2024
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Löff, J'unior; Griebler, Dalvan; Fernandes, Luiz Gustavo; Binder, Walter MPR: An MPI Framework for Distributed Self-adaptive Stream Processing Inproceedings doi In: Euro-Par 2024: Parallel Processing, pp. 400-414, Springer, Madrid, Spain, 2024. @inproceedings{LOFF:Euro-Par:24,
title = {MPR: An MPI Framework for Distributed Self-adaptive Stream Processing},
author = {J'unior Löff and Dalvan Griebler and Luiz Gustavo Fernandes and Walter Binder},
url = {https://doi.org/10.1007/978-3-031-69583-4_28},
doi = {10.1007/978-3-031-69583-4_28},
year = {2024},
date = {2024-08-01},
booktitle = {Euro-Par 2024: Parallel Processing},
pages = {400-414},
publisher = {Springer},
address = {Madrid, Spain},
series = {Euro-Par'24},
abstract = {Stream processing systems must often cope with workloads varying in content, format, size, and input rate. The high variability and unpredictability make statically fine-tuning them very challenging. Our work addresses this limitation by providing a new framework and runtime system to simplify implementing and assessing new self-adaptive algorithms and optimizations. We implement a prototype on top of MPI called MPR and show its functionality. We focus on horizontal scaling by supporting the addition and removal of processes during execution time. Experiments reveal that MPR can achieve performance similar to that of a handwritten static MPI application. We also assess MPR's adaptation capabilities, showing that it can readily re-configure itself, with the help of a self-adaptive algorithm, in response to workload variations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stream processing systems must often cope with workloads varying in content, format, size, and input rate. The high variability and unpredictability make statically fine-tuning them very challenging. Our work addresses this limitation by providing a new framework and runtime system to simplify implementing and assessing new self-adaptive algorithms and optimizations. We implement a prototype on top of MPI called MPR and show its functionality. We focus on horizontal scaling by supporting the addition and removal of processes during execution time. Experiments reveal that MPR can achieve performance similar to that of a handwritten static MPI application. We also assess MPR's adaptation capabilities, showing that it can readily re-configure itself, with the help of a self-adaptive algorithm, in response to workload variations. |
Guder, Larissa; Aires, João Paulo; Meneguzzi, Felipe; Griebler, Dalvan Dimensional Speech Emotion Recognition from Bimodal Features Inproceedings doi In: Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde, pp. 579-590, SBC, Goiânia, Brasil, 2024. @inproceedings{GUDER:SBCAS:24,
title = {Dimensional Speech Emotion Recognition from Bimodal Features},
author = {Larissa Guder and João Paulo Aires and Felipe Meneguzzi and Dalvan Griebler},
url = {https://doi.org/10.5753/sbcas.2024.2779},
doi = {10.5753/sbcas.2024.2779},
year = {2024},
date = {2024-07-01},
booktitle = {Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde},
pages = {579-590},
publisher = {SBC},
address = {Goiânia, Brasil},
abstract = {Considering the human-machine relationship, affective computing aims to allow computers to recognize or express emotions. Speech Emotion Recognition is a task from affective computing that aims to recognize emotions in an audio utterance. The most common way to predict emotions from the speech is using pre-determined classes in the offline mode. In that way, emotion recognition is restricted to the number of classes. To avoid this restriction, dimensional emotion recognition uses dimensions such as valence, arousal, and dominance to represent emotions with higher granularity. Existing approaches propose using textual information to improve results for the valence dimension. Although recent efforts have tried to improve results on speech emotion recognition to predict emotion dimensions, they do not consider real-world scenarios where processing the input quickly is necessary. Considering these aspects, we take the first step towards creating a bimodal approach for dimensional speech emotion recognition in streaming. Our approach combines sentence and audio representations as input to a recurrent neural network that performs speechemotion recognition. Our final architecture achieves a Concordance Correlation Coefficient of 0.5915 for arousal, 0.1431 for valence, and 0.5899 for dominance in the IEMOCAP dataset.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Considering the human-machine relationship, affective computing aims to allow computers to recognize or express emotions. Speech Emotion Recognition is a task from affective computing that aims to recognize emotions in an audio utterance. The most common way to predict emotions from the speech is using pre-determined classes in the offline mode. In that way, emotion recognition is restricted to the number of classes. To avoid this restriction, dimensional emotion recognition uses dimensions such as valence, arousal, and dominance to represent emotions with higher granularity. Existing approaches propose using textual information to improve results for the valence dimension. Although recent efforts have tried to improve results on speech emotion recognition to predict emotion dimensions, they do not consider real-world scenarios where processing the input quickly is necessary. Considering these aspects, we take the first step towards creating a bimodal approach for dimensional speech emotion recognition in streaming. Our approach combines sentence and audio representations as input to a recurrent neural network that performs speechemotion recognition. Our final architecture achieves a Concordance Correlation Coefficient of 0.5915 for arousal, 0.1431 for valence, and 0.5899 for dominance in the IEMOCAP dataset. |
Gomes, Carlos Falcao Azevedo; Araujo, Adriel Silva; Ahmad, Sunna Imtiaz; Magnaguagno, Mauricio Cecilio; Teixeira, Vinicius Crisosthemos; Rajapuri, Anushri Singh; Roederer, Quinn; Griebler, Dalvan; Dutra, Vinicius; Turkkahraman, Hakan; Pinho, Marcio Sarroglia Multiview Machine Learning Classification of Tooth Extraction in Orthodontics Using Intraoral Scans Inproceedings doi In: 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1977-1982, IEEE, Osaka, Japan, 2024. @inproceedings{GOMES:COMPSAC:24,
title = {Multiview Machine Learning Classification of Tooth Extraction in Orthodontics Using Intraoral Scans},
author = {Carlos Falcao Azevedo Gomes and Adriel Silva Araujo and Sunna Imtiaz Ahmad and Mauricio Cecilio Magnaguagno and Vinicius Crisosthemos Teixeira and Anushri Singh Rajapuri and Quinn Roederer and Dalvan Griebler and Vinicius Dutra and Hakan Turkkahraman and Marcio Sarroglia Pinho},
url = {https://doi.org/10.1109/COMPSAC61105.2024.00316},
doi = {10.1109/COMPSAC61105.2024.00316},
year = {2024},
date = {2024-07-01},
booktitle = {2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)},
pages = {1977-1982},
publisher = {IEEE},
address = {Osaka, Japan},
abstract = {Orthodontic treatment planning often involves de-ciding whether to extract teeth, a critical and irreversible decision. Integrating machine learning (ML) can enhance decision-making. This study proposes using Intraoral Scans (IOS) 3D models to predict extraction/non-extraction binary decisions with ML models. We leverage a multiview approach, using images taken from multiple points of view of the 3D model. The methodology involved a dataset composed of preprocessed IOS from 181 subjects and an experimental procedure that evaluated multiple ML models in their ability to classify subjects using either grayscale pixel intensities or radiomic features. The results indicated that a logistic model applied to the radiomic features from the back and frontal views of the 3D models was one of the best model candidates, achieving a test accuracy of 70 % and F1 score of. 73 and. 65 for non-extraction and extraction cases, respectively. Overall, these findings indicate that a multiview approach to IOS 3D models can be used to predict extraction/non-extraction decisions. In addition, the results suggest that radiomic features provide useful information in the analysis of IOS data.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Orthodontic treatment planning often involves de-ciding whether to extract teeth, a critical and irreversible decision. Integrating machine learning (ML) can enhance decision-making. This study proposes using Intraoral Scans (IOS) 3D models to predict extraction/non-extraction binary decisions with ML models. We leverage a multiview approach, using images taken from multiple points of view of the 3D model. The methodology involved a dataset composed of preprocessed IOS from 181 subjects and an experimental procedure that evaluated multiple ML models in their ability to classify subjects using either grayscale pixel intensities or radiomic features. The results indicated that a logistic model applied to the radiomic features from the back and frontal views of the 3D models was one of the best model candidates, achieving a test accuracy of 70 % and F1 score of. 73 and. 65 for non-extraction and extraction cases, respectively. Overall, these findings indicate that a multiview approach to IOS 3D models can be used to predict extraction/non-extraction decisions. In addition, the results suggest that radiomic features provide useful information in the analysis of IOS data. |
Fim, Gabriel Rustick; Griebler, Dalvan Proposta de Paralelismo de Stream Multi-GPU em Multi-Cores Inproceedings doi In: Anais da XXIV Escola Regional de Alto Desempenho da Região Sul, pp. 101-102, Sociedade Brasileira de Computação, Florianópolis, Brazil, 2024. @inproceedings{FIM:ERAD:24,
title = {Proposta de Paralelismo de Stream Multi-GPU em Multi-Cores },
author = {Gabriel Rustick Fim and Dalvan Griebler},
url = {https://doi.org/10.5753/eradrs.2024.238680},
doi = {10.5753/eradrs.2024.238680},
year = {2024},
date = {2024-04-01},
booktitle = {Anais da XXIV Escola Regional de Alto Desempenho da Região Sul},
pages = {101-102},
publisher = {Sociedade Brasileira de Computação},
address = {Florianópolis, Brazil},
abstract = {Considerando a necessidade de tempos de processamento mais rápidos, a utilização de ambientes multi-aceleradores vem se tornando cada vez mais proeminente na literatura, infelizmente programar para estes tipos de ambientes apresenta uma série de desafios que fazem com que o desenvolvimento de códigos direcionados a multi-GPUs exija um maior esforço de programação. Propomos investigar como utilizar anotações C++ para simplificar a geração de código multi-GPU sem comprometer o desempenho.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Considerando a necessidade de tempos de processamento mais rápidos, a utilização de ambientes multi-aceleradores vem se tornando cada vez mais proeminente na literatura, infelizmente programar para estes tipos de ambientes apresenta uma série de desafios que fazem com que o desenvolvimento de códigos direcionados a multi-GPUs exija um maior esforço de programação. Propomos investigar como utilizar anotações C++ para simplificar a geração de código multi-GPU sem comprometer o desempenho. |
Araujo, Gabriell; Griebler, Dalvan; Fernandes, Luiz Gustavo Em direção a um modelo de programação paralela único para CPUs e GPUs em processamento de stream Inproceedings doi In: Anais da XXIV Escola Regional de Alto Desempenho da Região Sul, pp. 103-104, Sociedade Brasileira de Computação, Florianópolis, Brazil, 2024. @inproceedings{ARAUJO:ERAD:24,
title = {Em direção a um modelo de programação paralela único para CPUs e GPUs em processamento de stream },
author = {Gabriell Araujo and Dalvan Griebler and Luiz Gustavo Fernandes},
url = {https://doi.org/10.5753/eradrs.2024.238670},
doi = {10.5753/eradrs.2024.238670},
year = {2024},
date = {2024-04-01},
booktitle = {Anais da XXIV Escola Regional de Alto Desempenho da Região Sul},
pages = {103-104},
publisher = {Sociedade Brasileira de Computação},
address = {Florianópolis, Brazil},
abstract = {Este trabalho apresenta resultados parciais da pesquisa em andamento, a qual está utilizando a Linguagem Específica de Domínio (DSL) SPar para prototipar um modelo de programação paralela único direcionado a CPUs e GPUs em processamento de stream. Por meio do protótipo inicial, já é possível gerar código paralelo para CPUs e GPUs em processamento de stream.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Este trabalho apresenta resultados parciais da pesquisa em andamento, a qual está utilizando a Linguagem Específica de Domínio (DSL) SPar para prototipar um modelo de programação paralela único direcionado a CPUs e GPUs em processamento de stream. Por meio do protótipo inicial, já é possível gerar código paralelo para CPUs e GPUs em processamento de stream. |
