Abstract
We propose a solution for generating dynamic heightmap data to simulate deformations for soft objects, with a focus on the human skin. The solution utilizes mesostructure-level wrinkles and procedural textures to add static microstructure details. It offers flexibility beyond human skin during animations to mimic other material deformations, such as leather and rubber. Various methods suffer from self-intersections and increased storage requirements during synthesizing wrinkles. Although manual intervention using wrinkles and tension maps offers control, it lacks information on principal deformation directions. Physics-based simulations can generate detailed wrinkle maps, but may limit artistic control. Our research presents a procedural method to enhance the generation of dynamic deformation patterns, including wrinkles, with better control and without reliance on captured data. Incorporating static procedural patterns improves realism, and the proposed approach can be used in other application areas.

Abstract
Artificial intelligence encapsulates a black box of undiscovered knowledge, propelling the exploration of Explainable Artificial Intelligence (XAI) in generative data synthesis and deep learning. Focused on unveiling these black box areas, pointed into interpretability and validation in synthetic data generation, shedding light on the intricacies of generative processes. XAI techniques illuminate decision-making in complex algorithms, enhancing transparency and fostering a comprehensive understanding of non-linear relationships. Addressing the complexity of explaining deep learning models, this paper proposes an XAI solution for deep synthetic data generation explanation. The model integrates a clustering approach to identify similar training instances, reducing interpretation time for large datasets. Explanations, available in various formats, are tailored to diverse user profiles through integration with language models, generating texts with different technical detail levels. This research contributes to ethically deploying AI, bridging the gap between advanced model complexities and human interpretability in the dynamic landscape of artificial intelligence.

Abstract
Teaching the processes of designing digital electronic systems is becoming an increasingly challenging task. Design methodologies and tools have evolved to cope with the ever-growing complexity and density, raising the abstraction level of the source design far away from the logic circuit. However, it is of paramount importance that fresh students start by understanding the fundamental concepts of Boolean algebra, design, and optimization of combinational and sequential gatelevel circuits, before moving to higher abstract concepts and tools. For this, hands-on practice with simple real digital circuits is essential to understanding the essentials of the operation of digital circuits and how digital data is propagated and transformed from block to block. In this paper we present a distributed infrastructure based on the network protocol MQTT to support the deployment of distributed digital systems built with parts located in different physical locations. Thus, promoting the implementation of collaborative online learning/teaching activities will be one of our main goals. Experimental results show latencies between remote sites in the range of a few tens of milliseconds, which is acceptable for running simple digital systems at low speeds, which is necessary for being perceived and understanded by people.

Abstract
With the proliferation of Internet of Things (IoT) devices, there is an increasing need to prioritize their security, especially in the context of identity and authentication mechanisms. However, IoT devices have unique limitations in terms of computational capabilities and susceptibility to hardware attacks, which pose significant challenges to establishing strong identity and authentication systems. Paradoxically, the very hardware constraints responsible for these challenges can also offer potential solutions. By incorporating hardware-based identity implementations, it is possible to overcome computational and energy limitations, while bolstering resistance against both hardware and software attacks. This research addresses these challenges by investigating the vulnerabilities and obstacles faced by identity and authentication systems in the IoT context, while also exploring potential technologies to address these issues. Each identified technology underwent meticulous investigation, considering known security attacks, implemented countermeasures, and an assessment of their pros and cons. Furthermore, an extensive literature survey was conducted to identify instances where these technologies have effectively supported device identity. The research also includes a demonstration that evaluates the effectiveness of hardware trust anchors in mitigating various attacks on IoT identity. This empirical evaluation provides valuable insights into the challenges developers encounter when implementing hardware-based identity solutions. Moreover, it underscores the substantial value of these solutions in terms of mitigating attacks and developing robust identity frameworks. By thoroughly examining vulnerabilities, exploring technologies, and conducting empirical evaluations, this research contributes to understanding and promoting the adoption of hardware-based identity and authentication systems in secure IoT environments. The findings emphasize the challenges faced by developers and highlight the significance of hardware trust anchors in enhancing security and facilitating effective identity solutions.

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.

Abstract
Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating energy storage systems (ESSs) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This article compares the performance of hybrid energy storage systems (HESSs) to a single battery, evaluating their energy supply cost and environmental impact through optimization problems. The optimization model is based on a MILP incorporating the energy and degradation terms. It generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load. Seven technologies are assessed, with an example applied to an industrial site combining a vanadium redox flow battery (VRFB) and lithium battery considering the demand of a local load (building). The results indicate that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.