Abstract
Home energy management systems (HEMSs) are important platforms to allow consumers the use of flexibility in their consumption to optimise the total energy cost. The optimisation procedure embedded in these systems takes advantage of the nature of the existing loads and the generation equipment while complying with user preferences such as air temperature comfort configurations. The complexity in finding the best schedule for the appliances within an acceptable execution time for practical applications is leading not only to the development of different formulations for this optimisation problem, but also to the exploitation of non-deterministic optimisation methods as an alternative to traditional deterministic solvers. This study proposes the use of genetic algorithms (GAs) and the cross-entropy method (CEM) in low-power HEMS to solve a conventional mixed-integer linear programming formulation to optimise the total energy cost. Different scenarios for different countries are considered as well as different types of devices to assess the HEMS operation performance, namely, in terms of outputting fast and feasible schedules for the existing devices and systems. Simulation results in low-power HEMS show that GAs and the CEM can produce comparable solutions with the traditional deterministic solver requiring considerably less execution time.

Abstract
ProcessPAIR is a novel method and tool for automating the performance analysis in software development. Based on performance models structured by process experts and calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. However, the current calibration method is not fully automatic, because, in the case of performance indicators that affect other indicators in a conflicting way, the process expert has to manually calibrate the optimal value in a way that balances those impacts. In this paper we propose a novel method to automate this step, taking advantage of training data sets. We demonstrate the feasibility of the method with an example related with the Code Review Rate indicator, with conflicting impacts on Productivity and Quality.

Abstract
Solitons are localized wave solutions that appear in nonlinear systems when self-focusing effects balance the usual pulse dispersion of common optical media. Their stability and particle-like behavior make them ideal candidates for applications that range from communication to optical computing, but in real world physical systems, dissipative processes makes these otherwise stable solutions unstable, and true solitons are particularly hard to observe in systems featuring non-negligible dissipation. In these cases a special type of localized stable solutions, called dissipative solitons, are still possible to obtain, if in addition to a balance between diffraction and nonlinearity, an equilibrium between gain and loss is also present. In this work we discuss theoretically how a 4-level atomic system and an incoherent pumping process can be an ideal experimental testbed for studying this interesting class of solutions, featuring tunable optical properties and controllable gain/loss dynamics that allow to study both classes of temporal and spatial dissipative optical solitons.

Abstract
Virtual Reality (VR) has been evolving over the years, becoming more and more accessible, in a wide area of applications. One of these areas where VR can have a major impact is training and certification. Hydrogen vehicles are becoming a reality and first responders still lack proper tools and resources to train emergency responses for the purpose. VR can play here a crucial role in ensuring a proper hydrogen emergency response training due to the advantages associated with VR training programs such as resource optimization, repeatability, and replicability. This paper proposes using VR for hydrogen emergency response training by developing a solution composed of three components: tutorial mode, training mode, and certification mode. A usability study is further conducted to evaluate its usability and user satisfaction. The results show that the use of this application regards usability and user satisfaction were extremely positive.

Abstract
Unmanned aerial vehicles have become a popular remote sensing platform for agricultural applications, with an emphasis on crop monitoring. Although there are several methods to detect vegetation through aerial imagery, these remain dependent of manual extraction of vegetation parameters. This article presents an automatic method that allows for individual tree detection and multi-temporal analysis, which is crucial in the detection of missing and new trees and monitoring their health conditions over time. The proposed method is based on the computation of vegetation indices (VIs), while using visible (RGB) and near-infrared (NIR) domain combination bands combined with the canopy height model. An overall segmentation accuracy above 95% was reached, even when RGB-based VIs were used. The proposed method is divided in three major steps: (1) segmentation and first clustering; (2) cluster isolation; and (3) feature extraction. This approach was applied to several chestnut plantations and some parameterssuch as the number of trees present in a plantation (accuracy above 97%), the canopy coverage (93% to 99% accuracy), the tree height (RMSE of 0.33 m and R-2 = 0.86), and the crown diameter (RMSE of 0.44 m and R-2 = 0.96)were automatically extracted. Therefore, by enabling the substitution of time-consuming and costly field campaigns, the proposed method represents a good contribution in managing chestnut plantations in a quicker and more sustainable way.

Abstract
Forecasting is an important task across several domains. Its generalised interest is related to the uncertainty and complex evolving structure of time series. Forecasting methods are typically designed to cope with temporal dependencies among observations, but it is widely accepted that none is universally applicable. Therefore, a common solution to these tasks is to combine the opinion of a diverse set of forecasts. In this paper we present an approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. We present an approach for retrieving out-of-bag predictions that significantly improves its data efficiency. Finally, since diversity is a fundamental component in ensemble methods, we propose a method for explicitly handling the inter-dependence between experts when aggregating their predictions. Results from extensive empirical experiments provide evidence of the method's competitiveness relative to state of the art approaches. The proposed method is publicly available in a software package.