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Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)最新文献

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Short-term Inbetweening of 3D Human Motions 三维人体运动的短期中介
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18286
Fabio Neves Rocha, Valdinei Freire, K. V. Delgado
{"title":"Short-term Inbetweening of 3D Human Motions","authors":"Fabio Neves Rocha, Valdinei Freire, K. V. Delgado","doi":"10.5753/eniac.2021.18286","DOIUrl":"https://doi.org/10.5753/eniac.2021.18286","url":null,"abstract":"Creating computer generated human animations without the use of motion capture technology is a tedious and time consuming activity. Although there are several publications regarding animation synthesis using data driven methods, not many are dedicated towards the task of inbetweening, which consists of generating transition movements between frames. A modified version of LSTM, called Recurrent Transition Network (RTN), solves the inbetweening task for walking motion based on ten initial frames and two final frames. In this work, we are interested on the short-term inbetweening task, where we need to use the least amount of frames to generate the missing frames for short-term transitions. We are also interested on different kinds of movements, such as martial arts and Indian dance. Thus, we adapt the Recurrent Transition Network (RTN) to require only the two firts frames and the last one, called ARTN, and propose a simple post processing method combining ARTN with linear interpolation, called ARTN+. The results show that the average error of ARTN+ is less than the average error of each method (RTN and interpolation) separately in the martial arts and Indian dance dataset.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131854726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Age-Invariant Face Recognition to Aid Visually Impaired People 帮助视障人士的年龄不变人脸识别
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18244
Gonçalo Ferreira Neto, R. Veras, K. Aires, L. B. Britto Neto
{"title":"Age-Invariant Face Recognition to Aid Visually Impaired People","authors":"Gonçalo Ferreira Neto, R. Veras, K. Aires, L. B. Britto Neto","doi":"10.5753/eniac.2021.18244","DOIUrl":"https://doi.org/10.5753/eniac.2021.18244","url":null,"abstract":"Este trabalho propõem uma abordagem para auxiliar pessoas com deficiência visual no reconhecimento de pessoas independente da idade. O objetivo é desenvolver um sistema que utilize uma abordagem de reconhecimento facial, com foco na invariância na idade, que retorne bons resultados comparados aos resultados obtidos na revisão da literatura. A abordagem estudada utiliza Redes Neurais Convolucionais profundas CCNs, pré-treinadas pelo conjunto de dados VGGFace2, para extrair descritores de características de imagens de faces e classificar com o algoritmo de classificação Linear SVM. Como pode ser visto no decorrer do trabalho, a abordagem retornou 89,9% de acurácia, utilizando o conjunto de dados FG-NET, com 1002 imagens. E utilizando o conjunto de dados CACD, que contém 163.446 imagens divididas em quatro subconjuntos diferentes, três conjuntos para treino e um para teste, a abordagem retornou 85,2%, 82,4% e 88,2% de acurácia para cada modelo treinado com um conjunto de treinamento diferente.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133231739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Interpretability of Attention Mechanisms in a Portuguese-Based Question Answering System about the Blue Amazon 蓝色亚马逊流域葡萄牙语问答系统中注意机制的可解释性
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18302
Stefano Spindola, M. M. José, A. Oliveira, Flávio Nakasato Cação, F. G. Cozman
{"title":"Interpretability of Attention Mechanisms in a Portuguese-Based Question Answering System about the Blue Amazon","authors":"Stefano Spindola, M. M. José, A. Oliveira, Flávio Nakasato Cação, F. G. Cozman","doi":"10.5753/eniac.2021.18302","DOIUrl":"https://doi.org/10.5753/eniac.2021.18302","url":null,"abstract":"The Brazilian Exclusive Economic Zone, or the \"Blue Amazon\", with its extensive maritime area, is the primary means of transport for the country's foreign trade and is important due to its oil reserves, gas and other mineral resources, in addition to the significant influence on the Brazilian climate. We have manually built a question answering (QA) dataset based on crawled articles and have applied an off-the-shelf QA system based on a fine-tuned BERTimbau Model, achieving an F1-score of 47.0. More importantly, we explored how the proper visualization of attention weights can support helpful interpretations of the system's answers, which is critical in real environments.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132876806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Thompson Sampling in Heuristic Selection for the Quadratic Assignment Problem 二次分配问题的汤普森抽样启发式选择
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18249
C. D. Pohlod, Sandra M. Venske, C. Almeida
{"title":"Thompson Sampling in Heuristic Selection for the Quadratic Assignment Problem","authors":"C. D. Pohlod, Sandra M. Venske, C. Almeida","doi":"10.5753/eniac.2021.18249","DOIUrl":"https://doi.org/10.5753/eniac.2021.18249","url":null,"abstract":"Este trabalho propõe uma Hiper-Heurística (HH) de seleção baseada na abordagem Thompson Sampling (TS) para a solução do Problema Quadrático de Alocação (PQA). O PQA tem como objetivo a alocação de instalações em um conjunto de possíveis localidades já conhecidas, a fim de minimizar o custo total de todas as movimentações entre as instalações. A HH proposta é aplicada na configuração automática de um algoritmo memético, atuando na seleção de uma combinação de heurísticas de baixo nível. Cada combinação envolve a seleção de uma heurística de recombinação, de uma estratégia de busca local e de uma heurística de mutação. O algoritmo foi analisado em 15 instâncias do benchmark Nug e o desempenho da HH é superior àquele obtido por qualquer combinação de heurísticas aplicada de forma isolada, demonstrando a sua eficiência na configuração automática do algoritmo. Os experimentos mostram que o desempenho da TS é afetado pela qualidade do conjunto de heurísticas de baixo nível. A melhor versão da HH obtém a solução ótima em 9 instâncias e o desvio médio percentual da solução ótima (gap), considerando todas as 15 instâncias foi de 8,6%, sendo que os maiores gaps foram encontrados para as três maiores instâncias.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123472094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Tessarine and Quaternion-Valued Deep Neural Networks for Image Classification 图像分类的Tessarine和四元数值深度神经网络
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18266
Fernando Ribeiro de Senna, M. E. Valle
{"title":"Tessarine and Quaternion-Valued Deep Neural Networks for Image Classification","authors":"Fernando Ribeiro de Senna, M. E. Valle","doi":"10.5753/eniac.2021.18266","DOIUrl":"https://doi.org/10.5753/eniac.2021.18266","url":null,"abstract":"Many image processing and analysis tasks are performed with deep neural networks. Although the vast majority of advances have been made with real numbers, recent works have shown that complex and hypercomplex-valued networks may achieve better results. In this paper, we address quaternion-valued and introduce tessarine-valued deep neural networks, including tessarine-valued 2D convolutions. We also address initialization schemes and hypercomplex batch normalization. Finally, a tessarine-valued ResNet model with hypercomplex batch normalization outperformed the corresponding real and quaternion-valued networks on the CIFAR dataset.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125504378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A Model for Traffic Forwarding through Service Function Chaining using Deep Reinforcement Learning Techniques 基于深度强化学习技术的业务功能链流量转发模型
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18289
Silvio Romero de Araújo Júnior, Reinaldo A. C. Bianchi
{"title":"A Model for Traffic Forwarding through Service Function Chaining using Deep Reinforcement Learning Techniques","authors":"Silvio Romero de Araújo Júnior, Reinaldo A. C. Bianchi","doi":"10.5753/eniac.2021.18289","DOIUrl":"https://doi.org/10.5753/eniac.2021.18289","url":null,"abstract":"The development of new communication networks to offer innovative services has increased the volume of data. With the introduction of Deep Reinforcement Learning and Service Function Chaining architecture, new research opportunities have emerged to propose solutions to the new challenges. This work proposes a model through computational simulations how these techniques can be applied. The model was evaluated using two variations of the Deep Q-Network algorithm over the CIC-Darknet dataset. Results showed that both variations are a promising mechanism to make the networks more autonomous and intelligent. to demonstrate","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130172827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Classification of chest X-ray images using Machine Learning and Histogram of Oriented Gradients 基于机器学习和定向梯度直方图的胸部x射线图像分类
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18240
Fellipe M. C. Barbosa, Anne M. P. Canuto
{"title":"Classification of chest X-ray images using Machine Learning and Histogram of Oriented Gradients","authors":"Fellipe M. C. Barbosa, Anne M. P. Canuto","doi":"10.5753/eniac.2021.18240","DOIUrl":"https://doi.org/10.5753/eniac.2021.18240","url":null,"abstract":"Este trabalho propõe um modelo de aprendizado de máquina para classificar e detectar a presença de pneumonia a partir de uma coleção de amostras de radiografias do tórax. Ao contrário da maioria dos trabalhos que utilizam abordagens de aprendizado profundo para classificar se a imagem é de um pulmão com pneumonia ou não, ou seja, duas classes para assim alcançar um desempenho de classificação notável, este modelo utiliza Histograma de Gradientes Orientados para extrair características de uma determinada imagem de raio-X de tórax e classificá-la em três classes, determinando se uma pessoa está ou não infectada com pneumonia viral ou bacteriana. Apesar de uma maior complexidade e utilização de modelos tradicionais de aprendizado de máquina, a maior acurácia alcançada foi de 91.32% superior a de trabalhos que utilizam redes profundas e buscam resolver o mesmo grau de complexidade.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"6 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130012416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis 机器学习对COVID-19患者预后的早期分析
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18241
J. Figuerêdo, R. F. Araujo-Calumby, R. Calumby
{"title":"Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis","authors":"J. Figuerêdo, R. F. Araujo-Calumby, R. Calumby","doi":"10.5753/eniac.2021.18241","DOIUrl":"https://doi.org/10.5753/eniac.2021.18241","url":null,"abstract":"This work proposes a machine learning approach to predict the prognosis of patients with COVID-19. To assist in this task, a descriptive analysis and relative risk estimation were performed. In addition, the importance of variables in the perspective of machine learning algorithms was computed and discussed. The experiments were performed with large-scale nation-wide dataset from Brazil. The results reveal that the model developed was able to predict the patient's prognosis with an AUC = 0.8382. The results also point out that the chance of death is greater among patients over 60 years old, with comorbidities, and symptoms such as dyspnea and Oxygen saturation (< 95%), confirming results observed in other regions of the world.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122696465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Impact of the variation of the number of agents in the cooperative learning of optimal paths using LRTA-star 基于LRTA-star的智能体数量变化对最优路径合作学习的影响
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18242
Luan C. Klein, Cesar Augusto Tacla, Mariela Morveli-Espinoza
{"title":"Impact of the variation of the number of agents in the cooperative learning of optimal paths using LRTA-star","authors":"Luan C. Klein, Cesar Augusto Tacla, Mariela Morveli-Espinoza","doi":"10.5753/eniac.2021.18242","DOIUrl":"https://doi.org/10.5753/eniac.2021.18242","url":null,"abstract":"Algoritmos de aprendizado de caminhos ótimos estão presentes em diversos cenários. Diante disso, o LRTA* (learning real time A*) surge como uma opção que concilia planejamento e ação. O presente artigo estuda como a variação da quantidade de agentes impacta nas distâncias percorridas por eles para encontrar o caminho ótimo utilizando o LRTA* em ambientes estáticos. Através de experimentos, observou-se a existência de uma relação de que ao aumentar o número de agentes, a quantidade de movimentos totais e per capita tendem a curvas matemáticas, sendo elas uma linear e uma exponencial decrescente, respectivamente. Por meio dessa relação, é possível definir a melhor quantidade de agentes na busca do caminho ótimo em termos de desempenho.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124189830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparison of GANs for Covid-19 X-ray classification GANs对Covid-19 x线分类的比较
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18238
Luiz Felipe Cavalcanti, Lilian Berton
{"title":"Comparison of GANs for Covid-19 X-ray classification","authors":"Luiz Felipe Cavalcanti, Lilian Berton","doi":"10.5753/eniac.2021.18238","DOIUrl":"https://doi.org/10.5753/eniac.2021.18238","url":null,"abstract":"Image classification has been applied to several real problems. However, getting labeled data is a costly task, since it demands time, resources and experts. Furthermore, some domains like disease detection suffer from unbalanced classes. These scenarios are challenging and degrade the performance of machine learning algorithms. In these cases, we can use Data Augmentation (DA) approaches to increase the number of labeled examples in a dataset. The objective of this work is to analyze the use of Generative Adversarial Networks (GANs) as DA, which are capable of synthesizing artificial data from the original data, under an adversarial process of two neural networks. The GANs are applied in the classification of unbalanced Covid-19 radiological images. Increasing the number of images led to better accuracy for all the GANs tested, especially in the multi-label dataset, mitigating the bias for unbalanced classes.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126278283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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