Abstract
This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency.

Abstract
Formal verification has become increasingly crucial in ensuring the accurate and secure functioning of modern software systems. Given a specification of the desired behaviour, i.e. a contract, a program is considered to be correct when all possible executions guarantee the specification. Should the software fail to behave as expected, then a bug is present. Most existing research assumes that the bug is present in the implementation, but it is also often the case that the specified expectations are incorrect, meaning that it is the specification that must be repaired. Research and tools for providing alternative specifications that fix details missing during contract definition, considering that the implementation is correct, are scarce. This paper presents a preliminary tool, focused on Dafny programs, for automatic specification repair in contract programming. Given a Dafny program that fails to verify, the tool suggests corrections that repair the specification. Our approach is inspired by a technique previously proposed for another contract programming language and relies on Daikon for dynamic invariant inference. Although the tool is focused on Dafny, it makes use of specification repair techniques that are generally applicable to programming languages that support contracts. Such a tool can be valuable in various scenarios, such as when programmers have a reference implementation and need to analyse their contract options, or in educational contexts, where it can provide students with hints to correct their contracts. The results of the evaluation show that the approach is feasible in Dafny and that the overall process has reasonable performance but that there are stages of the process that need further improvements.

Abstract
Computer Science degrees are often seen as challenging by students, especially in what concerns subjects such as programming, data structures or algorithms. Many reasons can be pointed out for this, some of which related to the abstract nature of these subjects and the lack of previous related knowledge by the students. In this paper we tackle this challenge using gamification in the teaching/learning process, with two main goals in mind. The first is to increase the intrinsic motivation of students to learn, by making the whole process more fun, enjoyable and competitive. The second is to facilitate the learning process by providing intuitive tools for the visualization of data structures and algorithmic output, together with a tool for automated assessment that decreases the dependence on the teacher and allows them to work more autonomously. We validated this approach over the course of three academic years in a Computer Science degree of the Polytechnic of Porto, Portugal, through the use of a questionnaire. Results show that the effects of using games and game elements have a generally positive effect on motivation and on the overall learning process.

Abstract
Bone marrow edema (BME) or bone marrow lesion is the term attributed to an observed signal change within the bone marrow in magnetic resonance imaging (MRI). BME can be originated from multiple mechanisms, with pain being the main symptom. The presence of BME is an unspecific but sensitive sign with a wide differential diagnosis, that may act as a guide that leads to a systematic and correct interpretation of the magnetic resonance examination. An automatic approach for BME detection and quantification aims to reduce the overload of clinicians, decreasing human error and accelerating the time to the correct diagnosis. In this work, the bone region on the MRI slice was split into several patches and a CNN-based model was trained to detect BME in each patch from the MRI slice. The learning model developed achieved an AUC of 0.853 ± 0.056, showing that the CNN-based model can be used to detect BME in the MRI and confirming the patch strategy implemented to deal with the small data size and allowing the neural network to learn the specific information related with the classification task by reducing the region of the image to be considered. A learning model that can help clinicians with BME identification will decrease the time and the error for the diagnosis, and represent the first step for a more objective assessment of the BME. © 2023 IEEE.

Abstract
Although extensively used in the academic literature, workaholism as a concept has been explained in different ways, which has influenced the development and use of some measurement tools. As such, this article aims to address the subject through a systematic study review focusing on articles where the main objective was to develop, adapt, or analyze the psychometric properties of a workaholism scale. The main purpose is to describe the state of the art concerning workaholism measurement tools, highlighting trends and research perspectives for further research. In essence, this study may serve as a summary and starting point for scholars interested in measuring workaholism. It was observed that the discrepancy concerning the definition of workaholism has resulted in scales that attempt to evaluate diverging conceptualizations. Moreover, each scale has been readapted when tested in different countries. For further investigations, it is important to converge the concept of workaholism and validate the scales across differing contexts, regarding the industry, culture, and country of the sample.

Abstract
The adoption of immersive virtual experiences (IVEs) opened new research lines where the impact of realism is being studied, allowing developers to focus resources on realism factors proven to improve the user experience the most. We analyzed papers that compared different levels of realism and evaluated their impact on user experience. Exploratorily, we also synthesized the realism terms used by authors. From 1,300 initial documents, 79 met the eligibility criteria. Overall, most of the studies reported that higher realism has a positive impact on user experience. These data allow a better understanding of realism in IVEs, guiding future R&D.