Abstract
O Exame Nacional de Desempenho de Estudantes (ENADE) compõe uma das bases avaliativas do Sistema Nacional de Avaliação da Educação Superior e tem como principal propósito, avaliar o rendimento dos estudantes a partir da sua formação nos cursos de graduação. Por isso, este trabalho identificou a possibilidade de se basear nesses resultados para aprimorar os processos de ensino-aprendizagem. Para tanto, este artigo apresenta o planejamento, criação e avaliação de uma plataforma de aprendizagem Web responsiva, baseada no ENADE e que utiliza elementos de gamificação com o propósito de aumentar o engajamento dos estudantes. O software foi avaliado, por meio de questionários de aceitação tecnológica, aplicados com 38 usuários e que apresentou resultados promissores, com média geral de 4,71 entre 5 pontos possíveis.

Abstract
Indoor Air Quality (IAQ) pertains to the air quality within a specific space and is directly linked to the well-being and comfort of its occupants. In line with this objective, this research presents a real-time system dedicated to monitoring and predicting IAQ, encompassing both thermal comfort and gas concentration. The system initiates with a data acquisition, wherein a set of sensors captures environmental parameters and transmits this data for storage in a database. The measured parameters are analyzed by a neural network algorithm that predicts anomalies based on historical data. The neural network model generated predictions from 75.9% to 98.1% (depending on the parameter) of precision during regular situations. After that, a test with smoke in the same place was done to validate the model, and the results showed it could detect anomalies. Finally, prediction data are stored in a new database and displayed on a dashboard for monitoring in real-time measured and prediction data. © 2023 IEEE.

Abstract
It is important to properly test developed software because this may contribute to fewer bugs going unreported in deployed software. Often, little attention is spent on the topic of software testing in curricula, yielding graduate students without adequate preparation to deal with the quality standards required by the industry. This problem could be tackled by introducing bite-sized software testing education capsules that allow teachers to introduce software testing to their students in a less time-consuming manner and with a hands-on component that will facilitate learning. In order to design appropriate software testing educational tools, it is necessary to consider both the software testing needs of the industry and the cognitive models of students. This work-in-progress paper proposes an experimental design to gain an understanding of the cognitive strategies used by students during test case design based on real-life cases. Ultimately, the results of the experiment will be used to develop educational support for teaching software testing.

Abstract
Inclusive mobility represents an essential component of the smart and sustainable mobility ecosystem. Moreover, smart parking has gained greater importance given the vital contribution to reducing the carbon footprint. However, currently, existing solutions are not yet inclusive as they do not include the required information for the comfort and safety of people with reduced mobility, for whom the time it takes to park the vehicle is sometimes not the most important factor when compared to the suitability of the parking space considering the displacement objectives. The main contribution of this paper is a conceptual and technological architecture for an inclusive and real-time solution for parking assistance in a small urban environment. The architecture uses a crowd-sourcing approach, a Geographic Information System, a set of external APIs, the GPS, and a mobile solution for interaction with the citizen. The solution will be built from a previous work developed in the city of Viana do Castelo in Portugal and intends to be evaluated by the Sustainable Urban Mobility Indicators (SUMI) proposed by the European Commission.

Abstract

Abstract
Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.