Moreira, DAB, Ferreira, AIS, Silva, JVS, Santos, GO, Pereira, LFM, Gondim, JM, Bonil, G., Maia, H. de A., Silva, NFF, Hashiguti, ST, Santos, JA, Pedrini , H., Avila S. FairPIVARA: Reducing and Assessing Biases in CLIP-Based Multimodal Models. 35th British Machine Vision Conference (BMVC 2024) Privacy, Fairness, Accountability and Transparency in Computer Vision (PFATCV) Workshop, 2024. [LINK]
Araújo, P.; Haddadi; S.; Reis, Marcelo S.; dos Reis, JC 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, JC; 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 BD 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 AF 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 BD da Costa, Luis FG 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, FS, Dos Reis, JC, & 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, SF & 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, France. 2024. [LINK]
Rossi, LL; Rohmer, E.; Costa, PDP; Gudwin, RR, Da Silva Simões, A., Colombini, EL 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, LL; Rohmer, E.; Costa, PDP; Gudwin, R.R.; Simoes, AS; Colombini, EL Piagetian experiments to DevRobotics, Journal paper poster track, ICDL, Austin, US, 2024. [LINK]
Claudio Capanema, Joahannes BD 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, AM, 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 BD, Luis FG 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). Vienna, Austria. 2024. [LINK]
Aissa H Mohamed, Joahannes BD 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, France. 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, France. 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, France. 2024. [LINK]
2023
Santos, GOD, Moreia, DA, Ferreira, AI, 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, TB; Raimundo, MM Measuring fairness of synthetic minority oversampling on credit datasets. NeurIPS 2023 Algorithmic Fairness through the Lens of Time Workshop (AFT 2023), 2023. [LINK]
Sakabe, EY, da Silva, AA, Coletta, LF, da Silva Simões, A., Colombini, EL, Costa, PDP, & Gudwin, RR (2023, October). An Episode Tracker for Cognitive Architectures. In Biologically Inspired Cognitive Architectures Meeting (pp. 750-758). Call: Springer Nature Switzerland. [LINK]
Mohamed, AH, de Souza, AM, Da Costa, JB, Villas, LA, & Dos Reis, JC (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, RW, de Souza, AM, Da Costa, JB, & 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, CG, de Souza, AM, Silva, FA, Villas, LA, & Loureiro, AA (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, AM, Bittencourt, LF, & Villas, LA (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, AH, Assumpçao, NR, Astudillo, CA, de Souza, AM, Bittencourt, LF, & Villas, LA (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, EL, da Silva Simões, A., Gudwin, RR, & Costa, PDP (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, JB, de Souza, AM, Rosário, D., Sommer, C., & Villas, LA (2022, September). Flexe: Investigating federated learning in connected autonomous vehicle simulations. In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). IEEE. [LINK]
2021
Berto, LM, Costa, PD, Simoes, AS, Gudwin, RR, & Colombini, EL (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]