Brazil

O objetivo da presente pesquisa consiste em verificar a eficácia das armas de fogo, fuzil e pistola, em eventos de tiroteios em massa em ambiente fechado e quais os parâmetros que influenciam no número de vítimas, a fim de apoiar a análise e elaboração de políticas públicas. Para isso, realizou-se a Simulação Multiagente através do software NetLogo. Os resultados mostram que o tipo de arma utilizada no evento tem grande influência no número de vítimas. Além disso, também observou-se que a diferença no número de vítimas das duas armas diminui à medida em que se aumenta a quantidade de munição e se diminui a dimensão da porta de saída, devido ao efeito funil.

BRACIS 2020

Multiagent Simulation to Support Disarmament Policies

políticas públicas

tiroteios em massa

netlogo

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.

technical paper

The increase in transmission rates required for new Internet-related services is driving further studies on network optimization. Optimization algorithms with local search have shown promise in an attempt to design networks that satisfy different objectives such as: cost, blocking probability, energy expenditure and robustness indicators. This work proposes a new strategy for selecting individuals for local search, aiming to improve Pareto's quality in terms of hypervolume, and also to obtain efficiency gains. The proposed strategy was compared with a base method, in a simulated scenario of a backbone network for the state of Pernambuco. According to the results, the proposed strategy was able to make the algorithm more effective, improving its performance in all objectives, and more efficient; surpassing the base method in both aspects studied.

A Novel Strategy for Selecting Individuals in Manyobjective Algorithms with Local Search Applied to the Network Design Problem

This work presents the development of an explainable image classifier,
trained for the task of determining whether a cell is infected by malaria. The
classifier consists of a residual neural network, with a classification accuracy
of 96%, trained on the National Institute of Health Malaria Dataset. Explainable
Artificial Intelligence (XAI) techniques are employed to make classifications
more interpretable. These explanations are generated by using two different
methodologies: Local Interpretable Model Agnostic Explanations (LIME) and
SquareGrid. The explanations provide novel, key insights into the decision making
patterns of such a high performing model for a medical task.

Explainable Classifications for Images of Cells Infected by Malaria

We propose an integrated framework, named Multi-Document Aspect-based Sentiment Extractive Summarization (MD-ASES for short), to automatically generate extractive review summaries based on aspects of a large database with reviews of items such as films, businesses, and companies. Such summaries are got by extracting a subset of sentences as they are in the reviews, based on some relevance criteria. In MD-ASES, initially sentences are grouped in terms of aspects identified as predominant in the reviews. Then, sentences are selected by the similarity of the sentiment expressed about a particular aspect to the overall sentiment of the dataset reviews.
Our results show that MD-ASES can successfully preserve the average sentiment of the reviews while including the most important aspects in the summary.

A Framework for Multi-Document Extractive Summarization of Reviews with Aspect-Based Sentiment Analysis

Reinforcement Learning (RL) is a well-known technique to train autonomous
agents through interactions with the environment. However, the learning process
has a high sample-complexity to infer an effective policy, especially when multiple agents are simultaneously actuating in the environment. We here propose to take advantage of previous knowledge, so as to accelerate learning in multiagent RL problems. Agents may reuse knowledge gathered from previously solved tasks, and they may also receive guidance from more experienced friendly agents to learn faster. However, specifying a framework to integrate knowledge reuse into the learning process requires answering challenging research questions, such as: How to abstract task solutions to reuse them later in similar yet different tasks? How to define when advice should be given? How to select the previous task most similar to the new one and map correspondences? and How to defined if received advice is trustworthy? Although many methods exist to reuse knowledge from a specific knowledge source, the literature is composed of methods very specialized in their own scenario that are not compatible. We propose in this thesis to reuse knowledge both from previously solved tasks and from communication with other agents. In order to accomplish our goal, we propose several flexible methods to enable each of those two types of knowledge reuse. Our proposed methods include: Ad Hoc Advising, an inter-agent advising framework, where agents can share knowledge among themselves through action suggestions; and an extension of the object-oriented representation to multiagent RL and methods to leverage it for reusing knowledge. Combined, our methods provide ways to reuse knowledge from both previously solved tasks and other agents with state-of-the-art performance. Our contributions are first steps towards more flexible and broadly applicable multiagent transfer learning methods, where agents will be able to consistently combine reused knowledge from multiple sources, including solved tasks and other learning agents.

Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

Network-based representations can model different types of relationships between texts, they are capable of capturing patterns that are hardly captured by vector space model, and network-based clustering algorithms, such as label propagation, have linear complexity. However, network-based clustering has not been explored (i) specifically in clustering texts; and (ii) with different possibilities of text representations on networks. Thus, this article’s objective is to explore and analyze clustering techniques applied to different types of network representations. Experiments were performed using 30 collections from different domains, represented in the bag-of-words format, similarity networks of the type k-Nearest Neighbors, and bipartite networks. The label propagation approach applied in similarity networks presented the best results for most evaluation measures and most text collections.

Unsupervised Machine Learning Based on Heterogeneous Networks for Text Clustering

Ferramentas de geração de bases de dados sintéticos ganharam atenção como uma alternativa para a avaliação eficaz de classificadores. Como são altamente configuráveis, as ferramentas possibilitam a criação de bases com características específicas, permitindo a avaliação do classificador em diferentes cenários. Este trabalho propõe a CbDGen, uma ferramenta para geração de bases sintéticas baseada em complexidade de dados. Com uma interface simples e parametrizável, a CbDGen possibilita a inserção de informações básicas (i.e., número de classes, atributos), a seleção de diferentes distribuições e nível de ruído. O seu grande diferencial é permitir que a escolha do tipo de complexidade desejado (i.e., balanceamento, separabilidade), bem como o nível desta complexidade. Os resultados mostraram que a CbDGen é capaz de produzir bases sintéticas aderentes às escolhas realizadas pelo usuário, se mostrando uma plataforma propícia para a avaliação de classificadores.

CbDGen: A Complexity-based Synthetic Dataset Generation Tool

Brazil has the third-largest prison population globally, and it has been growing steadily for more than two decades. Constant growth and low jail investment generated significant problems, such as overcrowding and widespread diseases. This study proposes the construction of a Random Forest classifier to predict the occurrence of deaths in prisons. We extracted data from the National Survey of Penitentiary Information for the years 2015 to 2016. The best-fitted classifier achieved accuracy equals 87% being able to identify correctly up to 84% of deaths occurrences. In the present work, it was possible to establish a relationship between prisons' reality and the data mined, determining areas in need of investment in the penitentiary system.

Death Registry Prediction in Brazilian Male Prisons with a Random Forest Ensemble

The definition of policies to control the spread of infectious diseases, such as COVID19, is an important issue for society and for the government agents, responsable for the controlling measures. The majority of the vast number of recent research on this topic, uses past data to estimate the disease behavior in the future, considering an arbitrary policy. However, the use of automated planning techniques based on Markov Decision Processes (MDPs) has been shown to be more efficient to compute optimized control policies. One of the most common model used to control infectious diseases is the "SIR with vaccination model" in which the dynamics of the susceptible, infected and recovered individuals amount is controled by vaccination policies. But since this is a problem of saving lives, it is also necessary to take into account the attitude towards risk of a decision maker. Thus, the purpose of this work is to use risk-sensitive MDPs to find optimized policies to control the spread of the considered infectious diseases, taking into account the parameters of the SIR with risk model. The results show that vaccination policies depend on both the basic reproductive rate R0, as usual, and the attitude towards the risk of the government agents.

Risk Sensitive Policies for the Control of the Spread of Infectious Diseases

Inspection of weld radiographs is an essential task for preventing leaks in oil industry pipelines. Automatic inspection is important to assist specialists whose performance can be influenced by fatigue, visual acuity, and experience. A challenging issue in automatic inspection is the availability of laudable images that can be used for training classifiers. Data augmentation is an alternative however it can produce images with little or even negative impact on the training. This article presents a method for filtering images, aiming to favor the classifier relearning process, which is done by eliminating images with low performance from the training set and further retraining the segmentation model, with a positive impact on its generalization capabilities. Experimental results of the method show that data filtering accelerates the learning process and produces an improvement in the segmentation performance.


Training Data Filtering for Deep Learning Applied toInspection of Welded Joints in Oil Pipelines

A previsão de receita tributária é uma tarefa desafiadora às secretarias de fazenda. Por conta disso, neste trabalho se inicia um projeto de investigação do uso do modelo LSTM para a previsão de receita tributária, em especial o tributo ICMS. As previsões obtidas pelo modelo LSTM são comparadas às realizadas pela SEFAZ e pelo modelo de rede neural MLP. A rede LSTM apresentou resultados promissores para a previsão do tributo ICMS.

Multiagent Simulation to Support Disarmament Policies

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Multiagent Simulation to Support Disarmament Policies

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Next from BRACIS 2020

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Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

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