Abstract
This paper describes an exploration process aligned with the core domain of Service Science inside a critical sector of Society, aiming at developing City in a sustainable, responsible, inclusive way. The paper focuses on defining the Public Safety as a Service concept in an inclusive and responsible value co-creation urban design vision for liveable cities. It explains how service intelligence can act on immaterial artefacts to transform data into information to generate value co-creation processes whose outcomes are applied to the evolution of knowledge in public safety services. Public safety is approached within a service ecosystem perspective, following the global targets of the Sendai Framework for Disaster Risk Reduction as an application perspective. Managerial implication are approached from two perspectives: establishment of governance principles with the help of Elinor Ostrom's works, and a Viable Systems Approach on the response to disasters operating rules.

Abstract
Geared by the increasing need for enhanced performance, both optical and computational, new dynamic control laws have been researched in recent years for next generation adaptive optics systems on current 10 m-class and extremely large telescopes up to 40 m. We provide an overview of these developments and point out prospects to making such controllers drive actual systems on-sky.

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.

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.