Abstract

Abstract
The second workshop on HCI Engineering Education continued the effort of the IFIP Working Group 2.7/13.4 on User Interface Engineering by discussing the issues and identifying the opportunities in teaching and learning Human-Computer Interaction (HCI) Engineering. The workshop attracted eight papers covering different teaching contexts, ranging from massive university courses, passing through different teaching experiences in specific academic curricula, and even teaching engineering concepts to children. In addition, the workshop received input for improving and adapting the repository material to the dynamic nature of this field. The discussion after the presentation of the contributions focused on how to model competencies, the support to interdisciplinary work, the overall course design, the recruitment of the students and the provision of educational resources, paving the way for further editions of the workshop.

Abstract
X-Wing is a hybrid key-encapsulation mechanism based on X25519 and ML-KEM-768. It is designed to be the sensible choice for most applications. The concrete choice of X25519 and ML-KEM-768 allows X-Wing to achieve improved efficiency compared to using a generic KEM combiner. In this paper, we introduce the X-Wing hybrid KEM construction and provide a proof of security. We show (1) that X-Wing is a classically IND-CCA secure KEM if the strong Diffie-Hellman assumption holds in the X25519 nominal group, and (2) that X-Wing is a post-quantum IND-CCA secure KEM if ML-KEM-768 is itself an IND-CCA secure KEM and SHA3-256 is secure when used as a pseudorandom function. The first result is proved in the ROM, whereas the second one holds in the standard model. Loosely speaking, this means X-Wing is secure if either X25519 or ML-KEM-768 is secure. We stress that these security guarantees and optimizations are only possible due to the concrete choices that were made, and it may not apply in the general case.

Abstract
Forest fires in Portugal are a recurring tragedy, especially during the summer, leaving a devastating trail affecting the environment and local communities. In addition to the loss of vast forest areas, these disasters harm wildlife, pollute the air, and compromise soil and water quality, contributing to environmental degradation and increasing the risk of soil erosion and landslides. Furthermore, fires have significant economic impacts, affecting communities that depend on the forest for subsistence, tourism, and agricultural activities. To address this issue, an innovativeWeb Service has been developed that uses artificial intelligence algorithms to calculate real-time fire risk. This service integrates up-todate weather data with historical fire patterns, providing an accurate and timely assessment of fire potential in specific areas. The machine learning model behind the service was trained with historical fire data from mainland Portugal between 2017 and 2023, allowing for a more accurate and predictive analysis of fire risk. The Web Service facilitates proactive emergency prevention and decision-making response by integrating realtime weather information with historical fire data. Authorities can use the information provided by the service to implement preventive policies to help elderly people.

Abstract
With four companions at separations from 16 to 71 au, HR 8799 is a unique target for direct imaging, presenting an opportunity for a comparative study of exoplanets with a shared formation history. Combining new VLTI/GRAVITY observations obtained within the ExoGRAVITY program with archival data, we performed a systematic atmospheric characterisation across all four planets. We explored different levels of model flexibility to understand the temperature structure, chemistry, and clouds of each planet using both petitRADTRANS atmospheric retrievals and fits to self-consistent radiative-convective equilibrium models. Using Bayesian model averaging to combine multiple retrievals (a total of 89 across all four planets), we find that the HR 8799 planets are highly enriched in metals, with [M/H] greater than or similar to 1, and have stellar to superstellar atmospheric C/O ratios. The C/O ratio increases with increasing separation from 0.55(-0.10)(+0.12) for d to 0.78(-0.04)(+0.03) for b, with the exception of the innermost planet, which has a C/O ratio of 0.87 +/- 0.03. Such high metallicities are unexpected for these massive planets, and challenge planet-formation models. By retrieving a quench pressure and using a disequilibrium chemistry model, we derive vertical mixing strengths compatible with predictions for high-metallicity, self-luminous atmospheres. Bayesian evidence comparisons strongly favour the presence of HCN in HR 8799 c and e, as well as CH4 in HR 8799 c, with detections at > 5 sigma confidence. All of the planets are cloudy, with no evidence of patchiness. The clouds of c, d, and e are best fit by silicate clouds lying above a deep iron cloud layer, while the clouds of the cooler HR 8799 b are more likely composed of Na2S. With well-defined atmospheric properties, future exploration of this system is well positioned to unveil further details of these planets, extending our understanding of the composition, structure, and formation history of these siblings.

Abstract
Robotic competitions stand as platforms to propel the forefront of robotics research while nurturing STEM education, serving as hubs of both applied research and scientific innovation. In Portugal, the Portuguese Robotics Open (FNR) is an event with several robotic competitions, including the Robot@Factory 4.0 competition. This competition presents an example of deploying autonomous robots on a factory shop floor. Although the literature has works proposing frameworks for the original version of the Robot@Factory competition, none of them proposes a system framework for the Robot@Factory 4.0 version that presents the hardware, firmware, and software to complete the competition and achieve autonomous navigation. This paper proposes a complete robotic framework for the Robot@Factory 4.0 competition that is modular and open-access, enabling future participants to use and improve it in future editions. This work is the culmination of all the knowledge acquired by winning the 2022 and 2023 editions of the competition.