Abstract
The current energy scenario requires actions towards the reduction of energy consumption and the use of renewable resources. In this context, a microgrid is a self-sustained network that can operate connected to the smart grid or in isolation. The long-term scheduling of on/off cycles of devices is a critical problem that has been commonly addressed by centralized approaches. In this work, we propose a novel agent-based method to solve the long-term scheduling problem as a distributed constraint optimization problem (DCOP) by modelling future system configurations rather than reacting to changes. Moreover, with respect to approaches based on decentralised reinforcement learning, we can directly encode system-wide hard constraints (such as for example the Kirchhoff law) which are not easy to represent in a factored representation of the problem. We compare different multi-agent DCOP algorithms showing that the proposed method can find optimal/near-optimal solutions for a specific case study.

Abstract
We report on the direct intensity modulation characteristics of a high-speed resonant tunneling diode-photodetector (RTD-PD) with an oscillation frequency of 79 GHz. This work demonstrates both electrical and optical modulation and shows that RTD-PD oscillators can be utilized as versatile optoelectronic/radio interfaces. This is the first demonstration of optical modulation of an RF carrier using integrated RTD-PD oscillators at microwave frequencies. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

Abstract
Students born in a digital era require adjusted teaching and learning methodologies incorporating new technologies. A common difficulty found by students is how to test their controller designs in a real system. Thus, the development of affordable, portable and easy to use feedback control kits is highly desirable. The idea is that both lecturers and students can perform simple practical experiments anytime and anywhere. The APMonitor temperature control lab is an Arduino based control kit which fulfils these requirements. Proportional, integrative and derivative control is in operation in the vast majority of industrial process control loops. Thus, it is a mandatory topic in most undergraduate introductory feedback control courses. A teaching/learning PID control experiment for undergraduate Biomedical Engineering student's based on the temperature control lab is reported here. Results received from students are presented.

Abstract
Regression testing is of paramount importance to ensure that the quality of software does not suffer when code changes are implemented. However, having a large set of tests is mostly done by hand and is time-consuming. Regression tests are written to test functionality that is already implemented and thus are a prime target for automatic test generation. Mutation testing is a technique that evaluates the quality of tests by applying simple changes to source code and checking if any test detects those changes. This paper presents an approach focused on GUI Testing that takes the idea behind mutation testing and applies it, not to the source code, but the actual tests. Generated tests are then analyzed, and those that generate different outcomes are chosen. The set of initial test cases is obtained from the interactions of the actual users of the service under analysis. In the end, an evaluation of the approach is presented.

Abstract
Numerous sophisticated machine learning tools (e.g. ensembles or deep networks) have shown outstanding performance in terms of accuracy on different numeric forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Frequently, decision makers require more than a black box model to be able to “trust” the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning and Data Mining research. This paper proposes a series of visualisation tools that help in understanding the predictive performance of non-interpretable regression models. More specifically, these tools allow the user to relate the expected error of any model to the values of the predictor variables. This type of information allows end-users to correctly assess the risks associated with the use of the models, by showing how concrete values of the predictors may affect the performance of the models. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end-user. © 2019, Springer Nature Switzerland AG.

Abstract
This work aims to present and summarize the identified main research fields about player engagement enhancement with video games. The expansion of video game diversity, complexity and applicability increased development costs. New approaches aim to automatize the design process by developing algorithms that can understand players requirements and redesign games on the fly. Multiplayer games have the added benefit of socially engage all involved parties through game-play. But balancing becomes more important as feeling overwhelmed by a stronger opponent may be demotivating, as feeling underwhelmed by a weaker adversary that cannot provide enough challenge and stimulation. Our research concludes that there is still lack of research effort in the identified fields. This may be due to the lack of academy incentive on the subject. The entertainment industry depends on game quality to increase their revenue, but lack interest on sharing their knowledge. We identify potential application on Serious Games. © Springer Nature Switzerland AG 2019.