Abstract
Real world optimization problems like Scheduling are generally complex, large scaled, and constrained in nature. Thereby, classical operational research methods are often inadequate to efficiently solve them. Metaheuristics (MH) are used to obtain near-optimal solutions in an efficient way, but have different numerical and/or categorical parameters which make the tuning process a very time-consuming and tedious task. Learning methods can be used to aid with the parameter tuning process. Racing techniques have been used to evaluate, in a refined and efficient way, a set of candidates and discard those that appear to be less promising during the evaluation process. Case-based Reasoning (CBR) aims to solve new problems by using information about solutions to previous similar problems. A novel Racing+CBR approach is proposed and brings together the better of the two techniques. A computational study for the resolution of the scheduling problem is presented, concluding about the effectiveness of the proposed approach.

Abstract
Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

Abstract
In the last decade, the level of variable renewable energy sources (RESs) integrated in distribution network systems have been continuously growing. This adds more uncertainty to the system, which also faces all traditional sources of uncertainty and those pertaining to other emerging technologies such as demand response and electric vehicles. As a result, distribution system operators are finding it increasingly difficult to maintain an optimal daily operation of such systems. Such challenges/limitations are expected to be alleviated when distribution systems undergo the transformation process to smart grids, equipped with appropriate technologies such as energy storage systems (ESSs) and switchable capacitor banks (SCBs). These technologies offer more flexibility in the system, allowing effective management of the uncertainty in RESs. This paper presents a stochastic mixed integer linear programming (SMILP) model, aiming to optimally operate distribution network systems, featuring variable renewables, and minimizing the impact of RES uncertainty on the system's overall performance via ESSs and SCBs. A standard 41-bus distribution system is employed to show the effectiveness of the proposed S-MILP model. Simulation results indicate that strategically placed ESSs and SCBs can substantially alleviate the negative impact of RES uncertainty in the considered system.

Abstract
The safety-critical real-time embedded domain increasingly demands the use of parallel architectures to fulfill performance requirements. Such architectures require the use of parallel programming models to exploit the underlying parallelism. This paper evaluates the applicability of using OpenMP, a widespread parallel programming model, with Ada, a language widely used in the safety-critical domain. Concretely, this paper shows that applying the OpenMP tasking model to exploit fine-grained parallelism within Ada tasks does not impact on programs safeness and correctness, which is vital in the environments where Ada is mostly used. Moreover, we compare the OpenMP tasking model with the proposal of Ada extensions to define parallel blocks, parallel loops and reductions. Overall, we conclude that the OpenMP tasking model can be safely used in such environments, being a promising approach to exploit fine-grain parallelism in Ada tasks, and we identify the issues which still need to be further researched.

Abstract
In this paper we introduce a new Android library, called ULTEMAT, for the delivery of ecological momentary assessments (EMAs) on mobile devices and we present its use in the MoodBuster app developed in the H2020 E-COMPARED project. We discuss context-aware, or event-based, triggers for the presentation of EMAs and discuss the potential they have to improve the effectiveness of mobile provision of mental health interventions as they allow for the delivery of assessments to the patients when and where these are most appropriate. Following this, we present the abilities of ULTEMAT to use such context-aware triggers to schedule EMAs and we discuss how a similar approach can be used for Ecological Momentary Interventions (EMIs).

Abstract
We design a graph-based framework for the analysis of access control policies that aims at easing the specification and verification tasks for security administrators. We consider policies in the category-based access control model, which has been shown to subsume many of the most well known access control models (e.g., MAC, DAC, RBAC). Using a graphical representation of category-based policies, we show how answers to usual administrator queries can be automatically computed, and properties of access control policies checked. We show applications in the context of emergency situations, where our framework can be used to analyse the interaction between access control and emergency management.