Sousa, John; Romulo, Bustincio; Bastos, Lucas; Morais, Renan; Rosário, Denis ; Cerqueira, Eduardo. Enhancing Robustness in Federated Learning using Minimal Repair and Dynamic Adaptation in a Scenario with Client Failures. Annals of Telecommunications. 2025. [accepted for publication]
Gustavo Morais; Eduardo Yuji; Paula Costa; Alexandre Simões; Ricardo Gudwin; Esther Colombini. A general framework for reinforcement learning in cognitive architecture. Cognitive Systems Research. 2025. [LINK]
Letícia Berto, Ana Tanevska; Azamor Cirne; Paula Costa; Alexandre Simões; Ricardo Gudwin, Esther Colombini, Alessandra Scutti. Curiosity and Affect-Driven Cognitive Architecture for HRI. IEEE Transactions on Affective Computing. 2025. [LINK]
2024
Barros, Alex ; Veiga, Rafael; Morais, Renan; Rosário, Denis; Cerqueira, Eduardo . MESFLA: Model Efficiency through Selective Federated Learning Algorithm. JOURNAL OF INTERNET SERVICES AND APPLICATIONS, 2024. [LINK]
Veiga, Rafael ; Sousa, John ; Morais, Renan ; Bastos, Lucas; Lobato, Wellington ; Rosário, Denis ; Cerqueira, Eduardo . A Robust Client Selection Mechanism for Federated Learning Environments. Journal of the Brazilian Computer Society (ONLINE), 2024. [LINK]
Jugurta Montalvão, Dami Duarte, Levy Boccato. A coincidence detection perspective for the maximum mean discrepancy. Pattern Recognition Letters. 2024. [LINK]
Otávio Napoli, Dami Duarte, Patrick Alves, Darlinne Hubert Palo Soto, Henrique Evangelista de Oliveira, Anderson Rocha, Levy Boccato, and Edson Borin. A benchmark for domain adaptation and generalization in smartphone-based human activity recognition. Scientific Data – Nature Data, 2024. [LINK]
Bernardo, B., Costa, P. (2024). A Speech-Driven Talking Head based on a Two-Stage Generative Framework. In Proceedings of the 16th International Conference on Computational Processing of Portuguese. [LINK]
Berto, L., Costa, P., Simões A., Gudwin, R., Colombini, E. (2024). A motivational-based learning model for mobile robots. Cognitive Systems Research. [LINK]
de Souza, AM, Maciel, F., da Costa, JB, Bittencourt, LF, Cerqueira, E., Loureiro, AA, & Villas, LA (2024). Adaptive client selection with personalization for efficient communication Federated Learning. Ad Hoc Networks. [LINK]
Berto, L., Rossi, L., Rohmer, E., Costa, P., Gudwin, R., Simões, A., & Colombini, E. (2024). Piagetian experiments to DevRobotics. Cognitive Systems Research. [LINK]
Maciel, F., de Souza, AM, Bittencourt, LF, Villas, LA, & Braun, T. (2024). Federated learning energy saving through client selection. Pervasive and Mobile Computing. [LINK]
Talasso, G., & Villas, L. (2024). Solutions for Heterogeneous Data in Federated Learning through Model Similarity and Client Grouping. Electronic Journal of Scientific Initiation in Computing. [LINK]
by Lellis Rossi, L., Rohmer, E., Dornhofer Paro Costa, P., Colombini, EL, da Silva Simões, A., & Gudwin, RR (2024). A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents. Journal of Intelligent & Robotic Systems. [LINK]
Haddadi, S.J., Farshidvard, A., dos Santos Silva, F., dos Reis, J.C., & da Silva Reis, M. (2024). Customer churn prediction in imbalanced datasets with resampling methods: A comparative study. Expert Systems with Applications. [LINK]
2023
Prudencio, RF, Maximo, MR, & Colombini, EL (2023). A survey on offline reinforcement learning: Taxonomy, review, and open problems. IEEE Transactions on Neural Networks and Learning Systems. [LINK]
Rossi, LDL, Berto, LM, Rohmer, E., Costa, PP, Gudwin, RR, Colombini, EL, & Simoes, ADS (2023). Incremental procedural and sensorimotor learning in cognitive humanoid robots. arXiv preprint arXiv. [LINK]
Berto, L., Costa, P., Simões, A., Gudwin, R., & Colombini, E. (2023). Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots. arXiv preprint arXiv. [LINK]
2022
Camargo, E., Sakabe, E.Y., & Gudwin, R. (2022). Existence, Hypotheses and Categories in Knowledge Representation. Procedia Computer Science. [LINK]
by Santana Correia, A., & Colombini, EL (2022). Attention, please! A survey of neural attention models in deep learning. Artificial Intelligence Review. [LINK]
2021
de Santana Correia, A., & Colombini, E. (2021). Neural attention models in deep learning: Survey and taxonomy. arXiv preprint arXiv. [LINK]
