Abstract
The Puzzle -based Learning approach has been applied to several fields of knowledge. In education research papers, the instructional usage of puzzles is considered to improve learners' motivation and engagement and help them to develop critical skills but difficulties concerning learners' interaction with puzzles have also been pointed out. Our paper investigates the dynamics of the concept of a puzzle and its interface to provide a better understanding of its form and functions, and help learners interact with puzzles. We consider Puzzle -based Learning tenets as well as their educational impacts on both critical thinking and learner engagement and provide an original proposal concerning the understanding of puzzles. Our proposal centered on the dynamics of puzzles bears conceptual and educational facets. Conceptually, puzzle dynamics is viewed as composed of two elements: a mechanism, the Puzzle Trigger, and a process, the Puzzle -Solving. From an educational point of view, the rationale for integrating Puzzle Triggers in Puzzle -based Learning is meant to help learners interact with puzzles and consequently become motivated and engaged in the Puzzle -Solving process. This way, learners' critical thinking skills are reinforced and focused on finding solutions to challenges. We illustrate the implementation of Puzzle Triggers and Puzzle -Solving by considering two instructional activities in a Software Development undergraduate course of an online learning Informatics Engineering Program.

Abstract
The proposed framework (Human-Centered Trustworthy Framework) provides a novel human-computer interaction approach to incorporate positive and meaningful trustful user experiences in the system design process. It helps to illustrate potential users' trust concerns in artificial intelligence and guides nonexperts to avoid designing vulnerable interactions that lead to breaches of trust.

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations.

Abstract
Artificial intelligence (AI) has gained significant evolution in recent years that, if properly harnessed, may meet or exceed expectations in a wide range of application fields. However, because Machine Learning (ML) models have a black-box structure, end users frequently seek explanations for the predictions made by these learning models. Through tools, approaches, and algorithms, Explainable Artificial Intelligence (XAI) gives descriptions of black-box models to better understand the models’ behaviour and underlying decision-making mechanisms. The AI development in companies enables them to participate in Industry 4.0. The need to inform users of transparent algorithms has given rise to the research field of XAI. This paper provides a brief overview and introduction to the subject of XAI while highlighting why this topic is generating more and more attention in many sectors, such as industry.

Abstract
Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics.

Abstract
The decline in student attendance and engagement in Higher Education (HE) is a pressing concern for educational institutions worldwide. Traditional lecture-style teaching is no longer effective, and students often become disinterested and miss classes, impeding their academic progress. While Gamification has improved learning outcomes, the integration of Artificial Intelligence (AI) has the potential to revolutionise the educational experience. The combination of AI and Gamification offers numerous research opportunities and paves the way for updated academic approaches to increase student engagement and attendance. Extensive research has been conducted to uncover the correlation between student attendance and engagement in HE. Studies consistently reveal that regular attendance leads to better academic performance. On the other hand, absenteeism can lead to disengagement and poor academic performance, stunting a student's growth and success. This position paper proposes integrating Gamification and AI to improve attendance and engagement. The approach involves incorporating game-like elements into the learning process to make it more interactive and rewarding. AI-powered tools can track student progress and provide personalised feedback, motivating students to stay engaged. This approach fosters a more engaging and fruitful educational journey, leading to better learning outcomes. This position paper will inspire further research in AI-Gamification integration, leading to innovative teaching methods that enhance student engagement and attendance in HE.