Abstract
Public lighting plays a very important role for society's safety and quality of life. The identification of faults in public lighting is essential for the maintenance and prevention of safety. Traditionally, this task depends on human action, through checking during the day, representing expenditure and waste of energy. Automatic detection with deep learning is an innovative solution that can be explored for locating and identifying of this kind of problem. In this study, we present a first approach, composed of several steps, intending to obtain the segmentation of public lighting, using Seville (Spain) as case study. A dataset called NLight was created from a nighttime image taken by the JL1-3B satellite, and four U-Net and FPN architectures were trained with different backbones to segment part of the NLight. The U-Net with InceptionResNetv2 proved to be the model with the best performance, obtained 761 of 815, correct locations (93.4%). This model was used to predict the segmentation of the remaining dataset. This study provides the location of lamps so that we can identify patterns and possible lighting failures in the future.

Abstract
Selective thinning is a crucial operation to reduce forest ignitable material, to control the eucalyptus species and maximise its profitability. The selection and removal of less vigorous stems allows the remaining stems to grow healthier and without competition for water, sunlight and nutrients. This operation is traditionally performed by a human operator and is time-intensive. This work simplifies selective thinning by removing the stem selection part from the human operator's side using a computer vision algorithm. For this, two distinct datasets of eucalyptus stems (with and without foliage) were built and manually annotated, and three Deep Learning object detectors (YOLOv5, YOLOv7 and YOLOv8) were tested on real context images to perform instance segmentation. YOLOv8 was the best at this task, achieving an Average Precision of 74% and 66% on non-leafy and leafy test datasets, respectively. A computer vision algorithm for automatic stem selection was developed based on the YOLOv8 segmentation output. The algorithm managed to get a Precision above 97% and a 81% Recall. The findings of this work can have a positive impact in future developments for automatising selective thinning in forested contexts.

Abstract
The challenge is a fundamental aspect of almost every gameplay, and immersion is one of the most widely recognized concepts in the video game industry. Since this is currently a work in progress, this study aims to preliminary research how player's perceived level of challenge affects narrative immersion during gameplay in the role-playing game (RPG) genre. This study will outline the procedures that will be undertaken, including the utilization of the Challenge Originating from Recent Gameplay Interaction Scale (CORGIS) instrument and a questionnaire to measure player immersion. These instruments will enable the assessment of the impact of the perceived challenge on narrative immersion in each use case. © 2023 Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2023, IHCI 2023; Computer Graphics, Visualization, Computer Vision and Image Processing 2023, CGVCVIP 2023; and Game and Entertainment Technologies 2023, GET 2023. All rights reserved.

Abstract
A significant concern that is widely discussed in the field of Higher Education is declining student participation. In several institutions, attendance is optional, allowing students to attend lectures at their convenience. This study proposes the integration of Artificial Intelligence and Gamification to improve student engagement and attendance rates. The initiative combines advanced technological strategies with conventional educational methodologies to enhance the lecture experience. The initiative is significant as formal lectures often witness dwindling student interest and frequent absenteeism, undermining the educational process and student's future career prospects. This combination has the potential to revolutionise Higher Education by providing a more interactive and engaging learning experience. While gamification has positively impacted learning in various contexts, integration with Artificial Intelligence is a game-changer, paving the way for a modernised educational experience. This innovative exploration of the AI-gamification blend sets the stage for future research and the implementation of updated academic strategies, ultimately addressing student engagement and attendance. This position paper presents the bases and foundations for understanding the problem of student attendance and engagement and the role of AI and gamification in Higher Education in alleviating it.;Uma inquietação relevante e extensamente discutida no domínio do Ensino Superior é a diminuição da participação dos estudantes. Em diversas instituições, a assiduidade é facultativa, permitindo aos estudantes a frequência às aulas segundo a sua disponibilidade. Este estudo propõe a integração da Inteligência Artificial e da Gamification como meios para melhorar o envolvimento e as taxas de assiduidade dos estudantes. A iniciativa em causa articula estratégias tecnologicamente avançadas com metodologias educativas convencionais no intuito de enriquecer a experiência de ensino. Tal iniciativa assume importância dada à constante diminuição do interesse dos estudantes e a assiduidade irregular nas aulas formais, fatores que afetam negativamente o processo de ensino e as perspetivas de carreira dos estudantes. Esta combinação ostenta o potencial de revolucionar o Ensino Superior, proporcionando uma experiência de aprendizagem mais interativa e envolvente. Embora a Gamification já tenha impactado positivamente o processo de aprendizagem em diversos contextos, a sua integração com a Inteligência Artificial surge como um elemento transformador, abrindo caminho para uma experiência educacional modernizada. Esta investigação inovadora que combina a IA e Gamification prepara as bases para investigações futuras e a implementação de estratégias académicas aprimoradas, concentrando-se principalmente no envolvimento e na assiduidade dos estudantes. Este artigo de posicionamento apresenta as bases e os fundamentos necessários para a compreensão do problema da frequência e envolvimento dos estudantes no Ensino Superior, assim como o papel da IA e da Gamification na sua mitigação.

Abstract
Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive syn-thesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increas-ingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and rec-ommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.

Abstract
The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna's functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental's EOL.