Abstract
Augmented and virtual reality have been experiencing rapid growth in recent years, but there is still no deep knowledge regarding their capabilities and in what fields they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of Microsoft's HoloLens 2 (augmented reality device) and HTC Vive (virtual reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, whereas, in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimeter range, while the HoloLens 2 revealed not very accurate measurements (around 2 cm). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not the system's controller made a huge impact. The results are considered a significant step for the ongoing project of developing a human-robot interface by demonstrating an industrial robot using extended reality, which shows great potential to succeed based on our data.

Abstract
Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, particularly with the application of machine learning techniques to help identify risk levels or risk factors of a project before the project development begins, with the intent of improving the likelihood of success of software projects. This paper provides an overview of various concepts related to risk and risk management in software projects, including traditional techniques used to identify and control risks in software projects, as well as machine learning techniques and methods which have been applied to provide better estimates and classification of the risk levels and risk factors that can be encountered during the development of a software project. The paper also presents an analysis of machine learning oriented risk management studies and experiments found in the literature as a way of identifying the type of inputs and outputs, as well as frequent algorithms used in this research area.

Abstract
The selection of the resources system (SRS) is an important element in the integration/project of Agile/Virtual Enterprises (A/V E) because its performance is dependent of this selection, and even of its creation. However, it remains a difficult matter to solve because is still a very complex and uncertain problem. We propose that using Value Analysis (VA) in the pre-selection of resources phase represents a significant improvement of the SRS process. The current literature fails to formally address the pre-selection phase and none of the resource selection models incorporate the resources value and its consequence in the complexity of the selection process. Whereby, ours developed model with VA constitutes an innovative approach towards greater sustainability in the configuration of A/V E in the context of Industry 4.0, where a massive interconnection among enterprises is expected and consequently the increase of the selection process complexity. After the construction of a demonstrator tool for a set of the problem formulations, this paper verifies by computational results the thesis regarding the benefits of applying VA to the SRS process: VA reduces the complexity of the SRS process, even ensuring that the final system of resources achieve higher quality/value grade.

Abstract
The transport sector of the European Union is the only sector of the economy that has been increasing its emissions since 2014. To reduce the use of fossil fuels and achieve the greenhouse gas emissions mitigation target, many countries are focusing on the deployment of electric vehicles. This paper aims at analysing recent literature on the deployment of electric vehicles (EV) and typifying objectives, methods and indicators generally exploited, to better understand the state of the art on this topic. The Web of Science database was used and the results showed that the interest in the topic of electric vehicles has been increasing exponentially since 2010. The main significant indicators and the assessment methodologies were analysed. The indicators identified were aggregated in four main clusters: environmental, economic, social and technical indicators. Although the factors that contribute to EV deployment can vary depending on the regions specific characteristics, most of the research studies pointed out that the main contributors are the high density of recharging points, the existence of government monetary incentives and the lower operational cost of EV.

Abstract
The Natural Language Processing task of determining Who did what to whom is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed for major improvements in this task over the previous state of the art. However, for low resource languages, like Portuguese, currently available semantic role labeling models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained Transformer-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state-of-the-art performance in Portuguese by over 15 F1. Additionally, we improve semantic role labeling results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models, and transfer learning from dependency parsing in Portuguese, evaluating the various proposed approaches empirically.

Abstract
Although advances have been made in reducing the time needed for manhole inspection, the procedure is still mostly done manually, with workers having to enter and visually assess the areas being inspected. There is also a growing need to have these structures inspected regularly, in order to prevent casualties and services interruption, as well as the higher cost of rebuilding instead of repairing these structures, which is possible only if pathologies are identified at early stages. This situation renders the task a good target for automation. This paper reviews a set of existing manhole, tunnel and duct inspection systems to ascertain the main features required for the task, as well as the technologies currently used. Most of the present-day solutions are rather expensive and cumbersome, requiring the deployment of relatively heavy equipment and specialized personnel to operate them. With the recent development of laser range sensors and depth (RGBD) cameras with small form factors and weights, the development of solutions with higher portability and lower cost become feasible. Such a solution could improve considerably the rate at which manholes are inspected, and the technology could be used to generate textured models to be analyzed and reported by a remotely located specialist, both online and offline. The work presented here lays the ground for the development of such a system in our research group who has been working on low-cost systems for the generation of 3D textured models for automated inspection. © 2021, Springer Nature Switzerland AG.