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29 results on '"Adriano L"'

1. Detecting Malicious HTTP Requests Without Log Parser Using RequestBERT-BiLSTM

Author

Ramos Júnior, Levi S., Macêdo, David, Oliveira, Adriano L. I., Zanchettin, Cleber, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Xavier-Junior, João Carlos, editor, and Rios, Ricardo Araújo, editor

Published
2022
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2. Ulysses-RFSQ: A Novel Method to Improve Legal Information Retrieval Based on Relevance Feedback

Author

Vitório, Douglas, Souza, Ellen, Martins, Lucas, da Silva, Nádia F. F., de Leon Ferreira de Carvalho, André Carlos Ponce, Oliveira, Adriano L. I., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Xavier-Junior, João Carlos, editor, and Rios, Ricardo Araújo, editor

Published
2022
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3. Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-Generated Text

Author

Costa, Rosimeire, Albuquerque, Hidelberg Oliveira, Silvestre, Gabriel, Silva, Nádia Félix F., Souza, Ellen, Vitório, Douglas, Nunes, Augusto, Siqueira, Felipe, Pedro Tarrega, João, Vitor Beinotti, João, de Souza Dias, Márcio, Pereira, Fabíola S. F., Silva, Matheus, Gardini, Miguel, Silva, Vinicius, de Carvalho, André C. P. L. F., Oliveira, Adriano L. I., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Marreiros, Goreti, editor, Martins, Bruno, editor, Paiva, Ana, editor, Ribeiro, Bernardete, editor, and Sardinha, Alberto, editor

Published
2022
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4. UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition

Author

Albuquerque, Hidelberg O., Costa, Rosimeire, Silvestre, Gabriel, Souza, Ellen, da Silva, Nádia F. F., Vitório, Douglas, Moriyama, Gyovana, Martins, Lucas, Soezima, Luiza, Nunes, Augusto, Siqueira, Felipe, Tarrega, João P., Beinotti, Joao V., Dias, Marcio, Silva, Matheus, Gardini, Miguel, Silva, Vinicius, de Carvalho, André C. P. L. F., Oliveira, Adriano L. I., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pinheiro, Vládia, editor, Gamallo, Pablo, editor, Amaro, Raquel, editor, Scarton, Carolina, editor, Batista, Fernando, editor, Silva, Diego, editor, Magro, Catarina, editor, and Pinto, Hugo, editor

Published
2022
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7. Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-Generated Text

Author

Costa, Rosimeire, primary, Albuquerque, Hidelberg Oliveira, additional, Silvestre, Gabriel, additional, Silva, Nádia Félix F., additional, Souza, Ellen, additional, Vitório, Douglas, additional, Nunes, Augusto, additional, Siqueira, Felipe, additional, Pedro Tarrega, João, additional, Vitor Beinotti, João, additional, de Souza Dias, Márcio, additional, Pereira, Fabíola S. F., additional, Silva, Matheus, additional, Gardini, Miguel, additional, Silva, Vinicius, additional, de Carvalho, André C. P. L. F., additional, and Oliveira, Adriano L. I., additional

Published
2022
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8. Evaluating Active Learning Sampling Strategies for Opinion Mining in Brazilian Politics Corpora

Author

Vitório, Douglas, Souza, Ellen, Oliveira, Adriano L. I., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moura Oliveira, Paulo, editor, Novais, Paulo, editor, and Reis, Luís Paulo, editor

Published
2019
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9. Characterization of Handwritten Signature Images in Dissimilarity Representation Space

Author

Souza, Victor L. F., Oliveira, Adriano L. I., Cruz, Rafael M. O., Sabourin, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rodrigues, João M. F., editor, Cardoso, Pedro J. S., editor, Monteiro, Jânio, editor, Lam, Roberto, editor, Krzhizhanovskaya, Valeria V., editor, Lees, Michael H., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2019
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10. The Impact of Dataset Complexity on Transfer Learning over Convolutional Neural Networks

Author

Wanderley, Miguel D. de S., Bueno, Leonardo de A. e, Zanchettin, Cleber, Oliveira, Adriano L. I., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lintas, Alessandra, editor, Rovetta, Stefano, editor, Verschure, Paul F.M.J., editor, and Villa, Alessandro E.P., editor

Published
2017
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13. Characterizing Opinion Mining: A Systematic Mapping Study of the Portuguese Language

Author

Souza, Ellen, Vitório, Douglas, Castro, Dayvid, Oliveira, Adriano L. I., Gusmão, Cristine, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Silva, João, editor, Ribeiro, Ricardo, editor, Quaresma, Paulo, editor, Adami, André, editor, and Branco, António, editor

Published
2016
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14. TOPIE: An Open-Source Opinion Mining Pipeline to Analyze Consumers’ Sentiment in Brazilian Portuguese

Author

Souza, Ellen, Alves, Tiago, Teles, Ingryd, Oliveira, Adriano L. I., Gusmão, Cristine, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Silva, João, editor, Ribeiro, Ricardo, editor, Quaresma, Paulo, editor, Adami, André, editor, and Branco, António, editor

Published
2016
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19. Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-Generated Text

Author

Rosimeire Costa, Hidelberg Oliveira Albuquerque, Gabriel Silvestre, Nádia Félix F. Silva, Ellen Souza, Douglas Vitório, Augusto Nunes, Felipe Siqueira, João Pedro Tarrega, João Vitor Beinotti, Márcio de Souza Dias, Fabíola S. F. Pereira, Matheus Silva, Miguel Gardini, Vinicius Silva, André C. P. L. F. de Carvalho, and Adriano L. I. Oliveira

Published
2022

20. UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition

Author

Hidelberg O. Albuquerque, Rosimeire Costa, Gabriel Silvestre, Ellen Souza, Nádia F. F. da Silva, Douglas Vitório, Gyovana Moriyama, Lucas Martins, Luiza Soezima, Augusto Nunes, Felipe Siqueira, João P. Tarrega, Joao V. Beinotti, Marcio Dias, Matheus Silva, Miguel Gardini, Vinicius Silva, André C. P. L. F. de Carvalho, and Adriano L. I. Oliveira

Published
2022

24. Evaluating Active Learning Sampling Strategies for Opinion Mining in Brazilian Politics Corpora

Author

Ellen Souza, Adriano L. I. Oliveira, and Douglas Vitório

Subjects
Change over time, Computer science, Sentiment analysis, 020207 software engineering, 02 engineering and technology, Data science, language.human_language, Continuous data, Politics, 0202 electrical engineering, electronic engineering, information engineering, language, Entropy (information theory), 020201 artificial intelligence & image processing, Portuguese, Stream data
Abstract

Politics is a commonly used domain in Opinion Mining applications, in which opinions may change over time. Nevertheless, the usual approaches for Opinion Mining are not able to deal with the characteristics and the challenges brought by continuous data streams; so, an alternative is the use of techniques such as Active Learning, which labels selected data rather than the entire data set. The Active Learning approach requires the choice of a sampling strategy to select the most valuable instances. However, no study has performed an analysis in order to identify the best strategies for Opinion Mining. In this sense, we evaluated eight Active Learning sampling strategies, from which Entropy achieved the best results. In addition, due to the lack of publicly available stream data sets written in Portuguese, we created and evaluated corpora from Twitter and Facebook about the 2018 Brazilian presidential elections.

Published
2019

25. The Impact of Dataset Complexity on Transfer Learning over Convolutional Neural Networks

Author

Miguel Wanderley, Adriano L. I. Oliveira, Cleber Zanchettin, and Leonardo de A. e Bueno

Subjects
Computer science, business.industry, Deep learning, 020207 software engineering, Pattern recognition, 02 engineering and technology, Machine learning, computer.software_genre, Convolutional neural network, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Transfer of learning, business, computer, Classifier (UML)
Abstract

This paper makes use of diverse domains’ datasets to analyze the impact of image complexity and diversity on the task of transfer learning in deep neural networks. As the availability of labels and quality instances for several domains are still scarce, it is imperative to use the knowledge acquired from similar problems to improve classifier performance by transferring the learned parameters. We performed a statistical analysis through several experiments in which the convolutional neural networks (LeNet-5, AlexNet, VGG-11 and VGG-16) were trained and transferred to different target tasks layer by layer. We show that when working with complex low-quality images and small datasets, fine-tuning the transferred features learned from a low complexity source dataset gives the best results.

Published
2017

26. Characterizing Opinion Mining: A Systematic Mapping Study of the Portuguese Language

Author

Cristine Gusmão, Ellen Souza, Adriano L. I. Oliveira, Douglas Vitório, and Dayvid Castro

Subjects
Computer science, business.industry, Sentiment analysis, 020207 software engineering, 02 engineering and technology, computer.software_genre, language.human_language, Plot (graphics), Task (project management), Naive Bayes classifier, Brazilian Portuguese, 0202 electrical engineering, electronic engineering, information engineering, language, 020201 artificial intelligence & image processing, Social media, The Internet, Artificial intelligence, Portuguese, business, computer, Natural language processing
Abstract

The growth of social media and user-generated content (UGC) on the Internet provides a huge quantity of information that allows discovering the experiences, opinions, and feelings of users or customers. Opinion Mining (OM) is a sub-field of text mining in which the main task is to extract opinions from UGC. Given that Portuguese is one of the most common spoken languages in the world, and it is also the second most frequent on Twitter, the goal of this work is to plot the landscape of current studies that relates the application of OM for Portuguese. A systematic mapping review (SMR) method was applied to search, select and to extract data from the included studies. Manual and automated searches retrieved 6075 studies up to year 2014, from which 25 articles were included. Almost 70 % of all approaches focus on the Brazilian Portuguese variant. Naive Bayes and Support Vector Machine were the main classifiers and SentiLex-PT was the most used lexical resource. Portugal and Brazil are the main contributors in processing the Portuguese language.

Published
2016

27. Characterizing User-Generated Text Content Mining: A Systematic Mapping Study of the Portuguese Language

Author

Adriano L. I. Oliveira, Ellen Souza, Cristine Gusmão, Douglas Vitório, Dayvid Castro, and Ingryd Teles

Subjects
Computer science, business.industry, Sentiment analysis, 020207 software engineering, Unstructured data, 02 engineering and technology, computer.software_genre, Enterprise data management, language.human_language, Plot (graphics), Naive Bayes classifier, Text mining, 0202 electrical engineering, electronic engineering, information engineering, language, 020201 artificial intelligence & image processing, Artificial intelligence, Portuguese, business, computer, Classifier (UML), Natural language processing
Abstract

Unstructured data accounts for more than 80 % of enterprise data and is growing at an annual exponential rate of 60 %. Text mining refers to the process of discovering new, previously unknown and potentially useful information from a variety of unstructured data including user-generated text content (UGTC). Given that Portuguese language is one of the most common languages in the world, and it is also the second most frequent language on Twitter, the goal of this work is to plot the landscape of current studies that relates the application of text mining to UGTC in the Portuguese language. The systematic mapping review method was applied to search, select, and to extract data from the included studies. Our manual and automated searches retrieved 6075 studies up to year 2014, from which 35 were included in the study. Text classification concentrates 79 % of all text mining tasks, having the Naive Bayes as the main classifier and Twitter as the main data source.

Published
2016

28. Smoke Detection in Environmental Regions by Means of Computer Vision

Author

Hani Camille Yehia, Carlos Alexandre Meireles Do Nascimento, Adriano L. Santos, Douglas A. G. Vieira, and Adriano C. Lisboa

Subjects
Smoke, Background subtraction, genetic structures, Pixel, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020101 civil engineering, Feature selection, 02 engineering and technology, 0201 civil engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

This paper presents a novel method for smoke detection in videos based on three main steps: background removal, color classification, and temporal/spatial persistence. First, the regions of interest are determined by detecting objects in movement through background subtraction. After that, the moving objects are classified according to their color. Only objects with smoke colors are analyzed by the last stage, which carries out analysis of space movement, verifying the temporal persistence of pixels of interest in the video frames. Pixels that satisfy the rules defined in the three stages are considered pixels of smoke. The system considered in this work is compared with other existing systems, through tests with a database of videos that contains smoke images and videos that do not contain smoke images, but contain objects that look like smoke.

Published
2016

29. TOPIE: An Open-Source Opinion Mining Pipeline to Analyze Consumers’ Sentiment in Brazilian Portuguese

Author

Ellen Souza, Adriano L. I. Oliveira, Tiago Alves, Cristine Gusmão, and Ingryd Teles

Subjects
Point (typography), business.industry, Computer science, Sentiment analysis, Supervised learning, 02 engineering and technology, Pipeline (software), Task (project management), World Wide Web, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, The Internet, Social media, business, Tertiary sector of the economy
Abstract

The growth of social media and user-generated content (UGC) on the Internet provides a huge quantity of information that allows discovering the experiences, opinions, and feelings of users or customers. These electronic Word of Mouth statements expressed on the web are prevalent in business and service industry to enable a customer to share his/her point of view. However, it is impossible for humans to fully understand it in a reasonable amount of time. Opinion mining (also known as Sentiment Analysis) is a sub-field of text mining in which the main task is to extract opinions from UGC. Thus, this work presents an open source pipeline to analyze the costumer’s opinion or sentiment in Twitter about products and services offered by Brazilian companies. The pipeline is based on General Architecture for Text Engineering (GATE) framework and the proposed hybrid method combines lexicon-based, supervised learning, and rule-based approaches. Case studies performed on Twitter real data achieved precision of almost 70 %.

Published
2016

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