Abstract
This position paper emphasizes the critical role of professional training in facilitating the effective adoption of Generative AI (GenAI) in the corporate world. GenAI, with its ability to create new content from existing data, holds immense potential for transforming business processes, enhancing decision-making, and driving innovation. However, the adoption of GenAI faces significant challenges, including a shortage of skilled professionals, high implementation costs, data privacy concerns, and the complexity of integrating these technologies into existing systems. To address these challenges, this paper highlights the importance of comprehensive education and training programs tailored to equip employees with the necessary skills and knowledge. Such programs should focus on developing technical competencies and understanding the operational implications of GenAI. By analyzing current literature and case studies, this paper identifies key strategies for effective training and outlines best practices for integrating GenAI into corporate environments. The findings underscore the need for a strategic approach to training that aligns with the evolving demands of AI-driven innovation. This includes continuous learning and development initiatives, the promotion of a culture of innovation, and the implementation of responsible AI practices. By investing in professional training, organizations can bridge the skills gap, mitigate risks, and fully leverage the transformative potential of GenAI technologies, ultimately gaining a competitive edge in the market. Through this comprehensive exploration, the paper advocates for the integration of robust training frameworks that support the sustainable adoption of GenAI, ensuring that businesses are well-prepared to navigate the complexities and opportunities of the digital age.

Abstract
In recent years, companies have faced increasing pressure from globalization, requiring them to adapt not only to survive but also to thrive in a highly competitive environment. This adaptation has been facilitated by the efficient integration of technology, achieved through digital processes and collaboration tools. Digital transformation has emerged as a critical element for maintaining competitiveness as economies become increasingly digital. To succeed in this everevolving environment, companies must balance leveraging existing strengths with seeking new organizational agility. Integrating advanced technologies like Artificial Intelligence (AI) and Web Technologies into education and professional training is a strategic response to the challenges posed by the current digital landscape. AI, with its adaptability and automation capabilities, offers benefits such as increased efficiency, personalized learning, and streamlined administrative processes. Continuous evaluation of teaching and learning, along with data extraction and predictive analysis, enhances e-learning quality and informs organizational decisions. This research aims to investigate how advanced technologies can predict and adapt organizational training needs to improve competency development and overall effectiveness. The research adopts a Design Science Research (DSR) methodology, focusing on the development and implementation of an AI-based framework for personalized training recommendations. Expected outcomes include integrating AI-driven predictive models with existing Human Resources Management Systems to identify and address training needs, fostering employee skill development, organizational agility, and competitiveness in a rapidly changing market. Additionally, addressing this issue promotes a more inclusive and empowering work environment, enabling employees to thrive in an increasingly digital world.

Abstract
Background: Virtual Reality (VR) is making education more engaging and accessible, especially for students with Autism Spectrum Disorders (ASD), promoting inclusion and the development of STEM skills in innovative ways. The literature still reveals a significant gap in terms of appropriate educational resources adapted to the specific needs of these students, resulting in difficulties in their inclusion. With the growing need for inclusive approaches in education, it is essential to find solutions to support these students. The aim of this study is to validate the data collection methodology that will enable the development of Virtual Learning Environments with STEM content for students with ASD. Methods: The Design Science Research (DSR) methodology was used to develop a VR artefact for students with ASD. In addition, the Delphi method was applied in the expert involvement phase, which will contribute to the validation of the artefact's specific requirements. Both will allow for an inclusive and distinctive approach to the development of an artefact, with the aim of offering an innovative educational experience, meeting the varied needs and learning styles of students with ASD, optimising the effectiveness of the proposed VLE. Results: The results show a strong acceptance among experts, highlighting the potential positive impact of this approach, although there are aspects to be improved to ensure a more comprehensive and effective approach. Conclusions: This study highlights the successful validation of an innovative virtual reality programme for students with ASD, highlighting the importance of interdisciplinary collaboration and the strong contribution to the advancement of inclusive education.

Abstract
Object detection is a fundamental task of computer vision that is constantly evolving, with a wide range of applications in fields such as security, medicine, and autonomous driving. This work presents an interactive self-learning course dedicated to exploring some crucial concepts for beginners in object detection. The course offers educational resources, including the possibility to follow a simple tutorial on the operation of an object detection model and definitions of the main concepts related to object detection technology. Users also have a brief description of object detection algorithms such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Networks), and SSD (Single Shot Detector) and the possibility to learn more about these in a tutorial prepared on a Google Colab notebook. The course aims to provide a learning experience accessible to beginners in the field of object detection, who want to take the first step in their learning about the subject. After completing the tutorial, the user answers a questionnaire, with the goal of analyzing the learning outcomes and extracting the user's impression of the website in general. With this paper, we want to show the advantages of using tools of this nature to foster learning regarding object detection.

Abstract
This study examines the effectiveness of employing Advanced Rider Assistance System (ARAS) for enhancing motorcycle safety by reducing crashes and improving rider safety. The system includes both single solution approaches, like braking systems, and multi-sensor solutions that integrate data from LiDARs, radars, and cameras through sensor fusion. A systematic literature review was conducted to collect data from 2008 to 2024 across various sources related to ARAS. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a comprehensive and transparent process. Data were extracted from the included studies, focusing on study design, sample size, intervention details, and outcomes. The risk of bias was assessed using a customized checklist. The review included 31 studies that met the inclusion criteria. Findings were summarized for single sensor solutions and sensor fusion approaches.The review indicates that single-solution systems are effective ARAS technologies. In contrast, the application of sensor fusion in motorcycles has been only minimally explored, making it difficult to draw definitive conclusions about its impact in this context. Evidence from four-wheeled vehicles, however, shows that sensor fusion can enhance perception robustness, improve performance under adverse conditions, and contribute to measurable safety gains. These results suggest that similar advantages could be realized for motorcycles as fusion-based ARAS technologies become more widely implemented. Moreover, sensor fusion holds the potential to provide riders with broader situational awareness and more comprehensive safety assistance than single-system solutions. Future research should focus on addressing the identified challenges and optimizing these systems for broader implementation. This review underscores the critical role of ARAS in reducing motorcycle-related incidents and improving rider safety, highlighting the need for ongoing research to refine sensor fusion algorithms and address technical challenges for real-world applications.

Abstract
Pose estimation by computer vision is essential in underwater robot navigation. Several works already use computer vision and ArUco markers for this purpose. The method is widely spread and developed. In terms of software, libraries have already been developed, for instance, the ArUco module in the OpenCV library. However, there is still a need to characterize the relationship between the performance of the system and the computer vision hardware itself, as well as the spatial arrangement of the markers. Another aspect to take into account is the environmental condition. This work seeks to relate these factors to the error resulting from the estimation of relative positions between cameras and markers.