Abstract
In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Abstract
Taxi demand prediction with scarce historic information is among the most encountered challenges of the present decade for the traffic network of a smart city. Lack of sufficient information results in the failure of conventional approaches in prediction for a new city. Additionally, the prevalent Deep Neural Network (DNN) Models resort to ineffectual approaches which fail to meet the required prediction performance for the network. Moreover, existing domain adaptation (DA) models could not sufficiently reap the domain-shared features well from multiple source, questioning the models' applicability. Complex structure of these DA models tends to a nominal performance gain due to inefficient resource utilization of the sources. The present paper introduces a domain adaptation deep neural network model, Bootstrap Zero-Shot (BTS-Z) learning model which focuses on capturing the latent spatio-temporal features of the whole city traffic network shared among every source city and maneuver them to predict for the target city traffic network with no prior information. The presented model proves the efficacy of the bootstrap algorithm in the prediction of demands for the unseen target over the computationally expensive MAML models. The experimental results on three real-world city taxi data on the standard benchmark metrics report a minimum of 23.41% improvement over the best performing competitive system. © 2024 IEEE.

Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation.

Abstract
Engineering plays a key role in society today, influencing social behaviour, economic systems, (un)sustainability and future construction. Faced with this central and powerful role of engineering, it is urgent to recognise the need for professionals in this area to be culturally competent and sociopolitically committed in the collective ethical construction of the common good. Engineering course curricula generally focus on technical-scientific training - as is the case in Portugal - not on including or valuing other educational dimensions (namely, social, ethical, cultural or political responsibility). However, to promote an ethically responsible and sustainable future, it is imperative that these dimensions are included in engineers' training, namely through ethical education that promotes a responsible professional practice that contributes to a viable common future. Intending to contribute to a culturally responsive engineering education, and to the development of the pedagogical dimension of the ethical education of engineering students, this study aims to develop a framework of the ethical skills necessary for the professional practice of engineering. The methodology used included a systematic literature review and document analysis. The developed framework allows systematising and interconnecting ethical skills, which can promote and facilitate the inclusion of ethical education in engineering courses. The framework helped to design a curricular module in engineering. It is a useful tool for professors of ethics in engineering, for those responsible for structuring engineering curriculum plans and for anyone responsible for enhancing this field of engineering education.

Abstract
Motifs are overrepresented and statistically significant sub-patterns in a network, whose identification is relevant to uncover its underlying functional units. Recently, its extraction has been performed on higher-order networks, but due to the complexity arising from polyadic interactions, and the similarity with known computationally hard problems, its practical application is limited. Our main contribution is a novel approach for hyper-subgraph census and higher-order motif discovery, allowing for motifs with sizes 3 or 4 to be found efficiently, in real-world scenarios. It is consistently an order of magnitude faster than a baseline state-of-art method, while using less memory and supporting a wider range of base algorithms.

Abstract
Since creating universally capable robots is challenging for a single manufacturer, a diverse fleet of robots from various manufacturers is utilized. However, these heterogeneous fleets encounter communication and interoperability issues. As a result, there is a growing need for a standardized interface that is capable of communicating, controlling and managing a diverse fleet without these interoperability issues. This paper presents a translation software module capable of controlling an autonomous mobile robot and communicating with a ROS-based robot fleet manager using the VDA5050 Standard and exchanging information via the MQTT communication protocol, aiming at flexibility and control across different robot brands. The effectiveness of the software in controlling a mobile robot via the VDA5050 standard was demonstrated by the results. It accurately analysed data from the Robot Fleet Manager, converted it into VDA 5050 JSON messages and skilfully translated it back into ROS messages. The robot's behavior remained consistent before and after the VDA5050 implementation.