Brazil

The use of sensors in environments where they require constant monitoring has been increasing in recent years. The main goal is to guarantee the effectiveness, safety, and smooth functioning of the system. To identify the occurrence of abnormal events, we propose a methodology that aims to detect patterns that can lead to abrupt changes in the behavior of the sensor signals. To achieve this objective, we provide a strategy to characterize the time series, and we use a clustering technique to analyze the temporal evolution of the sensor system. To validate our methodology, we propose the clusters&#39; stability index by windowing. Also, we have developed a parameterizable time series generator, which allows us to represent different operational scenarios for a sensor system where extreme anomalies may arise.

BRACIS 2020

Extreme Events Characterization on Time Series

time series analysis

extreme events

characterization of time series

cluster analysis

anomaly detection

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The use of sensors in environments where they require constant monitoring has been increasing in recent years. The main goal is to guarantee the effectiveness, safety, and smooth functioning of the system. To identify the occurrence of abnormal events, we propose a methodology that aims to detect patterns that can lead to abrupt changes in the behavior of the sensor signals. To achieve this objective, we provide a strategy to characterize the time series, and we use a clustering technique to analyze the temporal evolution of the sensor system. To validate our methodology, we propose the clusters' stability index by windowing. Also, we have developed a parameterizable time series generator, which allows us to represent different operational scenarios for a sensor system where extreme anomalies may arise.

technical paper

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.

This paper describes a real application of graphical neural networks (GNNs) in the dynamic estimation of spatially distributed buoys that are of central importance in maritime navigation. We describe the techniques we used to process both data and background knowledge about the domain, indicating why GNNs are particularly well suited for this sort of task. We report our empirical results, demonstrating that GNNs profitably use the avaible relational structure.

Prediction of Environmental Conditions for Maritime Navigation using a Network of Sensors: A Practical Application of Graph Neural Networks

Sentiment analysis on social media data can be a challenging task, among other reasons, because labeled data for training is not always available. Transfer learning approaches address this problem by leveraging a labeled source domain to obtain a model for a target domain that is different but related to the source domain. However, the question that arises is how to choose proper source data for training the target classifier, which can be made considering the similarity between source and target data using distance metrics. This article investigates the relation between these distance metrics and the classifiers’ performance. For this purpose, we propose to evaluate four metrics combined with distinct dataset representations. Computational experiments, conducted in the Twitter sentiment analysis scenario, showed that the cosine similarity metric combined with bag-of-words normalized with term frequency-inverse document frequency presented the best results in terms of predictive power, outperforming even the classifiers trained with the target dataset in many cases.

Transfer learning for Twitter sentiment analysis: Choosing an effective source dataset

As demonstrações contábeis são parte essencial do processo de análise de risco de crédito de empresas. Tais documentos são apresentados em alta variedade de modelos, fator que limita a extração automática de seus dados. Para superar essa limitação, analisou-se agrupamentos desses documentos gerados com o algoritmo estado da arte RNG-HDBSCAN*. A pesquisa concluiu que os agrupamentos encontrados são aderentes ao processo de extração automática de dados e podem superar as limitações conhecidas, ou seja, têm potencial para melhorar a qualidade desse processo.

Improving automatic data extraction from financial statements with clustering analysis

Text simplification (TS) is a natural language transformation process that reduces linguistic complexity while preserving semantics and retaining its original meaning. This work aims to present a research proposal for automatic simplification of texts, precisely a split-and-rephrase approach based on an encoder-decoder neural network model. The proposed method was trained against the WikiSplit English corpus with the help of a part-of-speech tagger and obtained a BLEU score of 74.72%. We also experimented with this trained model to split-and-rephrase sentences written in Portuguese with relative success, showing the method’s potential.

Experimenting Sentence Split-and-Rephrase Using Part-of-Speech Labels

Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.

Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

Brain-Computer Interfaces (ICM) allow the control of devices by modulating brain activity. Commonly, when based on motor imagery (IM) these systems use the energy (de)synchronization in the electroencephalogram signal (EEG), voluntarily caused by the individual, to identify and classify their motor intention. Therefore, the EEG segment used in the training of the learning algorithms plays a fundamental role in the description of the characteristics and, consequently, in the recognition of patterns in the signal. In this context, the objective of this work is to demonstrate the correlation between the temporal properties of the input EEG segment and the classification performance of a ICM-IM system. An auxiliary sliding window was used in order to obtain the variation of performance in function of the variation in the time and to support the decision making about the appropriate window. Simulations based on public EEG data point to significant variability in the location and width of the ideal window and suggest the need for individualized selection according to the cognitive patterns of each subject.

Towards ideal time window for classifying motor imagery in brain-computer interfaces

The worldwide COVID-19 pandemic has struck people’s lives overnight. With an alarming contagious rate and no effective treatments or vaccines, it has evoked all sorts of reactions. In this paper, we propose a framework to analyze how political polarization affects groups’ behavior with opposed stances, using the Brazilian COVID polarized scenario as a case study. Two Twitter groups represent the pro/against social isolation stances referred to as Chloroquiners and Quarenteners. The framework encompasses: a) techniques to automatically infer from users political orientation, b) topic modeling to discover the homogeneity of concerns expressed by each group; c) network analysis and community detection to characterize their behavior as a social network group and d) analysis of linguistic characteristics to identify psychological aspects. Our main findings confirm that Cloroquiners are right-wing partisans, whereas Quarenteners are more related to the left-wing. The political polarization of Chloroquiners and Quarenteners influence the arguments of economy and life, and support/opposition to the president. As a group, the network of Chloroquiners is more closed and connected, and Quarenteners have a more diverse political engagement. In terms of psychological aspects, polarized groups come together on cognitive issues and negative emotions.

Quarenteners vs. Cloroquiners: a framework to analyze the effect of political polarization on social distance stances

It has been advocated that post-hoc explanation techniques are crucial for increasing the trust in complex Machine Learning (ML) models. However, it is so far not well understood whether such explanation techniques are useful or easy for users to understand. In this work, we explore the extent to which SHAP's explanations, a state-of-the-art post-hoc explainer, help humans to make better decisions. In the malaria classification scenario, we have designed an experiment with 120 volunteers to understand whether humans, starting with zero knowledge about the classification mechanism, could replicate the complex ML classifier's performance after having access to the model explanations. Our results show that this is indeed the case, i.e., when presented with the ML model outcomes and the explanations, humans can improve their classification performance, indicating that they understood how the ML model makes its decisions.

Evaluation of Post-hoc Explanations for Malaria Detection

Machine translation is intrinsically associated with the study and development of computerized methodologies for idiomatic translations' production. The most common approaches are the statistical and methods based on neural networks. One of the deficiencies pointed out by these methods is the possible lack of coherence between the translated sentences. In this project, we propose using techniques based on Graph Theory to preserve the coherence in the translation of texts from English to Portuguese. The studied method presents large performance variability; however, some translations produce sentences 90% better evaluated than the statistical translator Moses and 10% superior to Google Translate.

Post-processing of machine translation texts based on graph theory

This study presents an algorithmic strategy to analyze a small set of social network information to monitor the dengue disease. Previous studies have achieved similar results based on large datasets of Twitter microblogs. In this study, we successfully map dengue cases using a small data collection of tweets from a medium-size city. A set of modules were constructed to collect, categorize, and display dengue-related tweets. We compared the collected tweets with real data from confirmed dengue cases. We showed a significant correlation between the number of confirmed dengue cases and the number of dengue-related tweets, even considering such a small dataset. The results of this approach may be relevant in public health policies.

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