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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.
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.
This article presents a proposal on how the European Union’s regulatory framework on genetically modified (GM) plants should be reformed in light of recent developments in genomic plant breeding techniques. The reform involves a three-tier system reflecting the genetic changes and resulting traits of GM plants. The article is intended to contribute to the ongoing debate over how best to regulate plant gene editing techniques in the EU.
Vergeschlechtlichte Ungleichheiten in der Wissenschaft sind seit langem bekannt und bestehen trotz aller Gegenmaßnahmen fort. Gender Gaps zeigen sich nicht nur in der universitären Personalstruktur, sondern auch in der Wissenschaftspraxis: Akademische Leistungen von Frauen erhalten oft weniger Anerkennung. Hierfür hat sich die Bezeichnung Matilda-Effekt etabliert. Dieser Beitrag nimmt die für die Reputationsverteilung zentrale Praxis des Zitierens netzwerkanalytisch in den Blick und fragt, ob es in der deutschsprachigen Humangeographie einen Gender Citation Gap gibt.
Empathie – häufig verstanden als die Fähigkeit, sich vorstellen und nachempfinden zu können, was in einer anderen Person vor sich gehen könnte (vgl. Wirtz 2013, S. 447) – ist ein Begriff, der im pädagogischen Schulalltag bedeutsam ist und zur Professionalisierung von Lehrkräften beiträgt. So ist Empathie beispielsweise essenziell für die Gestaltung sozialer Interaktionen (vgl. Baron-Cohen & Wheelwright 2004, S. 163), die Qualität der Interaktion zwischen Lehrenden und Lernenden (vgl. Warren 2013, S. 6), deren Vertrauensverhältnis (vgl. Gassner 2006, S. 8) und die Lehrer/-innen-Schüler/-innen-Beziehung (vgl. Liekam 2004, S. 21). Über welches subjektive Verständnis des Empathiebegriffs verfügen jedoch Lehrpersonen selbst? Für wie wichtig erachten sie es, dass Lehrkräfte empathisch sind und warum? Was macht aus ihrer Sicht eine empathische Lehrkraft aus, welche Faktoren werden als empathiefördernd bzw. empathiehemmend empfunden und welche Begründungslinien führen sie für die Einschätzung ihrer eigenen Empathiefähigkeit bzw. der von KollegInnen an? Ergebnisse einer qualitativen Inhaltsanalyse von Expertinneninterviews mit österreichischen Grundschullehrkräften legen strukturiert subjektive Empathiedefinitionen von Lehrpersonen dar, fassen Begründungslinien für die Wichtigkeit einer empathischen Lehrperson im Schulalltag zusammen, beschreiben empathisch eingestufte Ausdrucksweisen und Grundhaltungen der befragten Lehrpersonen und identifizieren subjektive Förder- und Hemmfaktoren auf Empathie. Weitere Aspekte im Empathiebewusstsein von Lehrpersonen werden vorgestellt sowie Veränderungen im Empathiebewusstsein von Lehrkräften aufgrund von Expertise und Berufserfahrung herausgearbeitet. Diese Arbeit leistet einen Beitrag zur Grundlagenforschung und Empathiediskussion im bildungswissenschaftlichen wie grundschulpädagogischen und -didaktischen Feld. Erkenntnisse könnten für die Lehramtsausbildung sowie die Weiterbildung von Pädagoginnen und Pädagogen bedeutsam sein und neue Impulse liefern, beispielsweise was die Forcierung sozialer Kompetenzen betrifft.
Der Beitrag „Parzival, multimodal. Digitale Zugänge zu illustrierten Parzival-Handschriften“ von Andrea Sieber und Julia Siwek widmet sich an der Schnittstelle von fachwissenschaftlicher Expertise und kompetenzorientierter Anwendung digitalen Zugängen zu illustrierten Parzival-Handschriften. Er analysiert die multimodalen Besonderheiten der Digitalisate und zeigt auf, wie diese in einem digitalen Lehr-Lern-Medium im H5P-Format für die Förderung multimodaler Kompetenz eingesetzt werden können.
Abstract 1: This paper investigates whether market quality, uncertainty, investor sentiment and attention, and macroeconomic news affect bitcoin price discovery in spot and futures markets. Over the period December 2017 – March 2019, we find significant time variation in the contribution to price discovery of the two markets. Increases in price discovery are mainly driven by relative trading costs and volume, and by uncertainty to a lesser extent. Additionally, medium-sized trades contain most information in terms of price discovery. Finally, higher news-based bitcoin sentiment increases the informational role of the futures market, while attention and macroeconomic news have no impact on price discovery.
Abstract 2: We investigate whether local religious norms affect stock liquidity for U.S. listed companies. Over the period 1997–2020, we find that firms located in more religious areas have higher liquidity, as reflected by lower bid-ask spreads. This result persists after the inclusion of additional controls, such as governance metrics, and further sensitivity and endogeneity analyses. Subsample tests indicate that the impact of religiosity on stock liquidity is particularly evident for firms operating in a poor information environment. We further show that firms located in more religious areas have lower price impact of trades and smaller probability of information-based trading. Overall, our findings are consistent with the notion that religiosity, with its antimanipulative ethos, probably fosters trust in corporate actions and information flows, especially when little is known about the firm. Finally, we conjecture an indirect firm value implication of religiosity through the channel of stock liquidity.
Abstract 3: This study shows that higher physical distance to institutional shareholders is associated with higher stock price crash risk. Since monitoring costs increase with distance, the results are consistent with the monitoring theory of local institutional investors. Cross-sectional analyses show that the effect of proximity on crash risk is more pronounced for firms with weak internal governance structures. The significant relation between distance and crash risk still holds under the implementation of the Sarbanes-Oxley Act, however, to a lower extent. Also, the existence of the channel of bad news hoarding is confirmed. Finally, I show that there is heterogeneity in distance-induced monitoring activities of different types of institutions.
Teaching Journalism Literacy in Schools: The Role of Media Companies as
Media Educators in Germany
(2023)
German journalism is facing major challenges including declining circulation, funding, trust, and political allegations of spreading disinformation. Increased media literacy in the population is one way to counter these issues and their implications. This especially applies to the sub‐concept of journalism literacy, focusing on the ability to consume news critically and reflectively, thus enabling democratic participation. For media companies, promoting journalism literacy seems logical for economic and altruistic reasons. However, research on German initiatives is scarce. This article presents an explorative qualitative survey of experts from seven media companies offering journalistic media education projects in German schools, focusing on the initiatives’ content, structure, and motivation. Results show that initiatives primarily aim at students and teachers, offering mostly education on journalism (e.g., teaching material) and via journalism (e.g., journalistic co‐production with students). While these projects mainly provide information on the respective medium and journalistic practices, dealing with disinformation is also a central goal. Most initiatives are motivated both extrinsically (e.g., reaching new audiences) and intrinsically (e.g., democratic responsibility). Despite sometimes insufficient resources and reluctant teachers, media companies see many opportunities in their initiatives: Gaining trust and creating resilience against disinformation are just two examples within the larger goal of enabling young people to be informed and opinionated members of a democratic society.
Der Beitrag „Semantische Vielfalt im ‚freien‘ Erzählraum. Semiotische Aspekte der Early-Literacy-Förderung im Kontext von Multimodalität“ von Romina Seefried betrachtet verschiedene Storytelling-Formate, die für eine handlungs- und produktionsorientierte Förderung des mündlichen Erzählens im Rahmen der Early-Literacy-Förderung genutzt werden und analysiert deren multimodales Potenzial.
Der Beitrag „Multimodalität im Literaturunterricht“ von Magdalena Schlintl und Markus Pissarek beleuchtet, wie multimodale, literarische und nicht-literarische Texte zum Erwerb von prozeduralem Handlungswissen im Bereich der semiotisch fundierten Textanalyse und -interpretation beitragen. Neben der multimodalen Transferfähigkeit zeigt er grundlegend auf, welche Relevanz das Phänomen Multimodalität als Unterrichtsmedium, Unterrichtsgegenstand und Unterstützungsinstrument im Deutschunterricht hat.