Abstract
In the context of SaaS (Software as a Service) where software has to be up and running 7 days a week and 24 h a day, keeping the requirements specification up to date can be difficult. Managing requirements in this context have additional challenges that need to be taken into account, for instance, re-prioritize requirements continuously and identify/update new dependencies among them. We claim that extracting and analyzing the usage of the SaaS can help to maintain requirements updated and contribute to improve the overall quality of the services provided. This paper presents REQAnalytics, a recommendation system that collects the information about the usage of a SaaS, analyses it and generates recommendations more readable than reports generated by web analytic tools. The overall approach has been applied on several case studies with promising results.

Abstract
Systems are error-prone. Big systems have lots of errors. The Internet-of-Things poses us one of the biggest and widespread systems, where errors directly impact people's lives. Testing and validating is how one deals with errors; but testing and validating a planetary-scale, heterogeneous, and ever-growing ecosystem has its own challenges and idiosyncrasies. As of today, the solutions available for testing these systems are insufficient and fragmentary. In this paper we provide an overview on test approaches, tools and methodologies for the Internet-of-Things, its software and its devices. Our conclusion is that we are still lagging behind on the best practices and lessons learned from the Software Engineering community in the past decades.

Abstract
A hybrid population-based metaheuristic, Hybrid Canonical Differential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) problems. Despite the inherent difficulties of tackling these real-world problems, they must be solved several times a day taking into account operation and security conditions. A combination of the C-DEEPSO metaheuristic coupled with a multipoint search operator is proposed to better exploit the search space in the vicinity of the best solution found so far by the current population in the first stages of the search process. A simple diversity mechanism is also applied to avoid premature convergence and to escape from local optima. A experimental design is devised to fine-tune the parameters of the proposed algorithm for each instance of the SCOPF problem. The effectiveness of the proposed hC-DEEPSO is tested on the IEEE 57-bus, IEEE 118-bus and IEEE 300-bus standard systems. The numerical results obtained by hC-DEEPSO are compared with other evolutionary methods reported in the literature to prove the potential and capability of the proposed hC-DEEPSO for solving the SCOPF at acceptable economical and technical levels.

Abstract
The development of the smart grid concept implies major changes in the operation and planning of distribution systems, particularly in Low Voltage (LV) networks. The majority of small-scale Distributed Energy Resources (DER)-microgeneration units, energy storage devices, and flexible loads-are connected to LV networks, requiring local control solutions to mitigate technical problems resulting from its integration in the system. Simultaneously, LV connected DER can be aggregated in small cells in order to globally provide new functionalities to system operators. Within this view, the Microgrid (MG) concept has been pointed out as a solution to extend and decentralize the distribution network monitoring and control capability. An MG is a highly flexible, active, and controllable LV cell, incorporating microgeneration units based on Renewable Energy Sources (RES) or low carbon technologies for small-scale combined heat and power applications, energy storage devices, and loads. The coordination of MG local resources, achieved through an appropriated network of controllers and communication system, endows the LV system with sufficient autonomy to operate interconnected to the upstream network or autonomously-emergency operation. In this case, the potentialities of DER can be truly realized if the islanded operation is allowed and bottom-up black start functionalities are implemented. To achieve this operational capability, this chapter presents the control procedures to be used in such a system to deal with the islanded operation and to exploit the local generation resources as a way to help in power system restoration in case of an emergency situation. A sequence of actions for a black start procedure is identified, and it is expected to be an advantage for power system operation regarding reliability as a result from the presence of a huge amount of dispersed generation.

Abstract

Abstract
Falls are very rare and extremely difficult to acquire in free living conditions. Due to this, most of prior work on fall detection has focused on simulated datasets acquired in scenarios that mimic the real-world context, however, the validation of systems trained with simulated falls remains unclear. This work presents a transfer learning approach for combining a dataset of simulated falls and non-falls, obtained from young volunteers, with the real-world FARSEEING dataset, in order to train a set of supervised classifiers for discriminating between falls and non-falls events. The objective is to analyze if a combination of simulated and real falls could enrich the model. In the real-world, falls are a sporadic event, which results in imbalanced datasets. In this work, several methods for imbalance learning were employed: SMOTE, Balance Cascade and Ranking models. The Balance Cascade obtained less misclassifications in the validation set.There was an improvement when mixing the real falls and simulated non-falls compared to the case when only simulated falls were used for training. When testing with a mixed set with real falls and simulated non-falls, it is even more important to train with a mixed set. Moreover, it was possible to onclude that a model trained with simulated falls generalize better when tested with real falls, than the opposite. The overall accuracy obtained for the combination of different datasets were above 95 %.