1393 research outputs found
Sort by
Romance Scam Victimization: A Survey-Based Examination of Financial, Psychological, and Reporting Factors
Get PDFRomance scams are a growing type of cybercrime in which perpetrators develop and exploit fraudulent romantic relationships with victims to obtain financial resources. These schemes cause substantial economic and psychological damage, yet they are significantly underreported. Official 2022 reports indicate only \\u3c $~0.001) confirmed that victims who depend exclusively on informal support were significantly less likely to engage formal reporting mechanisms. In addition, greater financial losses appeared to be linked to higher rates of suicidal ideation.
The findings suggest that the increased performance and perceived helpfulness of formal institutions may increase their use, provide more accurate reporting, and lead to better outcomes. Further research should evaluate how improvements, including a centralized victim assistance organization, can improve victim outcomes and mitigate financial and psychological consequences
The Impact of Organizational Culture on Artificial Intelligence (AI) Adoption and Performance: A Qualitative Approach
No full textThe rapid evolution of artificial intelligence (AI) has increasingly become an integral part of the digital strategy for organizations. However, many organizations are still struggling to adopt AI and leverage its capabilities in their operations. Although previous research has explored the notion of technology adoption, very little literature exist that examines the impact of organizational culture on AI adoption in organizations using a qualitative approach. To address this gap, we conducted a qualitative study through semi-structured interviews with 9 technology leaders from US-based organizations. The results indicate the importance of understanding how organizational culture, senior leadership support, technology capability, data governance, and policies, influences AI adoption and organizational performance. This study makes significant contributions to the information systems (IS) literature by examining the influence of organizational culture, particularly when adopting new technologies like AI, and it provides practical insights for managers to leverage AI and enhance organizational performance
If You Have Nothing to Hide, You Still Have Something to Fear: How Libraries Can Support Alternative Information Channels
No full textLibraries in the U.S. have seen massive increases in book challenges in recent years. Since books are one of the library’s vital resources, censoring books can have a great impact on libraries and patrons. To counteract the effects of censorship, libraries need to adopt and support alternative information channels so information can still be accessed through other channels even if one is shut down. When researching the behaviors of marginalized communities, most of them rely on the Internet to access vital information from various channels such as online support groups and social media. If libraries are to fully support marginalized patrons, then it is vital for them to provide Internet access while also protecting patron privacy. By adopting cybersecurity measures and implementing security culture within libraries, librarians can continue to provide access to information for marginalized communities
Towards an Artifact to Assess Differential Privacy in Microdata Streams
No full textAs continued data breaches allow state-level threat actors to assemble expansive dossiers on populations to carry out information warfare objectives, protecting personal privacy in published data sets and internal data stores is increasingly essential to civilian and societal safety. At the same time, the explosion of high-resolution, high-accuracy microdata streams, such as timestamped geolocation coordinates collected simultaneously by hardware platforms, operating systems, and a multitude of on-device applications and sites establishes a layered, highly-correlated pattern of life that can uniquely identify individuals and allow for targeted information warfare actions. Differential privacy (DP) is an advanced but highly effective technique in protecting sensitive data streams. This robust approach preserves privacy in published data sets through additive statistical noise sampled from Gaussian or Laplacian probability distributions. Data sets that contain highly correlated event-based data require specialized techniques to preserve mathematical DP guarantees in microdata streams beyond “user-level” applications available in most off-the-shelf approaches. Because practitioners need more tools to assess the robustness of differentially private outputs in microdata streams, application errors may result in future reidentification and privacy loss for data subjects. This research yields an artifact that can reassociate events in microdata streams when insufficient naive approaches are used. It also serves as a tool for implementers to validate their approaches in highly correlated event data
Setup and Design of GROWSTEM at DSU Year One Summary Report
Get PDFGROWSTEM is a DSU scholarship program funded by the National Science Foundations S-STEM program. The goal of the program is for participating scholars to build a sense of belonging through engagement. The program activities designed to accomplish this goal include faculty mentorship, a summer bridge program, study tables and an undergraduate research seminar
China vs Democracy: The Strategic Use of Cyber Influence Campaigns
Get PDFThe People\u27s Republic of China (PRC) has increasingly expanded its cyber influence campaigns against democratic states. Scholars largely agree that these campaigns align with the PRC’s broader goal of regional and global hegemony and that the PRC employs a range of cyber strategies to target democratic rivals. However, little research has examined what drives the PRC to adjust its online influence operations. This paper addresses that gap. Specifically, I compare the PRC’s cyber influence campaigns between two democracies to identify the demographic factors shaping its strategic choices. Using a comparative case-study approach, I analyze the PRC\u27s influence efforts in Canada and the United Kingdom, assessing whether the PRC adapts its tactics based on specific domestic conditions in each target state. My findings suggest that the PRC’s primary objective is not to influence election outcomes in rival states. Instead, it seeks to monitor and repress Chinese diaspora communities, and it attempts to undermine confidence in democratic institutions as a means to maintaining its authoritarian legitimacy. The composition of these communities and the political goals of the PRC overwhelmingly shape the nature of its cyber campaigns
Framework for Benchmarking Machine Learning Models: Integrating Performance Metrics, Explainability Techniques, and Robustness Assessments
No full textMachine learning (ML) models are widely used across various critical domains for their ability to process large-scale data and deliver high predictive accuracy. While traditional benchmarking of ML models focuses on performance metrics like accuracy and precision, these metrics often fall short in accounting for the model’s transparency and robustness when the models get complex with varying conditions. This research proposes a comprehensive framework for benchmarking supervised machine learning models that incorporate the model’s performance metrics, explainability techniques, and robustness assessments. The proposed framework combines traditional accuracy- and precision-based performance evaluation with explainability and robustness to ensure the model\u27s efficiency, transparency, and stability in the presence of noise and data shifts. By holistically addressing performance, explainability, and robustness in tandem, the framework supports data-driven decision-making in selecting the model appropriate to organizational contexts, addressing stakeholder concerns related to model interpretability, trustworthiness, and resilience—particularly in critical domains such as healthcare
Distilled Ensembles and Quantized Prototypical Networks for Adaptive, Edge-Based Weed Classification
No full textPrecision agriculture increasingly relies on automated weed detection to optimize herbicide usage and reduce environmental impact. This paper tackles edge-device constraints and domain shifts by combining knowledge distillation, few-shot meta-learning, and quantization. An EfficientNet-B7 teacher network provides high-quality features, which are distilled into an ensemble of three lightweight student networks, thereby inheriting strong representational power with minimal overhead. A weighted ensemble merges their outputs into a single embedding, enabling rapid adaptation to new weed species using only a handful of annotated examples. Dynamic quantization further reduces the model footprint, making it practical for resource-constrained devices. Experimental results on varied weed datasets confirm robust performance under real-world conditions. By uniting teacher–student distillation, ensemble embeddings, few-shot adaptation, and quantization, the methodology advances precision agriculture across diverse field scenarios. Additionally, our approach surpasses existing classification benchmarks. This synergy fosters advanced weed classification on constrained hardware deployments ensuring more sustainable farmland management practices
Exploring the Interoperability for Information Exchange Between Acute and Post-Acute Care Settings
Get PDFThe seamless transfer and assimilation of healthcare data are foundational to delivering holistic, timely, and effective patient care across the healthcare spectrum. However, disparities in Electronic Health Record (EHR) system adoption, especially in long-term and post-acute care (LTPAC) settings, consistently obstruct interoperability. This study explores the factors that impede and facilitate health information exchange in LTPAC environments, focusing on technical and organizational facets. Grounded theory guided our qualitative case study research, involving 35 stakeholder interviews. Key technical findings highlight the need for integrated, reliable data structures, robust infrastructure, and standardized practices. Organizational insights reveal a shift towards integrative, patient-centric strategies and emphasize the importance of effective stakeholder and vendor dynamics. The findings provide actionable insights for advancing health information exchange in LTPAC scenarios, advocating for a harmonized approach that converges technology and organizational strategies to enhance patient care. These insights are crucial for policy-making, healthcare operations, and further research in healthcare interoperability
Bridging Domains: Advances in Explainable, Automated, and Privacy-Preserving AI for Computer Science and Cybersecurity
Get PDFArtificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems