Abstract
O nosso estudo propôs a implementação de Gamificação numa Plataforma Social Académica de uma Universidade Virtual, para verificar de que forma esta influenciava a Interação e a Aprendizagem Social. Para isso usámos uma Metodologia de Design Based Research numa configuração de Métodos Mistos. Começámos por recolher opiniões dos utilizadores dessa Plataforma. Esses resultados informaram na construção de um protótipo gamificado. Seguidamente efetuaram-se testes de usabilidade, recolhendo dados da performance e das opiniões dos utilizadores e foi construída uma nova Plataforma. Nesta fase foi efetuada uma Observação sistemática e recolhidas Analytics do uso. Foram discutidos os resultados e de que forma estes podem ser usados para posteriores intervenções.;Our work proposed the Gamification of an Academic Social Platform from a Virtual University, to inspect the impact on the Interaction and Social Learning of members. We employed Design Based Research with Mixed-Methods. First we gathered information about the users of the original platform, them we designed a prototype. After, we made usability tests and implemented a second platform with Gamification Elements. On this second platform we made a Systematic Observation and gathered the Analytics. We discuss the findings and report ways where they can be used for future implementations.

Abstract
Missing data consists in the lack of information in a dataset and since it directly influences classification performance, neglecting it is not a valid option. Over the years, several studies presented alternative imputation strategies to deal with the three missing data mechanisms, Missing Completely At Random, Missing At Random and Missing Not At Random. However, there are no studies regarding the influence of all these three mechanisms on the latest high-performance Artificial Intelligence techniques, such as Deep Learning. The goal of this work is to perform a comparison study between state-of-the-art imputation techniques and a Stacked Denoising Autoencoders approach. To that end, the missing data mechanisms were synthetically generated in 6 different ways; 8 different imputation techniques were implemented; and finally, 33 complete datasets from different open source repositories were selected. The obtained results showed that Support Vector Machines imputation ensures the best classification performance while Multiple Imputation by Chained Equations performs better in terms of imputation quality. © Springer Nature Switzerland AG 2018.

Abstract
This paper presents a testbed implementation and evaluation of coding for secrecy schemes in a real environment through software defined radio platforms. These coding schemes rely on interleaving and scrambling with randomly generated keys to shuffle information before transmission. These keys are then encoded jointly with data and then hidden (erased) before transmission, thus only being retrievable through parity information resulting from encoded data. An advantage of the legitimate receiver (e.g. a better signal-to-noise ratio) on the reception of those keys provides the means to achieve secrecy against an adversary eavesdropper. Through this testbed implementation, we show the practical feasibility of coding for secrecy schemes in real-world environments, unveiling the usefulness of interleaving and scrambling with a hidden key to reduce the required advantage over an eavesdropper. We further describe and present solutions to a set of issues that appear when doing practical implementations of security schemes in software defined radio platforms. © 2018 IEEE.

Abstract
This paper describes the development of a Co-training (semi-supervised approach) method that uses multiple learners for single target regression on data streams. The experimental evaluation was focused on the comparison between a realistic supervised scenario (all unlabelled examples are discarded) and scenarios where unlabelled examples are used to improve the regression model. Results present fair evidences of error measure reduction by using the proposed Co-training method. However, the error reduction still is relatively small.

Abstract
This paper presents a synchronous resonant control strategy based on the inherent characteristics of permanent magnet synchronous generators (PMSG) for the control of power converters to provide stable operating conditions for the power grid under high penetration of renewable energy resources (RERs). The proposed control technique is based on the small signal linearization of a dynamic model with grid specifications, load-current-based voltages, and power converter currents. A combination of the linearized dynamic model with the PMSG swing equation and resonant controller leads to a control technique with synchronous features and appropriate inertia for the control of converter-based power generators. As the main contribution of this work, an extra functionality is proposed in the control loop of the proposed model to solve the inherent inconveniences of conventional synchronous generators. Also, a comprehensive collaboration between interfaced converter specifications and PMSG features is achieved as another contribution of the proposed control technique, and this can guarantee accurate performance under various conditions. A current perturbation curve is introduced to assess the variations of the grid frequency and voltage magnitude under operation of the interfaced converters controlled by the proposed control technique. Moreover, by taking into account the load-based voltages, the effects of the current perturbation components are investigated. The proposed model is simulated in MATLAB/Simulink environment to verify the high performance of the proposed control technique over the other existing control methods.

Abstract
The integration of functional imaging modality provided by Positron Emission Tomography (PET) and associated anatomical imaging modality provided by Computed Tomography (CT) has become an essential procedure both in the evaluation of different types of malignancy and in radiotherapy planning. The alignment of these two exams is thus of great importance. In this research work, three registration approaches (1) intensity-based registration, (2) rigid translation followed by intensity-based registration and (3) coarse registration followed by fine-tuning were evaluated and compared. To characterize the performance of these methods, 161 real volume scans from patients involved in Hodgkin Lymphoma staging were used: CT volumes used for radiotherapy planning were registered with PET volumes before any treatment. Registration results achieved 78%, 60%, and 91% of accuracy for methods (1), (2) and (3), respectively. Registration methods validation was extended to a corresponding landmarks points distance calculation. Methods (1), (2) and (3) achieved a median improvement registration rate of 66% mm, 51% mm and 70% mm, respectively. The accuracy of the proposed methods was further confirmed by extending our experiments to other multimodal datasets and in a monomodal dataset with different acquisition conditions.