Abstract

Abstract
Os desafios e as potencialidades de uma realidade hi perconectada numa era de hiperinteligência nos fazem pen sar num outro tipo de docência para o ensino superior e para a pós-graduação, dadas as transformações digitais pelas quais temos passado. Com o objetivo de compreender como formar o professor para a Docência OnLIFE, o percurso da pesquisa- -desenvolvimento-formação se fundamenta no método car tográfico de pesquisa-intervenção. Os artigos que constituem esta obra têmcomo foco a formação do professor-pesquisador, nesse sentido abordam: umhistórico das pesquisas realizadas pelo GPe-dU UNISINOS, uma discussão sobre amodalidade da Pós-Graduação stricto sensu, a construção de um programa de formação docente que tem início como uma ação de ex tensão em 2018 e se vai constituindo e ampliando para uma Especialização, em nível lato sensu, proposta em 2020, num processo de inventividade e inovação curricular e metodológi ca, com um público também diferenciado e pouco estudado: o professor-pesquisador de pós-graduação. Os capítulos deste livro vão problematizar as competên cias necessárias à docência na contemporaneidade, propondo a Rede de Competências para a Docência OnLIFE (Schlemmer, 2021). Essas competências são organizadas por dimensões, que apresentamos como “os 5D's da Formação Docente OnLI FE” (Schlemmer, 2019). Essas competências, organizadas nas dimensões, vão demandar, como se verá, outra concepção de currículo, que se apresenta de forma reticular. As metodologias e práticas inventivas que são construídas nesse escopo nos levam a propor uma docência OnLIFE.

Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm's performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.

Abstract
Imbalanced data are present in various business sectors and must be handled with the proper resampling methods and classification algorithms. To handle imbalanced data, there are numerous resampling and learning method combinations; nonetheless, their effective use necessitates specialised knowledge. In this paper, several approaches, ranging from more accessible to more advanced in the domain of data resampling techniques, will be considered to handle imbalanced data. The application developed delivers recommendations of the most suitable combinations of techniques for a specific dataset by extracting and comparing dataset meta-feature values recorded in a knowledge base. It facilitates effortless classification and automates part of the machine learning pipeline with comparable or better results than state-of-the-art solutions and with a much smaller execution time.

Abstract
Stress is a common feeling in people's day-to-day life, especially at work, being the cause of several health problems and absenteeism. Despite the difficulty in identifying it properly, several studies have established a correlation between stress and perceivable human features. The problem of detecting stress has attracted significant attention in the last decade. It has been mainly addressed through the analysis of physiological signals in the execution of specific tasks in controlled environments. Taking advantage of technological advances that allow to collect stress-related data in a non-invasive way, the goal of this work is to provide an alternative approach to detect stress in the workplace without requiring specific controlled conditions. To this end, a video-based plethysmography application that analyses the person's face and retrieves several physiological signals in a non-invasive way was used. Moreover, in an initial phase, additional information that complements and labels the physiological data was obtained through a brief questionnaire answered by the participants. The data collection pilot took place over a period of two months, having involved 28 volunteers. Several stress detection models were developed; the best trained model achieved an accuracy of 86.8% and a F1 score of 87% on a binary stress/non-stress prediction.