Abstract
Phobias are characterized as the excessive or irrational fear of an object or situation, and specific phobias affect about 10% of the world population. Blood-injection-injury phobia is a specific phobia that has a unique physical response to phobic stimuli, that is, a vasovagal syncope that causes the person to faint. Phobos is a serious game intended for blood phobia treatment that was created to be played in virtual reality with an HTC Vive that has photorealistic graphics to provide a greater immersion. We also developed a console application in C# for electrocardiography sensor connectivity and data acquisition, which gathers a 1 min baseline reading and then has continuous data acquisition during gameplay. Usability tests were conducted with self-reported questionnaires and with a case study population of 10 testers, which gave insight into the previous game experience of the tester for both digital games and virtual reality games, evaluating the discomfort for hardware on both the sensor and the virtual reality headset, as well as the game regarding usability, user experience, level of immersion, and the existence of motion sickness and its source. The results corroborate that the immersion of the game is good, which suggests that it will help with triggering the phobia.

Abstract
This article introduces an immersive Virtual Reality (VR) application designed to assess the interaction capabilities of users with physical and cognitive limitations, including older adults and individuals with disabilities, as well as ICU patients. The VR application encompasses six tasks varying in complexity, each designed to evaluate different aspects of VR interaction skills, such as movements of the head, arms, and fingers, alongside more intricate activities like pick-and-place, pointing, and painting.The paper details the VR application's specifications, including its system architecture, deployment framework, and data structure. The application's efficacy was tested through three pilot studies in a retirement home setting. The analysis focused on examining correlations among various factors, including age, cognitive abilities (evaluated using the Mini-Mental Status Examination), and previous VR experience. The findings reveal significant correlations, illuminating the effects of age, cognitive capacity, and past VR interactions on task performance. The results emphasize the importance of accounting for user-specific attributes, prior experiences, and cognitive abilities in the design of VR-based therapeutic interventions.

Abstract
The increasing relevance of remote sensing and artificial intelligence (AI) for archaeological research and cultural heritage management is undeniable. However, there is a critical gap in this field. Many studies conclude with identifying hundreds or even thousands of potential sites, but very few follow through with crucial fieldwork validation to confirm their existence. This research addresses this gap by proposing and implementing a fieldwork validation pipeline. In northern Portugal's Alto Minho region, we employed this pipeline to verify 237 potential burial mounds identified by an AI-powered algorithm. Fieldwork provided valuable information on the optimal conditions for burial mounds and the specific factors that led the algorithm to err. Based on these insights, we implemented two key improvements to the algorithm. First, we incorporated a slope map derived from LiDAR-generated terrain models to eliminate potential burial mound inferences in areas with high slopes. Second, we trained a Vision Transformer model using digital orthophotos of both confirmed burial mounds and previously identified False Positives. This further refines the algorithm's ability to distinguish genuine sites. The improved algorithm was then tested in two areas: the original Alto Minho validation region and the Barbanza region in Spain, where the location of burial mounds was well established through prior field work.

Abstract
O ensino de conteúdos de matemática na educação básica apresenta alguns desafios. Muitos desses vêm sendo superados com a utilização de tecnologias da informação e comunicação. Nesse contexto, a robótica educacional vem ganhando espaço, estando cada vez mais presente em ambientes escolares. Porém, há escassez de materiais que auxiliem os professores no uso dessa tecnologia em sala de aula. Para começar a suplantar esse problema, neste artigo, apresenta-se o desenvolvimento de uma ontologia capaz de auxiliar o ensino e aprendizagem da disciplina de matemática utilizando robótica educacional. A ontologia denominada Ontologia de Conteúdo de Matemática Combinada com Robótica Educacional (Onto-ENSINARE) foi construída com base na metodologia Ontology Development 101 com os aspectos de completude, consistência e concisão. Para validar a ontologia foram utilizadas consultas SPARQL para obtenção de respostas úteis aos professores de matemática da educação básica.

Abstract
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing's unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.

Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm's performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.