03 Contributo in periodico (Part of a journal)

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Speech-Driven 3D Facial Animation with Natural Head Movements

Speech-driven 3D facial animation has been studied for a long time, yet many challenges still prevent achieving truly natural results. One major issue is that current methods often overlook head motion during speech. To address this, we conducted a preliminary investigation and identified several key obstacles: the scarcity of 3D facial animation datasets with head motion and the limitations of regular sequence prediction models (e.g., Transformer, LSTM, and GRU), which are designed for discr...

A novel multi-target fire smoke segmentation method with imbalance handling for UAV remote sensing images

In the field of early fire and smoke detection using unmanned aerial vehicle (UAV) remote sensing, existing research mainly focuses on single-target segmentation, and there are problems of neglecting the inherent correlation and coexistence between fire and smoke in real scenarios, as well as class imbalance. Furthermore, traditional convolutional neural networks (CNNs) suffer from fixed receptive fields, which make it difficult to balance the detection of large-scale smoke and small-scale fl...

Beyond the Data: Architectural Choices for Imbalanced Learning in Vulnerability Detection

Static code analyzers, such as deep learning-based techniques, have proven to be essential in the security testing process. These methods rely on training an effective and reliable deep learning model to detect weaknesses and vulnerabilities. In the domain of source code analysis, the use of an adequate dataset of vulnerable and non-vulnerable code is crucial. However, it is recognized that these datasets are difficult to obtain, contain noise such as duplications, and are often strongly imba...

Modeling function-level relationships for vulnerability detection in graph neural networks

Context: Deep learning, and in particular graph-based models, has advanced software vulnerability detection by effectively capturing structural code features. Nonetheless, most existing approaches treat source code as independent components, overlooking inter-function relationships and thereby missing potential vulnerability propagation across function boundaries. Objective: To address this limitation, we propose SCVdet , a Graph Attention Network (GAT) based method that integrates inter-func...

Boosting Team Modeling through Tempo–Relational Representation Learning

Team modeling remains a fundamental challenge at the intersection of Artificial Intelligence and Social Sciences. Although a variety of computational models have been proposed in the last two decades, most fail to sufficiently integrate Social Sciences insights, such as the critical role of temporal interactions in shaping team dynamics, and do not meet key practical requirements for real-world applications, including the ability to provide real-time, actionable recommendations to enhance tea...

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