Abstract
The physiotherapeutic process is widely discussed and of fundamental importance for the recovery of patients who suffer from any injury or adverse muscle condition. For best results, it is of primary importance that the patient maintains a pace of treatment and remains engaged in the activities required by the Physiotherapist. In this context, an approach that improves such engagement with concern for usability and acceptance by patients is explored in this article. A gamified platform was created, capable of capturing the time of exposure to the movement of squeezing a handgrip and expressing the patient's results in a swordsman-themed versus game, through the reading of neuromuscular signals captured by a MYO armband. © 2020 IEEE.

Abstract
A Internet das Coisas (IoT ou Internet of Things) tem se revelado uma tecnologia potencialmente disruptiva em vários campos da atividade  humana, o que inclui os processos educacionais. O processamento de dados produzidos e armazenados por objetos cotidianos e sensores e processados na web pode potencializar a análise pedagógica do professor e conferir maior liberdade para intervir nos percursos formativos dos alunos. Contudo, as pesquisas relacionando IoT e educação são incipientes, não conferindo uma visão clara sobre como concretizar este potencial. Este artigo apresenta uma revisão sistemática da literatura para melhor evidenciar as possibilidades de conexão entre IoT e Educação, recolhendo produções e analisando o conteúdo das mesmas e agrupando-as por ano e país de publicação,  contexto e nível educacional, foco dos estudos, tecnologias adotadas, aspectos metodológicos, aspectos teóricos e desenvolvimento de competências proporcionado por tais tecnologias. Constatou-se que a  maioria das pesquisas foca em aspectos técnicos e utilitários da IoT, mas existem propostas com referencial teórico-metodológico de caráter pedagógico. Os resultados apontam para a necessidade de aprofundamento da interligação entre Internet das Coisas e Educação, indicam possibilidades de pesquisas futuras e a quais áreas esse aprofundamento deve atender, possibilitando a utilização desse potencial tecnológico para a promoção de novas abordagens na área de ensino e aprendizagem.

Abstract
Missing data is an issue often addressed with imputation strategies that replace the missing values with plausible ones. A trend in these strategies is the use of generative models, one being Variational Autoencoders. However, the default loss function of this method gives the same importance to all data, while a more suitable solution should focus on the missing values. In this work an extension of this method with a custom loss function is introduced (Variational Autoencoder with Weighted Loss). The method was compared with state-of-the-art generative models and the results showed improvements higher than 40% in several settings. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

Abstract

Abstract
The use of optical sensors inside the needle can improve targeting precision and can bring real-time information about the location of the needle tip if necessary, since a needle bends through insertion into the tissue. Therefore, the precise location of the needle tip is so important in percutaneous treatments. In the current experiment, a fiber sensor based on a Fabry-Perot (FP) cavity is described to measure the needle curvature. The sensor is fabricated by producing an air bubble between two sections of multimode fiber. The needle with the sensor therein was attached at one end and deformed by the application of movements. The sensor presents a sensitivity of -0.152 dB/m(-1) to the curvature measurements, with a resolution of 0.089 m(-1). The sensory structure revealed to be stable, obtaining a cross-sensitivity to be 0.03 m(-1)/degrees C.

Abstract
This paper proposes a comprehensive framework for generation and transmission planning of renewable dominated power systems, which is formulated as a stochastic multi-objective problem. In this regard, a Normalized Normal Constraint (NNC) solution approach is proposed to solve the introduced stochastic multiobjective generation and transmission planning (GTP) problem. The NNC is utilized in this paper as a relation between different objective functions with different dimensions to find the optimal weighting factors of these objectives. The NNC is applied for solving the GTP problem with objective functions including the investment and operation costs along with the transmission losses, while considering the cost of unserved energy, as well as the uncertainty of load and Renewable Energy Resources (RERs). A fuzzy-based decision making framework is utilized to select the best solution among the optimal non-dominated solution points. A scenario-based approach is used to model the uncertainties. The Garver 6-bus and IEEE 118-bus test systems are utilized to perform the numerical analysis. The simulation results validate the performance and importance of the proposed model, as well as the effectiveness of the NNC to find the evenly distributed Pareto solutions of the multiobjective problems.