Brazil

Summarized the main findings of the paper Face Reconstruction with Variational Autoencoder and Face Masks, answering key questions of how the set up of Face Masks and combined loss functions between L1, L2, and SSIM affects the image reconstruction quality.

BRACIS 2021

Face Reconstruction with Variational Autoencoder and Face Masks

ssim

image reconstruction

variational autoencoders

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

Training deep neural networks is a relevant problem with open questions related to convergence and quality of learned representations. Gradient-based optimization methods are used in practice, but cases of failure and success are still to be investigated. In this context, we set out to better understand the convergence properties of different optimization strategies, under different parameter options. Our results show that (i) feature embeddings are impacted by different optimization settings, (ii) suboptimal results are achieved by the use of default parameters, (iii) significant improvement is obtained by making educated choices of parameters, (iv) learning rate decay should always be considered. Such findings offer guidelines for training and deployment of deep networks.

Optimization Matters: Guidelines to Improve Representation Learning with Deep Networks

Automatic gender recognition from speech is a problem related to the
area of speech analysis and has a variety of applications thats extends from the
personalisation of product recommendation to forensics. Identifying the efficiency and costs of different approaches that deal with this problem is imperative. This work aims to investigate and compare the efficiency and costs of different deep learning architectures in the task of gender recognition from speech.

A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals

Algorithms for learning optimal paths are present in several scenarios. Given this, LRTA* (learning real time A*) appears as an option for reconciling planning and action. This article investigate how the variation of the number of agents impacts on the distances traveled by them to find optimal path using the LRTA* in static environments. Experiments show the existence of a relationship that the grater number of agents, the total quantity and per capita movements behave like mathematical curves, the first one like a linear and the second as a decreasing exponential. Through the relationship, it is possible to define the best number of agents in the search for the optimal path in terms of performance.

Impact of the variation of the number of agents in the cooperative learning of optimal paths using LRTA*

Electronic Legal Proceedings are worldwide legal phenomena, allowing
the use of computerized systems for the creation and monitoring of procedural
acts in the most diverse legal bodies. On one hand, it allows greater
transparency in the conduct of procedural acts, on the other, it has contributed
to the bottleneck of open but unresolved lawsuits each year. Nowadays, Information
Retrieval to automate the processing of these procedural objects is at the
forefront of computer systems for Law. In this study, we present MISLA2, a system
to retrieve orders and preliminaries from judicial labor sentences through
ontological models built from previous cases. Instead of tied and difficult-to-maintain
domain specification models, we demonstrate how light ontologies, in
conjunction with regular expressions for extracting significant portions of the
text, can achieve the desired results. In addition, empirical experiments carried
out with real labor lawsuits evidence that results are quite promising.

MISLA2: A System to Information Retrieval in Labor Lawsuits using Legal Ontologies and Regular Expressions

In a multi-agent system (MAS), it is very usual that agents delegate tasks to each other. However, due to the subjectivity of the information used by agents during the decision-making process, an agent may end up delegating a task to an untrustworthy partner. In this work, we present a trust computing approach based on quantitative argumentation with votes (\hbox{QuAD-V}), where a trust measure is estimated according with the agents' opinions about the service provided by a partner. Besides, such an approach provides a mechanism to evaluate the credibility of agents that play as information sources. In our results, we demonstrate how our trust computing approach can be employed for detecting lying agents, which are able to slander or promote other agents.

Trust Computing Based on Argumentation Debates with Votes for Detecting Lying Agents

A taxa de perfuração (ROP) de um poço de petróleo é uma métrica muito importante de se controlar, por influenciar na produtividade do poço, no desgaste da broca e na segurança do poço e operação. Este trabalho avalia a utilização do algoritmo de floresta aleatória para previsão da ROP em poços de petroleo offshore como alternativa para modelos tradicionalmente utilizados pela indústria. Modelos de floresta aleatória foram avaliados com diferentes hiper-parametros para 4 poços offshore no pré-sal da Bacia de Santos. Não houve uma única combinação de hiper-parâmetros ótima comum entre os poços.

Previsão de Taxa de Perfuração em Poços de Petróleo Offshore Utilizando Algoritmos de Aprendizado de Máquina

The core point of the research process are data. They are records fromscientific investigation, which support the results published in journals and con-ferences. Making research data available in open access digital repositories hasmany advantages, such as increasing the visibility of associated publications,reproducing experiments, and validating results. In Brazil, full and unrestrictedsharing of them is not yet accepted by most researchers. This paper presents aninitial study to describe a model analyzing the attitude of Brazilian researchersconcerning open access research data. A clustering algorithm was used to iden-tify different research profiles. The achieved results indicate the main reasonswhy the researchers object to share their data

A clustering algorithm to evaluate the attitude of Brazilian researchers regarding open access research data

Este trabalho propõem uma abordagem para auxiliar pessoas com deficiência visual no reconhecimento de pessoas independente da idade. O objetivo é desenvolver um sistema que utilize uma abordagem de reconhecimento facial, com foco na invariância na idade, que retorne bons resultados comparados aos resultados obtidos na revisão da literatura. A abordagem estudada utiliza Redes Neurais Convolucionais profundas - CCNs, pré-treinadas pelo conjunto de dados VGGFace2, para extrair descritores de características de imagens de faces e classificar com o algoritmo de classificação Linear SVM. Como pode ser visto no decorrer do trabalho, a abordagem retornou 89,9% de acurácia, utilizando o conjunto de dados FG-NET, com 1002 imagens. E utilizando o conjunto de dados CACD, que contém 163.446 imagens divididas em quatro subconjuntos diferentes, três conjuntos para treino e um para teste, a abordagem retornou 85,2%, 82,4% e 88,2% de acurácia para cada modelo treinado com um conjunto de treinamento diferente.

Age-Invariant Face Recognition to Aid Visually Impaired Pleople

The employment of video surveillance cameras by public safety agencies enables incident detection in monitored cities by using object detection for scene description, enhancing the protection to the general public. Object detection has its drawbacks, such as false positives. Our work aims to enhance object detection and image classification by employing IoU (Intersection over Union) to minimize the false positives and identify weapon holders or fire in a frame, adding more information to the scene.

Detection of weapon possession and fire in Public Safety surveillance cameras

Handwritten signature authentication systems are important in many real world scenarios to avoid frauds. 
Thanks to Deep Learning, state-of-art solutions have been proposed to this problem by making use of Convolutional Neural Networks, but other models in this Machine Learning subarea are still to be furtherexplored. 
In this perspective, the present article introduces a Conditional Deep
Convolutional Generative Adversarial Networks (cDCGAN) approach whose experimental results in a realistic dataset with skilled forgeries have Equal Error Rate (EER) of 18.53 % and balanced accuracy of 87.91 %. 
These results validate a writer-dependent cDCGAN-based solution to the signature authentication problem in a real world scenario where no forgeries are available nor required in training time.

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