Abstract
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resourceconstrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge. © 2023 Reyna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract
Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an Fl-score of 87.89% in the SWaT dataset.

Abstract
One of the Industry 4.0 landmarks, concerns the optimization of manufacturing processes by increasing the operator's productivity. But productivity is highly affected by the operator's emotions. Positive emotions (e.g. happiness) are positively related to productivity, in contrast negative emotions (e.g. frustration) are negative related to productivity and positive related to misconducts and misbehaviors on the workplace. Thus perhaps, automatic recommendation systems can suggest actions or instructions to eliminate or attenuate undesired negative emotions on the workplace. These systems might support their actions based on the reliability of emotion detectors. In this paper, emotions are detected thought a speech system. Our solution was built over deep speech recognition layers, namely the first two convolutional layers of the pre-trained 2015 Baidu's speech recognition model. In re-utilizing these first two convolutional layers, robust meta-features are expected to be extracted. Our deep learning model attempts to predict the seven primary emotions on the MELD test set.Furthermore, our solution did not use any contextual data and yet it achieved robust results. The proposed weighted TrBaidu algorithm achieved state-of-art results on the detection of joy and surprise emotions, a F1-score rate of 23 % for both emotions.