Universität Stuttgart
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Item Open Access Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows(2023) Kschidock, HelenaIn this work, we implement, test, and validate an Euler-Lagrangian point-particle tracking framework for the commercial aerodynamics and aeroacoustics simulation tool ultraFluidX, which is based on the Lattice Boltzmann Method and optimized for GPUs. Our framework successfully simulates one-way and two-way coupled particle-laden flows based on drag forces and gravitation. Trilinear interpolation is used for determining the fluid's macroscopic properties at the particle position. Object and domain boundary conditions are implemented using a planar surface approximation. The whole particle framework is run within three dedicated GPU kernels, and data is only copied back to the CPU upon output. We show validation for the velocity interpolation, gravitational acceleration, back-coupling forces and boundary conditions, and test runtimes and memory requirements. We also propose the next steps required to make the particle framework ready for use in engineering applications.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 Thermodynamical stability analysis of a model quasicrystal(2022) Holzwarth, MoritzThe thermodynamical stability of a simple 2D model quasicrystal is analysed using the theory of the phason elastic free energy. Atoms in the crystal interact via a double-well potential called the Lennard-Jones Gauß-potenital. The essential mechanisms that support the quasicrystal's free energy are atom jumps called phasonic flips. The distribution of such flips in a crystal is computed in dependency of the crystal lattice, which is parameterized by a 2x2-matrix called the phasonic strain. This computation is fully analytic and is based on the popular cut-and-project-scheme for quasicrystals. The quasicrystal is found to be instable at low temperature but stabilized at high temperature due to large entropy. This is in accordance with an MD-simulation from 2008 that used the LJG-Interaction-potential for the first time.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 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 Tailoring language for user backgrounds : human and LLM evaluations of paraphrased texts(2025) Carlon, FrancescaIn an increasingly interdisciplinary world, the ability to tailor technical and field-specific language to diverse audiences has become essential. Both academic research and industry benefit from the knowledge and expertise of individuals from varied technical backgrounds, supporting progress in research and product development. Therefore, it has become essential to make domain-specific concepts accessible and understandable to different audiences. This study investigates how Large Language Models (LLMs) can generate effective paraphrases by tailoring language to specific target audiences. It also explores which linguistic elements enhance conceptual understanding and whether LLMs can produce high-quality synthetic data for evaluating text quality. This research specifically focuses on tailoring language from Linguistics, Computer Science, and Computational Linguistics texts for audiences with diverse academic backgrounds, using a variety of LLMs. Methods employed include Prompt Engineering, linguistic metrics, and evaluations conducted by both human judges and LLM-as-a-judge approaches. The experiments focus on assessing prompt quality, examining the relationship between human evaluations and linguistic features, and analyzing the correlation between human judgments and LLM-as-a-judge assessments. The results demonstrate that combining Role Prompting with Chain-of-Thought (CoT) prompting effectively produces high-quality paraphrased texts. Additionally, syntactic and lexical features play a central role in enhancing text accessibility and users’ understanding of unfamiliar concepts. Finally, while the correlation between LLM-as-a-judge evaluations and human judgments is promising, it varies across different user groups. Therefore, this research demonstrates the potential of Prompt Engineering techniques for paraphrasing and tailoring language to users with different backgrounds, explores the linguistic features that enhance text comprehension, and examines the extent to which LLM-as-a-judge correlates with human preferences and can support the automatic retrieval of high-quality data.Item Open Access Migrating monolithic architectures to microservices : a study on software quality attributes(2022) Koch, DanielEs gibt viele Beweggründe für die Migration von einer monolithischen zu einer Microservice-Architektur, z. B. hohe Skalierbarkeit oder verbesserte Wartbarkeit. Dabei müssen jedoch mehrere Faktoren im Migrationsprozess berücksichtigt werden, darunter auch Qualitätsmerkmale. Da die Migration zu einer Microservice-Architektur keine einfache Aufgabe ist, können definierte Qualitätsziele dabei helfen, einen geeigneten Migrationsansatz auszuwählen und anschließend geeignete Architekturentscheidungen zu treffen. Ziel dieser Arbeit ist es, zu untersuchen, wie Qualitätsattribute in den Migrationsprozess eingebunden werden können, um Praktiker und Softwarearchitekten dabei zu unterstützen. Ebenso wird untersucht, welche Rolle sie im Migrationsprozess spielen. Dazu wurde zunächst eine Literaturrecherche durchgeführt, um die Qualitätsattribute zu identifizieren, die für eine Microservice-Architektur relevant sind. Anschließend wurden die Qualitätsattribute den Migrationsansätzen zugeordnet, die sie in Richtung der Zielarchitektur optimieren. Ebenso wurden die Qualitätsattribute den Architekturmustern und Best Practices zugeordnet. Auf der Grundlage der zuvor gesammelten Ergebnisse wurde ein Qualitätsmodell erstellt, das auch die Interdependenzen und Kompromisse zwischen ihnen berücksichtigt. Auf diese Weise soll das Qualitätsmodell als Leitfaden dienen, der die Auswahl geeigneter Techniken und architektonischer Entscheidungen auf der Grundlage der definierten Qualitätsziele erleichtert. Das entwickelte Qualitätsmodell wurde anschließend in ein Tool integriert, das Praktiker durch den Migrationsprozess leiten sollte. Um die Nutzbarkeit des Werkzeugs in Bezug auf das Qualitätsmodell zu untersuchen, wurde eine Evaluierung in Form einer Umfrage mit vier Praktikern aus der Industrie durchgeführt. Das Ergebnis der Evaluation zeigt, dass das integrierte Qualitätsmodell den Migrationsprozess auf Basis der definierten Qualitätsziele in der Praxis unterstützen kann und die Erweiterung des Tools eine hohe Usability aufweist.Item Open Access Migration monolithischer Anwendungen in Microservices-basierte Architekturen : Fallstudie einer Service/Sales-Applikation(2023) Knodel, MarvinViele Altsysteme in der Industrie sind heutzutage in einer monolithischen Architektur implementiert. Manche Unternehmen setzen darauf ihre großen Applikationen in eine Microservices-Architektur zu migrieren, da sie sich hiervon viele Vorteile versprechen. So ist auch das Unternehmen L-mobile aus Sulzbach an der Murr dazu gewillt ihre Service/Sales-Applikation auf einen möglichen Microservices-Betrieb hin zu führen. Da es viele Ansätze gibt einen Monolithen in eine Microservices Applikation zu migrieren, hat die Abteilung Empirical Software Engineering des Institute of Software Engineering der Universität Stuttgart ein Framework für die Microservices Migration entwickelt, welches insbesondere Ansätze aus dem wissenschaftlichen Umfeld beinhaltet. Mithilfe dieses Frameworks wird in dieser Arbeit eine Teilmigration der Service/Sales-Applikation von L-mobile im Rahmen eines Proof of Concept durchgeführt. Dafür wurde zuerst eine Literaturrecherche durchgeführt um die Grundlagen von Monolithen, Microservices und dahingehende Migrationen im Allgemeinen zu erörtern. Anschließend wurde das Framework für Microservices Migration, für eine Teilmigration der Service/Sales-Applikation, durchgeführt. In dieser Durchführung wurde ein Service-Identifikationsansatz und eine Migrationsstrategie für die Applikation von L-mobile durch das Framework empfohlen. Während der Migration sind auch Herausforderungen aufgetreten. Einige der aufgetretene Herausforderungen wie die Migration der Datenbank werden auch in der wissenschaftlichen Literatur genannt, andere Herausforderungen, wie mangelnde Erfahrung mit Architekturbewertungen und der Implementierung von Microservices sind L-mobile spezifische Herausforderungen. Durch das Erheben strukturierter Feldnotizen während der Anwendung des Frameworks und durch verschiedene Reviews nach der Migration wurde das Framework hinsichtlich seiner Eignung für die Migration der Service/Sales-Applikation geprüft. Diese Evaluation ergab, dass sich das Framework für die Migration im Rahmen des Proof of Concept geeignet hat, da es umfangreich durch die Migration führt, eine Architekturbewertung berücksichtigt, geeignete Methoden für die Service-Identifizierung und Migration vorschlägt und durch das Vorschlagen von Patterns und Best Practices bei der Erstellung der Architektur unterstützt. Das Framework eignet sich auch für die komplette Migration der Service/Sales-Applikation