05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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    Visuelle Analyse gemeinsamer Sequenzen
    (2020) Galuschka, Marcel
    In den sozialen Medien verbreiten sich Texte schnell. Dieses Phänomen lässt sich auch in historischen Zeitungsartikeln erkennen. Forscher aus der Literaturwissenschaft und der Kulturwissenschaft befassen sich mit diesem Thema und untersuchen eine Menge von Texten. In dieser Arbeit erstellen wir dazu einen Prototyp, mit welchem es möglich ist, diese Texte untereinander zu vergleichen und zwei Texte im Vergleich genauer zu betrachten. Außerdem diskutieren wir hier, aus welchem Grund wir diese Darstellung gewählt und wie wir diese verwirklicht haben. Darüber hinaus haben wir diesen Prototyp einer Literaturwissenschaftlerin vorgestellt, welche mit diesem sprachliche Veränderungen zwischen den Texten erkennen kann.
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    Modelllierung von TOSCA-basierten Deployment Modellen in Java
    (2020) Fuhrmann, Jan
    In den letzten Jahren gewinnt das Thema Cloud Computing in vielen Bereichen der Gesellschaft zunehmend an Bedeutung. Immer mehr Unternehmen verlagern Anwendungen sowie Teile der lokalen Infrastruktur in die Cloud. Die Automatisierung des Deployments solcher Cloud-Anwendungen wird jedoch durch deren steigende Komplexität und zugleich hohe Diversität signifikant erschwert, weshalb eine einheitliche Strukturierung erforderlich ist. Die „Topology and Orchestration Specification for Cloud Applications (TOSCA)“ definiert einen Standard für die Beschreibung und das Management sowie die automatisierte Bereitstellung von Cloud-Anwendungen. Alle Komponenten einer Anwendung, in TOSCA Node Templates genannt, inklusive Abhängigkeiten und Verbindungen (Relationship Templates) werden dabei innerhalb sogenannter Topology Templates dargestellt. Bisher erfolgt die Modellierung von TOSCA Topologien ausschließlich in XML oder YAML, aber in einer objektorientierten Programmiersprache wie Java ist dies bislang nicht möglich. In dieser Arbeit wird ein Konzept vorgestellt, das eine automatisierte Konvertierung von TOSCA Node Types und Relationship Types in die Programmiersprache Java beschreibt. Diese Umwandlung findet im sogenannten „TOSCA2JAVA-Transformator“ statt, der im Rahmen dieser Arbeit entwickelt wurde. Er bildet damit die Grundlage für eine zukünftige Kreierung und Konfiguration von TOSCA Topologien in der objektorientierten Umgebung von Java.
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    Determining carbon dioxide footprint using data center simulators
    (2022) Keppler, Michael
    The modern world relies more and more on highly available software and digital data. To make this possible, the demands on data centers are increasing to provide the services in the required quality. This is associated with an increase in their power consumption and thus also their CO2 emissions. Today, they are already among the largest consumers of electricity and producers of CO2. Due to climate change and the set target of limiting global warming to a maximum of 2° Celsius, the aim is to reduce CO2 emissions. To calculate the CO2 footprint of data centers, data center simulators are already used in their planning phases. Simulators provide operators with important information about the future data center and show room for improvement. This thesis deals with the calculation of the Co2 footprint of data centers, the associated factors and provides an extended approach to the calculation of the CO2 footprint. Using a life cycle assessment approach, it is shown how the carbon footprint of data centers can be comprehensively calculated. For this purpose, several life cycle stages are determined that contribute to the CO2 footprint during the life cycle of a data center. Based on the life cycle stages, requirements for simulators are developed and, with the help of these, four current data center simulators are evaluated in their ability to calculate a CO2 footprint. Since current simulators calculate the CO2 footprint only partially and incompletely, the analysis of this work shows the need for a new simulator. Based on the analyzed life cycle stages, a new simulator is therefore designed. The subsequent evaluation of the simulator implemented as a proof-of-concept against the established requirements shows that the CO2 emissions of a data center can be comprehensively calculated with the demonstrated concept. In addition, a case study is carried out based on the High-Performance Computing Center Stuttgart and compared with the environmental statement of the HLRS.
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    Usable and fast interactive mental face reconstruction
    (2023) Strohm, Florian; Bâce, Mihai; Bulling, Andreas
    We introduce an end-to-end interactive system for mental face reconstruction - the challenging task of visually reconstructing a face image a person only has in their mind. In contrast to existing methods that suffer from low usability and high mental load, our approach only requires the user to rank images over multiple iterations according to the perceived similarity with their mental image. Based on these rankings, our mental face reconstruction system extracts image features in each iteration, combines them into a joint feature vector, and then uses a generative model to visually reconstruct the mental image. To avoid the need for collecting large amounts of human training data, we further propose a computational user model that can simulate human ranking behaviour using data from an online crowd-sourcing study (N=215). Results from a 12-participant user study show that our method can reconstruct mental images that are visually similar to existing approaches but has significantly higher usability, lower perceived workload, and is faster. In addition, results from a third 22-participant lineup study in which we validated our reconstructions on a face ranking task show a identification rate of , which is in line with prior work. These results represent an important step towards new interactive intelligent systems that can robustly and effortlessly reconstruct a user’s mental image.
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    Challenges of computational social science analysis with NLP methods
    (2022) Dayanik, Erenay; Padó, Sebastian (Prof. Dr.)
    Computational Social Science (CSS) is an emerging research area at the intersection of social science and computer science, where problems of societal relevance can be addressed by novel computational methods. With the recent advances in machine learning and natural language processing as well as the availability of textual data, CSS has opened up to new possibilities, but also methodological challenges. In this thesis, we present a line of work on developing methods and addressing challenges in terms of data annotation and modeling for computational political science and social media analysis, two highly popular and active research areas within CSS. In the first part of the thesis, we focus on a use case from computational political science, namely Discourse Network Analysis (DNA), a framework that aims at analyzing the structures behind complex societal discussions. We investigate how this style of analysis, which is traditionally performed manually, can be automated. We start by providing a requirement analysis outlining a roadmap to decompose the complex DNA task into several conceptually simpler sub-tasks. Then, we introduce NLP models with various configurations to automate two of the sub-tasks given by the requirement analysis, namely claim detection and classification, based on different neural network architectures ranging from unidirectional LSTMs to Transformer based architectures. In the second part of the thesis, we shift our focus to fairness, a central concern in CSS. Our goal in this part of the thesis is to analyze and improve the performances of NLP models used in CSS in terms of fairness and robustness while maintaining their overall performance. With that in mind, we first analyze the above-mentioned claim detection and classification models and propose techniques to improve model fairness and overall performance. After that, we broaden our focus to social media analysis, another highly active subdomain of CSS. Here, we study text classification of the correlated attributes, which pose an important but often overlooked challenge to model fairness. Our last contribution is to discuss the limitations of the current statistical methods applied for bias identification; to propose a multivariate regression based approach; and to show that, through experiments conducted on social media data, it can be used as a complementary method for bias identification and analysis tasks. Overall, our work takes a step towards increasing the understanding of challenges of computational social science. We hope that both political scientists and NLP scholars can make use of the insights from this thesis in their research.
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    Distributed Deep Reinforcement Learning for Learn-to-optimize
    (2023) Mayer, Paul
    In the context of increasingly complex applications, e.g., robust performance tuning in Integrated Circuit Design, conventional optimization methods have difficulties in achieving satisfactory results while keeping to a limited time budget. Therefore, learning optimization algorithms becomes more and more interesting, replacing the established way of hand-crafting or tweaking algorithms. Learned algorithms reduce the amount of assumptions and expert knowledge necessary to create state-of-the-art solvers by decreasing the need of hand-crafting heuristics and hyper-parameter tuning. First advancements using Reinforcement Learning have shown great success in outperforming typical zeroth- and first-order optimization algorithms, especially with respect to generalization capabilities. However, training still is very time consuming. Especially challenging is training models on functions with free parameters. Changing these parameters (that could represent, e.g., conditions in a real world example) affects the underlying objective function. Robust solutions therefore depend on thorough sampling, which tends to be the bottleneck considering time consumption. In this thesis we identified the runtime bottleneck of the Reinforcement Learning Algorithm and were able to decrease runtime drastically by distributing data collection. Additionally, we studied the effects of combining sampling strategies in regards to generalization capabilities of the learned algorithm.
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    Endowing a NAO robot with practical social-touch perception
    (2022) Burns, Rachael Bevill; Lee, Hyosang; Seifi, Hasti; Faulkner, Robert; Kuchenbecker, Katherine J.
    Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants’ feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO’s left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors’ physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot’s presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.
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    Generating code for distributed deployments of cyber-physical systems using the MechatronicUML
    (2022) Stürner, David
    Models are applied in engineering disciplines to describe systems from a higher level of abstraction. In Model-Driven Software Engineering (MDSE), formal models are used to design and verify software systems and to infer platform-specific models and implementations. The MechatronicUML is an MDSE method specifically designed for distributed cyber-physical systems (CPS). This thesis explores how the MechatronicUML may be used for generating code. The exact state of previous code generation approaches is not precisely known. The objective of this thesis is to design and implement a MechatronicUML-based code generator for distributed deployments of CPS. Previous code generation approaches are analyzed for this purpose and one approach is selected and extended to support a particular type of robot car as a target platform. A taxonomy for model-based code generation is proposed to structure the analysis of the previous approaches. Based on the selected previous approach, a code generator is presented and implemented. Additionally, an automotive application scenario is used as a case study for evaluating the concept and the implementation of the presented code generator. This code generator supports modeling the distributed deployment of a CPS with the MechatronicUML and generates platform-specific source code which can be successfully compiled and deployed on the Arduino-based robot cars. Ultimately, the thesis presents a proof of concept to generate the code for a distributed CPS based on the MechatronicUML.
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    Formal security analysis of the OpenID FAPI 2.0 Security Profile with FAPI 2.0 Message Signing, FAPI-CIBA, Dynamic Client Registration and Management : technical report
    (2023) Hosseyni, Pedram; Küsters, Ralf; Würtele, Tim
    Building on our recent formal security analysis of the FAPI 2.0 Security Profile, we here extend the analysis effort to FAPI 2.0 Message Signing, combined with Dynamic Client Registration, Dynamic Client Management, and FAPI-CIBA. Overall, we model an ecosystem which uses all these profiles and extensions in parallel. Like the previous work on the FAPI 2.0 Security Profile, this analysis is based on the Web Infrastructure Model, a Dolev-Yao style model of the web infrastructure - in fact, it is the most comprehensive and detailed model of the web infrastructure to date. We identify several attacks, propose fixes and prove the fixed protocols secure with respect to authorization, authentication, session integrity for both authorization and authentication, and non-repudiation for the messages covered by FAPI 2.0 Message Signing. The attacks and proposed fixes have been reported to the responsible FAPI Working Group at the OpenID Foundation, and fixes have since been incorporated into the specifications.
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    Service-Based Translation of Quantum Circuits
    (2022) Kuhn, Maximilian Jakob Johannes
    Recently, quantum advantage has started to attract attention to the field of quantum computing. While current devices are still noisy and error-prone, numerous vendors have already established themselves, each offering their various approaches with different characteristics and optimizations. In the era of Noisy Intermediate-Scale Quantum (NISQ) computers, quantum circuits must be compiled and executed as efficiently as possible, to best utilize the limited quantum resources available. Therefore, selecting a fitting vendor is a major part of programming for quantum devices. However, different vendors offer different, often incompatible frameworks. Compiled circuits are also highly complex, making manual comparison non-trivial. The NISQ Analyzer has been presented as a solution to this issue. It automates the compilation process of a circuit over a subset of usually incompatible providers. For this purpose it utilizes translation, allowing it to access multiple frameworks even with a circuit only provided in one language. In this thesis, we extend upon this functionality. We make new frameworks available for translation, employing existing translation functionality where possible. For proof of concept, we also implement compilation for a new vendor using the NISQ Analyzer, utilizing our translations. Additionally, we include a detailed evaluation of the reliability of translation frameworks, as well as a case study showing how our extensions can be put to use.