Abstract
In the past few years, the literature has shown that the practice of reuse through requirement patterns is an effective alternative to address specification quality issues, with the additional benefit of time savings. Due to the interactions between requirements engineering and other phases of the software development life cycle (SDLC), these benefits may extend to the entire development process. This paper describes a revisited systematic literature mapping (SLM) that identifies and analyzes research that demonstrates those benefits from the use of requirement patterns for software design, construction, testing, and maintenance. In this extended version, the SLM protocol includes automatic search over two additional sources of information and the application of the snowballing technique, resulting in ten primary studies for analysis and synthesis. In spite of this new version of the SLM protocol, results still point out a small number of studies on requirement patterns at the SDLC (excluding requirements engineering). Results indicate that there is yet an open field for research that demonstrates, through empirical evaluation and usage in practice, the pertinence of requirement patterns at software design, construction, testing, and maintenance.

Abstract
This work consists in assessing a real islanded power system from a dynamic security point of view, to support the planned installation, in the near future, of a hydro power plant with pumped storage, aiming to increase the integration of renewable energy. The analysed hydro power plant will include a Pelton turbine as it is a high-head hydro facility. Due to economic reasons, the adopted water pumping technology consists in fixed speed pumps coupled to induction motors with direct grid connection. It was possible to verify through detailed simulations of this power system's time domain behaviour that, even though the expected installation of this new power plant will bring additional frequency stability constraints, a robust technical solution may be found dealing with the new constraints without increasing the complexity of operation in this islanded power system. The conclusions obtained from this specific case are also valid for similar isolated power systems, namely when hydro pumping stations are being considered to increase time-variable renewable generation penetration.

Abstract
To ensure interoperability and the correct end-to-end behavior of heterogenous distributed systems, it is important to conduct integration tests that verify the interactions with the environment and between the system components in key scenarios. The automation of such integration tests requires that test components are also distributed, with local testers deployed close to the system components, coordinated by a central tester. In such a test architecture, it is important to maximize the autonomy of the local testers to minimize the communication overhead and maximize the fault detection capability. A test scenario is called locally observable and locally controllable, if conformance errors can be detected locally and test inputs can be decided locally, respectively, by the local testers, without the need for exchanging coordination messages between the test components during test execution (i.e., without any communication overhead). For test scenarios specified by means of UML sequence diagrams that don’t exhibit those properties, we present in this paper an approach with tool support to automatically find coordination messages that, added to the given scenario, make it locally controllable and locally observable.

Abstract
The field of automated learning has been steadily growing in robotic tasks. This phenomenon is supported by the evolution of computational resources and new reinforcement learning algorithms. Researchers have drawn their attentions to methods that are easy to implement and tune, while achieving state-of-the-art performance. This trend also affects the world of robotic soccer, where new papers delve systematically into the optimization of basic skills. However, when learning higher-level strategies, there is space for improvement on two fronts. First, the simulation environment should allow the agent to abstract from low-level details. Second, the existing methods to train this kind of behaviors are still scarce. This paper contributes with innovative problem-solving methods, specifically in the rewards field. To test alternative approaches, an extended version of the RoboCup's official Soccer Server simulator was used. The results have confirmed the importance of the proposed reward components and their relationship with the episodes' initial conditions.

Abstract
MixAR, a full-stack system capable of providing visualization of virtual reconstructions seamlessly integrated in the real scene (e.g. upon ruins), with the possibility of being freely explored by visitors, in situ, is presented in this article. In addition to its ability to operate with several tracking approaches to be able to deal with a wide variety of environmental conditions, MixAR system also implements an extended environment feature that provides visitors with an insight on surrounding points-of-interest for visitation during mixed reality experiences (positional rough tracking). A procedural modelling tool mainstreams augmentation models production. Tests carried out with participants to ascertain comfort, satisfaction and presence/immersion based on an in-field MR experience and respective results are also presented. Ease to adapt to the experience, desire to see the system in museums and a raised curiosity and motivation contributed as positive points for evaluation. In what regards to sickness and comfort, the lowest number of complaints seems to be satisfactory. Models' illumination/re-lightning must be addressed in the future to improve the user's engagement with the experiences provided by the MixAR system.

Abstract
Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks.