Abstract

Abstract
Acquired brain injury (ABI) is associated with severe functional consequences at several levels: personal, psychological, social, physical, economic, and systemic. The main objective of the present study is to show the potential of applying virtual reality (VR) in neuropsychological rehabilitation and the consequent psychological and cognitive improvement of the person with ABI. A total of 27 participants of both sexes with moderate or severe ABI participated in an investigation in a clinical trial-type design with pre-test and post-test. Participants in the experimental group (n = 8) underwent a remote holistic neuropsychological intervention program supported by a VR platform: the Virtual Centre for the Rehabilitation of Road Accident Victims (VICERAVI). The experimental group results were compared with a first control group (n = 10) that underwent a conventional holistic neuropsychological intervention face-to-face program; and with a second control group that did not have any neuropsychological intervention (n = 9). We conclude that the VR-based neuropsychological rehabilitation program (NRP) at a distance produces better cognitive results in general cognitive functioning, learning, memory, and executive functioning than the conventional face-to-face NRP. Still, at the psychosocial level, the conventional NRP obtained results similar to those of the VR-based NRP, which did not produce significant improvements. Thus, the study results suggest that the development of VR-based holistic NRPs may benefit the autonomy of people with ABI.

Abstract
This paper proposes a modular framework to generate robust biped locomotion using a tight coupling between an analytical walking approach and deep reinforcement learning. This framework is composed of six main modules which are hierarchically connected to reduce the overall complexity and increase its flexibility. The core of this framework is a specific dynamics model which abstracts a humanoid's dynamics model into two masses for modeling upper and lower body. This dynamics model is used to design an adaptive reference trajectories planner and an optimal controller which are fully parametric. Furthermore, a learning framework is developed based on Genetic Algorithm (GA) and Proximal Policy Optimization (PPO) to find the optimum parameters and to learn how to improve the stability of the robot by moving the arms and changing its center of mass height. A set of simulations are performed to validate the performance of the framework using the official RoboCup 3D League simulation environment. The results validate the performance of the framework, not only in creating a fast and stable gait but also in learning to improve the upper body efficiency.

Abstract

Abstract
The introduction of Cyber-Physical Systems (CPS) in the industry through the digitalization of equipment, also known as Digital Twins, allows for a more customized production. Due to high market fluctuation, the implementation of a CPS should guarantee a high flexibility in both hardware and software levels to achieve a high responsiveness of the system. The software reconfiguration, specifically, introduces a question: With heterogeneous equipment with different capabilities namely processing and memory capabilities - where a certain software module should execute? ; that question fits on the task/resource allocation area applied to CPS software reconfiguration. Although in task allocation issue several approaches address such a problem, only a few of them focus on CPS resources optimization. Given that, an approach based on the Dutch Auction algorithm is proposed, implemented at the CPS level enables the software reconfiguration of the CPS according to the existing equipment resources. This approach, besides the optimization of the CPS resources and the energy consumption, transforms the CPS in more reliable and fault-tolerant systems. As shown by the results, despite the demonstration of its suitability in task/resource allocation problems in decentralized architectures, the proposed approach also as a major advantage of quickly finding a near-optimal solution.

Abstract
The Natural Language Processing (NLP) community has witnessed huge improvements in the last years. However, most achievements are evaluated on benchmarked curated corpora, with little attention devoted to user-generated content and less-resourced languages. Despite the fact that recent approaches target the development of multi-lingual tools and models, they still underperform in languages such as Portuguese, for which linguistic resources do not abound. This paper exposes a set of challenges encountered when dealing with a real-world complex NLP problem, based on user-generated complaint data in Portuguese. This case study meets the needs of a country-wide governmental institution responsible for food safety and economic surveillance, and its responsibilities in handling a high number of citizen complaints. Beyond looking at the problem from an exclusively academic point of view, we adopt application-level concerns when analyzing the progress obtained through different techniques, including the need to obtain explainable decision support. We discuss modeling choices and provide useful insights for researchers working on similar problems or data.