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Die theologisch-ethische Bearbeitung von Themen – wie Freiheit, Sünde und Vergebung – muss erklären können, wie mit biblischen Texten umzugehen ist, wenn durch sie ein erfahrenes Interaktionsgeschehen mit Gott zum Ausdruck kommt. Schließlich besteht die Aufgabe, normative Aussagen für heutiges Handeln begründen zu können. Der Beitrag argumentiert für eine Methodik verhältnismäßiger, historischer Erfahrung.
Eva Hörmanns Beitrag „Die Rohingya-Krise: Eine Analyse der Reaktion der Vereinten Nationen auf die Massenverbrechen" widmet sich der agenda-setting-Rolle der UN für den Nachweis von Massengräueln.
Dariush Movahedian untersucht in „Dröhnendes Schweigen - Deutsche Politik und Medien zu Massenverbrechen im Tigray-Konflikt" Deutschlands Rolle in Bezug auf die Massengräuel in Äthiopien.
Sheila Riek beleuchtet in "German Colonialism in Namibia and the Continuity of Structural lnjustice: Analysing the Parliament's Engagement with the Herero and Nama Genocide" die Erinnerungspolitik Deutschlands, was die Kolonialverbrechen des Deutschen Reiches in Namibia angeht.
Dabei betreten die Beiträge oft auch theoretisches Neuland. Schließlich ist „Schweigen" bislang nicht prominent in den Internationalen Beziehungen bearbeitet worden. Während Dariush Movahedian mit der Diskursgebundenen Identitätstheorie Schweigen in der Politik, nicht aber in den Medien entdecken kann, arbeitet Eva Hörmann mit dem Spiralmodell des Transnationalen Konstruktivismus, um Schweigen gegenüber Massenverbrechen analytisch zu fassen. Sheila Riek bevorzugt dahingegen eine kritische Perspektive, indem sie Ansätze von Structural Injustice und die kritische Diskursanalyse nutzt, um Schweigen zur Herrschaftssicherung aufzudecken.
Mit diesen Studien wird die Bandbreite und der Reichtum von IB-Perspektiven deutlich - und wir verstehen wieder ein kleines bisschen besser, wann, wie und warum wir schweigen.
Inhalt:
Eva Hörmann
Die Rohingya-Krise
Eine Analyse der Reaktion der Vereinten Nationen auf die Massenverbrechen
Sheila-Ann Riek
German Colonialism in Namibia and the Continuity of Structural Injustice
Analysing the Parliament's Engagement with the Herero and Nama Genocide
Dariush Movahedian Moghadam
Dröhnendes Schweigen
Deutsche Politik und Medien zu Massenverbrechen im Tigray-Konflikt
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.
My dissertation examines literary mythologies of privacy in the authoritarian Russia of 1953–1985. This era was marked by an expansion of “non-state spheres,” or areas of life of which the Communist state increasingly released its control after Joseph Stalin’s death in 1953. Political elites, architects, and designers, as well as ordinary citizens co-constructed and explored the rising spheres through the languages of their respective fields—by producing regulations and laws, designing and erecting new types of buildings, developing new and modernizing already familiar everyday objects as well as devising new ways of integrating these objects into private and public spaces. Alongside these voices, transformations in the cultural sphere in general, and literature in particular, were most vocal. The late Soviet era witnessed the dissolution of the ossified ideology of socialist realism that focused on the glorification of the “new Soviet man” and had held culture in its tight grips since the 1930s. Starting from the 1950s, writers increasingly focused on portraying areas of life that lay beyond one’s public commitments and experimented with new meanings, codes, and forms to give shape to novel spheres of experience of the “late Soviet man” that can be subsumed under the concept of the “private sphere.”
The analytical framework of my dissertation is built around the journey to understand the mechanisms and architecture that powered the imagination of models of distancing oneself from the state and the society at large—scenarios of privacy, as we may call them today. I examine Russian prose and drama of the 1950s–1980s as a laboratory for the ideas of privacy, which was increasingly sought in the society disillusioned by the Communist doctrine and thus progressively alienating from active participation in the public sphere. I analyze the meanings that writers incorporated into new and old forms of domesticity—private flats that became progressively widespread throughout the 1950s–1980s, rooms in communal apartments, individual houses—to determine the spectrum of concepts that nurtured the idea of privacy in the late Soviet literary imagination. I also examine representations of reciprocal paradigms of relations between subjects from which the state and society at large were increasingly excluded. In the examples that I analyze, forms of private withdrawals variate from establishing control over liminal spaces or escaping into the world of feelings and emotions and building a connection to a person or space (significant for the characters for private rather than public reasons) to experimenting with language and pursuing one’s idiolect despite the ubiquitous “officialese,” as well as living in temporalities asynchronous with the public time.
Beyond revealing the visions of different, non-state existences in the late Soviet era, my text also advocates examining the role of official literature as a platform for subversion and change that was no less important in an authoritarian state than dissident literature. I see officially published texts as a cultural subaltern who defies the state of affairs and slowly but firmly turns the “state sphere” into a public one by pushing its own agenda through publications that test and gain ground for bolder visions of Soviet life that are not predicated on the commitment to the public sphere. With individual mechanisms of power assertion employed by the state or literature in the late Soviet era well-researched, the framework is still missing that would capture the shifts of borders between the private and public spheres under the influence of these actors. In devising such framework, I build upon sociological theories of disattendability and civil inattention that Erving Goffman conceived to describe conventions of individual behavior and social interaction in public. Extending these theories toward the studies of literary politics, I argue that by envisioning scenarios of a private retreat and bringing them into officially published editions, literature normalized privacy as a late Soviet imaginary and, therefore, continuously heightened its own disattendability, thereby expanding the borders of the private sphere. On the side of the state, the border was defined by the triggers of disturbance of civil inattention: privacy was conceded in return for disattendability. Under such conditions, literature became a bizarre “private kitchen” that performed private and public functions simultaneously—similar to the kitchens in newly-built individual apartments that were popular loci of socialization in the late Soviet era. It turned into a place where one can escape—it became one of the “niches of privacy” where it was possible to discuss and negotiate the world, in which the society lived or to which it should strive. At the same time, it assumed the role of a surrogate for the public sphere within the “state” sphere by pushing its own agenda through the publication of literary texts that sought to imagine a person rather than a cog in the Communist machine and thus transformed socialist realism into a literary current “with a human face.”
In my research, privacy, literature, and politics are bound together to reveal a vibrant spectacle of the continuous interaction between the state, cultural elites, and the citizens, in which thresholds are erected and crossed incessantly. Fictional private sites were battlefields for the production and contestation of ideologies, and the exposure of these literary wars to the public eye played a fundamental role in shifting the borders between the private and the public spheres in an authoritarian late Soviet Russia. The patterns of relations between the state and culture that I uncover in my dissertation resonate in neo-authoritarian twenty-first-century Russia, making privacy an important lens for our insight into the role of culture in rising authoritarian and failing democratic systems across the globe.
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.