Thiago Barcelos

[This retracts the article DOI: 10.1007/s00500-021-05810-5.].

Iniciativas voltadas ao desenvolvimento do Pensamento Computacional (PC) em crianças e adolescentes em idade escolar têm sido organizadas em torno do conceito de Computação Criativa, que busca contextualizar o desenvolvimento do PC em torno da criação de produtos que tenham conexão pessoal com o aprendiz, utilizando a criatividade e imaginação, além dos conceitos computacionais. Este relato descreve a experiência da reformulação de um curso de Computação Criativa, previamente oferecido como uma ação de Extensão presencial, para a modalidade online devido ao isolamento social provocado pela pandemia da COVID-19 durante o ano de 2020. Apesar dos objetivos da oficina terem sido mantidos, o seu conteúdo passou a ser organizado em uma estratégia de curadoria de conteúdos online, principalmente relacionados ao site code.org, associada à produção de videoaulas próprias. O artigo descreve a experiência do oferecimento do curso reformulado a duas turmas entre os meses de maio e agosto de 2020. São discutidos os resultados obtidos a partir de um questionário aplicado às duas turmas e da análise do código produzido pelos participantes. Dificuldades iniciais relacionadas à comunicação remota com as crianças foram contornadas com a inclusão de momentos de interação com as monitoras do projeto. Ainda, os projetos finais cumpriram satisfatoriamente o objetivo do curso, tendo em vista a complexidade do código produzido pelos participantes.

Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a “black-box” method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19.

Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.

A análise automatizada de código desenvolvido por alunos em atividades de desenvolvimento do Pensamento Computacional vem mostrando potencial em identificar práticas e estratégias utilizadas para a resolução dos problemas propostos. No entanto, ainda não tem sido explorada em detalhes a evolução do código produzido por alunos e sua relação com a aquisição de competências do Pensamento Computacional. Neste estudo foram empregados mapas auto-organizáveis na análise de jogos produzidos por alunos em uma Oficina de Produção de Jogos Digitais nos anos de 2013 e 2017. O resultado do processo de treinamento produziu agrupamentos no mapa que apresentaram coerência com características dos jogos vinculadas a estratégias utilizadas pelos alunos que foram previamente observadas em sala de aula. Implicações dos resultados para estudos futuros também são discutidas.

Proposta de Aplicação e Avaliação de Conceitos do Pensamento Computacional em Crianças Hospitalizadas

Relações entre o Pensamento Computacional e a Matemática: uma Revisão Sistemática da Literatura

Desarrollo de un Software de Apoyo el Desarrollo de la Función Ejecutiva en Niños con Trastornos del Espectro Autista