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

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Triple-VAE: A Triple Variational Autoencoder to Represent Events in One-Class Event Detection 三vae:在一类事件检测中表示事件的三变分自编码器
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18291
M. Gôlo, R. G. Rossi, R. Marcacini
{"title":"Triple-VAE: A Triple Variational Autoencoder to Represent Events in One-Class Event Detection","authors":"M. Gôlo, R. G. Rossi, R. Marcacini","doi":"10.5753/eniac.2021.18291","DOIUrl":"https://doi.org/10.5753/eniac.2021.18291","url":null,"abstract":"Events are phenomena that occur at a specific time and place. Its detection can bring benefits to society since it is possible to extract knowledge from these events. Event detection is a multimodal task since these events have textual, geographical, and temporal components. Most multimodal research in the literature uses the concatenation of the components to represent the events. These approaches use multi-class or binary learning to detect events of interest which intensifies the user's labeling effort, in which the user should label event classes even if there is no interest in detecting them. In this paper, we present the Triple-VAE approach that learns a unified representation from textual, spatial, and density modalities through a variational autoencoder, one of the state-ofthe-art in representation learning. Our proposed Triple-VAE obtains suitable event representations for one-class classification, where users provide labels only for events of interest, thereby reducing the labeling effort. We carried out an experimental evaluation with ten real-world event datasets, four multimodal representation methods, and five evaluation metrics. Triple-VAE outperforms and presents a statistically significant difference considering the other three representation methods in all datasets. Therefore, Triple-VAE proved to be promising to represent the events in the one-class event detection scenario.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"376 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":"124694250","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
A Machine Learning-based System for Financial Fraud Detection 基于机器学习的金融欺诈检测系统
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18250
João Paulo A. Andrade, Leonardo S. Paulucio, T. M. Paixão, Rodrigo Berriel, T. Carneiro, Raphael V. Carneiro, A. D. Souza, C. Badue, Thiago Oliveira-Santos
{"title":"A Machine Learning-based System for Financial Fraud Detection","authors":"João Paulo A. Andrade, Leonardo S. Paulucio, T. M. Paixão, Rodrigo Berriel, T. Carneiro, Raphael V. Carneiro, A. D. Souza, C. Badue, Thiago Oliveira-Santos","doi":"10.5753/eniac.2021.18250","DOIUrl":"https://doi.org/10.5753/eniac.2021.18250","url":null,"abstract":"Companies created for money-laundering or as a means for taxevasion are harmful to the country's economy and society. This problem is usually tackled by governmental agencies by having officials to pore over companies' financial data and to single out those that exhibit fraudulent behavior. Such work tends to be slow-paced and tedious. This paper proposes a machine learning-based system capable of classifying whether a company is likely to be involved in fraud or not. Based on financial and tax data from various companies, four different classifiers – k-Nearest Neighbors, Random Forest, Support Vector Machine (SVM), and a Neural Network – were trained and then used to indicate fraud. The best-performing model achieved a macro-averaged F1-score of 92.98% with the Random Forest.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"103 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":"124719381","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: 4
A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals 基于音频信号的自动性别识别的深度学习架构比较
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18297
A. I. S. Ferreira, Frederico S. Oliveira, Nádia Silva, A. S. Soares
{"title":"A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals","authors":"A. I. S. Ferreira, Frederico S. Oliveira, Nádia Silva, A. S. Soares","doi":"10.5753/eniac.2021.18297","DOIUrl":"https://doi.org/10.5753/eniac.2021.18297","url":null,"abstract":"O reconhecimento de gênero a partir da fala é um problema relacionado à análise de fala humana, e possui diversas aplicações que vão desde a personalização na recomendação de produtos à ciência forense. A identificação da eficiência e custos de diferentes abordagens que lidam com esse problema é imprescindível. Este trabalho tem como foco investigar e comparar a eficiência e custos de diferentes arquiteturas de deep learning para o reconhecimento de gênero a partir da fala. Os resultados mostram que o modelo convolucional unidimensional consegue os melhores resultados. No entanto, constatou-se que o modelo fully connected apresentou resultados próximos com menor custo, tanto no uso de memória, quanto no tempo de treinamento.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"10 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":"125563243","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
Detecting Misinformation in Tweets Related to COVID-19 检测与COVID-19相关的推文中的错误信息
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18260
Ramon Souza da Cruz, Gilberto Nunes Neto, Rafael Torres Anchiêta
{"title":"Detecting Misinformation in Tweets Related to COVID-19","authors":"Ramon Souza da Cruz, Gilberto Nunes Neto, Rafael Torres Anchiêta","doi":"10.5753/eniac.2021.18260","DOIUrl":"https://doi.org/10.5753/eniac.2021.18260","url":null,"abstract":"A propagação de desinformação trouxe e ainda traz diversos problemas para a sociedade, sendo considerada uma infodemia pela Organização Mundial da Saúde (OMS). A grande maioria dos trabalhos desenvolvidos para lidar com desinformação são focados para a língua inglesa. A fim de preencher essa lacuna, este trabalho investiga estratégias baseadas em aprendizado de máquina supervisionado para detectar desinformação em tweets escritos na língua portuguesa. Além disso, criou-se um corpus que foi manualmente anotado para esta tarefa, a fim de avaliar as abordagens desenvolvidas e compará-las com trabalhos relacionados. Os resultados alcançados são competitivos com trabalhos correlatos, indicando que a abordagem produz um interessante baseline para o corpus construído.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"134 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":"124179577","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
Offline Handwritten Signature Authentication with Conditional Deep Convolutional Generative Adversarial Networks 基于条件深度卷积生成对抗网络的离线手写签名认证
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18277
David C. Yonekura, Elloá B. Guedes
{"title":"Offline Handwritten Signature Authentication with Conditional Deep Convolutional Generative Adversarial Networks","authors":"David C. Yonekura, Elloá B. Guedes","doi":"10.5753/eniac.2021.18277","DOIUrl":"https://doi.org/10.5753/eniac.2021.18277","url":null,"abstract":"Handwritten signature authentication systems are important in many real world scenarios to avoid frauds. Thanks to Deep Learning, state-of-art solutions have been proposed to this problem by making use of Convolutional Neural Networks, but other models in this Machine Learning subarea are still to be further explored. In this perspective, the present article introduces a Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) approach whose experimental results in a realistic dataset with skilled forgeries have Equal Error Rate (EER) of 18.53% and balanced accuracy of 87.91%. These results validate a writerdependent cDCGAN-based solution to the signature authentication problem in a real world scenario where no forgeries are available nor required in training time.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"93 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":"126379768","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
Universal Approximation Theorem for Tessarine-Valued Neural Networks tessarine值神经网络的普遍逼近定理
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18256
R. Carniello, Wington L. Vital, M. E. Valle
{"title":"Universal Approximation Theorem for Tessarine-Valued Neural Networks","authors":"R. Carniello, Wington L. Vital, M. E. Valle","doi":"10.5753/eniac.2021.18256","DOIUrl":"https://doi.org/10.5753/eniac.2021.18256","url":null,"abstract":"The universal approximation theorem ensures that any continuous real-valued function defined on a compact subset can be approximated with arbitrary precision by a single hidden layer neural network. In this paper, we show that the universal approximation theorem also holds for tessarine-valued neural networks. Precisely, any continuous tessarine-valued function can be approximated with arbitrary precision by a single hidden layer tessarine-valued neural network with split activation functions in the hidden layer. A simple numerical example, confirming the theoretical result and revealing the superior performance of a tessarine-valued neural network over a real-valued model for interpolating a vector-valued function, is presented in the paper.","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":"127614014","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
Data Cleansing of Multiple Environmental Monitoring Time Series Using Spatio-Temporal Correlation 基于时空相关的多环境监测时间序列数据清理
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18253
Ranier A. A. Moura, Domingos B. S. Santos, Daniel G. M. Lira, J. E. Maia
{"title":"Data Cleansing of Multiple Environmental Monitoring Time Series Using Spatio-Temporal Correlation","authors":"Ranier A. A. Moura, Domingos B. S. Santos, Daniel G. M. Lira, J. E. Maia","doi":"10.5753/eniac.2021.18253","DOIUrl":"https://doi.org/10.5753/eniac.2021.18253","url":null,"abstract":"Aplicações computacionais baseadas em dados de sensores são uma realidade, mas os dados coletados e transmitidos para as aplicações raramente chegam prontos para o uso devido a perdas e ruídos de vários tipos. Neste trabalho desenvolve-se uma abordagem baseada em correlação espaço temporal para limpeza de dados de múltiplas séries temporais de sensores quanto à ruído, dados ausentes e outliers. O método foi testato em seis conjuntos de dados reais publicamente disponíveis e o seu desempenho foi comparado com um método baseline, com um autoencoder denoising e com outro método publicado. Os resultados mostram que a abordagem proposta é competitiva e requer menos dados de treinamento do que os concorrentes.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"25 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":"132601816","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
Deep Learning and Mel-spectrograms for Physica Violence Detection in Audio 基于深度学习和mel -谱图的音频物理暴力检测
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18259
Tiago Lacerda, Pericles Miranda, André Câmara, Ana Paula C. Furtado
{"title":"Deep Learning and Mel-spectrograms for Physica Violence Detection in Audio","authors":"Tiago Lacerda, Pericles Miranda, André Câmara, Ana Paula C. Furtado","doi":"10.5753/eniac.2021.18259","DOIUrl":"https://doi.org/10.5753/eniac.2021.18259","url":null,"abstract":"Há um crescente interesse em sistemas de detecção de violência de forma automática por meio do áudio ambiente. Neste trabalho, construímos e avaliamos 4 classificadores com essa proposta. Porém, em vez de processar diretamente os sinais de áudio, nós os convertemos para imagens, conhecidas como mel-spectrograms, e em seguida utilizamos Redes Neurais Convolucionais (CNN) para tratar como um problema de classificação de imagens utilizando-se de redes pre-treinadas neste contexto. Testou-se as arquiteturas Inception v3, VGG-16, MobileNet v2 e ResNet152 v2, tendo o classificador oriundo da arquitetura MobileNet obtido os melhores resultados de classificação, quando avaliado no HEAR Dataset, criado para a realização desta pesquisa.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"266 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":"124325350","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
Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction 海洋平台偏移量预测的神经结构搜索方法评价
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18264
T. M. Suller, Eric O. Gomes, H. B. Oliveira, L. P. Cotrim, A. M. Sa'ad, Ismael H. F. Santos, Rodrigo A. Barreira, E. Tannuri, E. Gomi, A. H. R. Costa
{"title":"Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction","authors":"T. M. Suller, Eric O. Gomes, H. B. Oliveira, L. P. Cotrim, A. M. Sa'ad, Ismael H. F. Santos, Rodrigo A. Barreira, E. Tannuri, E. Gomi, A. H. R. Costa","doi":"10.5753/eniac.2021.18264","DOIUrl":"https://doi.org/10.5753/eniac.2021.18264","url":null,"abstract":"This paper proposes a solution based on Multi-Layer Perceptron (MLP) to predict the offset of the center of gravity of an offshore platform. It also performs a comparative study with three optimization algorithms – Random Search, Simulated Annealing, and Bayesian Optimization (BO) – to find the best MLP architecture. Although BO obtained the best architecture in the shortest time, ablation studies developed in this paper with hyperparameters of the optimization process showed that the result is sensitive to them and deserves attention in the Neural Architecture Search process.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"168 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":"116773592","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
Zeroth Order Policy Search Methods for Global Optimization Problems: An Experimental Study 全局优化问题的零阶策略搜索方法:实验研究
Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18254
Moésio W. da Silva Filho, Gabriel A. Barbosa, P. Miranda, André C. A. Nascimento, R. F. Mello
{"title":"Zeroth Order Policy Search Methods for Global Optimization Problems: An Experimental Study","authors":"Moésio W. da Silva Filho, Gabriel A. Barbosa, P. Miranda, André C. A. Nascimento, R. F. Mello","doi":"10.5753/eniac.2021.18254","DOIUrl":"https://doi.org/10.5753/eniac.2021.18254","url":null,"abstract":"Os métodos Policy Search (PS) vem sendo utilizados nos últimos anos para se aprender, automaticamente, algoritmos de otimização, obtendo resultados animadores. Neste trabalho, consideramos métodos PS para aprender algoritmos de otimização para problemas de otimização global, considerando um cenário pouco estudado: funções de alta dimensionalidade e os algoritmos de otimização não possuem acesso às derivadas da função a ser otimizada. Os resultados apontam, que apesar das dificuldades, os algoritmos de otimização aprendidos têm um desempenho promissor no cenário estudado.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"50 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":"122263695","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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