Brazil

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&#39; 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.

BRACIS 2021

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

quad-v framework

social evaluations

trust model

task delegation

**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

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.

technical paper

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.

Deep Learning and Mel-spectrograms for Physical Violence Detection in Audio

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.

Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction

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.

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

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