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Systems-focused error prevention efforts are internationally recognized in the healthcare industry, and industry efforts to identify and correct organizational defects through the process of CRM are well established in the U.S. and Germany. However, in both countries, there is no clear corresponding liability for healthcare organizations who fail to engage in systems-based learning through the process of clinical risk management (CRM). Although both jurisdictions do recognize organization-based theories of liability, liability for negligent CRM has not been explicitly recognized by courts in either jurisdiction to date. German legal scholars, recognizing this gap in liability for healthcare organizations, have written in support of finding liability for negligent CRM under existing tort law; however, there is no corresponding discussion in the American legal literature. This dissertation fills that gap with a comparative analysis of medical negligence law in the U.S. and Germany through the international lens of modern medical error prevention science and policy to articulate a legal basis and sketch the evidentiary framework for tort liability based on negligent CRM.
In the constrained planarity setting, we ask whether a graph admits a crossing-free drawing that additionally satisfies a given set of constraints. These constraints are often derived from very natural problems; prominent examples are Level Planarity, where vertices have to lie on given horizontal lines indicating a hierarchy, Partially Embedded Planarity, where we extend a given drawing without modifying already-drawn parts, and Clustered Planarity, where we additionally draw the boundaries of clusters which recursively group the vertices in a crossing-free manner. In the last years, the family of constrained planarity problems received a lot of attention in the field of graph drawing. Efficient algorithms were discovered for many of them, while a few others turned out to be NP-complete. In contrast to the extensive theoretical considerations and the direct motivation by applications, only very few of the found algorithms have been implemented and evaluated in practice.
The goal of this thesis is to advance the research on both theoretical as well as practical aspects of constrained planarity. On the theoretical side, we consider two types of constrained planarity problems. The first type are problems that individually constrain the rotations of vertices, that is they restrict the counter-clockwise cyclic orders of the edges incident to vertices. We give a simple linear-time algorithm for the problem Partially Embedded Planarity, which also generalizes to further constrained planarity variants of this type.
The second type of constrained planarity problem concerns more involved planarity variants that come down to the question whether there are embeddings of one or multiple graphs such that the rotations of certain vertices are in sync in a certain way. Clustered Planarity and a variant of the Simultaneous Embedding with Fixed Edges Problem (Connected SEFE-2) are well-known problems of this type. Both are generalized by our Synchronized Planarity problem, for which we give a quadratic algorithm. Through reductions from various other problems, we provide a unified modelling framework for almost all known efficiently solvable constrained planarity variants that also directly provides a quadratic-time solution to all of them.
For both our algorithms, a key ingredient for reaching an efficient solution is the usage of the right data structure for the problem at hand. In this case, these data structures are the SPQR-tree and the PC-tree, which describe planar embedding possibilities from a global and a local perspective, respectively. More specifically, PC-trees can be used to locally describe the possible cyclic orders of edges around vertices in all planar embeddings of a graph. This makes it a key component for our algorithms, as it allows us to test planarity while also respecting further constraints, and to communicate constraints arising from the surrounding graph structure between vertices with synchronized rotation.
Bridging over to the practical side, we present the first correct implementation of PC-trees. We also describe further improvements, which allow us to outperform all implementations of alternative data structures (out of which we only found very few to be fully correct) by at least a factor of 4. We show that this yields a simple and competitive planarity test that can also yield an embedding to certify planarity. We also use our PC-tree implementation to implement our quadratic algorithm for solving Synchronized Planarity. Here, we show that our algorithm greatly outperforms previous attempts at solving related problems like Clustered Planarity in practice. We also engineer its running time and show how degrees of freedom in the theoretical algorithm can be leveraged to yield an up to tenfold speed-up in practice.
The nationalism-patriotism distinction is one of the most influential distinctions in the field of political psychology. While frequently used, the distinction suffers from a number of shortcomings that have hitherto been devoted little attention to. This dissertation aims to contribute to fill this research gap by systematically addressing these pitfalls. Notably, it does not abandon the binary distinction as such, but aims to further refine it. Thoroughly revisiting the nationalism-patriotism distinction, it synthesises the field's two predominant research traditions, i.e. the work of Kosterman and Feshbach (1989) in the U.S. and the one of Blank and Schmidt (2003) in Germany, that have not been brought into dialogue. In so doing, and engaging with research on attachment, it calls for a more nuanced triad of attachments: nationalism, that revolves around the nation; patriotism, that refers to the homeland; and democratic patriotism with democracy as its object of attachment. In line with this triad, it introduces a novel three-factor measurement model that has been validated in three studies in Germany. Overall, the dissertation underlines the need to approach ambiguous and complex concepts such as nationalism and patriotism in a more theoretically consistent way before operationalizing them in a rigorous manner.
Data has become a necessary resource for firm operations in the modern digital world, explaining their growing data gathering efforts. Due to this development, consumers are confronted with decisions to disclose personal data on a daily basis, and have become increasingly intentional about data sharing. While this reluctance to disclose personal data poses challenges for firms, at the same time, it also creates new opportunities for improving privacy-related interactions with customers. This dissertation advocates for a more holistic perspective on consumers’ privacy-related decision-making and introduces the consumer privacy journey consisting of three subsequent phases: pre data disclosure, data disclosure, post data disclosure. In three independent essays, I stress the importance of investigating data requests (i.e., the first step of this journey) as they represent a largely neglected, yet, potentially powerful means to influence consumers’ decision-making and decision-evaluation processes. Based on dual-processing models of decision-making, this dissertation focuses on both consumers’ cognitive and affective evaluations of privacy-related information: First, Essay 1 offers novel conceptualizations and operationalizations of consumers’ perceived behavioral control over personal data (i.e., cognitive processing) in the context of Artificial Intelligence (AI)-based data disclosure processes. Next, Essay 2 examines consumers’ cognitive and affective processing of a data request that entails relevance arguments as well as relevance-illustrating game elements. Finally, Essay 3 categorizes affective cues that trigger consumers’ affective processing of a data request and proposes that such cues need to fit with a specific data disclosure situation to foster long-term decision satisfaction. Collectively, my findings provide research and practice with new insights into consumers’ privacy perceptions and behaviors, which are particularly valuable in the context of complex, new (technology-enabled) data disclosure situations.
Due to the increasing amount of distributed renewable energy generation and the emerging high demand at consumer connection points, e. g., electric vehicles, the power distribution grid will reach its capacity limit at peak load times if it is not expensively enhanced. Alternatively, smart flexibility management that controls user assets can help to better utilize the existing power grid infrastructure for example by sharing available grid capacity among connected electric vehicles or by disaggregating flexibility requests to hybrid photovoltaic battery energy storage systems in households. Besides maintaining an acceptable state of the power distribution grid, these smart grid applications also need to ensure a certain quality of service and provide fairness between the individual participants, both of which are not extensively discussed in the literature. This thesis investigates two smart grid applications, namely electric vehicle charging-as-a-service and flexibility-provision-as-a-service from distributed energy storage systems in private households.
The electric vehicle charging service allocation is modeled with distributed queuing-based allocation mechanisms which are compared to new probabilistic algorithms. Both integrate user constraints (arrival time, departure time, and energy required) to manage the quality of service and fairness. In the queuing-based allocation mechanisms, electric vehicle charging requests are packetized into logical charging current packets, representing the smallest controllable size of the charging process. These packets are queued at hierarchically distributed schedulers, which allocate the available charging capacity using the time and frequency division multiplexing technique known from the networking domain. This allows multiple electric vehicles to be charged simultaneously with variable charging currents. To achieve high quality of service and fairness among electric vehicle charging processes, dynamic weights are introduced into a weighted fair queuing scheduler that considers electric vehicle departure time and required energy for prioritization. The distributed probabilistic algorithms are inspired by medium access protocols from computer networking, such as binary exponential backoff, and control the quality of service and fairness by adjusting sampling windows and waiting periods based on user requirements.
The second smart grid application under investigation aims to provide flexibility provision-as-a-service that disaggregates power flexibility requests to distributed battery energy storage systems in private households. Commonly, the main purpose of stationary energy storage is to store energy from a local photovoltaic system for later use, e. g., for overnight charging of an electric vehicle. This is optimized locally by a home energy management system, which also allows the scheduling of external flexibility requests defined by the deviation from the optimal power profile at the grid connection point, for example, to perform peak shaving at the transformer. This thesis discusses a linear heuristic and a meta heuristic to disaggregate a flexibility request to the single participating energy management systems that are grouped into a flexibility pool. Thereby, the linear heuristic iteratively assigns portions of the power flexibility to the most appropriate energy management system for one time slot after another, minimizing the total flexibility cost or maximizing the probability of flexibility delivery. In addition, a multi-objective genetic algorithm is proposed that also takes into account power grid aspects, quality of service, and fairness among par-ticipating households. The genetic operators are tailored to the flexibility disaggregation search space, taking into account flexibility and energy management system constraints, and enable power-optimized buffering of fitness values.
Both smart grid applications are validated on a realistic power distribution grid with real driving patterns and energy profiles for photovoltaic generation and household consumption. The results of all proposed algorithms are analyzed with respect to a set of newly defined metrics on quality of service, fairness, efficiency, and utilization of the power distribution grid. One of the main findings is that none of the tested algorithms outperforms the others in all quality of service metrics, however, integration of user expectations improves the service quality compared to simpler approaches. Furthermore, smart grid control that incorporates users and their flexibility allows the integration of high-load applications such as electric vehicle charging and flexibility aggregation from distributed energy storage systems into the existing electricity distribution infrastructure. However, there is a trade-off between power grid aspects, e. g., grid losses and voltage values, and the quality of service provided. Whenever active user interaction is required, means of controlling the quality of service of users’ smart grid applications are necessary to ensure user satisfaction with the services provided.
Code injection attacks like the one used in the high-profile 2017 Equifax breach, have become increasingly common, ranking at the top of OWASP’s list of critical web application vulnerabilities. The injection attacks can also target embedded applications running on processors like ARM and Xtensa by exploiting memory bugs and maliciously altering the program’s behavior or even taking full control over a system. Especially, ARM’s support of low power consumption without sacrificing performance is leading the industry to shift towards ARM processors, which advances the attention of injection attacks as well.
In this thesis, we are considering web applications and embedded applications (running on ARM and Xtensa processors) as the target of injection attacks. To detect injection attacks in web applications, taint analysis is mostly proposed but the precision, scalability, and runtime overhead of the detection depend on the analysis types (e.g., static vs dynamic, sound vs unsound). Moreover, in the existing dynamic taint tracking approach for Java- based applications, even the most performant can impose a slowdown of at least 10–20% and often far more. On the other hand, considering the embedded applications, while some initial research has tried to detect injection attacks (i.e., ROP and JOP) on ARM, they suffer from high performance or storage overhead. Besides, the Xtensa has been neglected though used in most firmware-based embedded WiFi home automation devices.
This thesis aims to provide novel approaches to precisely detect injection attacks on both the web and embedded applications. To that end, we evaluate JavaScript static analysis frameworks to evaluate the security of a hybrid app (JS & native) from an industrial partner, provide RIVULET – a tool that precisely detects injection attacks in Java-based real-world applications, and investigate injection attacks detection on ARM and Xtensa platforms using hardware performance counters (HPCs) and machine learning (ML) techniques.
To evaluate the security of the hybrid application, we initially compare the precision, scalability, and code coverage of two widely-used static analysis frameworks—WALA and SAFE. The result of our comparison shows that SAFE provides higher precision and better code coverage at the cost of somewhat lower scalability. Based on these results, we analyze the data flows of the hybrid app via taint analysis by extending the SAFE’s taint analysis and detected a potential for injection attacks of the hybrid application.
Similarly, to detect injection attacks in Java-based applications, we provide Rivulet which monitors the execution of developer-written functional tests using dynamic taint tracking. Rivulet uses a white-box test generation technique to re-purpose those functional tests to check if any vulnerable flow could be exploited. We compared Rivulet to the state-of-the-art static vulnerability detector Julia on benchmarks and Rivulet outperformed Julia in both false positives and false negatives. We also used Rivulet to detect new vulnerabilities.
Moreover, for applications running on ARM and Xtensa platforms, we investigate ROP1 attack detection by combining HPCs and ML techniques. We collect data exploiting real- world vulnerable applications and small benchmarks to train the ML. For ROP attack detection on ARM, we also implement an online monitor which labels a program’s execution as benign or under attack and stops its execution once the latter is detected. Evaluating our ROP attack detection approach on ARM provides a detection accuracy of 92% for the offline training and 75% for the online monitoring. Similarly, our ROP attack detection on the firmware-only Xtensa processor provides an overall average detection accuracy of 79%.
Last but not least, this thesis shows how relevant taint analysis is to precisely detect injection attacks on web applications and the power of HPC combined with machine learning in the control flow injection attacks detection on ARM and Xtensa platforms.
Das chinesische Zivilgesetzbuch wurde am 28. Mai 2020 verkündet, mit vielen Änderungen gegenüber der vorherigen Gesetzgebung. In diesem Zusammenhang ist das Leistungsstörungsrecht, das ein Kerngebiet des Schuldrechts ist, neu zu erfassen. Dies soll insbesondere anhand des deutschen Leistungsstörungsrechts und eines Vergleichs zwischen beiden geschehen. Die vorliegende Arbeit befasst sich daher mit einer Reihe grundlegender Fragen des chinesischen Leistungsstörungsrechts. So kann man sich über die Herkunft des chinesischen Vertragsrechts, seinen gegenwärtigen Stand, das rechtliche Regelsystem sowie die einschlägige Behandlung in der Rechtsprechung informieren.
Was möchten Politikerinnen und Politiker mit dem Gesagten wirklich ausdrücken? Welchen spezifischen Zweck erfüllen bestimmte Worte im politischen Kontext?
In dieser Arbeit wird die sprachliche Signifikanz des Zusammenspiels von Frame-Semantik und Plastikwörtern im Kontext der Politik des 21. Jahrhunderts untersucht. Nach der Vorstellung dieser beiden Theorien werden grundlegende Definitionen aus der Frame-Semantik und der Forschung zu Plastikwörtern adaptiert, um das Vokabular in der politischen Kommunikation fachlich adäquat analysieren zu können. Für diese politolinguistische Analyse wurde eigenständig ein Untersuchungskorpus erstellt, das aus Reden und Redeanteilen von deutschen Politikerinnen und Politikern aus dem Zeitraum von 2005 bis 2020 besteht. Das Korpus umfasst Reden bzw. Redeanteile verschiedener Politikerinnen und Politiker. Die korpuslinguistische Analyse soll den Zusammenhang zwischen Frame-Semantik und Plastikwörtern in der Politik, sowie die dabei auftretenden sprachlichen Muster und Praktiken empirisch nachweisen. Hierzu wurde eine com-putergestützte Analyse mithilfe der Programmiersprachen Python und R durchgeführt.
Sprache in der Politik bietet einen relevanten Themenbereich der Sprachwissenschaft. Sie ist durch die Medien in der Gesellschaft omnipräsent und betrifft alle Bürgerinnen und Bürger. Aus diesem Grund ist die Bearbeitung dieses Fachgebietes von allgemeinem Interesse. Der kombinierte quantitative wie qualitative Einsatz von Frame-Semantik und Plastikwort-Forschung bei der Analyse eines politiksprachlichen Korpus stellt ein Forschungsdesiderat dar.
Welche Typen von Lernaufgaben werden von Jugendlichen als interessant und bedeutsam wahrgenommen? Lassen sich positive Zusammenhänge zwischen Aufgabenmerkmalen und aufgabenbezogenem situationalem Interesse von Schülerinnen und Schülern herstellen? Diese Arbeit untersucht auf der Basis von Interessentheorien die Wirkung von Aufgabenmerkmalen auf situationales Interesse im Fach Geschichte. Aufgrund hauptsächlich normativ gesetzter geschichtsdidaktischer Vorschläge zur Lernaufgabengestaltung fehlt bislang ein empirisch überprüftes Klassifikationsschema zur Beschreibung von unterschiedlichen Aufgabenmerkmalen. Die vorliegende Studie stellt die Entwicklung eines Aufgabenkategoriensystems vor und untersucht den Zusammenhang zwischen Aufgabenmerkmalen und aufgabenspezifischem situationalem Interesse [Aufgabeninteressantheit (AI) und subjektive Bedeutsamkeit (SB)] von Lernenden. Als Aufgabenmerkmale ließen sich ‚Aufgabenschwierigkeit‘, ‚Offenheit der Aufgabenstellung‘ (Aufgabenstruktur), ‚Kognitive Anforderungen‘, ‚Lebensweltbezug‘, ‚Oberflächenmerkmale‘ (grafische Elemente) und ‚Aufgabenumfang‘ (Leseaufwand und Schreibaufwand) feststellen und wurden hinsichtlich AI und SB näher untersucht.
Es wurden insgesamt 801 bayerische Realschülerinnen und Realschüler (30 Klassen) der 9. Jahrgangsstufe dreimal innerhalb eines Zeitraums von zwei bis drei Wochen im Rahmen eines lernaufgabengesteuerten Unterrichts befragt.
Nach Überprüfung des Klassifikationsschemas zeigten multiple Regressionsanalysen, dass in den Einzelanalysen (Betrachtung der einzelnen Aufgabenmerkmale) AI vorwiegend durch schwierige Aufgaben, wenig strukturierte Aufgaben, Aufgaben mit hohen kognitiven Anforderungen sowie Aufgaben mit reiner Textdarstellung und hohem Lese- und Schreibaufwand vorhergesagt wurde, während SB nur durch Aufgaben mit Lebensweltbezug und Aufgaben mit hohem Leseaufwand prädiziert wurde. Betrachtet man die Gesamtmodelle (Aufnahme aller Aufgabenmerkmale ins Modell) unter Berücksichtigung von Multikollinearität, zeigten sich positive Effekte auf SB bei Aufgaben mit dem Erfordernis eines historischen Werturteils, das ein In-Beziehung-Setzen des historischen Geschehens mit der eigenen Gegenwart impliziert. Hinsichtlich AI und SB zeigten sich positive Effekte bei Aufgaben mit hohem Leseaufwand. Negative Effekte auf AI ließen sich bei schwierigen Aufgaben und Aufgaben mit hohem Schreibaufwand feststellen. Hinsichtlich AI und SB zeigten sich negative Effekte bei Aufgaben mit reiner Textdarstellung.
Der signifikant positive Einfluss von Aufgaben mit hohem Leseaufwand auf AI und SB deutet auf einen Prädiktor hin, der Ansätze für Nachfolgestudien im Fach Geschichte bietet. Da besonders die Lesekompetenz eine Voraussetzung und Teil historischer Kompetenz darstellt, sollte z. B. auf die Textschwierigkeit mehr Aufmerksamkeit gerichtet werden. Auch die Ergebnisse zum Schreibaufwand weisen auf Untersuchungsansätze hin, welche Aufgabenkonstruktionen mit dem Erfordernis historischer Werturteile betreffen.