Moreira, D. A. B., Ferreira, A. I. S., Silva, J. V. S., Santos, G. O., Pereira, L. F. M., Gondim, J. M., Bonil, G., Maia, H. de A., Silva, N. F. F., Hashiguti, S. T., Santos, J. A., Pedrini, H., Avila S. FairPIVARA: Reducing and Assessing Biases in CLIP-Based Multimodal Models. 35th British Machine Vision Conference (BMVC 2024) Workshop Privacy, Fairness, Accountability and Transparency in Computer Vision (PFATCV), 2024. [LINK]
Araújo, P.; Haddadi; S.; Reis, Marcelo S.; dos Reis, J.C. Topic Modeling Influence in Sentiment Analysis from User-generated Product Reviews. 32nd International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2024). Reggio Emilia, Italy, 2024.
Santos, Fillipe; dos Reis, J.C.; Reis, Marcelo S. SERIEMA: A Framework to Enhance Clustering Stability, Compactness, and Separation by Fusing Multimodal Data. 29th International Conference on Natural Language & Information Systems (NLDB 2024), Turin, Italy, 2024. [LINK]
Carnot Braun, Joahannes B D da Costa, Leandro A Villas, Allan M de Souza. EcoPredict: Assessing Distributed Machine Learning Methods for Predicting Urban Emissions. IEEE 100th Vehicular Technology Conference (VTC-Fall), 2024.
Rafael O. Jarczewski, Eduardo Cerqueira, Luiz F. Bittencourt, Antonio A. F. Loureiro, Leandro A. Villas, Allan M. de Souza. Let’s Federate – Effective Communication Strategy for Dynamic Client Participation. IEEE International Conference on Machine Learning and Applications (ICMLA), 2024.
Wellington Lobato, Joahannes B D da Costa, Luis F G Gonzales, Eduardo Cerqueira, Denis Rosário, Christoph Sommer, Leandro A Villas. Entropy and Mobility-based Model Assignment for Multi-model Vehicular Federated Learning. IEEE International Symposium on Federated Learning Technologies and Applications (FLTA), 2024. [LINK]
Silva, F. S., Dos Reis, J. C., & Reis, M., SERIEMA: A Framework to Enhance Clustering Stability, Compactness, and Separation by Fusing Multimodal Data. International Conference on Natural Language & Information Systems (NLDB) 2024. [LINK]
Osorio, A., Grassiotto, F., Moraes, S., Munoz, A., Neto, S. F. & Gibaut, W. Transfer Learning for Human Activity Recognition in Federated Learning on Android Smartphones with Highly Imbalanced Datasets. In IEEE DistInSys 2024: The 4th IEEE International Workshop on Distributed Intelligent Systems. Paris, França. 2024. [LINK]
Rossi, L. L.; Rohmer, E.; Costa, P. D. P.; Gudwin, R. R., Da Silva Simões, A., Colombini, E. L. Drives And Impulses: Shaping Motivation And Procedural Learning For Humanoid Robots, ICDL, Austin, US, 2024. [LINK]
Berto, L. M., Tanevska, A. Costa, P. P Simoes, A. S, Gudwin, R., Colombini, E., Real, F., Sciutti, A. Curiosity and Affect-Driven Cognitive Architecture for HRI, ICDL,Austin, US, 2024. [LINK]
Berto, L. M.; Rossi, L. L.; Rohmer, E.; Costa, P. D. P.; Gudwin, R. R.; Simoes, A. S.; Colombini, E. L. Piagetian experiments to DevRobotics, Journal paper poster track, ICDL, Austin, US, 2024. [LINK]
Claudio Capanema, Joahannes B D da Costa, Fabricio A Silva, Leandro Villas, Antonio A Loureiro. A Modular Plugin for Concept Drift in Federated Learning. 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (IEEE DCOSS-IoT 2024). Abu Dhabi, UAE. 2024. [LINK]
Talasso, G., de Souza, A. M., Bittencourt, L., Cerqueira, E., Loureiro, A,, & Villas, L. FedSCCS: Hierarchical Clustering with Multiple Models for Federated Learning. IEEE International Conference on Communications (ICC) 2024. IEEE. [LINK]
Filipe Maciel, Joahannes B D, Luis F G Gonzalez, Allan M de Souza, Leandro A Villas, Luiz F Bittencourt. Adaptive Fit Fraction Based on Model Performance Evolution in Federated Learning. 11th International Conference on Future Internet of Things and Cloud (FiCloud). Viena, Áustria. 2024. [LINK]
Aissa H Mohamed, Joahannes B D da Costa, Allan M de Souza, Leandro A Villas, Julio C dos Reis. Combining Client Selection Strategy with Knowledge Distillation for Federated Learning in Non-IID Data. 29th IEEE Symposium on Computers and Communications (ISCC). Paris, França. 2024. [LINK]
Bruno S Martins, Leandro A Villas. Partial Training Mechanism to Handle the Impact of Stragglers in Federated Learning with Heterogeneous Clients. 29th IEEE Symposium on Computers and Communications (ISCC). Paris, França. 2024. [LINK]
Nicolas Assumpção, Leandro Villas. Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning. 29th IEEE Symposium on Computers and Communications (ISCC). Paris, França. 2024. [LINK]
2023
Santos, G. O. D., Moreia, D. A., Ferreira, A. I., Silva, J., Pereira, L., Bueno, P., & Avila, S. (2023). CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages. Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL). [LINK]
Miranda Filho, D.; Veronese, T. B.; Raimundo, M. M. Measuring fairness of synthetic minority oversampling on credit datasets. NeurIPS 2023 Workshop Algorithmic Fairness through the Lens of Time (AFT 2023), 2023. [LINK]
Sakabe, E. Y., da Silva, A. A., Coletta, L. F., da Silva Simões, A., Colombini, E. L., Costa, P. D. P., & Gudwin, R. R. (2023, October). An Episode Tracker for Cognitive Architectures. In Biologically Inspired Cognitive Architectures Meeting (pp. 750-758). Cham: Springer Nature Switzerland. [LINK]
Mohamed, A. H., de Souza, A. M., Da Costa, J. B., Villas, L. A., & Dos Reis, J. C. (2023, December). CCSF: Clustered Client Selection Framework for Federated Learning in non-IID Data. In Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing. [LINK]
Condori Bustincio, R. W., de Souza, A. M., Da Costa, J. B., & Bittencourt, L. (2023, December). EntropicFL: Efficient Federated Learning via Data Entropy and Model Divergence. In Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing. [LINK]
Veiga, R., Flexa, R., Bastos, L., Medeiros, I., Rosário, D., Cerqueira, E., & Villas, L. (2023, October). A Federated Learning Approach for Continuous User Identification. In 2023 IEEE 9th World Forum on Internet of Things (WF-IoT). IEEE. [LINK]
Capanema, C. G., de Souza, A. M., Silva, F. A., Villas, L. A., & Loureiro, A. A. (2023, June). FedPredict: Combining Global and Local Parameters in the Prediction Step of Federated Learning. In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). IEEE. [LINK]
Maciel, F., De Souza, A. M., Bittencourt, L. F., & Villas, L. A. (2023, June). Resource aware client selection for federated learning in IoT scenarios. In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). IEEE. [LINK]
Mohamed, A. H., Assumpçáo, N. R., Astudillo, C. A., de Souza, A. M., Bittencourt, L. F., & Villas, L. A. (2023, January). Compressed client selection for efficient communication in federated learning. In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC). IEEE. [LINK]
2022
Grassiotto, F., Colombini, E. L., da Silva Simões, A., Gudwin, R. R., & Costa, P. D. P. (2022, January). CogToM-CST: An implementation of the Theory of Mind for the Cognitive Systems Toolkit. In ICAART (3). [LINK]
Marques, Á., Coletta, L., Silva, A., Paraense, A., Berto, L., Costa, P., & Gudwin, R. (2022). Visualization Tools for Monitoring and Debugging a Cognitive Architecture using CST. Procedia Computer Science. [LINK]
Lobato, W., Da Costa, J. B., de Souza, A. M., Rosário, D., Sommer, C., & Villas, L. A. (2022, September). Flexe: Investigating federated learning in connected autonomous vehicle simulations. In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). IEEE. [LINK]
2021
Berto, L. M., Costa, P. D., Simoes, A. S., Gudwin, R. R., & Colombini, E. L. (2021, August). An iowa gambling task-based experiment applied to robots: A study on long-term decision making. In 2021 IEEE International Conference on Development and Learning (ICDL). IEEE. [LINK]
