Abstract
With the increased use of machine learning algorithms to make decisions which impact people's lives, it is of extreme importance to ensure that predictions do not prejudice subgroups of the population with respect to sensitive attributes such as race or gender. Discrimination occurs when the probability of a positive outcome changes across privileged and unprivileged groups defined by the sensitive attributes. It has been shown that this bias can be originated from imbalanced data contexts where one of the classes contains a much smaller number of instances than the other classes. It is also important to identify the nature of the imbalanced data, including the characteristics of the minority classes' distribution. This paper presents FAWOS: a Fairness-Aware oversampling algorithm which aims to attenuate unfair treatment by handling sensitive attributes' imbalance. We categorize different types of datapoints according to their local neighbourhood with respect to the sensitive attributes, identifying which are more difficult to learn by the classifiers. In order to balance the dataset, FAWOS oversamples the training data by creating new synthetic datapoints using the different types of datapoints identified. We test the impact of FAWOS on different learning classifiers and analyze which can better handle sensitive attribute imbalance. Empirically, we observe that this algorithm can effectively increase the fairness results of the classifiers while not neglecting the classification performance. Source code can be found at: https://github.com/teresalazar13/FAWOS

Abstract

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Abstract
Respiratory diseases are among the leading causes of death worldwide. Preventive measures are essential to avoid and increase the odds of a successful recovery. An important screening tool is pulmonary auscultation, an inexpensive, noninvasive and safe method to assess the mechanics and dynamics of the lungs. On the other hand, it is a difficult task for a human listener since some lung sound events have a spectrum of frequencies outside of the human hearing ability. Thus, computer assisted decision systems might play an important role in the detection of abnormal sounds, such as crackle or wheeze sounds. In this paper, we propose a novel system, which is not only able to detect abnormal lung sound events, but it is also able to classify them. Furthermore, our system was trained and tested using the publicly available ICBHI 2017 challenge dataset, and using the metrics proposed by the challenge, thus making our framework and results easily comparable. Using a Mel Spectrogram as an input feature for our convolutional neural network, our system achieved results in line with the current state of the art, an accuracy of 43 %, and a sensitivity of 51%.

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.