Universität Stuttgart
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Item Open Access Locking-enabled security analysis of cryptographic circuits(2024) Upadhyaya, Devanshi; Gay, Maël; Polian, IliaHardware implementations of cryptographic primitives require protection against physical attacks and supply chain threats. This raises the question of secure composability of different attack countermeasures, i.e., whether protecting a circuit against one threat can make it more vulnerable against a different threat. In this article, we study the consequences of applying logic locking, a popular design-for-trust solution against intellectual property piracy and overproduction, to cryptographic circuits. We show that the ability to unlock the circuit incorrectly gives the adversary new powerful attack options. We introduce LEDFA (locking-enabled differential fault analysis) and demonstrate for several ciphers and families of locking schemes that fault attacks become possible (or consistently easier) for incorrectly unlocked circuits. In several cases, logic locking has made circuit implementations prone to classical algebraic attacks with no fault injection needed altogether. We refer to this “zero-fault” version of LEDFA by the term LEDA, investigate its success factors in-depth and propose a countermeasure to protect the logic-locked implementations against LEDA. We also perform test vector leakage assessment (TVLA) of incorrectly unlocked AES implementations to show the effects of logic locking regarding side-channel leakage. Our results indicate that logic locking is not safe to use in cryptographic circuits, making them less rather than more secure.Item Open Access Philosophy of action and Its relationship to interactive visualisation and Molière’s theatre(2023) Feige, Daniel M.; Weiskopf, Daniel; Dickhaut, KirstenItem Open Access ILP-based resource optimization realized by quantum annealing for optical wide-area communication networks : a framework for solving combinatorial problems of a real-world application by quantum annealing(2024) Witt, Arthur; Kim, Jangho; Körber, Christopher; Luu, ThomasResource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ensuring increased network efficacy and robustness, while minimizing energy requirements coming from power-hungry transceivers. In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave Advantage™ quantum annealer system, demonstrating proof of principle. Our initial studies left open the possibility for improvement of D-Wave solutions via judicious choices of system run parameters. Here we report on our investigations for optimizing these system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. In particular, we use the Hamming distance to investigate correlations between various system-run parameters and solution vectors. We then apply a decision tree neural network (NN) to learn these correlations, with the goal of using the neural network to provide further guesses to solution vectors. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave. We find, however, for the 3-node network problem the NN is not able to enhance the quality of space of solutions.Item Open Access Building a fully-automatized active learning framework for the semantic segmentation of geospatial 3D point clouds(2024) Kölle, Michael; Walter, Volker; Sörgel, UweIn recent years, significant progress has been made in developing supervised Machine Learning (ML) systems like Convolutional Neural Networks. However, it’s crucial to recognize that the performance of these systems heavily relies on the quality of labeled training data. To address this, we propose a shift in focus towards developing sustainable methods of acquiring such data instead of solely building new classifiers in the ever-evolving ML field. Specifically, in the geospatial domain, the process of generating training data for ML systems has been largely neglected in research. Traditionally, experts have been burdened with the laborious task of labeling, which is not only time-consuming but also inefficient. In our system for the semantic interpretation of Airborne Laser Scanning point clouds, we break with this convention and completely remove labeling obligations from domain experts who have completed special training in geosciences and instead adopt a hybrid intelligence approach. This involves active and iterative collaboration between the ML model and humans through Active Learning, which identifies the most critical samples justifying manual inspection. Only these samples (typically ≪1%of Passive Learning training points) are subject to human annotation. To carry out this annotation, we choose to outsource the task to a large group of non-specialists, referred to as the crowd, which comes with the inherent challenge of guiding those inexperienced annotators (i.e., “short-term employees”) to still produce labels of sufficient quality. However, we acknowledge that attracting enough volunteers for crowdsourcing campaigns can be challenging due to the tedious nature of labeling tasks. To address this, we propose employing paid crowdsourcing and providing monetary incentives to crowdworkers. This approach ensures access to a vast pool of prospective workers through respective platforms, ensuring timely completion of jobs. Effectively, crowdworkers become human processing units in our hybrid intelligence system mirroring the functionality of electronic processing units .Item Open Access Datamator : an authoring tool for creating datamations via data query decomposition(2023) Guo, Yi; Cao, Nan; Cai, Ligan; Wu, Yanqiu; Weiskopf, Daniel; Shi, Danqing; Chen, QingDatamation is designed to animate an analysis pipeline step by step, serving as an intuitive and efficient method for interpreting data analysis outcomes and facilitating easy sharing with others. However, the creation of a datamation is a difficult task that demands expertise in diverse skills. To simplify this task, we introduce Datamator, a language-oriented authoring tool developed to support datamation generation. In this system, we develop a data query analyzer that enables users to generate an initial datamation effortlessly by inputting a data question in natural language. Then, the datamation is displayed in an interactive editor that affords users the ability to both edit the analysis progression and delve into the specifics of each step undertaken. Notably, the Datamator incorporates a novel calibration network that is able to optimize the outputs of the query decomposition network using a small amount of user feedback. To demonstrate the effectiveness of Datamator, we conduct a series of evaluations including performance validation, a controlled user study, and expert interviews.Item Open Access Cloudbezogenes IT-Service-Management als Gestaltungshebel für die Twin Transformation(2026) Koch de Souza, Larissa; Stuber, Kim; Petrik, Dimitri; Engstler, Martin; Herzwurm, GeorgDie Digitale und die Nachhaltigkeitstransformation sind längst keine getrennten Entwicklungspfade mehr, sondern miteinander verflochten, dies wird auch als Twin Transformation bezeichnet. Dabei stehen Unternehmen mit digitalen Geschäftsmodellen vor neuen Spannungsfeldern: Einerseits treiben datenintensive Services, KI-basierte Funktionalitäten und cloudbasierte Infrastrukturen die digitale Leistungsfähigkeit maßgeblich voran. Andererseits führt diese technologische Tiefe zu einem erheblich wachsenden ökologischen Fußabdruck. Ein zentraler, bislang wenig erforschter Hebel der Twin Transformation liegt in der systematischen Erfassung, Steuerung und Integration cloudbezogener ökologischer Auswirkungen in die Prozesse des IT-Service-Managements. Vor diesem Hintergrund untersucht der Beitrag, wie ökologische, cloudbezogene Nachhaltigkeit in das IT-Service-Management integriert werden kann, um eine wirksame Verbindung zwischen digitaler Effizienz und ökologischer Verantwortung herzustellen. Ziel ist es, einen Maßnahmenkatalog mit dazugehörigen Handlungsempfehlungen zu entwickeln, welcher die Integration eines nachhaltigkeitsorientierten Ansatzes zur Cloud-Infrastruktur-Nutzung in IT-Service-Management-Prozesse ermöglicht. Methodisch verfolgt der Beitrag hierfür ein zweistufiges Vorgehen: Explorative Interviews mit Expert:innen führender Beispielunternehmen in den Bereichen des IT-Service-Managements und Cloud-Infrastrukturdiensten werden zusammengetragen mit verschiedenen Gestaltungsoptionen für nachhaltige IT-Infrastruktur-Lösungen. Die aus Sicht der Industrie abgeleiteten Ergebnisse wurden in Maßnahmen und Empfehlungen überführt. Diese Empfehlungen sind entlang von fünf Dimensionen eines nachhaltigen, cloudbasierten IT-Service-Managements strukturiert und unterstützen Unternehmen mit digitalen Geschäftsmodellen bei ihrer Nachhaltigkeitstransformation. Insgesamt stellen die Ergebnisse einen Industrie-basierten Ansatz zur Verknüpfung digitaler und nachhaltiger Verbesserungen im Kontext der Twin Transformation dar.Item Open Access Knowledge graphs in the digital twin : a systematic literature review about the combination of semantic technologies and simulation in industrial automation(2024) Listl, Franz; Dittler, Daniel; Hildebrandt, Gary; Stegmaier, Valentin; Jazdi, Nasser; Weyrich, MichaelItem Open Access Data processing, analysis, and evaluation methods for co-design of coreless filament-wound building systems(2023) Gil Pérez, Marta; Mindermann, Pascal; Zechmeister, Christoph; Forster, David; Guo, Yanan; Hügle, Sebastian; Kannenberg, Fabian; Balangé, Laura; Schwieger, Volker; Middendorf, Peter; Bischoff, Manfred; Menges, Achim; Gresser, Götz T.; Knippers, JanItem Open Access Touching data with PropellerHand(2022) Achberger, Alexander; Heyen, Frank; Vidackovic, Kresimir; Sedlmair, MichaelImmersive analytics often takes place in virtual environments which promise the users immersion. To fulfill this promise, sensory feedback, such as haptics, is an important component, which is however not well supported yet. Existing haptic devices are often expensive, stationary, or occupy the user’s hand, preventing them from grasping objects or using a controller. We propose PropellerHand, an ungrounded hand-mounted haptic device with two rotatable propellers, that allows exerting forces on the hand without obstructing hand use. PropellerHand is able to simulate feedback such as weight and torque by generating thrust up to 11 N in 2-DOF and a torque of 1.87 Nm in 2-DOF. Its design builds on our experience from quantitative and qualitative experiments with different form factors and parts. We evaluated our prototype through a qualitative user study in various VR scenarios that required participants to manipulate virtual objects in different ways, while changing between torques and directional forces. Results show that PropellerHand improves users’ immersion in virtual reality. Additionally, we conducted a second user study in the field of immersive visualization to investigate the potential benefits of PropellerHand there.Item Open Access MDSuite : comprehensive post-processing tool for particle simulations(2023) Tovey, Samuel; Zills, Fabian; Torres-Herrador, Francisco; Lohrmann, Christoph; Brückner, Marco; Holm, ChristianParticle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available experimentally. However, in most cases, PB data needs to be processed after a simulation to extract these observables. One of the main challenges in post-processing PB simulations is managing the large amounts of data typically generated without incurring memory or computational capacity limitations. In this work, we introduce the post-processing tool: MDSuite. This software, developed in Python, combines state-of-the-art computing technologies such as TensorFlow, with modern data management tools such as HDF5 and SQL for a fast, scalable, and accurate PB data processing engine. This package, built around the principles of FAIR data, provides a memory safe, parallelized, and GPU accelerated environment for the analysis of particle simulations. The software currently offers 17 calculators for the computation of properties including diffusion coefficients, thermal conductivity, viscosity, radial distribution functions, coordination numbers, and more. Further, the object-oriented framework allows for the rapid implementation of new calculators or file-readers for different simulation software. The Python front-end provides a familiar interface for many users in the scientific community and a mild learning curve for the inexperienced. Future developments will include the introduction of more analysis associated with ab-initio methods, colloidal/macroscopic particle methods, and extension to experimental data.