Brazil

We propose a solution based on Multi-Layer Perceptron (MLP) to predict the offset of the center of gravity of an offshore platform. We also perform 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 with hyperparameters of the optimization process showed that the result is sensitive to them and deserves attention in the Neural Architecture Search process.

BRACIS 2021

Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction

floating oil production and offloading units

neural architecture search

deep neural networks

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

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

[Register now](http://c4ai.inova.usp.br/bracis2021/)

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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 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.

Performance analysis of machine learning algorithms trained on biased data

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.

A lightweight approach for predicting errors in chess matches

Policy Search (PS) methods have been used to learn optimization algorithms, obtaining encouraging results. In this work, we used PS methods to learn optimization algorithms for global optimization problems, considering a little-studied scenario: high-dimensional functions and the optimization algorithms do not have access to the derivatives of the function to be optimized. The results indicate that, despite the difficulties, the learned optimization algorithms have promising performance in the studied scenario.

Zeroth Order Policy Search Methods for Global Optimization Problems: An Experimental Study

There is a growing interest in systems that detect violence automati-
cally using ambient audio. In this work, we built and evaluated four classifiers
with this proposal. However, instead of directly processing the audio signals, we
converted them to images, known as Mel-spectrograms, and then used Convolu-
tional Neural Networks (CNN) to treat as an image classification problem using
pre-trained networks in this context. We tested the architectures Inception v3,
VGG-16, MobileNet v2, and ResNet152 v2, and the classifier coming from the
MobileNet architecture obtained the best classification results when evaluated
on the HEAR Dataset created to carry out this research.

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

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