TY - CHAP A1 - Lindauer, Friederike A1 - Damus, Martha A1 - Winkel, Carmen A1 - Frosch, Katharina T1 - AI-Driven Communication Training for Cybersecurity with the Talk to Transform Simulator T2 - KI-Forum 2025 : KI in Forschung und Lehre an Hochschulen N2 - Effective communication skills are increasingly recognized as critical for leadership in digital transformation contexts. Recently, AI-Chatbots such as Talk to Transform (T2T) have been developed to enhance leadership competencies through interactive role-plays and feedback. This paper proposes their adaptation for cybersecurity training. We discuss the current landscape of cybersecurity training, highlight the importance of communication, and present T2T as an innovative approach to bridge this gap through chatbot-driven role-plays. Y1 - 2025 U6 - https://doi.org/10.25968/opus-3790 PB - HsH Applied Academics ER - TY - CHAP A1 - Pepe, Alberto A1 - Lasenby, Joan A1 - Buchholz, Sven T1 - CGAPoseNet+GCAN: A Geometric Clifford Algebra Network for Geometry-aware Camera Pose Regression T2 - Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, US, 2024 N2 - We introduce CGAPoseNet+ GCAN, which enhances CGAPoseNet, an architecture for camera pose regression, with a Geometric Clifford Algebra Network (GCAN). With the addition of the GCAN we obtain a geometry-aware pipeline for camera pose regression from RGB images only. CGAPoseNet employs Clifford Geometric Algebra to unify quaternions and translation vectors into a single mathematical object, the motor, which can be used to uniquely describe camera poses. CGAPoseNet solves the issue of balancing rotation and translation components in the loss function, and can obtain comparable results to other approaches without the need of expensive tuning of the loss function or additional information about the scene, such as 3D point clouds, which might not always be available. CGAPoseNet, however, like several approaches in the literature, only learns to predict motor coefficients, and it is unaware of the mathematical space in which predictions sit in and of their geometrical meaning. By leveraging recent advances in Geometric Deep Learning, we modify CGAPoseNet with a GCAN: proposals of possible motor coefficients associated with a camera frame are obtained from the InceptionV3 backbone, and the GCAN downsamples them to a single motor through a sequence of layers that work in G_ 4, 0. The network is hence geometry-aware, has multivector-valued inputs, weights and biases and preserves the grade of the objects that it receives in input. CGAPoseNet+ GCAN has almost 4 million fewer trainable parameters, it reduces the average rotation error by 41% and the average translation error by 8.8% compared to CGAPoseNet. Similarly, it reduces rotation and translation errors by 32.6% and 19.9%, respectively, compared to the best performing PoseNet strategy. CGAPoseNet+ GCAN reaches the state-of-the-art results on 13 commonly employed datasets. To the best of our knowledge, it is the first experiment in GCANs applied to the problem of camera pose regression. Y1 - 2024 U6 - https://doi.org/10.1109/WACV57701.2024.00646 SP - 6593 EP - 6603 PB - IEEE ER - TY - CHAP A1 - Pepe, Alberto A1 - Buchholz, Sven A1 - Lasenby, Joan T1 - Clifford Group Equivariant Neural Network Layers for Protein Structure Prediction T2 - Northern Lights Deep Learning Conference, VOL 233, 09-11 Januar 2024, Tromso, Norwegen N2 - We employ Clifford Group Equivariant Neural Network (CGENN) layers to predict protein coordinates in a Protein Structure Prediction (PSP) pipeline. PSP is the estimation of the 3D structure of a protein, generally through deep learning architectures. Information about the geometry of the protein chain has been proven to be crucial for accurate predictions of 3D structures. However, this information is usually flattened as machine learning features that are not representative of the geometric nature of the problem. Leveraging recent advances in geometric deep learning, we redesign the 3D projector part of a PSP architecture with the addition of CGENN layers . CGENNs can achieve better generalization and robustness when dealing with data that show rotational or translational invariance such as protein coordinates, which are independent of the chosen reference frame. CGENNs inputs, outputs, weights and biases are objects in the Geometric Algebra of 3D Euclidean space, i.e. G3,0,0, and hence are interpretable from a geometrical perspective. We test 6 approaches to PSP and show that CGENN layers increase the accuracy in term of GDT scores by up to 2.1\%, with fewer trainable parameters compared to linear layers and give a clear geometric interpretation of their outputs. Y1 - 2024 UR - https://proceedings.mlr.press/v233/pepe24a/pepe24a.pdf SP - 1 EP - 7 PB - PMLR ER - TY - CHAP A1 - Meister, Vera G. T1 - Competency Acquisition in Applied Knowledge Engineering An Approach based on Learning from Errors T2 - Proceedings of the 2018 International Conference on Education Science and Social Development (ESSD 2018), Shanghai, China, in July 21-22, 2018 Y1 - 2018 SN - 978-94-6252-556-6 U6 - https://doi.org/10.2991/essd-18.2018.53 PB - Atlantis Press ER - TY - CHAP A1 - Doudkin, Alexander A1 - Kindsmüller, Martin Christof T1 - Do not touch! - An artistic climate data visualization using motion capturing and 3D computer graphics T2 - Mensch und Computer 2024 – Workshopband, Gesellschaft für Informatik e.V., 01.-04. September 2024, Karlsruhe, German N2 - This demo explores an innovative artistic installation that creatively visualizes global temperature data using graphical visualization and motion capture technologies. By combining video-based posture capturing of nearby individuals with a dynamically rendered 3D model of the planet Earth, this installation offers an interactive and immersive experience. The goal is to transform climate change data into an engaging visual format, making it more accessible and impactful for a wide range of audiences. Y1 - 2024 U6 - https://doi.org/10.18420/muc2024-mci-demo-319 SP - 1 EP - 3 PB - Gesellschaft für Informatik e.V. CY - Karlsruhe ER - TY - CHAP A1 - Tetzlaff, Laura A1 - Purohit, Anne-Maria A1 - Spallek, Jacob A1 - Holmberg, Christine A1 - Schrader, Thomas T1 - Evaluating Interoperability in German Critical Incident Reporting Systems T2 - German Medical Data Sciences 2023 – Science. Close to People. Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023, Heilbronn, Germany N2 - In industrialised countries, one in ten patients suffers harm during hospitalization. Critical Incident Reporting Systems (CIRS) aim to minimize this by learning from errors and identifying potential risks. However, a lack of interoperability among the 16 CIRS in Germany hampers their effectiveness. Y1 - 2023 U6 - https://doi.org/10.3233/SHTI230722 SP - 249 EP - 257 PB - IOS Press ER - TY - CHAP A1 - Kant, Daniel A1 - Johannsen, Andreas T1 - Evaluation of AI-based use cases for enhancing the cyber secu- rity defense of small and medium-sized companies (SMEs) T2 - Proc. IS & T International Symposium on Electronic Imaging: Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications KW - Cyber defense KW - cyber security KW - artificial intelligence KW - defensive AI KW - cyber security applications KW - cyber security use cases KW - security use cases KW - AI-based security applications KW - SME Y1 - 2022 U6 - https://doi.org/10.2352/EI.2022.34.3.MOBMU-387 SP - 387-1 EP - 387-8 CY - Springfield, VA ER - TY - CHAP A1 - Vollmer, Michael A1 - Shaw, Joseph A. T1 - Extended visual range: an observation during a total solar eclipse T2 - Fifteenth Conference on Education and Training in Optics and Photonics: ETOP 2019 T3 - Proceedings of SPIE - 11143 Y1 - 2019 U6 - https://doi.org/10.1117/12.2523167 SP - 111431Q-1 EP - 111431Q-6 CY - Quebec City, Quebec, Canada ER - TY - CHAP A1 - Markgraf, Klaus A1 - Dietrich, Benjamin A1 - Müller, Katja A1 - Flassig, Robert A1 - Flassig, Peter T1 - FINEconcepts - Wissenstransfer und Energiesystemoptimierung mithilfe des digitalen Zwillings T2 - NWK, HS Harz, 2023 N2 - Climate change, but also geopolitical circumstances, are moving topics such as energy efficiency and renewable energies more and more into the focus of the population, economy , and politics. As a result, the will to optimize new and existing energy systems extends from private individuals to companies and even entire communities. This work describes the development and usage of a new software called FINEconcepts which creates a digital twin of an energy system. This virtual model can then be used to optimize the energy system based on annual costs, CO2 emissions or other relevant criteria such as self-sufficiency. Because all system components, which include renewable technologies as well, can be added as a building block with chosen but changeable parameters, the software allows the user to explore and awaken interest and understanding of technologies that were previously considered too costly, irrelevant, or unrealistic. Implemented projects in small and large companies as well as in residential areas did prove, that the usage of FINEconcepts leads not only to more efficient energy systems by increasing the use of renewable energy, but also increased knowledge and understanding in terms of energy. Besides economics, ecology and security, understanding is an equally important factor in achieving a sustainable energy supply. Y1 - 2023 UR - https://www.hs-harz.de/dokumente/extern/Forschung/NWK2023/Beitraege/FINEconcepts_-_Wissenstransfer_und_Energiesystemoptimierung_mithilfe_des_digitalen_Zwillings.pdf SP - 428 EP - 435 PB - HS Harz ER - TY - CHAP A1 - Pepe, Alberto A1 - Lasenby, Joan A1 - Buchholz, Sven T1 - GA-ReLU: an activation function for Geometric Algebra Networks applied to 2D Navier-Stokes PDEs T2 - ICLR 2024 Workshop on AI4DifferentialEquations In Science, Wien, Österreich N2 - Many differential equations describing physical phenomena are intrinsically geometric in nature. It has been demonstrated how this geometric structure of data can be captured effectively through networks sitting in Geometric Algebra (GA) that work with multivectors, making them suitable candidates to solve differential equations. GA networks however, are still mostly uncharted territory. In this paper we focus on non-linearities, since applying them to multivectors is not a trivial task: they are generally applied in a point-wise fashion over each real-valued component of a multivector. This approach discards interactions between different elements of the multivector input and compromises the geometric nature of GA networks. To bridge this gap, we propose GA-ReLU, a GA approach to the rectified linear unit (ReLU), and show how it can improve the solution of Navier-Stokes PDEs. Y1 - 2024 UR - https://openreview.net/pdf?id=T54Hn1e98P SP - 1 EP - 6 ER -