Brazil

I will present the Doclass, a free and open-source software for the Web that aims to assist in labeling and classifying large sets of documents. As a result, the first version of a system with an architecture composed of a mobile frontend that communicates with a backend through a REST API was published, with satisfactory performance evaluation by the applicant. Other results involve the use of active learning techniques to reduce human effort when performing the classification of documents, as well as the Uncertainty strategy to choose the document to be labeled.

BRACIS 2020

Doclass: open-source software to support document labelingand classification

legal text

document labeling

document classification

annotation tool

active learning

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

technical paper

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

One of the main challenges for cell segmentation is to separate overlapping cells, which is also a challenging task for cytologists. Here we propose a method that combines different algorithms for cervical cell segmentation of Pap smear images and searches for the best result underlying the maximization of a similarity coefficient. We carried out experiments with three state-of-the-art segmentation algorithms on images with clumps of cervical cells. We extracted features such as coefficient of variation and overlapping ratios for each cell grouping and selected the most appropriate algorithm to segment each cell clump. For decision criterion, we identified the cell clumps of the training dataset and calculated the mentioned features. We segmented each clump by the algorithms and reckoned the Dice measure from each segmentation. Finally, we used the kNN classifier to predict the best algorithm among neighboring k-clumps by choosing the one with the largest number of wins. We validated our proposal on multifocal cervical cell images and obtained an average Dice around 76.6% without using a threshold value. These results demonstrated that the proposed ensemble of segmentation algorithms is promising and suitable for cervical cell image segmentation.

Ensemble of algorithms for multifocal cervical cell image segmentation

Data augmentation is a widely adopted method for improving model performance in image classification tasks. Although it still not as ubiquitous in Natural Language Processing (NLP) community, some methods have already been proposed to increase the amount of training data using simple text transformations or text generation through language models. However, recent text classification tasks need to deal with domains characterized by a small amount of text and informal writing, e.g., Online Social Networks content, reducing the capabilities of current methods. Facing these challenges by taking advantage of the pre-trained language models, low computational resource consumption, and model compression, we proposed the PRE-trained Data AugmenTOR (PREDATOR) method. Our data augmentation method is composed of two modules: the Generator, which synthesizes new samples grounded on a lightweight model, and the Filter, that selects only the high-quality ones. The experiments comparing Bidirectional Encoder Representations from Transformer (BERT), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Multinomial Naive Bayes (NB) in three datasets exposed the effective improvement of accuracy. It was obtained 28.5% of accuracy improvement with LSTM on the best scenario and an average improvement of 8% across all scenarios. PREDATOR was able to augment real-world social media datasets and other domains, overcoming the recent text augmentation techniques.

Pre-trained Data Augmentation for Text Classification

My presentation is about my solution to this year's KDD competition. There I cover the models, the methods of feature selection, validation, parameters optimization and also my final ensemble.

Solution KDD BR Competition 2020

O vídeo descreve o desafio da competição KDD-BR 2020, que trata da predição de períodos de indisponibilidade de aluguel de carros. Ele foi elaborado pela equipe de Ciência de Dados da empresa Localiza, que foi a proponente do problema.

KDD-BR: apresentação do problema pela empresa Localiza

KDD-BR 2020 solution using 1-dimensional convolutions.

Car availability prediction in rental angencies [KDD-BR 2020]

As commodities milho e soja são produtos consumidos em grande escala no mundo. As flutuações nos preços de mercado têm efeitos de longo alcance sobre os consumidores, agricultores e processadores de grãos. Assim, a previsão dos preços desses grãos tem atraído significativa atenção dos pesquisadores. Os modelos de previsão geralmente usam dados quantitativos de série temporal. No entanto, fatores qualitativos externos podem influenciar os dados em séries temporais, como eventos políticos, crises econômicas e o mercado de câmbio estrangeiro. Essas informações não são explícitas nos dados da série temporal e esses fatores podem influenciar a previsão dos valores das variáveis. Os dados textuais extraídos de notícias, fóruns e redes sociais podem ser uma fonte de conhecimento sobre fatores externos e potencialmente úteis para modelos de previsão de séries temporais. Alguns estudos apresentam técnicas de mineração de texto para combinar dados textuais com séries temporais. Porém, as representações existentes apresentam algumas limitações, como a maldição da dimensionalidade e atributos ineficazes. Este trabalho aplica métodos de pré-processamento em séries temporais e usa representações combinadas com dados textuais para prever o preço futuro do milho e da soja. Os resultados indicam que os métodos utilizados podem ser uma alternativa para melhorar o desempenho de previsão em tarefas de regressão.

Doclass: open-source software to support document labelingand classification

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Ensemble of algorithms for multifocal cervical cell image segmentation

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DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool

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Doclass: open-source software to support document labelingand classification

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Next from BRACIS 2020

Ensemble of algorithms for multifocal cervical cell image segmentation

Similar lecture

DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads