Abstract
Covid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.

Abstract

Abstract
Early identification of failures is a critical task in predictive maintenance, preventing potential problems before they manifest and resulting in substantial time and cost savings for industries. We propose an approach that predicts failures in the near future. First, a deep learning model combining long short-term memory and convolutional neural network architectures predicts signals for a future time horizon using real-time data. In the second step, an autoencoder based on convolutional neural networks detects anomalies in these predicted signals. Finally, a verification step ensures that a fault is considered reliable only if it is corroborated by anomalies in multiple signals simultaneously. We validate our approach using publicly available Air Production Unit (APU) data from Porto metro trains. Two significant conclusions emerge from our study. Firstly, experimental results confirm the effectiveness of our approach, demonstrating a high fault detection rate and a reduced number of false positives. Secondly, the adaptability of this proposal allows for the customization of configuration of different time horizons and relationship between the signals to meet specific detection requirements.

Abstract
To maintain the performance of the latest generation of onshore and offshore wind turbine systems, a new methodology must be proposed to enhance the maintenance policy. In this context, this paper introduces an approach to designing a decision support tool that combines predictive capabilities with anomaly explanations for effective IoT predictive maintenance tasks. Essentially, the paper proposes an approach that integrates a predictive maintenance model with an explicative decision-making system. The key challenge is to detect anomalies and provide plausible explanations, enabling human operators to determine the necessary actions swiftly. To achieve this, the proposed approach identifies a minimal set of relevant features required to generate rules that explain the root causes of issues in the physical system. It estimates that certain features, such as the active power generator, blade pitch angle, and the average water temperature of the voltage circuit protection in the generator's sub-components, are particularly critical to monitor. Additionally, the approach simplifies the computation of an efficient predictive maintenance model. Compared to other deep learning models, the identified model provides up to 80% accuracy in anomaly detection and up to 96% for predicting the remaining useful life of the system under study. These performance metrics and indicators values are essential for enhancing the decision-making process. Moreover, the proposed decision support tool elucidates the onset of degradation and its dynamic evolution based on expert knowledge and data gathered through Internet of Things (IoT) technology and inspection reports. Thus, the developed approach should aid maintenance managers in making accurate decisions regarding inspection, replacement, and repair tasks. The methodology is demonstrated using a wind farm dataset provided by Energias De Portugal.

Abstract
Data-driven decision models based on Artificial Intelligence (AI) have been widely used in the public and private sectors. These models present challenges and are intended to be fair, effective and transparent in public interest areas. Bias, fairness and government transparency are aspects that significantly impact the functioning of a democratic society. They shape the government's and its citizens' relationship, influencing trust, accountability, and the equitable treatment of individuals and groups. Data-driven decision models can be biased at several process stages, contributing to injustices. Our research purpose is to understand fairness in the use of causal discovery for public procurement. By analysing Portuguese public contracts data, we aim i) to predict the place of execution of public contracts using the PC algorithm with sp-mi, smc-chi(2) and mc-chi(2) conditional independence tests; ii) to analyse and compare the fairness in those scenarios using Predictive Parity Rate, Proportional Parity, Demographic Parity and Accuracy Parity metrics. By addressing fairness concerns, we pursue to enhance responsible data-driven decision models. We conclude that, in our case, fairness metrics make an assessment more local than global due to causality pathways. We also observe that the Proportional Parity metric is the one with the lowest variance among all metrics and one with the highest precision, and this reinforces the observation that the Agency category is the one that is furthest apart in terms of the proportion of the groups.

Abstract
In many data stream applications, there is a normal concept, and the objective is to identify normal and abnormal concepts by training only with normal concept instances. This scenario is known in the literature as one-class learning (OCL) for data streams. In this OCL scenario for data streams, we highlight two main gaps: (i) lack of methods based on graph neural networks (GNNs) and (ii) lack of interpretable methods. We introduce OPENCAST (One-class graPh autoENCoder for dAta STream), a new method for data streams based on OCL and GNNs. Our method learns representations while encapsulating the instances of interest through a hypersphere. OPENCAST learns low-dimensional representations to generate interpretability in the representation learning process. OPENCAST achieved state-of-the-art results for data streams in the OCL scenario, outperforming seven other methods. Furthermore, OPENCAST learns low-dimensional representations, generating interpretability in the representation learning process and results.