Abstract
The complexity and subjectivity of the judgement task conceals the existence of biases that undermines the quality of the process. This paper presents a weighted aggregation function that attempts to reduce the influence of biased judgements on the final score. We also discuss a set of desirable properties. The proposed weighted aggregation function is able to correct the “nationalism bias” found by Emerson et al. (Am Stat 63(2):124–131, 2009) in the 2000 Olympic Games diving competition and suggest the possibility of a “reputation bias”. Our results can be applied to judgement sports and other activities that require the aggregation of several personal evaluations. © 2016, Springer Science+Business Media New York.

Abstract
This paper presents a study of the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles. In particular, the aim is to find the number of battery (and super capacitor) cells to propel a light vehicle to run two different standard driving cycles. Three equivalent circuit models are considered to simulate the battery electrical performance: linear static, non-linear static and non-linear with first-order dynamics. When dimensioning a battery-based vehicle, less complex models may lead to a solution with more battery cells and higher costs. Despite the same tendency, when a hybrid vehicle is taken into account, the influence of the battery models is dependent on the sizing strategy. In this work, two sizing strategies are evaluated: dynamic programming and filter based. For the latter, the complexity of the battery model has a clear influence on the result of the sizing problem. On the other hand, a modest influence is observed when a dynamic programming strategy is followed.

Abstract
Theory Revision from Examples is the process of repairing incorrect theories and/or improving incomplete theories from a set of examples. This process usually results in more accurate and comprehensible theories than purely inductive learning. However, so far, progress on the use of theory revision techniques has been limited by the large search space they yield. In this article, we argue that it is possible to reduce the search space of a theory revision system by introducing stochastic local search. More precisely, we introduce a number of stochastic local search components at the key steps of the revision process, and implement them on a state-of-the-art revision system that makes use of the most specific clause to constrain the search space. We show that with the use of these SLS techniques it is possible for the revision system to be executed in a feasible time, while still improving the initial theory and in a number of cases even reaching better accuracies than the deterministic revision process. Moreover, in some cases the revision process can be faster and still achieve better accuracies than an ILP system learning from an empty initial hypothesis or assuming an initial theory to be correct.

Abstract
Energy consumption is a major concern in Wireless Sensor Networks (WSNs) since nodes are powered by batteries. Usually, batteries have low capacity and can not be replaced due to economic and/or logistical issues. In addition, batteries are complex devices as they depend on electrochemical reactions to generate energy. As a result, batteries exhibit non-linear behaviour over time, which makes difficult to estimate their lifetime. Analytical battery models are abstractions that allow estimating the battery lifetime through mathematical equations, taking into account important effects such as rate capacity and charge recovery. The recovery effect is very important since it enables charge gains in the battery after its electrochemical stabilization. Sleep scheduling approaches may take advantage of the recovery effect by adding sleep periods in the node activities in order to extend the network lifetime. This work aims to analyse the recovery effect within WSN context, particularly regarding low-power nodes. To do so, we use an analytical battery model for analysing the battery performance over time, during the node execution. © Springer International Publishing AG 2017.

Abstract
The MOOC (Massive Open Online Courses) are the latest training model offered. They are online training courses, open and free, and for massive access. But are these features enough to attract potential participants? What are the characteristics of those who are most likely to enroll in a MOOC? To address these and other underlying issues a quantitative methodology was adopted, in the form of an online survey. The study was applied to the adult population of Aveiro district (Portugal) with over nine years of schooling. The sample consists of 424 individuals, and its sociodemographic characteristics equivalent to the population under study. 86.6% of the participants were unaware of the MOOC concept, but there are no significant differences in perceptions about the MOOC among those who knew and those who did not know the concept. The intention to participate in a MOOC is higher among the younger, the ones who have an academic degree, the more autonomous in terms of learning, the ones that have higher Internet and social network skills, the ones who already knew the concept, and who predict change on their employment status. This study provides clues to the identification of target segments and promotion strategies for MOOCs offered in Portugal.

Abstract
Minimum-variance control of adaptive optics (AO) systems relies on a stochastic dynamical model of the per-turbation and on models of the components, including loop delays. Resulting LQG controllers have been imple-mented in SCAO and WFAO both on laboratory benches and on-sky. Their efficiency has been recognized in several modes of operation, e.g. I) on-sky control of TT or low-order modes with vibration mitigation (SPHERE, GPI, CANARY, Raven, GeMS, in H2 formulation at the McMath-Pierce solar telescope) ii) full SCAO mode (CANARY) and MOAO mode (CANARY, Raven) and iv) in general it is advocated to control the low-order modes in laser tomography systems (E-ELT HARMONI LTAO, NFIRAOS). We first point out two examples related to VLT AO controllers to illustrate the need for RTC exibility. The implementation of LQG control in the framework of the future ELTs raises many questions related both to real-time control computation and associated parameter updates (at a far lower rate), and to the performance that can be reached compared with simpler control strategies. By gathering many lab and on-sky results, we draw the performance trends observed so far. We then outline some promising research directions for control design and implementations for future ELTs AO systems.