Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)最新文献_第4页

Esperanto and the Web: approaches and approximations 世界语与网络:方法与近似

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227239

C. R. Barbosa, J.F. Coelho Neto, Edio Roberto Manfio

引用次数: 0

An Interval Type-2 Maximum Likelihood Fuzzy Clustering Algorithm 区间2型最大似然模糊聚类算法

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.226935

Ben-Hur Matthews Moreno Montel, G. Serra

引用次数: 0

Enhancing Designer Knowledge to Dialogue Management: A Comparison between Supervised and Reinforcement Learning Approaches 提高设计者的对话管理知识:监督学习和强化学习方法的比较

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227625

B. Nishimoto, Rogers Cristo, Alex F. Mansano, Eduardo R. Hruschka, Vinicius Fernandes Caridá, Anna Helena Reali Costa

{"title":"Enhancing Designer Knowledge to Dialogue Management: A Comparison between Supervised and Reinforcement Learning Approaches","authors":"B. Nishimoto, Rogers Cristo, Alex F. Mansano, Eduardo R. Hruschka, Vinicius Fernandes Caridá, Anna Helena Reali Costa","doi":"10.5753/eniac.2022.227625","DOIUrl":"https://doi.org/10.5753/eniac.2022.227625","url":null,"abstract":"Task-oriented dialogue systems are complex natural language applications employed in various fields such as health care, sales assistance, and digital customer servicing. Although the literature suggests several approaches to managing this type of dialogue system, only a few of them compares the performance of different techniques. From this perspective, in this paper we present a comparison between supervised learning, using the transformer architecture, and reinforcement learning using two flavors of Deep Q-Learning (DQN) algorithms. Our experiments use the MultiWOZ dataset and a real-world digital customer service dataset, from which we show that integrating expert pre-defined rules with DQN allows outperforming supervised approaches. Additionally, we also propose a method to make better usage of the designer knowledge by improving how interactions collected in warm-up are used in training phase. Our results indicate a reduction in training time by preserving the designer’s knowledge, expressed as pre-defined rules in memory during the initial steps of the DQN training procedure.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124824480","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

An analysis of reward shaping for reinforcement learning in a multi-agent framework for combinatorial optimization 组合优化中多智能体框架下强化学习的奖励形成分析

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227627

Jardell Fillipe da Silva, Maria Amélia Lopes Silva, S. R. Souza

引用次数: 0

Learning methods for remaining useful life prediction in a turbofan engine 涡扇发动机剩余使用寿命预测的学习方法

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227615

Andréia Seixas Leal, Lilian Berton, Luis Carlos de Castro Santos

{"title":"Learning methods for remaining useful life prediction in a turbofan engine","authors":"Andréia Seixas Leal, Lilian Berton, Luis Carlos de Castro Santos","doi":"10.5753/eniac.2022.227615","DOIUrl":"https://doi.org/10.5753/eniac.2022.227615","url":null,"abstract":"In industry 4.0 there is a growth in the Industrial Internet of Things (iIoT) with a lot of information generation and consequent big data challenges. Thus, it is imperative to have techniques able to process all this data and predict the maintenance of equipment and systems. The development of algorithms for remaining useful life (RUL) estimators is critical for the full functioning of the company’s assets. Especially the aeronautical sector needs to guarantee safety and quality flights. The turbofan, a propulsion engine, is a critical element for an airplane operation. This paper proposes a model to perform prediction of the remaining useful life of an aircraft’s turbo engine. In this work, we focus on the run-to-failure data from an N-CMAPSS turbofan, the data used were provided by NASA in 2021. After training and validating different algorithms such as MLP and CNN, we find CNN as the best approach with an RMSE of 9.11, a score of 5.14, and computed score of 1.17. The results have improved when compared to the literature over 25% in RMSE and 15% in computed score.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127942087","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

Generating X-ray Reports Using Global Attention 使用全局关注生成x射线报告

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227598

F. Zeiser, C. A. D. Costa, G. D. O. Ramos, Henrique C. Bohn, Ismael Santos, B. Donida, Ana Paula de Oliveira Brun, Nathália Zarichta

{"title":"Generating X-ray Reports Using Global Attention","authors":"F. Zeiser, C. A. D. Costa, G. D. O. Ramos, Henrique C. Bohn, Ismael Santos, B. Donida, Ana Paula de Oliveira Brun, Nathália Zarichta","doi":"10.5753/eniac.2022.227598","DOIUrl":"https://doi.org/10.5753/eniac.2022.227598","url":null,"abstract":"The use of images for the diagnosis, treatment, and decision-making in health is frequent. A large part of the radiologist’s work is the interpretation and production of potentially diagnostic reports. However, they are professionals with high workloads doing tasks operator dependent, that is being subject to errors in case of non-ideal conditions. With the COVID-19 pandemic, healthcare systems were overwhelmed, extending to the X-ray analysis process. In this way, the automatic generation of reports can help to reduce the workload of radiologists and define the diagnosis and treatment of patients with suspected COVID-19. In this article, we propose to generate suggestions for chest radiography reports evaluating two architectures based on: (i) Long short-term memory (LSTM), and (ii) LSTM with global attention. The extraction of the most representative features from the X-ray images is performed by an encoder based on a pre-trained DenseNet121 network for the ChestX-ray14 dataset. Experimental results in a private set of 6650 images and reports indicate that the LSTM model with global attention yields the best result, with a BLEU-1 of 0.693, BLEU-2 of 0.496, BLEU-3 of 0.400, and BLEU-4 of 0.345. The quantitative and qualitative results demonstrate that our method can effectively suggest high-quality radiological findings and demonstrate the possibility of using our methodology as a tool to assist radiologists in chest X-ray analysis.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121372043","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

A Four-Step Cascade Methodology to Classify MCN Codes Using NLP Techniques 使用NLP技术对MCN代码进行分类的四步级联方法

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227652

Pedro Pinheiro, Luan Siqueira, Marcos Amarís

{"title":"A Four-Step Cascade Methodology to Classify MCN Codes Using NLP Techniques","authors":"Pedro Pinheiro, Luan Siqueira, Marcos Amarís","doi":"10.5753/eniac.2022.227652","DOIUrl":"https://doi.org/10.5753/eniac.2022.227652","url":null,"abstract":"A NCM é uma Nomenclatura regional para categorização de mercadorias adotada por países do Mercosul. Essa nomenclatura divide produtos usando 8 dígitos, separados em 4 partes, Capítulo, Posição, Subposição e item/Subitem. Há indícios que cerca de 30% das mercadorias enviadas globalmente estão com seu código errado por ser um processo manual. Esse trabalho tem como objetivo desenvolver um processo para classificar as descrições textuais dos produtos presentes nas Notas Ficais eletrônicas (NF-e). A classificação foi feita utilizando as técnicas de Processamento de Linguagem Natural (PLN) e testada usando 2 diferentes algoritmos de aprendizado de máquina, Máquina de Vetores de Suporte (SVM) e Naive Bayes. Para os experimentos foi usada uma base de dados de 340.000 produtos distintos. Dividimos o processo em 4 modelos de classificação, feitos para classificar as 4 partes da NCM. Os dados foram divididos em 80% treinamento e 20% teste e Obteve-se um acurácia de 89% para um total de 98 classes dos 2 primeiros dígitos, e 76% de utilizando uma técnica de cascata para classificar os 8 dígitos.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115840215","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

On the benefits of automated tuning of hyper-parameters: an experiment related to temperature prediction on UAV computers 超参数自动调优的好处:无人机计算机温度预测相关实验

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227276

Renato S. Maximiano, Valdivino Alexandre de Santiago Júnior, E. H. Shiguemori

{"title":"On the benefits of automated tuning of hyper-parameters: an experiment related to temperature prediction on UAV computers","authors":"Renato S. Maximiano, Valdivino Alexandre de Santiago Júnior, E. H. Shiguemori","doi":"10.5753/eniac.2022.227276","DOIUrl":"https://doi.org/10.5753/eniac.2022.227276","url":null,"abstract":"Finding the best configuration of a neural network to solve a problem has been challenging given the numerous possibilities of values of the hyper-parameters. Thus, tuning of hyper-parameters is one important approach and researchers suggest doing this automatically. However, it is important to verify when it is suitable to perform automated tuning which is usually very costly financially and also in terms of hardware infrastructure. In this study, we analyze the advantages of using a hyper-parameter optimization framework as a way of optimizing the automated search for hyper-parameters of a neural network. To achieve this goal, we used data from an experiment related to temperature prediction of computers embedded in unmanned aerial vehicles (UAVs), and the models Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to perform these predictions. In addition, we compare the hyper-parameter optimization framework to the hyper-parameter exhaustive search technique varying the size of the training dataset. Results of our experiment shows that designing a model using a hyper-parameter optimizer can be up to 36.02% better than using exhaustive search, in addition to achieving satisfactory results with a reduced dataset.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132258210","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

Políticas Aproximadas e Parciais Sensíveis a Risco para o Controle da Propagação de Doenças Infecciosas 控制传染病传播的近似和部分风险敏感政策

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227062

H. Pastor, Karina Valdivia Delgado, Valdinei Freire, L. Barros

{"title":"Políticas Aproximadas e Parciais Sensíveis a Risco para o Controle da Propagação de Doenças Infecciosas","authors":"H. Pastor, Karina Valdivia Delgado, Valdinei Freire, L. Barros","doi":"10.5753/eniac.2022.227062","DOIUrl":"https://doi.org/10.5753/eniac.2022.227062","url":null,"abstract":"Os Processos de Decisão de Markov (MDPs) podem ser usados para controlar a propagação de doenças infecciosas e encontrar uma política ótima de controle de vacinação. No entanto, por se tratar de um problema que envolve vidas, é necessário levar em consideração a atitude do agente em relação ao risco. Assim, neste trabalho, são usados MDPs sensíveis ao risco com o modelo compartimental SIR e são propostos dois algoritmos eficientes para encontrar políticas de vacinação otimizadas que permitam controlar a propagação de uma doença infecciosa, ou seja, selecionar o número de indivíduos que devem ser vacinados a cada período considerando um parâmetro que representa a atitude frente ao risco. A primeira solução proposta encontra uma política de vacinação que é parcial e ótima dada uma determinada atitude de risco. A segunda solução proposta é aproximada e assim pode resolver problemas ainda maiores. Os resultados mostram que: (i) as políticas de vacinação dependem não apenas da taxa básica de reprodução R0, como esperado, mas também do custo e da atitude em relação ao risco de um agente; e (ii) ambas as soluções obtêm um grande ganho de tempo de execução e pouca perda de qualidade quando comparadas com as políticas completas e não aproximadas.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445362","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

On the performance of uncertainty estimation methods for deep-learning based image classification models 基于深度学习的图像分类模型不确定性估计方法的性能研究

Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022) Pub Date : 2022-11-28 DOI: 10.5753/eniac.2022.227603

L. F. P. Cattelan, Danilo Silva

引用次数: 2