Brazil

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.


BRACIS 2021

Named entity recognition in Portuguese legal documents using deep learning

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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The program also features 6 Tutorials and 26 Workshops.
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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**.

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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

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. Similarly, Arena et al. proved in 1996 that any continuous quaternion-valued function on a compact can be approximated with arbitrary precision by a single hidden layer quaternion-valued neural network whose hidden units are equipped with the so-called split activation function. Quaternions are four-dimensional hypercomplex numbers widely used to describe spatial rotations. Like quaternions, tessarines are four-dimensional hypercomplex numbers. However, in contrast quaternion algebra, the product of tessarines is commutative. The commutativity of tessarine's product may be advantageous for the design of neural networks because synaptic weights and neuron inputs can interchange their roles. In this paper, we show that the universal approximation theorem also holds for tessarine-valued neural networks. Precisely, we show that that 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.

Universal Approximation Theorem for Tessarine-Valued Neural Networks

Many image processing and analysis tasks are performed with deep neural networks. Although the vast majority of advances have been made with real numbers, recent works have shown that complex and hypercomplex-valued networks may achieve better results. In this paper, we address quaternion-valued and introduce tessarine-valued deep neural networks, including tessarine-valued 2D convolutions. We also address initialization schemes and hypercomplex batch normalization. Finally, a tessarine-valued ResNet model with hypercomplex batch normalization outperformed the corresponding real and quaternion-valued networks on the CIFAR dataset.

Tessarine and Quaternion-Valued Deep Neural Networks for Image Classification

Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algorithm in synthetic and real-world event logs. Our analyses suggest the embeddings-based vector space model can properly handle trace clustering in unstructured processes.

Vector space models for trace clustering: a comparative study

Morphological neural networks represent a class of artificial neural networks whose neurons perform an operation from mathematical morphology followed by the application of an activation function. This paper provides a comparative study of different approaches that use morphological neural networks. Specifically, according to the training rule, we review incremental approaches, approaches based on maximum descent methods, extreme learning machines, and convex-concave optimization procedures. Computational experiments showed that, on average, the reduced dilation-erosion perceptron with bagging and ensemble strategies had better results in several binary classification problems.

A Comparative Study on Morphological Neural Networks for Binary Classification

O objetivo desse trabalho é analisar conjuntos de variáveis que podem ter mais influência na previsão do resultado de uma partida de futebol, utilizando técnicas como cálculos de probabilidade e algoritmos de previsão, com a intenção de obter lucros em apostas. As técnicas utilizadas para o desenvolvimento foram redes bayesianas, com o algoritmo Naïve Bayes, e a de probabilidade, baseada no cálculo de Poisson. Os dados utilizados para treinamento foram do Campeonato Brasileiro, entre 2010 a 2017, sendo considerados os dados dos anos de 2018 e 2019 para testes. Os principais resultados atingidos foram de 53% de acerto do resultado de uma partida e as principais variáveis envolvidas foram força de ataque e força de defesa.

Análise de Variáveis em Partidas de Futebol: Previsão de Resultados com Naïve Bayes e Poisson

The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. 
The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. 
This work aims to assess which countries are in a similar situation regarding sustainable development. 
Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.

A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning

Companies created for money-laundering or as a means for tax-evasion are harmful to the country’s economy and society. This problem isusually tackled by governmental agencies by having officials to pore over com-panies’ financial data and to single out those that exhibit fraudulent behavior. Such work tends to be slow-paced and tedious. This paper proposes a machinelearning-based system capable of classifying whether a company is likely to beinvolved in fraud or not. Based on financial and tax data from various compa-nies, four different classifiers –k-Nearest Neighbors, Random Forest, SupportVector Machine (SVM), and a Neural Network – were trained and then used toindicate fraud. The best-performing model achieved a macro-averaged F1-scoreof 92.98% with the Random Forest.

A Machine Learning-based System for Financial Fraud Detection

DevOps practices have been increasingly used by software engineering teams in order to improve development stages. In processes that involve machine learning (ML), DevOps can also be applied to deploy machine learning models into production - a practice also known as MLOps. This systematic mapping aims to understand how DevOps has been applied to machine learning processes and what challenges are faced. Most of the 15 articles selected make use of CI/CD practices and propose architectures for the implementation of ML models. The biggest challenges are the characteristics inherent to the ML models and resistance to change.

Applying DevOps to Machine Learning Processes: A Systematic Mapping

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.

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Named entity recognition in Portuguese legal documents using deep learning

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