DCCIA - Artículos

Torchmil: A PyTorch-based library for deep multiple instance learning

Torchmil: A PyTorch-based library for deep multiple instance learning Castro Macías, Francisco M.; Sáez Maldonado, Francisco J.; Morales Álvarez, Pablo; Molina Soriano, Rafael Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest in deep MIL methods, the field lacks standardized tools for model development, evaluation, and comparison, which hinders reproducibility and accessibility. To address this, we present torchmil, an open-source Python library built on PyTorch. torchmil offers a unified, modular, and extensible framework, featuring basic building blocks for MIL models, a standardized data format, and a curated collection of benchmark datasets and models. The library includes comprehensive documentation and tutorials to support both practitioners and researchers. torchmil aims to accelerate progress in MIL and lower the entry barrier for new users.

Mitigating Linguistic Aggression in Group Decision-Making: A Comparative Analysis of AI-Driven Hostility Detection

Mitigating Linguistic Aggression in Group Decision-Making: A Comparative Analysis of AI-Driven Hostility Detection Trillo Vílchez, José Ramón; González-Quesada, Juan Carlos; Cabrerizo Lorite, Francisco Javier; Pérez Gálvez, Ignacio Javier The process of group decision-making is an integral component not only for quotidian interactions but also for strategic deliberations. However, it is profoundly shaped by the inherent semantic indeterminacy of natural language. This linguistic ambiguity starkly contrasts the syntactic and semantic precision characteristic of machine-generated language. Furthermore, the conveyance of affective states–such as aggressiveness or elation–via natural language introduces a layer of complexity that can significantly perturb the equilibrium of the group decision-making process. In response to these challenges, we propose an advanced consensus-reaching methodology based on sentiment analysis to quantify and mitigate aggressiveness in discourse. This study conducts a comparative evaluation of three state-of-the-art large language models: Gemini, Copilot, and ChatGPT for their efficacy in detecting and assessing hostility. By calibrating the influence of individual participants based on their degree of linguistic aggression, the proposed framework attenuates the disproportionate impact of dominant voices, thus fostering a more balanced and equitable deliberative environment. This methodological innovation not only incentivizes the adoption of a more dispassionate and constructive linguistic register but also safeguards the integrity of collective decision-making processes against the distortive effects of undue emotional influence. Across five repeated evaluations per comment, ChatGPT and Gemini exhibited < 5% variance, while Copilot showed ≈ 8 − 12%; in all cases, hostility-aware weighting reduced the most aggressive expert’s influence by ≈ 27 − 29%, yielding robust group rankings. These mechanisms improve consensus quality by reducing bias from aggressive discourse, and they are expected to foster higher group satisfaction through perceived fairness in deliberation. Potential improvements include benchmarking against gold standards, extending to multilingual and multimodal contexts, and enhancing transparency for end-users. This work has been supported by the grant PID2022–139297OB-I00 funded by MICIU/ AEI/10.13039/501,100,011,033 and by ERDF/EU. Moreover, it is part of the project C-ING-165-UGR23, co-funded by the Regional Ministry of University, Research and Innovation and by the European Union under the Andalusia ERDF Program 2021–2027. Funding for open access publishing: Universidad de Granada/CBUA.

Outlier Curve Detection in Functional Data Using Robust FPCA

Outlier Curve Detection in Functional Data Using Robust FPCA Pérez Rocano, Wilson; López Herrera, Antonio Gabriel; Escabias Machuca, Manuel We propose a robust method for outlier detection in functional data analysis. This approach uses the robust Minimum Covariance Determinant estimator to compute the Mahalanobis distance applied to functional principal component scores. The main contribution of this research is the detection of outlier curves using the robust covariance matrix of functional principal components, in contrast to existing methods that use principal components on the discrete dataset. The proposed method is practical because it considers the entire functional form of the data, through their functional principal components, providing a comprehensive analysis that can detect anomalies across the entire functional range. A simulation study compares this approach with existing methods to evaluate their performance, followed by applications to El Niño Sea Surface Temperature data and SCImago Journal Rank data. The results show that the proposed method provides greater accuracy, demonstrating its effectiveness in detecting outlier curves.

Fusion architectures for soft rot detection in melon plants using hyperspectral and multicolor fluorescence imaging

Fusion architectures for soft rot detection in melon plants using hyperspectral and multicolor fluorescence imaging Moreno Gutiérrez, Salvador; Barón, Matilde; Moreno Martín, María Trinidad; Pineda, Mónica The wide range of non-destructive sensors currently available in agriculture encourages the development of fusion models for better monitoring tools than individual devices. In this work, two deep-learning fusion architectures that combine hyperspectral and multicolor fluorescence imaging were designed and tested for the early detection of soft rot caused by Botrytis cinerea in melon (Cucumis melo) leaves, and compared them with four single-sensor architectures. Melon leaf sampling yielded 739 samples (431 infected, 308 healthy). Each sample combined an hyperspectral imaging spectrum (400–1000 nm, 300 bands) with four fluorescence images taken at 440, 520, 680, and 740 nm. Sampling was performed 1 and 2 days post-inoculation (dpi), when no visual symptoms were present, to train deep-learning models discriminating between infected and healthy plants. The six architectures were evaluated by training a large number of models, resulting from a combination of 48 hyperparameter fits and repeated 5-fold cross-validation, for a total of 4320 models. Statistical analyses showed that one fusion architecture significantly outperformed all others, achieving the highest mean cross-validated accuracy (0.8068), with individual runs reaching a maximum accuracy of 0.8919 for the significantly better-performing fusion architecture. These results confirm that the combination of reflectance spectra with four channels of fluorescence improves early soft rot detection in melon compared with single-sensor solutions.

Scientific Developments and Trends in the Study of Trauma and Neuroeducational Development in Unaccompanied Migrant Minors: A Scientometric Analysis Between 2008 and 2025

Scientific Developments and Trends in the Study of Trauma and Neuroeducational Development in Unaccompanied Migrant Minors: A Scientometric Analysis Between 2008 and 2025 Arenas Carranza, Sara; Expósito López, Jorge; Olmedo Moreno, Eva María Research on unaccompanied foreign minors remains limited due to the low scientific visibility of this population and the complexity of trauma-related neurodevelopment. This study presents a scientometric analysis of international literature (2007–2025) to identify trends, collaboration networks, thematic clusters, and research gaps on trauma and neuroeducation in this field. Using data from Scopus and Web of Science, methodological, contextual, and thematic variables were coded and analysed through bibliometric and network techniques. Results show a 91% growth in publications since 2008, following an exponential pattern (r2 = 0.91), with 90 authors organised into 23 collaboration clusters and an average collaboration index of 0.80. Despite growing inter-institutional networks, research remains concentrated in France, the United States, and the United Kingdom–Africa axis. The study concludes that the field is entering a phase of consolidation, shifting from predominantly clinical perspectives toward preventive and integrative neuroeducational approaches that combine relational, cultural, and educational dimensions to mitigate the effects of migratory trauma.