Abstract
The algorithm selection problem refers to the ability to predict the best algorithms for a new problem. This task has been often addressed by Metalearning, which looks for a function able to map problem characteristics to the performance of a set of algorithms. In the context of Collaborative Filtering, a few studies have proposed and validated the merits of different types of problem characteristics for this problem (i.e. dataset-based approach): using systematic metafeatures and performance estimations obtained by subsampling landmarkers. More recently, the problem was tackled using Collaborative Filtering models in a novel framework named CF4CF. This framework leverages the performance estimations as ratings in order to select the best algorithms without using any data characteristics (i.e algorithm-based approach). Given the good results obtained independently using each approach, this paper starts with the hypothesis that the integration of both approaches in a unified algorithm selection framework can improve the predictive performance. Hence, this work introduces CF4CF-META, an hybrid framework which leverages both data and algorithm ratings within a modified Label Ranking model. Furthermore, it takes advantage of CF4CF’s internal mechanism to use samples of data at prediction time, which has proven to be effective. This work starts by explaining and formalizing state of the art Collaborative Filtering algorithm selection frameworks (Metalearning, CF4CF and CF4CF-META) and assess their performance via an empirical study. The results show CF4CF-META is able to consistently outperform all other frameworks with statistically significant differences in terms of meta-accuracy and requires fewer landmarkers to do so. © 2018, Springer Nature Switzerland AG.

Abstract
LED-based technology is transforming public lighting networks, favouring smart city innovations. Beyond energy efficiency benefits, LED-based luminaries provide real time stateful data. However, most of the municipalities manage all their luminaries equally, independently of its state or the environmental conditions. Some existing approaches to street lighting management are already considering elementary features such as on-off control and individual dimming based on movement or ambient light. Nevertheless, our vision on public (street) lighting management, goes beyond basic consumption monitoring and dimming control, encompassing: a) adaptive lighting, by considering other potential influence factors such as work temperature of the luminaries or the arrangement of the luminaries on the street; b) Colour tuning, for public safety purposes and; c) emergency behaviour control. This paper addresses the first component (adaptive lighting) influence factors, in the scope of a real scenario in a Portuguese municipality.

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This paper considers the problem of allocating vehicles to collect and distribute fruit to producer associations in Colombia. In particular, the problem seeks to determine the optimal allocation of vehicles for fruit collection minimising both total transportation costs and CO2 emissions. This problem has multiple objectives, and the well-known e-constraint method has been used as solution technique for the proposed mathematical models. The efficiency of the former methodology has been tested by using a case study involving the distribution of blackberry (Rubus glaucus) by an association of producers in Cundinamarca Department, Colombia. In particular, we considered 12 different scenarios related to supply levels, route outsourcing, and collection frequency. The results show the efficiency of the proposed methodology in solving vehicle allocation problems related to collection and distribution. The case study reveals that, in general, collecting fruit three days/week yields lower costs and fewer emissions than performing collections four days/week. Furthermore, increased supply leads to greater differences between costs and emissions.

Abstract
More and more technological advances offer new paradigms for training, allowing novel forms of teaching and learning to be devised. A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. This forecast turns out to be an opportunity for human-computer interaction as a way to monitor and assess the user’s stress levels during high-risk tasks. The main effects of stress are increased physiological arousal, somatic complaints, mood disturbances (anxiety, fear and anger) and diminished quality of working life (e.g. reduced job satisfaction). To mitigate these problems, it is necessary to detect stressful users and apply coping measures to manage stress. Human-computer interaction could be improved by having machines naturally monitor their users’ stress, in a non-invasive and non-intrusive way. This article discusses the development of a random forest classifier with the goal of enabling the assessment of high school students’ stress during academic exams, through the analysis of mouse behaviour and decision-making patterns. © Springer Nature Switzerland AG 2019.