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Artigo de periodico
Do calibrated recommendations affect explanations?: A study on post-hoc adjustments (2025)
Atauchi, Paul Dany Flores ; Zanon, André Levi ; Rocha, Leonardo Chaves Dutra da ; Manzato, Marcelo Garcia
Fonte: Journal on Interactive Systems. Unidade: ICMC
Assunto: SISTEMAS DE RECOMENDAÇÃO
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ABNT
ATAUCHI, Paul Dany Flores et al. Do calibrated recommendations affect explanations?: A study on post-hoc adjustments. Journal on Interactive Systems, v. 16, n. 1, p. 441-460, 2025Tradução . . Disponível em: https://doi.org/10.5753/jis.2025.5563. Acesso em: 08 out. 2025.
APA
Atauchi, P. D. F., Zanon, A. L., Rocha, L. C. D. da, & Manzato, M. G. (2025). Do calibrated recommendations affect explanations?: A study on post-hoc adjustments. Journal on Interactive Systems, 16( 1), 441-460. doi:10.5753/jis.2025.5563
NLM
Atauchi PDF, Zanon AL, Rocha LCD da, Manzato MG. Do calibrated recommendations affect explanations?: A study on post-hoc adjustments [Internet]. Journal on Interactive Systems. 2025 ; 16( 1): 441-460.[citado 2025 out. 08 ] Available from: https://doi.org/10.5753/jis.2025.5563
Vancouver
Atauchi PDF, Zanon AL, Rocha LCD da, Manzato MG. Do calibrated recommendations affect explanations?: A study on post-hoc adjustments [Internet]. Journal on Interactive Systems. 2025 ; 16( 1): 441-460.[citado 2025 out. 08 ] Available from: https://doi.org/10.5753/jis.2025.5563
Artigo de periodico
Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures (2025)
Dias, Fabio Felix ; Ponti, Moacir Antonelli ; Minghim, Rosane
Fonte: Ecological Informatics. Unidade: ICMC
Assuntos: REDES NEURAIS, RECONHECIMENTO DE PADRÕES, ACÚSTICA, MONITORAMENTO AMBIENTAL, PÁSSAROS, ANURA
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ABNT
DIAS, Fabio Felix e PONTI, Moacir Antonelli e MINGHIM, Rosane. Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures. Ecological Informatics, v. 90, p. 1-12, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.ecoinf.2025.103232. Acesso em: 08 out. 2025.
APA
Dias, F. F., Ponti, M. A., & Minghim, R. (2025). Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures. Ecological Informatics, 90, 1-12. doi:10.1016/j.ecoinf.2025.103232
NLM
Dias FF, Ponti MA, Minghim R. Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures [Internet]. Ecological Informatics. 2025 ; 90 1-12.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.ecoinf.2025.103232
Vancouver
Dias FF, Ponti MA, Minghim R. Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures [Internet]. Ecological Informatics. 2025 ; 90 1-12.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.ecoinf.2025.103232
Trabalho de evento-anais periodico
Integrating multi-FPGA acceleration to OpenMP distributed computing (2024)
Rosso, Pedro Henrique ; Petrica, Lucian ; Lisa, Nusrat Jahan ; Pereira, Márcio ; Rigo, Sandro ; Yviquel, Hervé ; Bonato, Vanderlei ; Francesquini, Emilio ; Araujo, Guido
Fonte: Lecture Notes in Computer Science - LNCS. Nome do evento: International Workshop on OpenMP - IWOMP. Unidade: ICMC
Assuntos: COMPUTAÇÃO APLICADA, CLUSTERS, BENCHMARKS
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ABNT
ROSSO, Pedro Henrique et al. Integrating multi-FPGA acceleration to OpenMP distributed computing. Lecture Notes in Computer Science - LNCS. Cham: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/978-3-031-72567-8_4. Acesso em: 08 out. 2025. , 2024
APA
Rosso, P. H., Petrica, L., Lisa, N. J., Pereira, M., Rigo, S., Yviquel, H., et al. (2024). Integrating multi-FPGA acceleration to OpenMP distributed computing. Lecture Notes in Computer Science - LNCS. Cham: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. doi:10.1007/978-3-031-72567-8_4
NLM
Rosso PH, Petrica L, Lisa NJ, Pereira M, Rigo S, Yviquel H, Bonato V, Francesquini E, Araujo G. Integrating multi-FPGA acceleration to OpenMP distributed computing [Internet]. Lecture Notes in Computer Science - LNCS. 2024 ; 15195 49-63.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/978-3-031-72567-8_4
Vancouver
Rosso PH, Petrica L, Lisa NJ, Pereira M, Rigo S, Yviquel H, Bonato V, Francesquini E, Araujo G. Integrating multi-FPGA acceleration to OpenMP distributed computing [Internet]. Lecture Notes in Computer Science - LNCS. 2024 ; 15195 49-63.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/978-3-031-72567-8_4
Trabalho de evento
Addressing the gap between current language models and key-term-based clustering (2023)
Cabral, Eric Macedo ; Rezaeipourfarsangi, Sima ; Oliveira, Maria Cristina Ferreira de ; Milios, Evangelos E ; Minghim, Rosane
Fonte: Proceedings. Nome do evento: ACM Symposium on Document Engineering - DocEng. Unidade: ICMC
Assuntos: PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DE TEXTO, REDES NEURAIS, VISUALIZAÇÃO
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ABNT
CABRAL, Eric Macedo et al. Addressing the gap between current language models and key-term-based clustering. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3573128.3604900. Acesso em: 08 out. 2025.
APA
Cabral, E. M., Rezaeipourfarsangi, S., Oliveira, M. C. F. de, Milios, E. E., & Minghim, R. (2023). Addressing the gap between current language models and key-term-based clustering. In Proceedings. New York: ACM. doi:10.1145/3573128.3604900
NLM
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1145/3573128.3604900
Vancouver
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1145/3573128.3604900
Artigo de periodico
A classification and quantification approach to generate features in soundscape ecology using neural networks (2022)
Dias, Fabio Felix ; Ponti, Moacir Antonelli ; Minghim, Rosane
Fonte: Neural Computing and Applications. Unidade: ICMC
Assuntos: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ACÚSTICA, MONITORAMENTO AMBIENTAL, PÁSSAROS, ANURA
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ABNT
DIAS, Fabio Felix e PONTI, Moacir Antonelli e MINGHIM, Rosane. A classification and quantification approach to generate features in soundscape ecology using neural networks. Neural Computing and Applications, v. 34, n. 3, p. 1923-1937, 2022Tradução . . Disponível em: https://doi.org/10.1007/s00521-021-06501-w. Acesso em: 08 out. 2025.
APA
Dias, F. F., Ponti, M. A., & Minghim, R. (2022). A classification and quantification approach to generate features in soundscape ecology using neural networks. Neural Computing and Applications, 34( 3), 1923-1937. doi:10.1007/s00521-021-06501-w
NLM
Dias FF, Ponti MA, Minghim R. A classification and quantification approach to generate features in soundscape ecology using neural networks [Internet]. Neural Computing and Applications. 2022 ; 34( 3): 1923-1937.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s00521-021-06501-w
Vancouver
Dias FF, Ponti MA, Minghim R. A classification and quantification approach to generate features in soundscape ecology using neural networks [Internet]. Neural Computing and Applications. 2022 ; 34( 3): 1923-1937.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s00521-021-06501-w
Artigo de periodico
Visual active learning for labeling: a case for soundscape ecology data (2021)
Huancapaza, Liz ; Ribeiro, Milton Cezar ; Minghim, Rosane
Fonte: Information. Unidade: ICMC
Assuntos: VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, SOM, MONITORAMENTO BIOLÓGICO
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ABNT
HUANCAPAZA, Liz e RIBEIRO, Milton Cezar e MINGHIM, Rosane. Visual active learning for labeling: a case for soundscape ecology data. Information, v. 12, n. 7, p. 1-17, 2021Tradução . . Disponível em: https://doi.org/10.3390/info12070265. Acesso em: 08 out. 2025.
APA
Huancapaza, L., Ribeiro, M. C., & Minghim, R. (2021). Visual active learning for labeling: a case for soundscape ecology data. Information, 12( 7), 1-17. doi:10.3390/info12070265
NLM
Huancapaza L, Ribeiro MC, Minghim R. Visual active learning for labeling: a case for soundscape ecology data [Internet]. Information. 2021 ; 12( 7): 1-17.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/info12070265
Vancouver
Huancapaza L, Ribeiro MC, Minghim R. Visual active learning for labeling: a case for soundscape ecology data [Internet]. Information. 2021 ; 12( 7): 1-17.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/info12070265
Artigo de periodico
An adaptive boundary algorithm for the reconstruction of boundary and initial data using the method of fundamental solutions for the inverse Cauchy-Stefan problem (2021)
Reddy, Gujji Murali Mohan ; Nanda, P; Vynnycky, Michael ; Cuminato, José Alberto
Fonte: Computational and Applied Mathematics. Unidade: ICMC
Assuntos: PROBLEMAS INVERSOS, MÉTODOS NUMÉRICOS, ALGORITMOS
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ABNT
REDDY, Gujji Murali Mohan et al. An adaptive boundary algorithm for the reconstruction of boundary and initial data using the method of fundamental solutions for the inverse Cauchy-Stefan problem. Computational and Applied Mathematics, v. 40, p. 1-26, 2021Tradução . . Disponível em: https://doi.org/10.1007/s40314-021-01454-1. Acesso em: 08 out. 2025.
APA
Reddy, G. M. M., Nanda, P., Vynnycky, M., & Cuminato, J. A. (2021). An adaptive boundary algorithm for the reconstruction of boundary and initial data using the method of fundamental solutions for the inverse Cauchy-Stefan problem. Computational and Applied Mathematics, 40, 1-26. doi:10.1007/s40314-021-01454-1
NLM
Reddy GMM, Nanda P, Vynnycky M, Cuminato JA. An adaptive boundary algorithm for the reconstruction of boundary and initial data using the method of fundamental solutions for the inverse Cauchy-Stefan problem [Internet]. Computational and Applied Mathematics. 2021 ; 40 1-26.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s40314-021-01454-1
Vancouver
Reddy GMM, Nanda P, Vynnycky M, Cuminato JA. An adaptive boundary algorithm for the reconstruction of boundary and initial data using the method of fundamental solutions for the inverse Cauchy-Stefan problem [Internet]. Computational and Applied Mathematics. 2021 ; 40 1-26.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s40314-021-01454-1
Artigo de periodico
A visual mining approach to improved multiple-instance learning (2021)
Castelo, Sonia; Ponti, Moacir Antonelli ; Minghim, Rosane
Fonte: Algorithms. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, VISUALIZAÇÃO
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ABNT
CASTELO, Sonia e PONTI, Moacir Antonelli e MINGHIM, Rosane. A visual mining approach to improved multiple-instance learning. Algorithms, v. 14, n. 12, p. 1-28, 2021Tradução . . Disponível em: https://doi.org/10.3390/a14120344. Acesso em: 08 out. 2025.
APA
Castelo, S., Ponti, M. A., & Minghim, R. (2021). A visual mining approach to improved multiple-instance learning. Algorithms, 14( 12), 1-28. doi:10.3390/a14120344
NLM
Castelo S, Ponti MA, Minghim R. A visual mining approach to improved multiple-instance learning [Internet]. Algorithms. 2021 ; 14( 12): 1-28.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/a14120344
Vancouver
Castelo S, Ponti MA, Minghim R. A visual mining approach to improved multiple-instance learning [Internet]. Algorithms. 2021 ; 14( 12): 1-28.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/a14120344
Artigo de periodico
Big data and machine learning for materials science (2021)
Rodrigues Junior, José Fernando ; Florea, Larisa ; Oliveira, Maria Cristina Ferreira de ; Diamond, Dermot; Oliveira Junior, Osvaldo Novais de
Fonte: Discover Materials. Unidades: ICMC, IFSC
Assuntos: BIG DATA, INTERNET DAS COISAS, APRENDIZADO COMPUTACIONAL
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ABNT
RODRIGUES JUNIOR, José Fernando et al. Big data and machine learning for materials science. Discover Materials, v. 1, n. 1, p. 12-1-12-27, 2021Tradução . . Disponível em: https://doi.org/10.1007/s43939-021-00012-0. Acesso em: 08 out. 2025.
APA
Rodrigues Junior, J. F., Florea, L., Oliveira, M. C. F. de, Diamond, D., & Oliveira Junior, O. N. de. (2021). Big data and machine learning for materials science. Discover Materials, 1( 1), 12-1-12-27. doi:10.1007/s43939-021-00012-0
NLM
Rodrigues Junior JF, Florea L, Oliveira MCF de, Diamond D, Oliveira Junior ON de. Big data and machine learning for materials science [Internet]. Discover Materials. 2021 ; 1( 1): 12-1-12-27.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s43939-021-00012-0
Vancouver
Rodrigues Junior JF, Florea L, Oliveira MCF de, Diamond D, Oliveira Junior ON de. Big data and machine learning for materials science [Internet]. Discover Materials. 2021 ; 1( 1): 12-1-12-27.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s43939-021-00012-0

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