Press, 2024) Izza, Yacine; Meel, Kuldeep S.; Marques-Silva, JoaoExplainable Artificial Intelligence (XAI) is widely regarded as a cornerstone of trustworthy AI. Unfortunately, most work on XAI offers no guarantees of rigor. In high-stakes domains, e.g. uses of AI that impact humans, the lack of rigor of explanations can have disastrous consequences. Formal abductive explanations offer crucial guarantees of rigor and so are of interest in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PAXps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed solutions.