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 Task-oriented specialization techniques for entity retrieval(2020) Glaser, Andrea; Kuhn, Jonas (Prof. Dr.)Finding information on the internet has become very important nowadays, and online encyclopedias or websites specialized in certain topics offer users a great amount of information. Search engines support users when trying to find information. However, the vast amount of information makes it difficult to separate relevant from irrelevant facts for a specific information need. In this thesis we explore two areas of natural language processing in the context of retrieving information about entities: named entity disambiguation and sentiment analysis. The goal of this thesis is to use methods from these areas to develop task-oriented specialization techniques for entity retrieval. Named entity disambiguation is concerned with linking referring expressions (e.g., proper names) in text to their corresponding real world or fictional entity. Identifying the correct entity is an important factor in finding information on the internet as many proper names are ambiguous and need to be disambiguated to find relevant information. To that end, we introduce the notion of r-context, a new type of structurally informed context. This r-context consists of sentences that are relevant to the entity only to capture all important context clues and to avoid noise. We then show the usefulness of this r-context by performing a systematic study on a pseudo-ambiguity dataset. Identifying less known named entities is a challenge in named entity disambiguation because usually there is not much data available from which a machine learning algorithm can learn. We propose an approach that uses an aggregate of textual data about other entities which share certain properties with the target entity, and learn information from it by using topic modelling, which is then used to disambiguate the less known target entity. We use a dataset that is created automatically by exploiting the link structure in Wikipedia, and show that our approach is helpful for disambiguating entities without training material and with little surrounding context. Retrieving the relevant entities and information can produce many search results. Thus, it is important to effectively present the information to a user. We regard this step beyond the entity retrieval and employ sentiment analysis, which is used to analyze opinions expressed in text, in the context of effectively displaying information about product reviews to a user. We present a system that extracts a supporting sentence, a single sentence that captures both the sentiment of the author as well as a supportingfact. This supporting sentence can be used to provide users with an easy way to assess information in order to make informed choices quickly. We evaluate our approach by using the crowdsourcing service Amazon Mechanical Turk.Item Open Access A framework for similarity recognition of CAD models in respect to PLM optimization(2022) Zehtaban, Leila; Roller, Dieter (Univ.-Prof. Hon.-Prof. Dr.)Item Open Access Eine Methode zum Verteilen, Adaptieren und Deployment partnerübergreifender Anwendungen(2022) Wild, Karoline; Leymann, Frank (Prof. Dr. Dr. h. c.)Ein wesentlicher Aspekt einer effektiven Kollaboration innerhalb von Organisationen, aber vor allem organisationsübergreifend, ist die Integration und Automatisierung der Prozesse. Dazu zählt auch die Bereitstellung von Anwendungssystemen, deren Komponenten von unterschiedlichen Partnern, das heißt Abteilungen oder Unternehmen, bereitgestellt und verwaltet werden. Die dadurch entstehende verteilte, dezentral verwaltete Umgebung bedarf neuer Konzepte zur Bereitstellung. Die Autonomie der Partner und die Verteilung der Komponenten führen dabei zu neuen Herausforderungen. Zum einen müssen partnerübergreifende Kommunikationsbeziehungen realisiert und zum anderen muss das automatisierte dezentrale Deployment ermöglicht werden. Eine Vielzahl von Technologien wurde in den letzten Jahren entwickelt, die alle Schritte von der Modellierung bis zur Bereitstellung und dem Management zur Laufzeit einer Anwendung abdecken. Diese Technologien basieren jedoch auf einer zentralisierten Koordination des Deployments, wodurch die Autonomie der Partner eingeschränkt ist. Auch fehlen Konzepte zur Identifikation von Problemen, die aus der Verteilung von Anwendungskomponenten resultieren und die Funktionsfähigkeit der Anwendung einschränken. Dies betrifft speziell die partnerübergreifenden Kommunikationsbeziehungen. Um diese Herausforderungen zu lösen, stellt diese Arbeit die DivA-Methode zum Verteilen, Adaptieren und Deployment partnerübergreifender Anwendungen vor. Die Methode vereinigt die globalen und lokalen Partneraktivitäten, die zur Bereitstellung partnerübergreifender Anwendungen benötigt werden. Dabei setzt die Methode auf dem deklarativen Essential Deployment Meta Model (EDMM) auf und ermöglicht damit die Einführung deploymenttechnologieunabhängiger Modellierungskonzepte zur Verteilung von Anwendungskomponenten sowie zur Modellanalyse und -adaption. Das Split-and-Match-Verfahren wird für die Verteilung von Anwendungskomponenten basierend auf festgelegten Zielumgebungen und zur Selektion kompatibler Cloud-Dienste vorgestellt. Für die Ausführung des Deployments können EDMM-Modelle in unterschiedliche Technologien transformiert werden. Um die Bereitstellung komplett dezentral durchzuführen, werden deklarative und imperative Technologien kombiniert und basierend auf den deklarativen EDMM-Modellen Workflows generiert, die die Aktivitäten zur Bereitstellung und zum Datenaustausch mit anderen Partnern zur Realisierung partnerübergreifender Kommunikationsbeziehungen orchestrieren. Diese Workflows formen implizit eine Deployment-Choreographie. Für die Modellanalyse und -adaption wird als Kern dieser Arbeit ein zweistufiges musterbasiertes Verfahren zur Problemerkennung und Modelladaption eingeführt. Dafür werden aus den textuellen Musterbeschreibungen die Problem- und Kontextdefinition analysiert und formalisiert, um die automatisierte Identifikation von Problemen in EDMM-Modellen zu ermöglichen. Besonderer Fokus liegt dabei auf Problemen, die durch die Verteilung der Komponenten entstehen und die Realisierung von Kommunikationsbeziehungen verhindern. Das gleiche Verfahren wird auch für die Selektion geeigneter konkreter Lösungsimplementierungen zur Behebung der Probleme angewendet. Zusätzlich wird ein Ansatz zur Selektion von Kommunikationstreibern abhängig von der verwendeten Integrations-Middleware vorgestellt, wodurch die Portabilität von Anwendungskomponenten verbessert werden kann. Die in dieser Arbeit vorgestellten Konzepte werden durch das DivA-Werkzeug automatisiert. Zur Validierung wird das Werkzeug prototypisch implementiert und in bestehende Systeme zur Modellierung und Ausführung des Deployments von Anwendungssystemen integriert.Item Open Access Elastic parallel systems for high performance cloud computing(2020) Kehrer, Stefan; Blochinger, Wolfgang (Prof. Dr.)High Performance Computing (HPC) enables significant progress in both science and industry. Whereas traditionally parallel applications have been developed to address the grand challenges in science, as of today, they are also heavily used to speed up the time-to-result in the context of product design, production planning, financial risk management, medical diagnosis, as well as research and development efforts. However, purchasing and operating HPC clusters to run these applications requires huge capital expenditures as well as operational knowledge and thus is reserved to large organizations that benefit from economies of scale. More recently, the cloud evolved into an alternative execution environment for parallel applications, which comes with novel characteristics such as on-demand access to compute resources, pay-per-use, and elasticity. Whereas the cloud has been mainly used to operate interactive multi-tier applications, HPC users are also interested in the benefits offered. These include full control of the resource configuration based on virtualization, fast setup times by using on-demand accessible compute resources, and eliminated upfront capital expenditures due to the pay-per-use billing model. Additionally, elasticity allows compute resources to be provisioned and decommissioned at runtime, which allows fine-grained control of an application's performance in terms of its execution time and efficiency as well as the related monetary costs of the computation. Whereas HPC-optimized cloud environments have been introduced by cloud providers such as Amazon Web Services (AWS) and Microsoft Azure, existing parallel architectures are not designed to make use of elasticity. This thesis addresses several challenges in the emergent field of High Performance Cloud Computing. In particular, the presented contributions focus on the novel opportunities and challenges related to elasticity. First, the principles of elastic parallel systems as well as related design considerations are discussed in detail. On this basis, two exemplary elastic parallel system architectures are presented, each of which includes (1) an elasticity controller that controls the number of processing units based on user-defined goals, (2) a cloud-aware parallel execution model that handles coordination and synchronization requirements in an automated manner, and (3) a programming abstraction to ease the implementation of elastic parallel applications. To automate application delivery and deployment, novel approaches are presented that generate the required deployment artifacts from developer-provided source code in an automated manner while considering application-specific non-functional requirements. Throughout this thesis, a broad spectrum of design decisions related to the construction of elastic parallel system architectures is discussed, including proactive and reactive elasticity control mechanisms as well as cloud-based parallel processing with virtual machines (Infrastructure as a Service) and functions (Function as a Service). To evaluate these contributions, extensive experimental evaluations are presented.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%.Item Open Access Improving usability of gaze and voice based text entry systems(2023) Sengupta, Korok; Staab, Steffen (Prof. Dr.)Item Open Access Molekularstrahlepitaxie und Charakterisierung unverspannter Silizium-Germanium-Zinn-Legierungen auf virtuellem Germanium-Substrat(2022) Schwarz, Daniel; Schulze, Jörg (Prof. Dr. habil.)Item Open Access Data-efficient and safe learning with Gaussian processes(2020) Schreiter, Jens; Toussaint, Marc (Prof. Dr. rer. nat.)Data-based modeling techniques enjoy increasing popularity in many areas of science and technology where traditional approaches are limited regarding accuracy and efficiency. When employing machine learning methods to generate models of dynamic system, it is necessary to consider two important issues. Firstly, the data-sampling process should induce an informative and representative set of points to enable high generalization accuracy of the learned models. Secondly, the algorithmic part for efficient model building is essential for applicability, usability, and the quality of the learned predictive model. This thesis deals with both of these aspects for supervised learning problems, where the interaction between them is exploited to realize an exact and powerful modeling. After introducing the non-parametric Bayesian modeling approach with Gaussian processes and basics for transient modeling tasks in the next chapter, we dedicate ourselves to extensions of this probabilistic technique to relevant practical requirements in the subsequent chapter. This chapter provides an overview on existing sparse Gaussian process approximations and propose some novel work to increase efficiency and model selection on particularly large training data sets. For example, our sparse modeling approach enables real-time capable prediction performance and efficient learning with low memory requirements. A comprehensive comparison on various real-world problems confirms the proposed contributions and shows a variety of modeling tasks, where approximate Gaussian processes can be successfully applied. Further experiments provide more insight about the whole learning process, and thus a profound understanding of the presented work. In the fourth chapter, we focus on active learning schemes for safe and information-optimal generation of meaningful data sets. In addition to the exploration behavior of the active learner, the safety issue is considered in our work, since interacting with real systems should not result in damages or even completely destroy it. Here we propose a new model-based active learning framework to solve both tasks simultaneously. As basis for the data-sampling process we employ the presented Gaussian process techniques. Furthermore, we distinguish between static and transient experimental design strategies. Both problems are separately considered in this chapter. Nevertheless, the requirements for each active learning problem are the same. This subdivision into a static and transient setting allows a more problem-specific perspective on the two cases, and thus enables the creation of specially adapted active learning algorithms. Our novel approaches are then investigated for different applications, where a favorable trade-off between safety and exploration is always realized. Theoretical results maintain these evaluations and provide respectable knowledge about the derived model-based active learning schemes. For example, an upper bound for the probability of failure of the presented active learning methods is derived under reasonable assumptions. Finally, the thesis concludes with a summary of the investigated machine learning problems and motivate some future research directions.Item Open Access Verifikation softwareintensiver Fahrwerksysteme(2023) Hellhake, Dominik; Wagner, Stefan (Prof. Dr.)Kontext: Die zunehmende Signifikanz von softwarebasierten Funktionen in modernen Fahrzeugen ist der Auslöser vieler Veränderungen im automobilen Entwicklungsprozess. In der Vergangenheit bestand ein Fahrzeug aus mehreren Electronic Control Units (ECUs), welche jeweils individuelle und voneinander unabhängige Softwarefunktionen ausführten. Demgegenüber bilden heute mehrere ECUs funktional kohärente Subsysteme, welche übergreifende und vernetzte Softwarefunktionen wie zum Beispiel Fahrerassistenzfunktionen und automatisierte Fahrfunktionen implementieren. Dieser Trend hin zu einem hochvernetzten Softwaresystem sorgt in der Entwicklung moderner Fahrzeuge für einen hohen Bedarf an geeigneten Architekturmodellen und Entwurfsmethoden. Aufgrund der Entwicklung von ECUs durch verschiedene Entwicklungsdienstleister werden zusätzlich systematische Integrationstestmethoden benötigt, um das korrekte Interaktionsverhalten jeder individueller ECU im Laufe der Fahrzeugentwicklung zu verifizieren. Hierfür stellt Kopplung eine weit verbreitete Messgröße dar, um in komponentenbasierten Softwaresystemen Qualitätseigenschaften wie die Verständlichkeit, Wiederverwendbarkeit, Modifizierbarkeit und Testbarkeit widerzuspiegeln. Problembeschreibung: Während Kopplung eine geeignete Messgröße für die Qualität eines Softwaredesigns darstellt, existieren nur wenig wissenschaftliche Beiträge über den Mehrwert von Kopplung für den Integrationstestprozess des aus dem Design resultierenden Systems. Existierende Arbeiten über das Thema Integrationstest beschreiben die schrittweise Integration von White-Box Softwarekomponenten unter Verwendung von Eigenschaften und Messgrößen, welche aus der Implementierung abgeleitet wurden. Diese Abhängigkeit vom Quellcode und der Softwarestruktur sorgt jedoch dafür, dass diese Methoden nicht auf die Entwicklung von Fahrzeugen übertragen werden können, da Fahrzeugsysteme zu einem großen Anteil aus Black-Box Software bestehen. Folglich existieren auch keine Methoden zur Messung der Testabdeckung oder zur Priorisierung der durchzuführenden Tests. In der Praxis sorgt dies dafür, dass lediglich erfahrungsbasierte Ansätze angewendet werden, bei denen signifikante Anteile des Interaktionsverhaltens im Laufe der Fahrzeugentwicklung ungetestet bleiben. Ziele: Um Lösungen für dieses Problem zu finden, soll diese Arbeit systematische und empirisch evaluierte Testmethoden ausarbeiten, welche für die Integrationstests während der Fahrzeugentwicklung angewendet werden können. Dabei wollen wir in erster Linie auch einen Einblick in das Potential bieten, welche Messgrößen Kopplung für die Verwendung zur Testfall-Priorisierung bietet. Das Ziel dieser Arbeit ist es, eine Empfehlung für das systematische Integrationstesten von Fahrzeugsystemen zu geben, welches auf dem Interaktionsverhalten einzelner ECUs basiert. Methoden: Um diese Ziele zu erreichen, analysieren wir im ersten Schritt dieser Arbeit den Stand der Technik, so wie er gegenwärtig bei BMW für das Integrationstesten der Fahrwerkssysteme angewendet wird. Dem gegenüber analysieren wir den Stand der Wissenschaft hinsichtlich existierender Testmethoden, welche auf die Problemstellung der Integration von Fahrzeugsystemen übertragen werden können. Basierend auf diesem Set an wissenschaftlich evaluierten Methoden leiten wir anschließend konkrete Vorgehensweisen für die Messung der Testabdeckung und der TestfallPriorisierung ab. Im Rahmen dieser Arbeit werden beide Vorgehensweisen empirisch evaluiert basierend auf Test- und Fehlerdaten aus einem Fahrzeugentwicklungsprojekt. Beiträge: Zusammengefasst enthält diese Arbeit zwei Beiträge, welche wir zu einem zentralen Beitrag zusammenführen. Der erste Bereich besteht aus einer Methode zur Messung der Testabdeckung basierend auf dem inter-komponenten Datenfluss von Black-Box-Komponenten. Die Definition eines Datenfluss-Klassifikationsschemas ermöglicht es, Daten über die Verwendung von Datenflüssen in existierenden Testfällen sowie in Fehlern zu sammeln, welche in den verschiedenen Testphasen gefunden wurden. Der zweite Beitrag dieser Arbeit stellt eine Korrelationsstudie zwischen verschiedenen Messmethoden für Coupling und der Fehlerverteilung in einem Fahrwerkssystem dar. Dabei evaluieren wir die Coupling-Werte von individuellen Software-Interfaces sowie die der Komponenten, welche diese implementieren. Zusammengefasst spiegelt diese Studie das Potential wider, das solche Coupling-Messmethoden für die Verwendbarkeit zur Testpriorisierung haben. Die Erkenntnisse aus diesen Beiträgen werden in unserem Hauptbeitrag zu einer Coupling-basierten Teststrategie für Systemintegrationstests zusammengeführt. Fazit: Der Beitrag dieser Arbeit verbindet zum ersten Mal den Stand der Technik zur Systemintegration von verteilten Black-Box-Softwaresystemen mit dem Stand der Wissenschaft über systematische Ansätze zur Integration von Softwaresystemen. Das Messen der Testabdeckung basierend auf dem Datenfluss ist hierfür eine effektive Methode, da der Datenfluss in einem System das Interaktionsverhalten der einzelnen Komponenten widerspiegelt. Zusätzlich kann das mögliche Interaktionsverhalten aller Komponenten des Systems aus dessen Architektur-Spezifikationen abgeleitet werden. Aus den Studien über die Korrelation von Coupling zur Fehlerverteilung geht außerdem eine moderate Abhängigkeit hervor. Aufgrund dessen ist die Selektion von Testfällen basierend auf die im Testfall erprobten Komponenteninteraktionen und dessen Coupling ein sinnvolles Vorgehen für die Praxis. Jedoch ist die moderate Korrelation auch ein Indiz dafür, dass zusätzliche Aspekte bei der Auswahl von Testfällen für Integrationstests zu berücksichtigen sind.Item Open Access Models for data-efficient reinforcement learning on real-world applications(2021) Dörr, Andreas; Toussaint, Marc (Prof. Dr.)Large-scale deep Reinforcement Learning is strongly contributing to many recently published success stories of Artificial Intelligence. These techniques enabled computer systems to autonomously learn and master challenging problems, such as playing the game of Go or complex strategy games such as Star-Craft on human levels or above. Naturally, the question arises which problems could be addressed with these Reinforcement Learning technologies in industrial applications. So far, machine learning technologies based on (semi-)supervised learning create the most visible impact in industrial applications. For example, image, video or text understanding are primarily dominated by models trained and derived autonomously from large-scale data sets with modern (deep) machine learning methods. Reinforcement Learning, on the opposite side, however, deals with temporal decision-making problems and is much less commonly found in the industrial context. In these problems, current decisions and actions inevitably influence the outcome and success of a process much further down the road. This work strives to address some of the core problems, which prevent the effective use of Reinforcement Learning in industrial settings. Autonomous learning of new skills is always guided by existing priors that allow for generalization from previous experience. In some scenarios, non-existing or uninformative prior knowledge can be mitigated by vast amounts of experience for a particular task at hand. Typical industrial processes are, however, operated in very restricted, tightly calibrated operating points. Exploring the space of possible actions or changes to the process naively on the search for improved performance tends to be costly or even prohibitively dangerous. Therefore, one reoccurring subject throughout this work is the emergence of priors and model structures that allow for efficient use of all available experience data. A promising direction is Model-Based Reinforcement Learning, which is explored in the first part of this work. This part derives an automatic tuning method for one of themostcommonindustrial control architectures, the PID controller. By leveraging all available data about the system’s behavior in learning a system dynamics model, the derived method can efficiently tune these controllers from scratch. Although we can easily incorporate all data into dynamics models, real systems expose additional problems to the dynamics modeling and learning task. Characteristics such as non-Gaussian noise, latent states, feedback control or non-i.i.d. data regularly prevent using off-the-shelf modeling tools. Therefore, the second part of this work is concerned with the derivation of modeling solutions that are particularly suited for the reinforcement learning problem. Despite the predominant focus on model-based reinforcement learning as a promising, data-efficient learning tool, this work’s final part revisits model assumptions in a separate branch of reinforcement learning algorithms. Again, generalization and, therefore, efficient learning in model-based methods is primarily driven by the incorporated model assumptions (e.g., smooth dynamics), which real, discontinuous processes might heavily violate. To this end, a model-free reinforcement learning is presented that carefully reintroduces prior model structure to facilitate efficient learning without the need for strong dynamic model priors. The methods and solutions proposed in this work are grounded in the challenges experienced when operating with real-world hardware systems. With applications on a humanoid upper-body robot or an autonomous model race car, the proposed methods are demonstrated to successfully model and master their complex behavior.