http://hdl.handle.net/10071/136 2026-05-30T09:35:08Z http://hdl.handle.net/10071/37216 Título próprio: Prosociality in cyberspace: Developing emotion and behavioral regulation to decrease aggressive communication Autoria: Veiga Simão, A. M.; Ferreira, P.; Pereira, N.; Oliveira, S.; Paulino, P.; Rosa, H.; Ribeiro, R.; Coheur, L.; Carvalho, J. P.; Trancoso, I. Resumo: Different forms of verbal aggression are often present in cyberbullying, which may impair executive function skills that enable the regulation of emotions and behavior. Emotion and behavioral regulation has been associated with better social adjustment and more positive interactions between peers. This study aimed to understand if fostering emotion and behav- ioral regulation strategies could decrease aggressive communication. A quasi-experimental longitudinal design, based on a Twitter client mobile application, with pre-posttest measures was used. For the application, we explored different machine learning approaches, including computational intelligence methods. Multilevel linear modeling and frequency analyses were performed. A convenience sample of 218 adolescents (Mage = 14.67, SD = 0.84, 53% female) participated in the study. Results suggest that a Twitter client mobile application intervention based on emotion and behavioral regulation strategies may help decrease adolescents’ aggressive communication. Moreover, female and male participants who used the digital application tended to present distinct trajectories over time with regard to searching for information concerning prosocial behavior. These findings suggest that digital tools resorting to emotion and behavioral regulation strategies may be effective in reducing an aggressive communication style amongst adolescents, and consequently, promote resource seeking to engage in prosociality. These results can be significant for the design of intervention programs against cyberbullying. 2021-01-01T00:00:00Z http://hdl.handle.net/10071/37065 Título próprio: Tailored laser wakefield acceleration for decaying particles Autoria: Badiali, C; Almeida, R.; Malaca, B.; Fonseca, R.; Silva, T.; Vieira, J. Resumo: We introduce a plasma wakefield acceleration scheme capable of boosting initially subrelativistic particles to relativistic velocities within millimeter-scale distances. A subluminal light pulse drives a wake whose velocity is continuously matched to the beam speed through a tailored plasma density, thereby extending the dephasing length. We develop a theoretical model that is generalizable across particle mass, initial velocity, and the particular accelerating bucket being used, and we verify its accuracy with particle-in-cell simulations using laser drivers with energies in the joule range. 2026-01-01T00:00:00Z http://hdl.handle.net/10071/36168 Título próprio: Aprendizado por transferência para correção automática de redação Autoria: Silveira, I. C.; Ribeiro, E.; Mamede, N.; Baptista, J. Resumo: A tarefa de Correção Automática de Redação tem despertado crescente interesse na área de processamento de texto em português. Entre os conjuntos de dados disponíveis, destaca-se um corpus de redações narrativas produzidas por alunos do 5º ao 9º ano do ensino fundamental no Brasil. Essas redações são avaliadas segundo quatro competências: registro formal, coerência temática, estrutura retórica narrativa e coesão textual. Este trabalho explora a criação de um sistema baseado em conhecimentos derivados de outro dataset (desenvolvido com base em textos produzidos para o ENEM) e de outras tarefas (cálculo de complexidade textual e análise de legibilidade). O sistema desenvolvido combina modelos neurais, características (features) curadas calculadas por programas de análise textual e seleção de features em um modelo de Aprendizado em Dois Estágios. Com isso, foi possível elevar a performance em relação ao estado-da-arte, nomeadamente, em 9% para a primeira competência, 5,5% para a terceira e 8,9% para a quarta. 2025-01-01T00:00:00Z http://hdl.handle.net/10071/35633 Título próprio: A transformer-based deep learning approach for detecting online hate speech in Spanish Autoria: Sanchez-Gomez, J. M.; Batista, F.; Vega-Rodríguez, M. A.; Pérez, C. J. Resumo: The amount of content published on the Internet has grown exponentially in recent times. Social networks have enabled this content to reach an even wider audience. However, the freedom of communication provided by these networks can consequently facilitate the spread of offensive language and hate speech. Although social media platforms have attempted to implement mechanisms for detecting and addressing such content, it remains an ongoing challenge, particularly for languages other than English, such as Spanish. One promising approach to tackle this problem is the application of Natural Language Processing (NLP) tools, which rely on the use of language models and deep learning for text classification. In this work, an approach for detecting Spanish Hate Speech with ALBETO (SHS-ALBETO) is proposed. Experimentation is conducted with HatEval dataset. The performance of SHS-ALBETO is compared with other competing models, such as BERT, BETO, and DistilBETO, along with other proposals from the state-of-the-art. SHS-ALBETO has improved the existing results in the scientific literature, simultaneously providing reduced computing times. Additionally, analyses of the results have revealed its advantages together with challenging aspects that must be addressed to further improve the performance of this kind of approach. 2025-01-01T00:00:00Z