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  • Trabalho de evento

    Explainable NLLP: advancements in explainable AI for natural legal language processing (2025)

    Resck, Lucas ; Moreno-Vera, Felipe ; Veiga, Tobias; Paucar, Gerardo; Fajreldines, Ezequiel ; Klafke, Guilherme ; Nonato, Luis Gustavo ; Poco, Jorge

    Source: Proceedings. Conference titles: Workshop on Automated Semantic Analysis of Information in Legal Text - ASAIL. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, DIREITO, PROCESSAMENTO DE LINGUAGEM NATURAL, PESQUISA

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      RESCK, Lucas et al. Explainable NLLP: advancements in explainable AI for natural legal language processing. 2025, Anais.. Harvard: IAAIL, 2025. Disponível em: https://visualdslab.com/papers/ExplainableNLLP. Acesso em: 02 nov. 2025.

    • APA

      Resck, L., Moreno-Vera, F., Veiga, T., Paucar, G., Fajreldines, E., Klafke, G., et al. (2025). Explainable NLLP: advancements in explainable AI for natural legal language processing. In Proceedings. Harvard: IAAIL. Recuperado de https://visualdslab.com/papers/ExplainableNLLP

    • NLM

      Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. Explainable NLLP: advancements in explainable AI for natural legal language processing [Internet]. Proceedings. 2025 ;[citado 2025 nov. 02 ] Available from: https://visualdslab.com/papers/ExplainableNLLP

    • Vancouver

      Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. Explainable NLLP: advancements in explainable AI for natural legal language processing [Internet]. Proceedings. 2025 ;[citado 2025 nov. 02 ] Available from: https://visualdslab.com/papers/ExplainableNLLP
    GDS 17. Partnerships for the goals

  • Artigo de periodico

    Time series information visualization: a review of approaches and tools (2025)

    Ortigossa, Evandro Scudeleti ; Dias, Fabio Felix ; Nascimento, Diego Carvalho ; Nonato, Luis Gustavo

    Source: IEEE Access. Unidade: ICMC

    Subjects: ANÁLISE DE DADOS, INTERFACE HOMEM-COMPUTADOR, ATRIBUTOS VISUAIS (COMPUTAÇÃO GRÁFICA)

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      ORTIGOSSA, Evandro Scudeleti et al. Time series information visualization: a review of approaches and tools. IEEE Access, v. 13, p. 161653-161732, 2025Tradução . . Disponível em: https://doi.org/10.1109/ACCESS.2025.3609404. Acesso em: 02 nov. 2025.

    • APA

      Ortigossa, E. S., Dias, F. F., Nascimento, D. C., & Nonato, L. G. (2025). Time series information visualization: a review of approaches and tools. IEEE Access, 13, 161653-161732. doi:10.1109/ACCESS.2025.3609404

    • NLM

      Ortigossa ES, Dias FF, Nascimento DC, Nonato LG. Time series information visualization: a review of approaches and tools [Internet]. IEEE Access. 2025 ; 13 161653-161732.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/ACCESS.2025.3609404

    • Vancouver

      Ortigossa ES, Dias FF, Nascimento DC, Nonato LG. Time series information visualization: a review of approaches and tools [Internet]. IEEE Access. 2025 ; 13 161653-161732.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/ACCESS.2025.3609404

  • Artigo de periodico

    Visagreement: visualizing and exploring explanations (dis)agreement (2025)

    Silva, Priscylla ; Guardieiro, Vitoria ; Barr, Brian ; Silva, Claudio; Nonato, Luis Gustavo

    Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, MODELAGEM DE DADOS, SIMULAÇÃO, VISUALIZAÇÃO

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      SILVA, Priscylla et al. Visagreement: visualizing and exploring explanations (dis)agreement. IEEE Transactions on Visualization and Computer Graphics, v. 31, n. 10 , p. 7862-7875, 2025Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2025.3558074. Acesso em: 02 nov. 2025.

    • APA

      Silva, P., Guardieiro, V., Barr, B., Silva, C., & Nonato, L. G. (2025). Visagreement: visualizing and exploring explanations (dis)agreement. IEEE Transactions on Visualization and Computer Graphics, 31( 10 ), 7862-7875. doi:10.1109/TVCG.2025.3558074

    • NLM

      Silva P, Guardieiro V, Barr B, Silva C, Nonato LG. Visagreement: visualizing and exploring explanations (dis)agreement [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10 ): 7862-7875.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2025.3558074

    • Vancouver

      Silva P, Guardieiro V, Barr B, Silva C, Nonato LG. Visagreement: visualizing and exploring explanations (dis)agreement [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10 ): 7862-7875.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2025.3558074

  • Artigo de periodico

    LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals (2025)

    Resck, Lucas ; Moreno-Vera, Felipe ; Veiga, Tobias; Paucar, Gerardo; Fajreldines, Ezequiel ; Klafke, Guilherme ; Nonato, Luis Gustavo ; Poco, Jorge

    Source: Artificial Intelligence and Law. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, DIREITO, PESQUISA, PROCESSAMENTO DE LINGUAGEM NATURAL

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      RESCK, Lucas et al. LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals. Artificial Intelligence and Law, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10506-025-09446-w. Acesso em: 02 nov. 2025.

    • APA

      Resck, L., Moreno-Vera, F., Veiga, T., Paucar, G., Fajreldines, E., Klafke, G., et al. (2025). LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals. Artificial Intelligence and Law. doi:10.1007/s10506-025-09446-w

    • NLM

      Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals [Internet]. Artificial Intelligence and Law. 2025 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s10506-025-09446-w

    • Vancouver

      Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals [Internet]. Artificial Intelligence and Law. 2025 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s10506-025-09446-w
    GDS 17. Partnerships for the goals

  • Trabalho de evento-anais periodico

    Modeling and predicting crimes in the city of São Paulo using graph neural networks (2025)

    Hassan, Waqar ; Cabral, Marvin Mendes ; Ramos, Thiago Rodrigo ; Castelo, Antonio ; Nonato, Luis Gustavo

    Source: Lecture Notes in Artificial Intelligence - LNAI. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC

    Subjects: SEGURANÇA PÚBLICA, MODELAGEM DE DADOS, REDES NEURAIS, CRIME

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      HASSAN, Waqar et al. Modeling and predicting crimes in the city of São Paulo using graph neural networks. Lecture Notes in Artificial Intelligence - LNAI. Cham: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/978-3-031-79035-5_26. Acesso em: 02 nov. 2025. , 2025

    • APA

      Hassan, W., Cabral, M. M., Ramos, T. R., Castelo, A., & Nonato, L. G. (2025). Modeling and predicting crimes in the city of São Paulo using graph neural networks. Lecture Notes in Artificial Intelligence - LNAI. Cham: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. doi:10.1007/978-3-031-79035-5_26

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      Hassan W, Cabral MM, Ramos TR, Castelo A, Nonato LG. Modeling and predicting crimes in the city of São Paulo using graph neural networks [Internet]. Lecture Notes in Artificial Intelligence - LNAI. 2025 ; 15414 372-386.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/978-3-031-79035-5_26

    • Vancouver

      Hassan W, Cabral MM, Ramos TR, Castelo A, Nonato LG. Modeling and predicting crimes in the city of São Paulo using graph neural networks [Internet]. Lecture Notes in Artificial Intelligence - LNAI. 2025 ; 15414 372-386.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/978-3-031-79035-5_26

  • Artigo de periodico

    CounterCrime: Using counterfactual explanations to explore crime reduction scenarios (2025)

    Raimundo, Marcos M ; Garcia-Zanabria, Germain ; Nonato, Luis Gustavo ; Poco, Jorge

    Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, CRIMINALIDADE, TOMADA DE DECISÃO, ANÁLISE DE DADOS

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      RAIMUNDO, Marcos M et al. CounterCrime: Using counterfactual explanations to explore crime reduction scenarios. IEEE Transactions on Visualization and Computer Graphics, v. 31, n. 10, p. 9008-9023, 2025Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2025.3586202. Acesso em: 02 nov. 2025.

    • APA

      Raimundo, M. M., Garcia-Zanabria, G., Nonato, L. G., & Poco, J. (2025). CounterCrime: Using counterfactual explanations to explore crime reduction scenarios. IEEE Transactions on Visualization and Computer Graphics, 31( 10), 9008-9023. doi:10.1109/TVCG.2025.3586202

    • NLM

      Raimundo MM, Garcia-Zanabria G, Nonato LG, Poco J. CounterCrime: Using counterfactual explanations to explore crime reduction scenarios [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10): 9008-9023.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2025.3586202

    • Vancouver

      Raimundo MM, Garcia-Zanabria G, Nonato LG, Poco J. CounterCrime: Using counterfactual explanations to explore crime reduction scenarios [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10): 9008-9023.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2025.3586202

  • Artigo de periodico

    T-Explainer: a model-agnostic explainability framework based on gradients (2025)

    Ortigossa, Evandro Scudeleti ; Dias, Fabio Felix ; Barr, Brian ; Silva, Claudio Teixeira ; Nonato, Luis Gustavo

    Source: IEEE Intelligent Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, TOMADA DE DECISÃO, BENCHMARKS

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      ORTIGOSSA, Evandro Scudeleti et al. T-Explainer: a model-agnostic explainability framework based on gradients. IEEE Intelligent Systems, 2025Tradução . . Disponível em: https://doi.org/10.1109/MIS.2025.3564330. Acesso em: 02 nov. 2025.

    • APA

      Ortigossa, E. S., Dias, F. F., Barr, B., Silva, C. T., & Nonato, L. G. (2025). T-Explainer: a model-agnostic explainability framework based on gradients. IEEE Intelligent Systems. doi:10.1109/MIS.2025.3564330

    • NLM

      Ortigossa ES, Dias FF, Barr B, Silva CT, Nonato LG. T-Explainer: a model-agnostic explainability framework based on gradients [Internet]. IEEE Intelligent Systems. 2025 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/MIS.2025.3564330

    • Vancouver

      Ortigossa ES, Dias FF, Barr B, Silva CT, Nonato LG. T-Explainer: a model-agnostic explainability framework based on gradients [Internet]. IEEE Intelligent Systems. 2025 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/MIS.2025.3564330

  • Artigo de periodico

    TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees (2025)

    Guardieiro, Vitoria ; Oliveira, Felipe Inagaki de ; Doraiswamy, Harish ; Nonato, Luis Gustavo ; Silva, Claudio

    Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: ANÁLISE DE DADOS, TOPOLOGIA, PROJEÇÃO

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      GUARDIEIRO, Vitoria et al. TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees. IEEE Transactions on Visualization and Computer Graphics, v. 31, n. 1, p. 229-239, 2025Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2024.3456365. Acesso em: 02 nov. 2025.

    • APA

      Guardieiro, V., Oliveira, F. I. de, Doraiswamy, H., Nonato, L. G., & Silva, C. (2025). TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees. IEEE Transactions on Visualization and Computer Graphics, 31( 1), 229-239. doi:10.1109/TVCG.2024.3456365

    • NLM

      Guardieiro V, Oliveira FI de, Doraiswamy H, Nonato LG, Silva C. TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 1): 229-239.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2024.3456365

    • Vancouver

      Guardieiro V, Oliveira FI de, Doraiswamy H, Nonato LG, Silva C. TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 1): 229-239.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2024.3456365

  • Artigo de periodico

    Explainable artificial intelligence (XAI): from theory to methods and applications (2024)

    Ortigossa, Evandro Scudeleti ; Gonçalves, Thales ; Nonato, Luis Gustavo

    Source: IEEE Access. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, REPRESENTAÇÃO

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      ORTIGOSSA, Evandro Scudeleti e GONÇALVES, Thales e NONATO, Luis Gustavo. Explainable artificial intelligence (XAI): from theory to methods and applications. IEEE Access, v. 12, p. 80799-80846, 2024Tradução . . Disponível em: https://doi.org/10.1109/ACCESS.2024.3409843. Acesso em: 02 nov. 2025.

    • APA

      Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024). Explainable artificial intelligence (XAI): from theory to methods and applications. IEEE Access, 12, 80799-80846. doi:10.1109/ACCESS.2024.3409843

    • NLM

      Ortigossa ES, Gonçalves T, Nonato LG. Explainable artificial intelligence (XAI): from theory to methods and applications [Internet]. IEEE Access. 2024 ; 12 80799-80846.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/ACCESS.2024.3409843

    • Vancouver

      Ortigossa ES, Gonçalves T, Nonato LG. Explainable artificial intelligence (XAI): from theory to methods and applications [Internet]. IEEE Access. 2024 ; 12 80799-80846.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/ACCESS.2024.3409843

  • Trabalho de evento-anais periodico

    Exploring the relationship between feature attribution methods and model performance (2024)

    Silva, Priscylla; Silva, Claudio; Nonato, Luis Gustavo

    Source: Proceedings of Machine Learning Research. Conference titles: Annual AAAI Conference on Artificial Intelligence. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, APRENDIZAGEM PROFUNDA, ANÁLISE DE DESEMPENHO, EDUCAÇÃO

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      SILVA, Priscylla e SILVA, Claudio e NONATO, Luis Gustavo. Exploring the relationship between feature attribution methods and model performance. Proceedings of Machine Learning Research. Vancouver: AAAI. Disponível em: https://proceedings.mlr.press/v257/silva24a.html. Acesso em: 02 nov. 2025. , 2024

    • APA

      Silva, P., Silva, C., & Nonato, L. G. (2024). Exploring the relationship between feature attribution methods and model performance. Proceedings of Machine Learning Research. Vancouver: AAAI. Recuperado de https://proceedings.mlr.press/v257/silva24a.html

    • NLM

      Silva P, Silva C, Nonato LG. Exploring the relationship between feature attribution methods and model performance [Internet]. Proceedings of Machine Learning Research. 2024 ; 257 29-38.[citado 2025 nov. 02 ] Available from: https://proceedings.mlr.press/v257/silva24a.html

    • Vancouver

      Silva P, Silva C, Nonato LG. Exploring the relationship between feature attribution methods and model performance [Internet]. Proceedings of Machine Learning Research. 2024 ; 257 29-38.[citado 2025 nov. 02 ] Available from: https://proceedings.mlr.press/v257/silva24a.html

  • Trabalho de evento

    A visual methodology to assess spatial graph vertex ordering algorithms (2024)

    Salinas, Karelia Alexandra Vilca ; Barella, Victor Hugo ; Vieira, Thales ; Nonato, Luis Gustavo

    Source: Anais. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC

    Subjects: GRAFOLOGIA, ALGORITMOS, VISUALIZAÇÃO, ESPAÇO URBANO, ANÁLISE DE DADOS

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      SALINAS, Karelia Alexandra Vilca et al. A visual methodology to assess spatial graph vertex ordering algorithms. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/SIBGRAPI62404.2024.10716318. Acesso em: 02 nov. 2025.

    • APA

      Salinas, K. A. V., Barella, V. H., Vieira, T., & Nonato, L. G. (2024). A visual methodology to assess spatial graph vertex ordering algorithms. In Anais. Piscataway: IEEE. doi:10.1109/SIBGRAPI62404.2024.10716318

    • NLM

      Salinas KAV, Barella VH, Vieira T, Nonato LG. A visual methodology to assess spatial graph vertex ordering algorithms [Internet]. Anais. 2024 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716318

    • Vancouver

      Salinas KAV, Barella VH, Vieira T, Nonato LG. A visual methodology to assess spatial graph vertex ordering algorithms [Internet]. Anais. 2024 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716318

  • Artigo de periodico

    Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm (2024)

    Raimundo, Marcos M ; Nonato, Luis Gustavo ; Poco, Jorge

    Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS

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      RAIMUNDO, Marcos M e NONATO, Luis Gustavo e POCO, Jorge. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, v. 38, p. 2942-2974, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-022-00906-4. Acesso em: 02 nov. 2025.

    • APA

      Raimundo, M. M., Nonato, L. G., & Poco, J. (2024). Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, 38, 2942-2974. doi:10.1007/s10618-022-00906-4

    • NLM

      Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s10618-022-00906-4

    • Vancouver

      Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s10618-022-00906-4

  • Artigo de periodico

    MOUNTAINEER: topology-driven visual analytics for comparing local explanations (2024)

    Solunke, Parikshit ; Guardieiro, Vitoria ; Rulff, João ; Chan, Gromit Yeuk-Yin ; Barr, Brian ; Nonato, Luis Gustavo ; Silva, Claudio

    Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: MODELAGEM DE DADOS, APRENDIZADO COMPUTACIONAL, ESTATÍSTICA, SIMULAÇÃO, TOPOLOGIA

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      SOLUNKE, Parikshit et al. MOUNTAINEER: topology-driven visual analytics for comparing local explanations. IEEE Transactions on Visualization and Computer Graphics, v. 30, n. 12, p. 7763-7775, 2024Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2024.3418653. Acesso em: 02 nov. 2025.

    • APA

      Solunke, P., Guardieiro, V., Rulff, J., Chan, G. Y. -Y., Barr, B., Nonato, L. G., & Silva, C. (2024). MOUNTAINEER: topology-driven visual analytics for comparing local explanations. IEEE Transactions on Visualization and Computer Graphics, 30( 12), 7763-7775. doi:10.1109/TVCG.2024.3418653

    • NLM

      Solunke P, Guardieiro V, Rulff J, Chan GY-Y, Barr B, Nonato LG, Silva C. MOUNTAINEER: topology-driven visual analytics for comparing local explanations [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2024 ; 30( 12): 7763-7775.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2024.3418653

    • Vancouver

      Solunke P, Guardieiro V, Rulff J, Chan GY-Y, Barr B, Nonato LG, Silva C. MOUNTAINEER: topology-driven visual analytics for comparing local explanations [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2024 ; 30( 12): 7763-7775.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2024.3418653

  • Trabalho de evento

    Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data (2024)

    Santos, Tiago Paulino; Souza, João Matheus Siqueira ; Vieira, Thales ; Nonato, Luis Gustavo

    Source: Anais. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC

    Subjects: MINERAÇÃO DE DADOS, TOMADA DE DECISÃO, VISUALIZAÇÃO, SEGURANÇA PÚBLICA

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      SANTOS, Tiago Paulino et al. Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/SIBGRAPI62404.2024.10716319. Acesso em: 02 nov. 2025.

    • APA

      Santos, T. P., Souza, J. M. S., Vieira, T., & Nonato, L. G. (2024). Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data. In Anais. Piscataway: IEEE. doi:10.1109/SIBGRAPI62404.2024.10716319

    • NLM

      Santos TP, Souza JMS, Vieira T, Nonato LG. Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data [Internet]. Anais. 2024 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716319

    • Vancouver

      Santos TP, Souza JMS, Vieira T, Nonato LG. Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data [Internet]. Anais. 2024 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716319

  • Artigo de periodico

    LegalVis: exploring and inferring precedent citations in legal documents (2023)

    Domingues, Lucas Emanuel Resck ; Ponciano, Jean Roberto ; Poco, Jorge ; Nonato, Luis Gustavo

    Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, VISUALIZAÇÃO, SISTEMA JUDICIÁRIO

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      DOMINGUES, Lucas Emanuel Resck et al. LegalVis: exploring and inferring precedent citations in legal documents. IEEE Transactions on Visualization and Computer Graphics, v. 29, n. 6, p. 3105-3120, 2023Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2022.3152450. Acesso em: 02 nov. 2025.

    • APA

      Domingues, L. E. R., Ponciano, J. R., Poco, J., & Nonato, L. G. (2023). LegalVis: exploring and inferring precedent citations in legal documents. IEEE Transactions on Visualization and Computer Graphics, 29( 6), 3105-3120. doi:10.1109/TVCG.2022.3152450

    • NLM

      Domingues LER, Ponciano JR, Poco J, Nonato LG. LegalVis: exploring and inferring precedent citations in legal documents [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( 6): 3105-3120.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2022.3152450

    • Vancouver

      Domingues LER, Ponciano JR, Poco J, Nonato LG. LegalVis: exploring and inferring precedent citations in legal documents [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( 6): 3105-3120.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2022.3152450

  • Artigo de periodico

    Calibrate: interactive analysis of probabilistic model output (2023)

    Xenopoulos, Peter ; Rulff, João; Nonato, Luis Gustavo ; Barr, Brian; Silva, Claudio

    Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, VISUALIZAÇÃO, MODELOS PARA PROCESSOS ESTOCÁSTICOS

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    • ABNT

      XENOPOULOS, Peter et al. Calibrate: interactive analysis of probabilistic model output. IEEE Transactions on Visualization and Computer Graphics, v. 29, n. Ja 2023, p. 853-863, 2023Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2022.3209489. Acesso em: 02 nov. 2025.

    • APA

      Xenopoulos, P., Rulff, J., Nonato, L. G., Barr, B., & Silva, C. (2023). Calibrate: interactive analysis of probabilistic model output. IEEE Transactions on Visualization and Computer Graphics, 29( Ja 2023), 853-863. doi:10.1109/TVCG.2022.3209489

    • NLM

      Xenopoulos P, Rulff J, Nonato LG, Barr B, Silva C. Calibrate: interactive analysis of probabilistic model output [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( Ja 2023): 853-863.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2022.3209489

    • Vancouver

      Xenopoulos P, Rulff J, Nonato LG, Barr B, Silva C. Calibrate: interactive analysis of probabilistic model output [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( Ja 2023): 853-863.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/TVCG.2022.3209489

  • Artigo de periodico

    Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data (2023)

    Luna, Aderval Severino ; Torres, Alexandre Rodrigues ; Cunha, Camilla Lima ; Lima, Igor Conrado Alves de; Nonato, Luis Gustavo

    Source: Brazilian Journal of Analytical Chemistry. Unidade: ICMC

    Subjects: BIOCOMBUSTÍVEIS, ALGORITMOS, APRENDIZADO COMPUTACIONAL

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    • ABNT

      LUNA, Aderval Severino et al. Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data. Brazilian Journal of Analytical Chemistry, v. 10, n. 39, p. 52-69, 2023Tradução . . Disponível em: https://doi.org/10.30744/brjac.2179-3425.AR-30-2022. Acesso em: 02 nov. 2025.

    • APA

      Luna, A. S., Torres, A. R., Cunha, C. L., Lima, I. C. A. de, & Nonato, L. G. (2023). Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data. Brazilian Journal of Analytical Chemistry, 10( 39), 52-69. doi:10.30744/brjac.2179-3425.AR-30-2022

    • NLM

      Luna AS, Torres AR, Cunha CL, Lima ICA de, Nonato LG. Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data [Internet]. Brazilian Journal of Analytical Chemistry. 2023 ; 10( 39): 52-69.[citado 2025 nov. 02 ] Available from: https://doi.org/10.30744/brjac.2179-3425.AR-30-2022

    • Vancouver

      Luna AS, Torres AR, Cunha CL, Lima ICA de, Nonato LG. Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data [Internet]. Brazilian Journal of Analytical Chemistry. 2023 ; 10( 39): 52-69.[citado 2025 nov. 02 ] Available from: https://doi.org/10.30744/brjac.2179-3425.AR-30-2022

  • Trabalho de evento

    Crime prediction models in the metropolitan area of São Paulo - Brazil (2023)

    Zhao, Wellington Yuanhe; Nonato, Luis Gustavo ; Russo, Cibele Maria

    Source: Proceedings. Conference titles: International Workshop on Statistical Modelling. Unidades: ICMC, Interinstitucional de Pós-Graduação em Estatística

    Subjects: SÃO PAULO (SP), CRIMINALIDADE, PREDIÇÃO, REDES NEURAIS

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    • ABNT

      ZHAO, Wellington Yuanhe e NONATO, Luis Gustavo e RUSSO, Cibele Maria. Crime prediction models in the metropolitan area of São Paulo - Brazil. 2023, Anais.. Dortmund: TU Dortmund University, 2023. Disponível em: https://iwsm2023.statistik.tu-dortmund.de. Acesso em: 02 nov. 2025.

    • APA

      Zhao, W. Y., Nonato, L. G., & Russo, C. M. (2023). Crime prediction models in the metropolitan area of São Paulo - Brazil. In Proceedings. Dortmund: TU Dortmund University. Recuperado de https://iwsm2023.statistik.tu-dortmund.de

    • NLM

      Zhao WY, Nonato LG, Russo CM. Crime prediction models in the metropolitan area of São Paulo - Brazil [Internet]. Proceedings. 2023 ;[citado 2025 nov. 02 ] Available from: https://iwsm2023.statistik.tu-dortmund.de

    • Vancouver

      Zhao WY, Nonato LG, Russo CM. Crime prediction models in the metropolitan area of São Paulo - Brazil [Internet]. Proceedings. 2023 ;[citado 2025 nov. 02 ] Available from: https://iwsm2023.statistik.tu-dortmund.de

  • Artigo de periodico

    A river flooding detection system based on deep learning and computer vision (2022)

    Fernandes Junior, Francisco Erivaldo ; Nonato, Luis Gustavo ; Ueyama, Jó

    Source: Multimedia Tools and Applications. Unidade: ICMC

    Subjects: VISÃO COMPUTACIONAL, ENCHENTES URBANAS, SEMÂNTICA

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    • ABNT

      FERNANDES JUNIOR, Francisco Erivaldo e NONATO, Luis Gustavo e UEYAMA, Jó. A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, v. 81, p. 40231-40251, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11042-022-12813-3. Acesso em: 02 nov. 2025.

    • APA

      Fernandes Junior, F. E., Nonato, L. G., & Ueyama, J. (2022). A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, 81, 40231-40251. doi:10.1007/s11042-022-12813-3

    • NLM

      Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s11042-022-12813-3

    • Vancouver

      Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s11042-022-12813-3

  • Artigo de periodico

    SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level (2022)

    Yuan, Jun ; Chan, Gromit Yeuk-Yin ; Barr, Brian; Overton, Kyle; Rees, Kim; Nonato, Luis Gustavo ; Bertini, Enrico ; Silva, Claudio Teixeira

    Source: IEEE Computer Graphics and Applications. Unidade: ICMC

    Subjects: CLUSTERS, VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL

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    • ABNT

      YUAN, Jun et al. SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level. IEEE Computer Graphics and Applications, v. 42, n. 6, p. 24-36, 2022Tradução . . Disponível em: https://doi.org/10.1109/MCG.2022.3199727. Acesso em: 02 nov. 2025.

    • APA

      Yuan, J., Chan, G. Y. -Y., Barr, B., Overton, K., Rees, K., Nonato, L. G., et al. (2022). SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level. IEEE Computer Graphics and Applications, 42( 6), 24-36. doi:10.1109/MCG.2022.3199727

    • NLM

      Yuan J, Chan GY-Y, Barr B, Overton K, Rees K, Nonato LG, Bertini E, Silva CT. SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level [Internet]. IEEE Computer Graphics and Applications. 2022 ; 42( 6): 24-36.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/MCG.2022.3199727

    • Vancouver

      Yuan J, Chan GY-Y, Barr B, Overton K, Rees K, Nonato LG, Bertini E, Silva CT. SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level [Internet]. IEEE Computer Graphics and Applications. 2022 ; 42( 6): 24-36.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1109/MCG.2022.3199727

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