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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.
A Comprehensive Comparison of Fuzzy Extractor Schemes Employing Different Error Correction Codes
(2023)
This thesis deals with fuzzy extractors, security primitives often used in conjunction with Physical Unclonable Functions (PUFs). A fuzzy extractor works in two stages: The generation phase and the reproduction phase. In the generation phase, an Error Correction Code (ECC) is used to compute redundant bits for a given PUF response, which are then stored as helper data, and a key is extracted from the response. Then, in the reproduction phase, another (possibly noisy) PUF response can be used in conjunction with this helper data to extract the original key.
It is clear that the performance of the fuzzy extractor is strongly dependent on the underlying ECC. Therefore, a comparison of ECCs in the context of fuzzy extractors is essential in order to make them as suitable as possible for a given situation. It is important to note that due to the plethora of various PUFs with different characteristics, it is very unrealistic to propose a single metric by which the suitability of a given ECC can be measured.
First, we give a brief introduction to the topic, followed by a detailed description of the background of the ECCs and fuzzy extractors studied. Then, we summarise related work and describe an implementation of the ECCs under consideration. Finally, we carry out the actual comparison of the ECCs and the thesis concludes with a summary of the results and suggestions for future work.
In empirical research, scholars can choose between an exploratory causes-of-effects analysis, a confirmatory effects-ofcauses approach, or a mechanism-of-effects analysis that can be either exploratory or confirmatory. Understanding the choice between the approaches is important for two reasons. First, the added value of each approach depends on how much is known about the phenomenon of interest at the time of the analysis. Second, because of the specializations of methods, there are benefits to a division of labor between researchers who have expertise in the application of a given method. In this preregistered study, we test two hypotheses that follow from these arguments. We theorize that exploratory research is chosen when little is known about a phenomenon and a confirmatory approach is taken when more knowledge is available. A complementary hypothesis is that quantitative researchers opt for confirmatory designs and qualitative researchers for exploration because of their academic socialization. We test the hypotheses with a survey experiment of more than 900 political scientists from the United States and Europe. The results indicate that the state of knowledge has a significant and sizeable effect on the choice of the approach. In contrast, the evidence about the effect of methods expertise is more ambivalent.
Understanding of financial data has always been a point of interest for market participants to make better informed decisions. Recently, different cutting edge technologies have been addressed in the Financial Technology (FinTech) domain, including numeracy understanding, opinion mining and financial ocument processing.
In this thesis, we are interested in analyzing the arguments of financial experts with the goal of supporting investment decisions. Although various business studies confirm the crucial role of argumentation in financial communications, no work has addressed this problem as a computational argumentation task. In other words, the automatic analysis of arguments. In this regard, this thesis presents contributions in the three essential axes of theory, data, and evaluation to fill the gap between argument mining and financial text.
First, we propose a method for determining the structure of the arguments stated by company representatives during the public announcement of their quarterly results and future estimations through earnings conference calls. The proposed scheme is derived from argumentation theory at the micro-structure level of discourse. We further conducted the corresponding annotation study and published the first financial dataset annotated with arguments: FinArg.
Moreover, we investigate the question of evaluating the quality of arguments in this financial genre of text. To tackle this challenge, we suggest using two levels of quality metrics, considering both the Natural Language Processing (NLP) literature of argument quality assessment and the financial era peculiarities.
Hence, we have also enriched the FinArg data with our quality dimensions to produce the FinArgQuality dataset.
In terms of evaluation, we validate the principle of ensemble learning on the argument identification and argument unit classification tasks. We show that combining a traditional machine learning model along with a deep learning one, via an integration model (stacking), improves the overall performance, especially in small dataset settings.
In addition, despite the fact that argument mining is mainly a domain dependent task, to this date, the number of studies that tackle the generalization of argument mining models is still relatively small. Therefore, using our stacking approach and in comparison to the transfer learning model of DistilBert, we address and analyze three real-world scenarios concerning the model robustness over completely unseen domains and unseen topics.
Furthermore, with the aim of the automatic assessment of argument strength, we have investigated and compared different (refined) versions of Bert-based models that incorporate external knowledge in the decision layer. Consequently, our method outperforms the baseline model by 13 ± 2% in terms of F1-score through integrating Bert with encoded categorical features.
Beyond our theoretical and methodological proposals, our model of argument quality assessment, annotated corpora, and evaluation approaches are publicly available, and can serve as strong baselines for future work in both FinNLP and computational argumentation domains.
Hence, directly exploiting this thesis, we proposed to the community, a new task/challenge related to the analysis of financial arguments: FinArg-1, within the framework of the NTCIR-17 conference.
We also used our proposals to react to the Touché challenge at the CLEF 2021 conference. Our contribution was selected among the «Best of Labs».
To answer the research question, all SPIEGEL covers from 1965 to 2021 were examined for a reference to history topics. The report documents the assignments of the 533 covers recorded to the categories of history narrative, politics of memory and politics of the past.
Main article: https://doi.org/10.3167/jemms.2023.150107
Organic agriculture in Java, Indonesia, has been historically intertwined with social movements that struggled for more economically, ecologically, culturally, and socially sustainable agriculture. While these grassroots movements emerged under an authoritarian government that showed little interest in organic agriculture, the turn of the 21st century saw the rapid involvement of the Indonesian government in supporting, regulating and, arguably, commodifying organic agriculture. Institutionalization triggered diverse responses from competing organic actors, reflecting their different standpoints and knowledges. In this context, a transdisciplinary approach is deemed suitable to provide context-specific insights into organic agriculture.
This dissertation draws on anthropology and Science and Technology Studies (STS) to explore the politics of knowledge of organic agriculture in Yogyakarta, Indonesia, as a contribution to a critique of transdisciplinarity. My interest on the hierarchization of different knowledges is inspired by the work of anthropologists of knowledge that asks how the communities they study construct knowledge and how they themselves construct knowledge about these communities. Since transdisciplinary knowledge is co-produced by science and society and reflects their embedded power relations, transdisciplinary research needs to be open to different interpretations, and reflexive towards the unequal distribution of resources, accountability, and responsibility. By linking these two lines of thought, I examine the making of knowledges through reflexive transdisciplinary work. I reflect on how “epistemic living space” (Felt 2009) and “co-presence” (Chua 2015) affect research and shape the politics of knowledge of organic agriculture in Yogyakarta, Indonesia. I argue that the hierarchization of different knowledges of organic agriculture was intertwined with my shifting positionalities, as a field researcher in Indonesia and PhD student at Passau University, as I moved between these two different “field sites”.
This cumulative dissertation is divided into two parts. In Part I, “Knowledge in the making”, I present my contributions towards transdisciplinary knowledge production and politics of knowledge of organic agriculture. Part II, “Publications”, comprises the three stand-alone papers. The first contribution is my formulation of the notion of knowledge in the making. The second is my exploration of the ways that reflexive transdisciplinary work, and living and intersubjective experience shape knowledge in the making. The third is my demonstration of how an understanding of knowledge in the making sheds lights on the politics of knowledge of organic agriculture. This approach serves to examine the politics involved in synthesizing the conceptualizations of organic agriculture employed by different actors into one overarching narrative, such as sustainable agriculture or alternative agriculture. My final contribution is the notion of transdisciplinary moments, a conceptualization of transdisciplinary research practice that accounts for the politics of knowledge in which both scientific and extra-scientific actors are embedded. As a conclusion, I share the lessons learned from pursuing a PhD as a cumulative dissertation in an unstructured setting within a German–Indonesian research project on Indonesian organic agriculture. Finally, I identify bodies of literature and strands of thinking for future engagement within transdisciplinary research and discuss their potential to contribute to radical change in the institutional and value structures of contemporary academia.
In the ongoing 21st century, low- and middle-income countries will face two health challenges that are thoroughly different from what these countries have been dealing with in preceding centuries. First, they are confronted with surging rates of non-communicable diseases (NCDs), and second, climate change will take its toll and is predicted to cause catastrophic health impairments and exacerbate chronic health conditions further. Both will pose a disproportionate health and economic burden on low- and middle-income countries, which are also the countries least able to cope with them. By threatening individual health and socioeconomic improvements, and by putting an immense burden on already constrained health care systems, they impede the progress in poverty reduction and widen health inequities between the rich and the poor.
Against this background, this thesis investigates the potential of NCD prevention and treatment measures in the context of Southeast Asia, with case studies in Indonesia. Specifically, it seeks to understand what kind of health interventions have the potential to be (cost-)effective considering the cultural background, lifestyle, health literacy and health system capacities in the region. Further, this thesis analyzes the interplay between NCDs and climate change and assesses the financial burden that both might pose in the decades to come. Hence, this thesis contributes to a better understanding of how the two health challenges of the 21st century, NCDs and climate change, can be addressed in the context of Southeast Asia and offers insights into what type of health policies and interventions can play a supportive role.
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.
Religion can unite and divide, it can lead to a strengthening or a weakening of identity and legitimacy. Religion can stoke conflicts but it can also pacify them – within societies and in international politics. Religion endures and it can exist independently of states, it can constitute them, and it can provide new forms of states, societies, and empires. Arguably, religion shapes or even constitutes the international society of states, an aspect so far neglected in the field of International Relations. The dissertation provides a new definition of religion for International Relations and the English School in particular. Based upon this understanding of religion, the five publications presented in the dissertation provide new analytical and theoretical concepts and approaches to fill the research gap. Religion is integrated into the theoretical framework of the English School in the form of a “prime institution” and with the help of the “quilt model”. While the former expands the theoretical framework, the latter adds an analytical layer. Based upon this definition religion is also introduced as a concept (“hybrid actorness”) in Foreign Policy Analysis, opening it up to become less state-centrist and more transnational-oriented, thereby boosting its relevance considering the evolving international (global) society. In another step, the Securitization framework of analysis is expanded to include (freedom) of religion. By revisiting the publications, the dissertation is able to identify next steps in terms of avenues of research. Finally, the dissertation reveals areas of study which contribute to increasing the pertinence of IR, particularly of the English School.
Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.
In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances.
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.
After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.
Due to their high numbers, refugees’ labour market inclusion has become an important topic for Germany in recent years. Because of a lack of research on meso-level actors’ influences on labour market inclusion and the transcendent role of organizations in modern societies, the article focuses on the German professional chambers’ role in the process of refugee inclusion. The study shows that professional chambers are intermediaries between economic actors, the government and refugees, which all follow their own logics and ideas of labour market inclusion (the state, the market and the community logic). The measures taken by professional chambers mainly reflect a governmental logic (to reduce refugee unemployment) combined with a market logic (to provide human resources to economic actors). A community logic (altruism) only comes into play as a rather unintended consequence of measures addressing the other two logics. The measures of two types of professional chambers are compared. Close similarities between them reveal that the organization type is of theoretical relevance to explain the type of measures organizations opt for.
My study examines how the configuration of the capitalist frontier through extractivism shapes ethnicity, gender, and intersectionality in the areas surrounding a nickel mine in Sorowako (East Luwu District, South Sulawesi), logging and coal mining along the Lalang River (Murung Raya District, Central Kalimantan) Indonesia. The colonial frontier intersects with the capitalist frontier and provides the circumstances for its formation. The colonial restrictions, religions, commodities, the imposition of labor discipline, and political changes have molded ethnic identities and relationships with nature. Furthermore, using autoethnography and Feminist Political Ecology, I combine my experiences as a woman academic-activist with the experiences of the people in my research area. I identify how communities and individuals interact with the multiple-frontier in everyday life by defining the configuration of the frontier from above and below. Thus, my dissertation contributes to understanding how I, the community, and the capitalist frontier landscape are contained and can potentially transform into multidimensional resistance. I develop a link between the body as the interior frontier and the extractive landscape to be transformed into a perspective of “Tubuh-tanah air” as a future arena of engagement and resistance to extractivism.
This dissertation examines the overarching research question of how the suppliers’ brand management in the form of brand identity, brand culture, and brand essence influences buyer-seller relationships in three independent essays.
In Essay 1, I address the structure, capabilities, and outcomes of brand identity from a supplier perspective. Through qualitative interviews with suppliers, I examine how widespread the concept of brand identity is in practice and what exactly practitioners understand by it. Going further, I look at what capabilities and conditions are necessary for brand identity to be successful and what outcomes suppliers hope to achieve. Using an Information-Display-Matrix (IDM) test and a sample of Master of Business Administration (MBA) students, I examine the relevance of brand functions in more detail.
In Essay 2, I use a dyadic dataset with matched buyer-seller dyads to examine the causes and effects of perceptual congruence and incongruence of brand culture strength on the buyer-seller relationship, while considering relationship-specific investments and interaction mechanisms as moderating effects. I show that congruence and incongruence have different effects on customer loyalty and price sensitivity and that these are strongly context-dependent.
In Essay 3, I deal with brand essence strength interactions and their effects on the buyer-seller relationship. I use a dyadic dataset with matched buyer-seller dyads to show how brand essence strength influences customer loyalty and customer profitability, and how it interacts with key customer attitudes and other important buyer-seller relationship closeness indicators.
This dissertation makes a significant contribution to the literature on brand identity, brand culture, and brand essence in buyer-seller relationships. Furthermore, my dissertation offers practical implications for managers at B2B suppliers who (re)shape their brand management with a focus on the inner parts of the brand.
1. IT-Exposure and Firm Value: We analyze the joint influence of a firm’s information technology (IT)-Exposure and investment behavior on firm value. Estimating a firm’s (partial) IT-Exposure allows for distinguishing between firms with a business model that is challenged by IT above and below market average. Hence, we estimate the annual IT-Exposure of a firm using a 3-factor Fama-French model extended by an IT-proxy. Subsequently, we analyze the relationship with Tobin’s Q in a panel data context, accounting for the relationship between IT-Exposure and investments proxied by R&D as well as CapEx. We use more than 48,000 firm-year observations for firms in the Russell 3000 Index covering the period 1990 to 2018. Although IT-Exposure has a negative impact on firm value, this discount can be overcompensated by up to 2.1 times by sufficient investments through R&D and CapEx, giving a firm with an average Tobin’s Q a premium of 14.8% to 19.2%, while controlling for endogeneity.
2. Corporate Social Responsibility, Risk, and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of corporate social responsibility (CSR) on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on CSR scores from Refinitiv and MSCI, using up to 12,013 firm-year observations over the period 2002 to 2019 for all U.S. companies listed on NYSE, NASDAQ, and AMEX. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. For CSR we find a risk-reducing as well as value-enhancing effect. When applying fixed effects OLS, we can just partly confirm the risk-reducing and value-enhancing effect of CSR shown in the literature.
3. Heterogenous Effects of Religiosity on Firm Risk and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of religiosity on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on all U.S. companies listed on NYSE, NASDAQ, and AMEX for the period from 1980 through 2020. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. Overall, the risk-reducing effect of religiosity is more pronounced in the higher quantiles of the distribution. We further observe a value-reducing as well as value-enhancing religiosity effect. When applying fixed effects OLS, we can confirm the risk-reducing and non-existing value effect of religiosity shown in the literature. The robustness of our results is underpinned by a battery of additional tests.
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.
Feature binding has been proven to be a common and general mechanism underlying human information processing and action control. There is strong evidence showing that when humans perform a task, stimuli (e.g., the target, the distractor) and responses are bound together into an episodic representation, called an event file or a stimulus-response (S-R) episode, which can be retrieved upon feature repetition. As compared with the target and the distractor, the context (i.e., an additional stimulus presented together with the target and the distractor, but not associated with any response keys throughout the whole course of the task), which is considered as task-irrelevant, did not receive that much attention in previous studies. The current thesis was aimed to provide insights into the different roles the context plays in S-R binding and retrieval. Specifically, in Study One and Two, the role of context as an element that can be integrated into an S-R episode was investigated, with a focus on the saliency and the inter-trial variability of the contextual stimulus. Both properties were found to influence how the context is integrated into an S-R episode. More specifically, results show that both saliency and inter-trial variability determine whether the context is directly bound in a binary fashion with the response, or it enters in to a configural binding together with another stimulus and the response. In Study Three, intrigued by the role of context as an event segmentation factor in the event perception literature, whether the context can demarcate the integration window of an S-R episode was tested. Results provide consistent evidence that sharing a common context leads to a stronger binding between a stimulus and the response, as compared with the condition when these elements are separated by different contexts, thereby suggesting a binding principle of common context. Taken together, the current thesis specifies the role of context in S-R binding and retrieval, and sheds some light on how contextual information influences human behavior.