Brazil

Chess is becoming more popular and accessible by the day. For instance, online chess enables matches between players from different parts of the world, bringing new ways of learning the game and interacting with other Web users. With this growth in popularity, there is a possibility to empower amateur players with rich computer analysis and tools, which may assist them in their learning process. One of the ways to analyze chess matches is through the study of errors. In this context, we present a new approach for the task of error prediction in chess. Our motivation is that knowing when players are likely to make a mistake is knowing what types of situations lead to difficulties in making the right decision. To that end, we add an abstraction layer to the already studied error prediction task, providing graph-based features to the machine learning models. Our results show an increase in the accuracy of the tested models, improving the results obtained in recent studies.

BRACIS 2021

A lightweight approach for predicting errors in chess matches

graph-oriented machine learning

data science

data mining

machine learning

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.

BRACIS 2020

technical paper

**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. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

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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. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

Collaborative Filtering (CF) can be understood as the process of predicting the preferences of users and deriving useful patterns by studying their activities. In the survey context, it can be used to predict answers to questions as combinations of other available answers. In this paper, we aim to test five CF-based algorithms (item-item, iterative matrix factorization, neural collaborative filtering, logistic matrix factorization, and an ensemble of them) to estimate scores in four survey applications (checkpoints) composed of 700,000 employee’s ratings. These data have been collected from 2019 to 2020 by a large Brazilian tech company with more than 10,000 employees. The results show that collaborative filtering approaches provide relevant alternatives to score questions of surveys. They provided good quality estimates. This result can be further explored to eventually reduce the size of questionnaires, avoiding burden phenomena faced by respondents when dealing with large surveys.

Comparative Analysis of Collaborative Filtering-Based Predictors of Scores in Surveys of a Large Company

In Brazil, several minority languages suffer a serious risk of extinction. The appropriate documentation of such languages is a fundamental step to avoid that. However, for some of those languages, only a small amount of text corpora is digitally accessible.
Meanwhile, there are many issues related to the identification of indigenous languages, which may help to identify key similarities among them, as well as to connect related languages and dialects.
Therefore, this paper proposes to study and automatically classify 26
neglected Brazilian native languages, considering a small amount of training data, under a supervised and unsupervised setting. Our findings indicate that the use of machine learning models in the analysis of Brazilian Indigenous corpora is very promising, and we hope this work encourage more research on this topic in the next years.

Analysis of a Brazilian Indigenous corpus using machine learning methods

Over the past few years, information technology has been transforming the legal world, automating processes and, consequently, reducing the time
needed to create and analyse digital legal documents. One of the most studied
problems in this area is the recognition of named entities (NER) in unstructured
texts. Previous work has not addressed the detection of legal entities through the
application of models based on neural networks available in natural language
processing libraries. In this article, the use of the libraries Spacy and FLAIR
were analyzed in the context of NER in initial petitions. The models were trained with pre-defined architectures and evaluated in two corpora, one of them
developed in the scope of this work. The results obtained with these experiments
demonstrated good results with both platforms Spacy and FLAIR, with superior
performance when adopting BiLSTM-CRF with FLAIR embeddings.


Named entity recognition in Portuguese legal documents using deep learning

The initial petition is one of the most important components of a civil litigation process. Automating the analysis of these documents might reduce the time necessary for the postulatory phase's conclusion. The parties qualification body is the section in which are exposed the informations about the entities involved in the process. This paper suggests the employment of named entity extraction techniques on the problem of information extraction and recognition on initial petitions. With this in mind, was created a part description body corpora extracted from Brazilian courts. Seven BiLSTM-CRF models with distinct combinations of vector representations of words were trained, evaluated, and compared to investigate their effects on the performance of an algorithm with that architecture and, in this way, improve the recognition of legal entities in legal texts. Unlike other works based on BiLSTM-CRF for NER tasks in the legal domain, this research emphasizes not the architectures employed, but rather the text representation methods used. The experiments performed with the developed corpus show that the stacking of character, word, and pooled FLAIR embeddings is the preferred combination to extract the best possible performance from BiLSTM-CRF hybrid models.

A comparative analysis of text embedding approach to extract named entities in Portuguese legal documents

Textual information, despite being digital, are not computationally structured, requiring the use of techniques to structure and extract information. This work aims to evaluate machine learning NER (Named entity recognition) tools for the Brazilian and European Portuguese language variants. The Apache OpenNLP, Stanford CoreNLP and spaCy tools were selected for the evaluation. The HAREM corpus was used to train and evaluate the generated machine learning model; a tool was developed to preprocess the HAREM corpus. Two comparisons were performed: a general comparison and between the Portuguese variants. It was found that variants can confuse the training and evaluation process of NER models.

A Comparative Analysis of Machine Learning Named Entity Recognition Tools for the Brazilian and European Portuguese Language Variants

Automatically dealing with Natural Language User-Generated Con-tent (UGC) is a challenging task of utmost importance, given the amount of information available over the web. We present in this paper an effort on building tokenization and Part of Speech (PoS) tagging systems for tweets in Brazilian Portuguese, following the guidelines of the Universal Dependencies (UD)project. We propose a rule-based tokenizer and the customization of current state-of-the-art UD-based tagging strategies for Portuguese, achieving a 98%f-score for tokenization, and a 95% f-score for PoS tagging. We also introduceDANTEStocks, the corpus of stock market tweets on which we base our work, presenting preliminary evidence of the multi-genre capacity of our PoS tagger.

Universal Dependencies for Tweets in Brazilian Portuguese: Tokenization and Part of Speech Tagging

Species Distribution Modelling (SDM) is used for biodiversity preservation and wildlife management. In order to apply it, a large and reliable
dataset about the species occurrence is required. However, in some cases, this
can be difficult when there are only a few occurrence records. In this context,
uncertainty handling techniques can be applied. Thus, Bayesian inference was
used in this study to perform SDM in the Amazon Basin region near Manaus
(AM) with data collected by the GoAmazon 2014/15 project. The results were
compared with those obtained in statistical models and were similar.

Handling uncertainty through Bayesian inference for Species Distribution Modelling in the Amazon Basin region

Computational applications based on sensor data is a reality, but the
data collected and transmitted to the applications rarely arrives ready for use
due to losses and noise of various types. In this work, an approach based on spatial temporal correlation is developed for cleaning data from multiple temporal
series of sensors for noise, missing data and outliers. The method was tested
on six publicly available real datasets and its performance was compared with
a baseline method, an autoencoder denoising, and another published method.
The results show that the proposed approach is competitive and requires less
training data than competitors.

Data Cleansing of Multiple Environmental Monitoring Time Series Using Spatio-Temporal Correlation

Time series have great applicability in the most diverse scenarios, including the scientific, agricultural, economic domains, among others.
Therefore, creating effective representations of a time series is a challenging task, as it allows more accurate analysis and, consequently, more assertive results and conclusions in various machine learning tasks.
One of the main tasks is classification, which can be performed from different computational representations of time series.
The main objective of this work is to improve the efficiency of classification tasks using a representation of time series obtained by the Beam Angle Statistics algorithm, a contour feature extractor based on angular statistics.

Time Series Classification using Shape Features based on Angle Statistics

The use of Artificial Intelligence and Machine Learning algorithms in everyday life is common nowadays in several areas, bringing many possibilities and benefits to society. However, since there is room for learning algorithms to make decisions, the range of related ethical issues was also expanded. There are many complaints about Machine Learning applications that identify some kind of bias, disadvantaging or favoring some group, with the possibility of causing harm to a real person. The present work aims to shed light on the existence of biases, analyzing and comparing the behavior of different learning algorithms – namely Decision Tree, MLP, Naive Bayes, Random Forest, Logistic Regression and SVM – when being trained using biased data. We employed pre-processing algorithms for mitigating bias provided by IBM’s framework AI Fairness 360.

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