High-fidelity helicopter flight simulators are essential for preparing pilots for complex and hazardous environments, yet realistic urban wind dynamics are difficult to reproduce in real time when relying on precomputed computational fluid dynamics (CFD) data. We present the first integration of a Fourier Neural Operator (FNO) into a Level D full flight simulator for real-time, physics-based urban wind field generation. Trained on high-resolution urban flow simulations, the FNO predicts one-minute-averaged 3D wind fields that dynamically adapt to flight state and location, replacing static wind inputs in the simulator pipeline. Turbulence levels are computed from the predictions and injected directly into the simulation loop. Professional pilots evaluated the system in an urban scenario and reported that it reproduced wind effects they would expect, such as turbulence and directional changes when landing behind buildings. They highlighted its value for less experienced pilots to develop wind awareness and for realistic training in critical operations, including offshore platform landings.

As large language models (LLMs) become increasingly integrated into learning environments, their potential to enhance or hinder the acquisition of computational skills remains debated. This study investigates the role of generative AI (GenAI) tools, particularly Harvard's CS50 Duck, in supporting programming education. Through a mixed-methods approach, we examine students' perceptions, engagement, and practical application of the CS50 Duck within our Computational Thinking course. Our results indicate that while proficient students use GenAI tools to reinforce problem-solving skills, struggeling students may over-rely on them, potentially bypassing critical learning processes. Survey and assignment data suggest that students value the non-judgmental feedback provided by the CS50 Duck, yet express nuanced views on GenAI's role in formal assessments and programming education. We show that analysing chat histories can serve as a qualitative framework for examining the interactions between students and artificial tutors, while simultaneously offering critical insights into students' learning processes and the challenges they encounter. This study also underscores the need for instructional strategies that guide responsible GenAI use and highlights the importance of educator involvement in integrating these tools effectively.

In today's world, where digital technologies and artificial intelligence (AI) are everywhere, the education sector is challenged with updating its course formats to stay relevant and future-focused. This paper discusses an innovative educational approach in the Informatics and Design program, combining computer science elements with design education. The program differs from traditional computer science curriculums. It caters to students with heterogeneous levels of tech skills and emphasizes a balanced approach to teach both, design and software development. Central to this program is a unique project co-taught by a computer scientist and a service designer. The project centers on creating a chatbot, incorporating the latest in Natural Language Processing and Large Language Models. This initiative not only keeps pace with the latest tech developments but also stresses the importance of a human-centered design approach in merging design with technology. The outcomes of this educational approach demonstrate that when design and technology are taught in tandem, it leads to a more comprehensive understanding and skill set among students. This approach not only prepares them for the current landscape of digital technology but also instills a mindset of continuous adaptation and learning, which is crucial in the ever-evolving field of informatics and design.

Currently, the development of the metaverse lies in the hands of industry. Citizens have little influence on this process. Instead, to do justice to the pluralism of (digital) societies, we should strive for an open discourse including many different perspectives on the metaverse and its core technologies such as AI. We utilize a participatory speculative design (PSD) approach to explore Japanese citizens' perspectives on future metaverse societies, as well as social and ethical implications. Our contributions are twofold. Firstly, we demonstrate the effectiveness of PSD in engaging citizens in critical discourse on emerging technologies like the metaverse by presenting our workshop framework and participants' processes. Secondly, we identify key themes from participants' perspectives, providing insights for culturally sensitive design and development of virtual environments. Our analysis shows that participants imagine the metaverse to have the potential to solve a variety of societal issues; for example, breaking down barriers of physical environments for communication, social interaction, crisis preparation, and political participation, or tackling identity-related issues. Regarding future metaverse societies, participants' imaginations raise critical questions about human-AI relations, technical solutionism, politics and technology, globalization and local cultures, and immersive technologies. We discuss implications and contribute to expanding conversations on metaverse developments.

The great challenges of our time and age are vastly complex and thus cannot be solved by a single discipline alone. Instead, a combination of a variety of fields is necessary. As digitalization becomes increasingly pervasive in both our personal and professional lives, IT and potentially AI skills are needed for finding appropriate solutions. Consequently, job profiles emerge that require solid skills both in a traditionally non-IT discipline as well as in IT, AI, and digitalization in general. However, university education traditionally focuses on just one discipline in its study programs. Therefore, it is high time to systematically create study programs in which different disciplines join forces, to create a truly transdisciplinary education and to foster transdisciplinary profiles. Typically, developing and operating these transdisciplinary study programs requires close cooperation of different disciplines. Although this seems to be quite obvious at first sight and easy to put into practice, experience shows that in these efforts, there lurk a variety of challenges that, if ignored, endanger the success of any transdisciplinary attempts. In this work, we try to sensitize to typical challenges involved in creating transdisciplinary study programs. We provide advice and good practices that may help to overcome typical difficulties.

This poster presents work-in-progress in the field of usable security. The usability of security mechanisms is crucial to avoid unintended misuse of security mechanisms which lowers the security level of a system. It is the goal of the work presented in this poster to identify security design patterns with good usability. Requirements for security design patterns with good usability stem from existing usable security design guidelines. A collection of security usability failures is presented as well as examples of how misuse anti-patterns can be derived from these failures. Misuse cases will be used in future work to identify security design patterns with good usability.

We present a hybrid system that integrates speech and image understanding. Given spoken references, it is able to identify objects of a 3D scene perceived via a stereo camera. Central to our approach is the extraction of qualitative object features and spatial scene properties from acoustic and visual data. The interaction of the understanding processes is performed using a procedural semantic network that interfaces with signal recognition and reconstruction modules, thus integrating semantic, neural and Bayesian networks and Hidden Markov Models.

We present a method for camera calibration and metric reconstruction of the three-dimensional structure of scenes with several, possibly small and nearly planar objects from one or more images. We formulate the projection of object models explicitly according to the pin-hole camera model in order to be able to estimate the pose parameters for all objects as well as relative poses and the focal lengths of the cameras. This is accomplished by minimising a multivariate non-linear cost function. The only information needed is simple geometric object models, the correspondence between model and image features, and the correspondence of objects in the images if more than one view of the scene is used. Additionally, we present a new method for the projection of circles using projective invariants. Results using both simulated and real images are presented. keywords: Least-squares model fitting, model-based vision, 3-D reconstruction, camera calibration, projective invariants. 1 Introduction ...

We present a new approach towards using contextual information to enhance speech recognition and understanding. Dynamically inferred knowledge about the context is used in addition to the static linguistic and domain specific knowledge. Based on the results of image analysis of a given scene language models for constituents of possible utterances concerning that scene are generated.