05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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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 The impact of domain models on energy consumption of classical planners(2025) Tekin, SerhatThe increasing integration of artificial intelligence into real-world systems has intensified concerns about the ecological footprint of computational processes. As the capabilities of AI expand and their applications spread into diverse areas of society, questions of efficiency are no longer confined to algorithmic performance alone but extend to the broader impact of computation on energy usage. Within this context classical planning provides a particularly relevant case since it is a core technique in automated planning. Research in this field has traditionally emphasized runtime efficiency, plan quality and algorithmic design while the energetic dimension has remained largely neglected. This thesis examines that omission by shifting the focus from planners to the domain models that constitute their input. Through systematic modifications of syntactic, semantic and solvability related features it demonstrates that modeling decisions can exert a measurable influence on energy consumption. Rather than viewing energy use as an inherent property of planners, the study shows it to emerge from the interaction between algorithmic behavior and representational form. The work introduces a replicable framework that combines controlled domain transformations with fine grained energy measurements, thereby enabling systematic evaluations of energy usage in symbolic AI. The empirical analysis indicates that syntactic variations usually result in only minor fluctuations, whereas modeling inefficiencies can increase energy demand, with operator arity standing out as a recurring factor. The most pronounced effects arise from solvability constraints which, depending on the planner and the domain, can lead to substantial increases in energy usage or in some cases reductions. Taken together the results highlight that domain modeling is not only a matter of syntactic correctness or semantic adequacy but also of energetic efficiency. The contribution of this thesis is twofold. It establishes a framework for investigating the energy implications of domain features and provides empirical evidence that modeling choices shape the energy profile of planning systems. These findings offer a foundation for further research and provide practical guidance for approaching domain modeling with energy consumption in mind.Item Open Access Prompt-based personality profiling : filtering social media posts using reinforcement learning(2024) Hofmann, JanAuthor profiling is the task of inferring characteristics about individuals by analyzing content they share. To date, systems that perform this task automatically, predominantly use supervised machine learning approaches and borrow from advances in the field of natural language understanding. However, while for many language understanding tasks immense progress has been made in recent years (e.g. by using pre-train then fine-tune paradigm), such progress most often does not transfer to automated profiling systems directly. One reason for this is that author profiling is inherently different from typical text inference tasks due to the possibly large amounts of content associated with an author. Therefore, this work proposes a new method for profiling that tries to select the most relevant parts of content shared by an author before inferring a characteristic. Here, instead of relying on ground-truth labels, this work uses the feedback from the zero-shot capabilities of a large language model to learn such a selection model via reinforcement learning, and evaluates this approach for personality profiling in social media. In experiments predicting big five personality traits, this work finds that prediction quality of such a system is comparable yet slightly worse to using all content associated to a profile in a zero-shot setting, while prediction time is reduced significantly due to the limited amount of content used for inferring personality in the proposed method. In addition, this work finds that simply selecting content arbitrarily leads to performance degradation for most traits, and therefore, this work concludes that, to some extent, the proposed method is able to distinguish between relevant and irrelevant content. Further, this work compares the proposed approach to existing supervised approaches and finds that such methods outperform the proposed method substantially. Still, since the ability of the proposed system to distinguish between relevant and irrelevant content of authors is closely tied to the capabilities of large language models, it can be expected that, with advances of such models, prediction quality of the proposed approach will increase in the future.Item Open Access Enhancing HTN planning with deep reinforcement learning for method selection(2025) Bahrami, SepidehAutomated planning is a central area within Artificial Intelligence (AI), enabling intelligent behavior in domains such as cloud computing, autonomous systems, context-aware activity recognition, and smart environments. Hierarchical Task Network (HTN) planning, which decomposes complex tasks into simpler subtasks using predefined methods, has proven effective in such structured domains. However, its performance is often constrained by static method selection strategies that lack adaptability to varying planning contexts. To address this limitation, this thesis proposes a neuro-symbolic framework that integrates HTN planning with Deep Reinforcement Learning (DRL), combining the strengths of symbolic reasoning and data-driven learning. Among the available DRL algorithms, Deep Q-Learning (DQL) is particularly suitable due to its off-policy nature, batch-efficient learning, and robust generalization across symbolic planning states. These characteristics align well with deterministic and hierarchical planners, enabling offline learning from curated datasets without requiring interactive exploration. The proposed integration introduces a learning-based decision layer that improves adaptability while preserving the reproducibility and determinism of the underlying planner. The effectiveness of this approach is demonstrated through a comprehensive evaluation across planning efficiency, memory consumption, and plan quality. Results highlight the potential of reinforcement learning to enhance classical HTN systems and support intelligent decision-making in complex, structured environments.Item Open Access Enhancing automotive safety through an ADAS violation dashboard(2024) Senger, TobiasAutonomous Driving (AD) is an active area of research in which Advanved Driver Assistance Systems (ADAS) play an important role. Ensuring the safety of ADAS systems is critical. However, most ADAS systems nowadays make use of Deep Learning or other types of Machine Learning. Formally verifying these systems to ensure their safety is hardly possible. For this reason, Radic explored the use of Runtime Monitoring (RM) to ensure the safety of ADAS systems by detecting violations of several specified Safety Requirements (SR) at runtime. After performing a test run with the system, she manually analyzed the causes of each series of violations in the extracted Violations Report. As this was laborious and time-consuming, this thesis should explore available approaches and techniques to automatically derive the root causes of violation series. To do this, we first perform an exploratory literature search. This allows us to identify that the most suitable approach to address our problem is Root Cause Analysis (RCA) using Language Models (LMs), Large Language Models (LLMs), Knowledge Graphs (KGs), or a combination of them. We perform a Rapid Review (RR) to find concrete techniques for this approach. We then conduct a narrative data synthesis to explore the techniques retrieved with our RR. This allows us to derive a plan to automatically analyze the causes of SR violations in a Violations Report. Our solution is then incorporated into a web-based safety dashboard application. This application enables our safety engineers to configure ADAS use cases, test tracks, and test runs. Then, the safety engineer can select a test run to display an interactive view of the test run. The safety engineer can then select individual violation series and analyze their root causes using our automated RCA solution based on LLMs. To evaluate the effectiveness of our system, we conduct a simple experiment. This experiment shows that our system already achieves comparable performance to a human baseline provided by Radic. Our system, therefore, represents a valuable tool for safety engineers to identify and repair safety-critical problems in ADAS systems in the context of AD. We also propose modified variants of our system that allow researchers to improve our automated RCA system in the future, e.g., by incorporating a KG.Item Open Access Stationary vehicle classification based on scene understanding(2024) Wang, WeitianNavigating through dense traffic situations like merging onto highways and making unprotected left turns remains a challenge for the existing autonomous driving system. Classifying vehicles into parked, stopped, and moving vehicles can benefit the decision-making system in this case because they play different roles during the vehicle-to-vehicle negotiation process. Existing works in vehicle classification focused on trivial cases and used methods that are not generalized enough. To fill this gap, after analyzing this problem and summarizing the necessary information needed for this problem, we propose a multi-modal model that can leverage information from lidar, radar, camera, and high-definition maps. To meet the complexity of our task and the needs of our model, we collect the dataset in real driving scenario and then preprocess and label it. By utilizing a pretrained vision encoder for fine-grained visual feature extraction and vision foundation model (CLIP) for scene understanding, our model achieves a 97.63% test accuracy on our dataset. Through visualization methods, experiments, and quantitative analyses, we investigate the effectiveness and importance of different encoders used in our model. We interpret and explain the successes and failures of our model to give a better understanding of how different latent features contribute to the final result. In the end, the limitations of our model and potential improvements are discussed.Item Open Access Klassifizieren von Bewegungsabläufen mit Machine-Learning-Verfahren unter Verwendung der Movella-Dot-Sensoren(2025) Volpe, AnnaDiese Arbeit untersucht den Einfluss verschiedener Modellarchitekturen und Optimierungsverfahren auf die Klassifikation von Human Activity Recognition (HAR) anhand von Sensordaten der Movella DOT Sensoren. Im Zentrum stehen Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Residual Neural Network (ResNet) sowie klassische Verfahren wie Support Vector Machine (SVM) und Random Forest (RF). Neben dem Vergleich der Optimierer Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop) und Stochastic Gradient Descent (SGD) mit verschiedenen Lernraten, wird analysiert, wie sich unterschiedliche Sensorpositionen und deren Kombinationen auf die Klassifikationsgenauigkeit auswirken. Hierzu werden Sliding-Window-Techniken zur Segmentierung der Zeitreihen eingesetzt und die Ergebnisse mit Konfusionsmatrizen, Receiver-operating characteristic (ROC)-Kurven sowie Metriken bewertet. Zur Durchführung der Experimente wurde eine Graphical User Interface (GUI) implementiert, die die Auswahl von Modellen, Hyperparametern und Optimierungsverfahren ermöglicht und die Ergebnisse visualisiert. Die implementierten Modelle wurden auf verschiedenen Bewegungsabläufen trainiert und getestet, darunter Gehen, Joggen, Treppensteigen, Fahrradfahren, Liegestütze, vereinfachte Liegestütze, Bizeps-Curls, Hampelmann, Liegen und Autofahren. Zusätzlich wurden insgesamt acht Handrotationen erfasst, jeweils vier mit der linken und vier mit der rechten Hand. Dabei wurde zwischen Faust- und offener Handhaltung sowie zwischen Links- und Rechtsrotation unterschieden. Es konnte gezeigt werden, dass insbesondere CNNs und ResNets stabile und hohe Genauigkeiten erzielten, während LSTMs stark von der Wahl des Optimierers abhängig waren und klassische Verfahren wie RF solide Ergebnisse bei geringer Modellkomplexität lieferten. Fußsensoren erwiesen sich für geh-und laufbezogene Aktivitäten als besonders zuverlässig, während Handsensoren für armbezogene Bewegungen den größten Mehrwert lieferten. Damit konnte bestätigt werden, dass die gezielte Auswahl weniger Sensoren mit optimaler Platzierung für robuste Klassifikationen ausreicht. Ziel ist es, die Bedeutung der Sensorpositionen und der Hyperparameterauswahl für robuste Modelle zu charakterisieren und so Empfehlungen für die Auswahl geeigneter Modell- und Sensorkonfigurationen aufzustellen.Item Open Access Cross-device data collection(2024) Zhu, KaiyangProviding cross-device service has become the best practice for many organizations. Those cross device services often have different user interfaces (UIs) designed to deliver user experiences (UXs) adapted to the device context. User behavior tends to vary across devices due to the UI variations, which can considerably impact usability. While extensive research exists on the relationship between user behavior and usability within the same device context, little attention has been paid to cross-device scenarios. By examining usage across devices, it becomes possible to identify the distinct user behavioral patterns and assess usability across varying UIs. Usability can be measured explicitly through user responses to a usability questionnaire and implicitly via navigation behavior events and task completion time on web pages. To facilitate this analysis, we developed a recording system capable of capturing user navigation behavior, usability feedback, and screenshots for five popular individual web pages. Participants were recruited both online and onsite to generate a diverse user behavior dataset. A total of 79 participants were invited to the experiment, with 17 participating in a pilot study. The resulting dataset comprises 34 well-processed, complete user datasets for analysis. Using this novel dataset, we conducted a preliminary analysis to explore correlations between user behavior features and usability metrics, specifically System Usability Scale (SUS) scores and task completion times. Additionally, we performed a small case study to compare the five different UIs and determine which design provided the best user experience. We found that usability, measured in terms of SUS scores, showed no significant differences across devices or web pages. However, completion time varied significantly across devices. Additionally, we provided design recommendations, such as addressing erratic user behaviors derived from speed-related metrics like excessive acceleration, which negatively impact usability, particularly on mobile devices.Item Open Access Fully-polynomial-time approximation schemes for the Euclidean shortest path problem(2024) Schneewind, AxelThe shortest path problem is a well-studied problem in computer-science. For transport networks, there exist natural graph representations and highly efficient algorithms that can compute shortest paths on millions of nodes within milliseconds. In contrast, computing shortest paths in space (e.g. in R^2) poses some challenges. Shortest path computations in space have applications in robotics, naval routing or video games. As shortest paths in a continuum are hard to compute (with the Euclidean shortest-path problem in 3D even proven to be NP-hard), approximations can be necessary to obtain acceptable runtimes. In this thesis, an approximation scheme is studied that guarantees solutions with cost at most (1+ε) times the optimum. It uses a triangulation of the domain and, given ε, construct a discretization. By performing a Dijkstra search, one can then approximate shortest paths with the given quality guarantee. This scheme is implemented and its practicality evaluated on larger instances.Item Open Access Rigorous compilation for near-term quantum computers(2024) Brandhofer, Sebastian; Polian, Ilia (Prof.)Quantum computing promises an exponential speedup for computational problems in material sciences, cryptography and drug design that are infeasible to resolve by traditional classical systems. As quantum computing technology matures, larger and more complex quantum states can be prepared on a quantum computer, enabling the resolution of larger problem instances, e.g. breaking larger cryptographic keys or modelling larger molecules accurately for the exploration of novel drugs. Near-term quantum computers, however, are characterized by large error rates, a relatively low number of qubits and a low connectivity between qubits. These characteristics impose strict requirements on the structure of quantum computations that must be incorporated by compilation methods targeting near-term quantum computers in order to ensure compatibility and yield highly accurate results. Rigorous compilation methods have been explored for addressing these requirements as they exactly explore the solution space and thus yield a quantum computation that is optimal with respect to the incorporated requirements. However, previous rigorous compilation methods demonstrate limited applicability and typically focus on one aspect of the imposed requirements, i.e. reducing the duration or the number of swap gates in a quantum computation. In this work, opportunities for improving near-term quantum computations through compilation are explored first. These compilation opportunities are included in rigorous compilation methods to investigate each aspect of the imposed requirements, i.e. the number of qubits, connectivity of qubits, duration and incurred errors. The developed rigorous compilation methods are then evaluated with respect to their ability to enable quantum computations that are otherwise not accessible with near-term quantum technology. Experimental results demonstrate the ability of the developed rigorous compilation methods to extend the computational reach of near-term quantum computers by generating quantum computations with a reduced requirement on the number and connectivity of qubits as well as reducing the duration and incurred errors of performed quantum computations. Furthermore, the developed rigorous compilation methods extend their applicability to quantum circuit partitioning, qubit reuse and the translation between quantum computations generated for distinct quantum technologies. Specifically, a developed rigorous compilation method exploiting the structure of a quantum computation to reuse qubits at runtime yielded a reduction in the required number of qubits of up to 5x and result error by up to 33%. The developed quantum circuit partitioning method optimally distributes a quantum computation to distinct separate partitions, reducing the required number of qubits by 40% and the cost of partitioning by 41% on average. Furthermore, a rigorous compilation method was developed for quantum computers based on neutral atoms that combines swap gate insertions and topology changes to reduce the impact of limited qubit connectivity on the quantum computation duration by up to 58% and on the result fidelity by up to 29%. Finally, the developed quantum circuit adaptation method enables to translate between distinct quantum technologies while considering heterogeneous computational primitives with distinct characteristics to reduce the idle time of qubits by up to 87% and the result fidelity by up to 40%.