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
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Abstract

Abstract
Change point detection methods try to find any sudden changes in the patterns and features of a given time series. In this paper a new change point detection method is presented, where the window width is automatically calculated. The proposed algorithm, AutoSW, is based on a Sliding Window search method of the Python ruptures package and uses a subset of statistical concepts to compute a possibly optimal window width. The proposed algorithm is compared with some other popular methods such as PELT using different real-world and synthetic time series. Results show that AutoSW can perform better than PELT producing a better set of change points in the time series tested. © 2022 IEEE.

Abstract
The widespread adoption of distributed energy resources (DER) is creating an opportunity for aggregators to transform DER flexibility into electricity market services. In a scenario of high DER integration, aggregators will need to coordinate the optimisation of DER with the distribution system operator (DSO) in order to avoid congestion and voltage incursions in the distribution networks. This coordination task is notably complex since both network and DER operation are impacted by multiple sources of uncertainty. To address these challenges, this paper proposes a new bidding strategy for aggregators of prosumers to make robust network-secure bidding decisions in day-ahead energy and reserve markets. The bidding strategy computes robust network-secure bids without jeopardising the data privacy of aggregators and the DSO. The data privacy is preserved by using the alternating direction method of multipliers (ADMM) to decompose a stochastic network-secure bidding problem into bidding and network subproblems and solve them separately and in parallel. The uncertainty of the prosumers is incorporated in the bidding problem through scenarios of load, renewable generation, and DER preferences. Our experiments show that the proposed bidding strategy computes robust bids against distribution network problems, outperforming deterministic and stochastic state-of-the-art bidding strategies in terms of cost and network observability.

Abstract
Modelling complex information systems often entails the need for dealing with scenarios of inconsistency in which several requirements either reinforce or contradict each other. In this kind of scenarios, arising e.g. in knowledge representation, simulation of biological systems, or quantum computation, inconsistency has to be addressed in a precise and controlled way. This paper generalises Belnap-Dunn four-valued logic, introducing paraconsistent transition systems (PTS), endowed with positive and negative accessibility relations, and a metric space over the lattice of truth values, and their modal logic.