Abstract
Facial expressions are one of the most common way to externalize our emotions. However, the same emotion can have different effects on the same person and has different effects on different people. Based on this, we developed a system capable of detecting the facial expressions of a person in real-time, occluding the eyes (simulating the use of virtual reality glasses). To estimate the position of the eyes, in order to occlude them, Multi-task Cascade Convolutional Neural Networks (MTCNN) were used. A residual network, a VGG, and the combination of both models, were used to perform the classification of 7 different types of facial expressions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral), classifying the occluded and non-occluded dataset. The combination of both models, achieved an accuracy of 64.9% for the occlusion dataset and 62.8% for no occlusion, using the FER-2013 dataset. The primary goal of this work was to evaluate the influence of occlusion, and the results show that the majority of the classification is done with the mouth and chin. Nevertheless, the results were far from the state-of-the-art, which is expect to be improved, mainly by adjusting the MTCNN.

Abstract

Abstract
Color comparison is a key aspect in many areas of application, including industrial applications, and different metrics have been proposed. In many applications, this comparison is required to be closely related to human perception of color differences, thus adding complexity to the process. To tackle this, different approaches were proposed through the years, culminating in the CIEDE2000 formulation. In our previous work, we showed that simple color properties could be used to reduce the computational time of a color similarity decision process that employed this metric, which is recognized as having high computational complexity. In this paper, we show mathematically and experimentally that these findings can be adapted and extended to the recently proposed CIEDE2000 PF metric, which has been recommended by the CIE for industrial applications. Moreover, we propose new efficient models that not only achieve lower error rates, but also outperform the results obtained for the CIEDE2000 metric.

Abstract
The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3.

Abstract
In the last few decades, there has been a growing interest in exploring the sea. The activities of the so-called blue economy can go from applications such as offshore maritime wind farms to ocean environment monitoring, which are supported by sensed platforms such Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs) that require the use of reliable underwater communications. Currently, there is no suitable solution that is able to combine long-range and broadband underwater communications. The integration of different technologies, namely acoustics, RF, and optical on a multimodal approach, has been considered a suitable solution to overcome the limitations caused by the water propagation medium. Since missions at the ocean are usually expensive and demand large human and technological resources, it is important to have accurate simulation platforms for these multimodal underwater wireless networks. This paper presents the first version of a novel simulation framework - MultiUWSim (Beta) -, built upon ns-3, which integrates multiple communications technologies (RF, acoustics and optical). The current version of the simulation platform offers the possibility of simulating acoustic-based and radio-based physical wireless interfaces in a single node in a ns-3 simulation environment, enabling fully-customizable underwater network simulations.

Abstract
The COVID-19 pandemic places unique challenges to medical rescuers’ occupational health. Thus, it is crucial to assess its direct and indirect impacts on key psychological outcomes and adaptation strategies. This study aims to analyse the impact of this pandemic on medical rescuers’ coping and emotion regulation strategies, and their levels of work-related psychological outcomes, such as burnout, trauma and post-traumatic growth. Additionally, it aims to analyse the contribution of coping and emotion regulation strategies, employed to manage the COVID-19 pandemic, on burnout, trauma and post-traumatic growth. A sample of 111 medical rescuers answered the Brief Cope, Emotion Regulation Questionnaire, Oldenburg Burnout Inventory, Impact of Event Scale-Revised and Post-Traumatic Growth Inventory. Medical rescuers resorted moderately to coping and emotion regulation strategies, since the beginning of COVID-19. They presented moderate burnout and post-traumatic growth and low trauma. Coping presented a higher weight on burnout, trauma and post-traumatic growth, than emotion regulation. Expressive suppression and dysfunctional coping predicted burnout and trauma, and problem and emotion-focused coping predicted post-traumatic growth. Dysfunctional coping mediated and, thus, exacerbated the effect of expressive suppression on burnout and on trauma. Practitioners should pay closer attention to professionals with higher burnout and trauma. Occupational practices should focus on reducing dysfunctional coping and expressive suppression and promoting problem-focused coping. © 2022, Instituto Superior de Psicologia Aplicada. All rights reserved.