Abstract
The Sequential Monte Carlo Simulation (SMCS) is a powerful and flexible method commonly used for generating system adequacy assessment. By sampling outage events in sequence and their respective duration, this method can easily incorporate time-dependent issues such as renewable power production, the capacity of hydro units, scheduled maintenance, complex correlated load models, etc, and is the only method that provides probability distributions for the reliability indexes. Despite these advantages, the SMCS method requires considerably more simulation time than the Non-sequential Monte Carlo Simulation approach to provide accurate estimates for the reliability indexes. In an attempt to reduce the simulation time, the SMCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms. Two parallelization strategies are proposed: Strategy A, which creates and evaluates yearly samples in a completely parallel approach and while the estimates of the reliability indexes are computed in the CPU; and Strategy B, which consists on concurrently sampling the outage events for the generating units while the state evaluation and the index estimation stages are executed in serial. Simulation results for the IEEE RTS 79, IEEE RTS 96, and the new IEEE RTS GMLC test systems, show that both implementations lead to a significant acceleration of the SMCS method while keeping all its advantages. In addition, it was observed that Strategy B results in less simulation time than Strategy A for generation system adequacy assessment. © 2020 IEEE.

Abstract

Abstract
This paper addresses the integration of the planning decisions concerning inbound logistics in an industrial setting (from the suppliers to the mill) and outbound logistics (from the mill to customers). The goal is to find the minimum cost routing plan, which includes the cost-effective outbound and inbound daily routes (OIRs), consisting of a sequence of deliveries of customer orders, pickup of a full truck-load at a supplier, and its delivery to the mill. This study distinguishes between three planning strategies: opportunistic backhauling planning (OBP), integrated inbound and outbound planning (IIOP) and decoupled planning (DIOP), the latter being the commonly used, particularly in the case of the wood-based panel industry under study. From the point of view of process integration, OBP can be considered as an intermediate stage from DIOP to IIOP. The problem is modelled as a Vehicle Routing Problem with Backhauls, enriched with case-specific rules for visiting the backhaul, split deliveries to customers and the use of a heterogeneous fleet. A new fix-and-optimise matheuristic is proposed for this problem, seeking to obtain good quality solutions within a reasonable computational time. The results from its application to the wood-based panel industry in Portugal show that IIOP can help to reduce total costs in about 2.7%, when compared with DIOP, due to better use of the delivery truck and a reduction of the number of dedicated inbound routes. Regarding OBP, fostering the use of OIRs does not necessarily lead to better routing plans than DIOP, as it depends upon a favourable geographical configuration of the set of customers to be visited in a day, specifically, the relative distance between a linehaul that can be visited last in a route, a neighboring backhaul, and a mill. The paper further provides valuable managerial insights on how the routing plan is impacted by the values of business-related model parameters which are set by the planner with some degree of uncertainty. Results suggest that increasing the maximum length of the route will likely have the largest impact in reducing transportation costs. Moreover, increasing the value of a reward paid for visiting a backhaul can foster the percentage of OIR in the optimal routing plan.

Abstract
Over the last years, it has become clear that online games are one of the most used applications on the Internet. This increasing popularity has attracted an influx of players, with some of them trying to gain an unfair advantage for economic reasons, e.g., eSports tournaments, through the use of cheats and exploits. From a different perspective, it is of utmost importance to start analyzing attacks from a defensive perspective to create novel mechanisms that can stop such behaviors. In this work, we introduce a novel solution that extends current anti-cheat solutions through Intel SGX. Our solution moves the core cheat detection engine to a secure enclave provided by SGX while making use of a kernel module for the necessary primitives for system-wide protection. With this, we can prevent client-side tampering in both game code and configuration data by creating a trusted execution environment isolated from the hosting operating system. We are making it capable of preventing the attacker from modifying the cheat detection engine and associated game files. This solution blocks known attacks in games such as CS:GO while maintaining the performance, ensuring gameplay integrity and fairness.

Abstract
Mobile robotics has become an essential content in many subjects within most Bachelor's and Master's degrees in engineering. Visual sensors have emerged as a powerful tool to perform reliable localization and mapping tasks for a mobile robot. Moreover, the use of images permits achieving other high level tasks such as object and people detection, recognition, or tracking. Nonetheless, our teaching experience confirms that students encounter many difficulties before dealing with visual localization and mapping algorithms. Initial stages such as data acquisition (images and trajectory), preprocessing or visual feature extraction, usually imply a considerable effort for many students. Consequently, the teaching process is prolonged, whereas the active learning and the students' achievement are certainly affected. Considering these facts, we have implemented a Matlab software tool to generate an open variety of virtual environments. This allows students to easily obtain synthetic raw data, according to a predefined robot trajectory inside the designed environment. The virtualization software also produces a set of images along the trajectory for performing visual localization and mapping experiments. As a result, the overall testing procedure is alleviated and students report to take better advantage of the lectures and the practical sessions, thus demonstrating higher achievement in terms of comprehension of fundamental mobile robotics concepts. Comparison results regarding the achievement of students, engagement, satisfaction and attitude to the use of the tool, are presented.

Abstract
Communication is crucial in agile contexts due to the need for collaboration and the number of different people involved, ranging from developers to users and business people. This communication should be effective to ensure everyone is on the same page; otherwise it can lead to undesired results such as misunderstanding and loss of productivity. As such, there is a need to ensure good communication in agile contexts. This paper presents a pattern language to tackle this issue, alongside with the patterns of a section of the language. The pattern language aims to provide a set of practices that help with recurrent problems and increase the effectiveness of communication. The patterns presented in this paper (Information Radiators, Co-Located Team, Open Space, Informal Communication Space and Cubes) address the issues related with the physical environment surrounding an agile team. © 2020 ACM.