1393 research outputs found
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RUFF: Resource Usage Fuzzing Framework
Get PDFModern computer security is greatly enhanced through the use of fuzzing to uncover problems in underlying program logic. Traditional fuzzing primarily targets code coverage and crashing error conditions, which are inadequate for detecting other non-desired behaviors. This dissertation introduces a comprehensive, multi-dimensional fuzzing framework that extends conventional approaches to include resource usage metrics. By integrating rigorous statistical methods and leveraging multiple sources of fuzzing data, this framework offers a novel and theoretically robust means of identifying and evaluating complex program behaviors that impact system performance and security. The contributions of this dissertation include a generalized framework for resource usage fuzzing, statistical testing mechanisms for fuzzing corpora, and an assessment of LLM-generated program samples. We demonstrate that the chosen metrics are independent and able to identify different sources of fuzzing samples. To accomplish this, over 5 million labeled samples across 50 Python programs are included to provide evidence of effectiveness. The results offer guidelines for practical applications and future studies, contributing to the development of more resilient software systems and considerations when using LLMs for fuzzing
Factors Influencing Blockchain Implementation in Supply Chain Management: An Exploratory Study
Get PDFThis exploratory study investigates the key factors influencing blockchain technology implementation in supply chain management, employing a qualitative research approach based on grounded theory. Using semi-structured interviews with sixteen participants, including blockchain consultants and supply chain professionals, the study identifies twelve critical factors essential for successful blockchain implementation. These factors—Strategic Leadership, Organizational Strategy, Future Readiness, Infrastructure Readiness, Supportive Ecosystem, Data Management Practices, Change Management, Training and Education, User Acceptance, Decentralized System Vitality, Decentralized Security and Encryption, and Decentralized Governance—were uncovered through open, axial, and selective coding of the data. The study highlights the significance of these key factors, showing how Strategic Leadership and Organizational Strategy are crucial for setting a clear direction and allocating resources, while Infrastructure Readiness ensures the technical foundation is robust enough to support blockchain’s decentralized architecture. Meanwhile, Change Management, Training and Education are vital in facilitating user acceptance and smooth adoption of the technology across the supply chain. These factors not only influence blockchain’s successful implementation but also ensure that the system is sustainable, secure, and adaptable to future developments. The significant contribution of this study is the development of the Strategic Decentralized Resilience Theory (SDRT), which integrates strategic, organizational, and decentralized factors into a comprehensive framework for understanding blockchain implementation in supply chain management. This theory addresses the need to align blockchain initiatives with organizational goals through strategic leadership while also accounting for individual user engagement and the decentralized nature of the technology. By incorporating these three dimensions, SDRT provides a holistic approach to managing the complexities of blockchain implementation, ensuring that both organizational objectives and individual user needs are met in a decentralized environment. The findings show that a successful blockchain implementation requires more than just technological integration; it depends on a balance of strategic alignment, individual adaptability with organizational backing, and robust decentralized governance. Strategic leadership ensures that blockchain initiatives align with long-term business goals, while individual factors such as user-centric change management and continuous support foster adoption and engagement. Decentralized factors, including governance frameworks and security mechanisms, ensure transparency, accountability, and scalability across distributed networks. Together, these integrated elements form the backbone of SDRT, offering organizations a roadmap for sustainable, resilient blockchain implementation that promotes operational efficiency and competitive advantage
Utilizing Large Language Models to Synthesize Product Desirability Datasets
Get PDFThis research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production
Transforming Information Systems Management: A Reference Model for Digital Engineering Integration
Get PDFDigital engineering practices offer significant yet underutilized potential for improving information assurance and system lifecycle management. This paper examines how capabilities like model-based engineering, digital threads, and integrated product lifecycles can address gaps in prevailing frameworks. A reference model demonstrates applying digital engineering techniques to a reference information system, exhibiting enhanced traceability, risk visibility, accuracy, and integration. The model links strategic needs to requirements and architecture while reusing authoritative elements across views. Analysis of the model shows digital engineering closes gaps in compliance, monitoring, change management, and risk assessment. Findings indicate purposeful digital engineering adoption could transform cybersecurity, operations, service delivery, and system governance through comprehensive digital system representations. This research provides a foundation for maturing application of digital engineering for information systems as organizations modernize infrastructure and pursue digital transformation
Getting the Best Out of Joint Warfighter Development
Get PDFIs the Department of Defense (DOD) producing the best joint warfighters possible? During strategic competition that increasingly ebbs toward conflict, joint warfighting and joint warfighter development are more important than ever. Joint warfighter development is the catalyst necessary for the U.S. military to conduct joint operations. Joint operations, where forces from different military departments are interoperable and interdependent, convey a marked advantage over potential adversaries and leave them with few military options to counter. Joint operations leverage the unique capabilities of each DOD Service, but they require joint-minded leaders—joint warfighters—who can collaboratively orient toward common objectives rather than fighting separate Service-centric campaigns. The present and future security environments demand warfighters who are truly joint-minded—capable, comfortable, and confident—when operating across joint functions, fighting domains, and culture
Enhancing Smart Home Security Through Risk-Based Access Control (RBAC): “Closing the Gap”
Get PDFThis dissertation addresses the evolving security challenges brought about by the widespread adoption of smart home technology. Despite the transformative impact on daily life, the rapid evolution has created unprecedented security risks. The research focuses on filling a crucial gap in the existing literature by delving into Risk-Based Access Control (RBAC) specifically tailored to smart homes. While RBAC has been explored in broader contexts and within the Internet of Things (IoT), there is a notable absence of in-depth research on its application in securing smart homes. Employing a mixed-methods approach, including literature review, expert interviews, and risk assessment, the study develops and evaluates a comprehensive RBAC model, incorporating a novel risk factor called Contextual Device Behavioral Risk (CDBR) and utilizing Bayesian Device Behavioral Modeling for risk estimation. Validation involves expert reviews, comparisons with existing literature, and a meticulous examination of the proposed RBAC model\u27s effectiveness in addressing dynamic risks in smart home environments. The anticipated contribution lies in providing a nuanced understanding of smart home technology\u27s dynamic risks and offering an adaptive security approach through the innovative RBAC model, empowering users against evolving cyber threats. The study aims to advance the discourse on modern access control systems, safeguarding the integrity and privacy of smart home ecosystems. Furthermore, the research extends its scope to propose practical guidelines for implementing the RBAC model in real-world smart home environments. By addressing deployment challenges, user adoption concerns, and interoperability issues, the study aims to bridge the gap between theoretical advancements and practical applications
The Impact of Organizational Culture on AI adoption and Organizational Performance: A Mixed Methods Approach
No full textIn recent years, the rapid advancement of artificial intelligence (AI) has created many opportunities for organizations to leverage existing enterprise databases and improve organizational performance. However, many organizations are experiencing numerous challenges to adopt AI and gain a competitive advantage. AI can potentially transform how organizations operate and compete in the business landscape, largely due to the availability of big data, innovative techniques, and infrastructure. In the organization\u27s context, organizational culture is argued to significantly impact AI adoption and organizational performance. Organizational culture (OC) refers to the set of shared values, beliefs, and norms an organization holds and their impact on decision-making toward novel technologies. While prior research mainly focuses on AI\u27s technological capabilities (Mikalef & Gupta, 2021), this study examines the interplay between organizational culture, AI adoption, and organizational performance. Specifically, the study seeks to investigate the following research questions: 1) To what extent does organizational culture impact AI adoption in the organization? And 2) To what extent does the adoption of AI in the organization impact organizational performance? Figure 1. depicts the proposed theoretical model. The study employs a mixed-method approach. Mixed methods can enable a deeper understanding of organizations\u27 perceptions, attitudes, and beliefs about AI adoption. The study expects the findings to show that organizational culture has a positive, significant mediating or moderating impact on AI adoption and organizational performance. This study makes two main contributions, the first is that it contributes theory by proposing a model to assess the impact of organizational culture on AI adoption and organizational performance. The second contribution is that the study employs a mixed methods approach integrating both quantitative and qualitative data for triangulation to gain a richer understanding of the phenomena
Gamifying Online Discussions: A Model for Improving Engagement Using Leaderboards
No full textAn increasingly popular tool for improving engagement in collaborative learning is the addition of game elements such as leaderboards. However, the effectiveness of leaderboards promoting engagement is mixed. Users at or near the top of leaderboards tend to become more engaged while discouraging users at or near the bottom. Accordingly, this study uses grounded theory to discover design principles for leaderboards which lead to positive engagement in a video discussion. The results provide a theoretical model, Leaderboard System Engagement Model (LSEM), comprising four themes (clear goals, challenge/skill balance, timely feedback, and social influences) with multiple design principles for each theme. The model provides both designers and discussion leaders with valuable insights on improving collaborative learning via the addition of effective leaderboards
Exploring large language models for ontology learning
Get PDFOntology Learning aims to facilitate automatic or semi-automatic ontology development based on machine learning techniques in context of big data. Recent evolution of technology has introduced Generative Artificial Intelligence (AI) capable of creating new data, extracting insights from the existing data, and generating coherent texts from various inputs. This ability supports analysis of text data, providing insights and annotations that reduce human effort. This study explores the emerging field of Generative AI, specifically, Large Language Models for ontology learning. We conducted a survey of the current state of Generative AI research with focus on applicability and efficacy for ontology development tasks, and assessment of evaluation techniques. We discussed challenges related to explainability and interpretability of Generative AI and outlined directions for future research
Tunneling time in coupled-channel systems
No full textIn present work, we present a couple-channel formalism for the description of tunneling time of a quantum particle through a composite compound with multiple energy levels or a complex structure that can be reduced to a quasi-one-dimensional multiple-channel system