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Enabling Novel Authentication Interfaces in AR/VR While Incorporating Complexity and Usability Theories
No full textPassword-based Knowledge-based Authentication (KBA) is likely the most recognizable user interface in computing and the standard form of authentication for more than five decades. Despite it\u27s prominence, it has many problems in the context of usable security. As we shift to a model of computing that utilizes augmented and virtual realities (AR/VR), this form of authentication has remained despite the nearly infinite possibilities these devices provide. We argue that the lack of a robust, standardized model that enables the consideration of unique AR/VR capabilities is partially to blame. We propose a theory-based model for describing and evaluating novel KBA schemes that connect both the mathematical and human complexities that are present. We then draw on theory to provide design principles to guide future advances
A Review of Reasoning in Artificial Agents using Large Language Models
Get PDFThe increasing sophistication and the use of large language models (LLMs) in artificial agents highlights the need to investigate their reasoning capabilities and limitations. Understanding these aspects is crucial, given the integral role of reasoning in decision-making processes, which are central to a software or embodied agent. This research paper presents a systematic review of the topic. We review the literature by selecting and analyzing highly cited papers using both PRISMA and snowballing. The gathered literature is categorized using a detailed framework of facets and categories. In the results section, we elaborate on our findings and illustrate the mapping through bubble chart visualizations. The paper concludes by highlighting research gaps and suggesting directions for future studies
Highlighting Competency Gaps in Health Informatics Education Using Advanced Text-Embedding Models
Get PDFThe healthcare sector anticipates substantial growth, with 125,000 job openings by 2026, including 69,000 middle-skilled positions. Despite this growth, data use and integration inefficiencies cost the sector $750 billion annually. Health informatics, an interdisciplinary field blending healthcare, computer, information, and cognitive sciences, can address these challenges through enhanced healthcare management via information technology. However, there is a critical disparity between competencies taught in educational programs and those demanded by the job market. This study examines competencies from job postings on Indeed.com and accredited health informatics programs, comparing them with the Health Information Technology Competencies (HITComp) database. Utilizing advanced text-embedding models, the study found that while educational programs focus on foundational competencies, the job market demands practical applications. Forty-six specific competencies, particularly in administrative, direct patient care, informatics engineering, and research domains, are identified as gaps. Addressing these gaps is essential to prepare the workforce for the evolving healthcare industry
Multi-level Post Quantum Encryption for Images with Quantum Fourier Transform
No full textThe rise of quantum computing poses an imminent threat to traditional cryptographic algorithms such as Advanced Encryption Standard (AES) and Rivest, Shamir, Adleman (RSA), necessitating innovative solutions to ensure data security in the post-quantum era. This paper introduces a novel multi-level image encryption system that combines post-quantum and conventional techniques to address this challenge. By leveraging the Quantum Fourier Transform (QFT) to decompose images, encrypting low-frequency components with Advanced Encryption Standard (AES), and securing AES keys with the quantum-resistant Learning with Errors (LWE) method, this approach offers robust protection against classical and quantum attacks. Empirical evaluations using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), entropy, and correlation coefficients demonstrate the system\u27s ability to maintain image quality while delivering exceptional security. Scalable and effective, this framework is well-suited for safeguarding critical applications in fields such as medicine, defense, and finance
Natural Bone Human Education Skeletons: Investigating Restoration and Ancestry
No full text•Three real human skeletons owned by DSU were restored to a fully functional state for classroom usage, using anatomical guides and museum preservation techniques. •These skeletons have inspired the development of a new curriculum that promotes interactive learning, allowing students to engage with the skeletal remains in a hands-on environment. •Non-invasive DNA extraction methods are being employed to gather additional information about the heritage of the skeletons, enhancing the understanding of their origins and significance.https://scholar.dsu.edu/research-symposium/1063/thumbnail.jp
China vs Democracy: The Strategic Use of Malign Cyber Influence Campaigns
Get PDFHow and why do China’s methods for malign influence vary between different democracies?https://scholar.dsu.edu/research-symposium/1055/thumbnail.jp
The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations
Get PDFState-sponsored influence operations (SIOs) have become a pervasive and complex challenge in the digital age, particularly on social media platforms where information spreads rapidly and with minimal oversight. These operations are strategically employed by nation-state actors to manipulate public opinion, exacerbate social divisions, and project geopolitical narratives, often through the dissemination of misleading or inflammatory content. Despite increasing awareness of their existence, the specific linguistic and emotional strategies employed by these campaigns remain underexplored. This study addresses this gap by conducting a comprehensive analysis of sentiment, emotional valence, and abusive language across 2 million tweets attributed to influence operations linked to China, Iran, and Russia, using Twitter’s publicly released dataset of state-affiliated accounts. We identify distinct affective and rhetorical patterns that characterize each nation’s digital propaganda. Russian campaigns predominantly deploy negative sentiment and toxic language to intensify polarization and destabilize discourse. In contrast, Iranian operations blend antagonistic and supportive tones to simultaneously incite conflict and foster ideological alignment. Chinese activities emphasize positive sentiment and emotionally neutral rhetoric to promote favorable narratives and subtly influence global perceptions. These findings reveal how state actors tailor their information warfare tactics to achieve specific geopolitical objectives through differentiated content strategies
Introducing Axlerod: An LLM-Based Chatbot for Assisting Independent Insurance Agents
No full textThe insurance industry is undergoing a paradigm shift through the adoption of artificial intelligence (AI) technologies, particularly in the realm of intelligent conversational agents. Chatbots have evolved into sophisticated AI-driven systems capable of automating complex workflows, including policy recommendation and claims triage, while simultaneously enabling dynamic, context-aware user engagement. This paper presents the design, implementation, and empirical evaluation of Axlerod, an AI-powered conversational interface designed to improve the operational efficiency of independent insurance agents. Leveraging natural language processing (NLP), retrieval-augmented generation (RAG), and domain-specific knowledge integration, Axlerod demonstrates robust capabilities in parsing user intent, accessing structured policy databases, and delivering real-time, contextually relevant responses. Experimental results underscore Axlerod\u27s effectiveness, achieving an overall accuracy of 93.18% in policy retrieval tasks while reducing the average search time by 2.42 seconds. This work contributes to the growing body of research on enterprise-grade AI applications in insurtech, with a particular focus on agent-assistive rather than consumer-facing architectures
Autonomous Penetration Testing: Solving Capture-the-Flag Challenges with LLMs
No full textThis study evaluates the ability of GPT-4o to au-tonomously solve beginner-level offensive security tasks by con-necting the model to OverTheWire\u27s Bandit capture-the-flag game. Of the 25 levels that were technically compatible with a single-command SSH framework, GPT-4o solved 18 unaided and another two after minimal prompt hints for an overall 80% success rate. The model excelled at single-step challenges that involved Linux filesystem navigation, data extraction or decoding, and straightforward networking. The approach often produced the correct command in one shot and at a human-surpassing speed. Failures involved multi-command scenarios that required persistent working directories, complex network reconnaissance, daemon creation, or interaction with non-standard shells. These limitations largely reflect the architectural harness choices rather than a lack of general exploit knowledge. The results demonstrate that large language models (LLMs) can automate a substantial portion of novice penetration-testing workflow, potentially lowering the expertise barrier for attackers and offering productivity gains for defenders who use LLMs as rapid reconnaissance aides. Further, the unsolved tasks reveal specific areas where secure-by-design environments might frustrate simple LLM -driven attacks, informing future hardening strategies. Beyond offensive cyberse-curity applications, results suggest the potential to integrate LLMs into cybersecurity education as practice aids
Small-Object Detection at the Edge: A Pareto-Efficient Benchmark of Lightweight YOLO Models on UAV and Overhead Datasets
No full textSmall-object detection plays a critical role in real-time aerial, Unmanned Aerial Vehicle (UAV), and satellite vision systems, as well as many other domains, where hardware constraints and deployment environments demand high accuracy, low latency, and energy efficiency. Although numerous lightweight object detectors have been proposed, there exists a lack of rigorous, cross-platform benchmarks that evaluate these models under consistent conditions for small-object-focused applications. This paper presents a comprehensive comparative evaluation of eight recent lightweight You Look Only Once (YOLO)-based models, including YOLOX-Nano and YOLOv6–v12 variants. Models are trained using a unified pipeline and evaluated on three representative datasets: VisDrone2019, DOTA-v1.5, and xView. Performance is assessed on both cloud GPUs (NVIDIA A100) and an edge AI platform (Jetson Orin Nano), using PyTorch and TensorRT (FP16) runtimes. The evaluation metrics include [email protected], [email protected]:0.95, F1-score, latency, FPS, memory footprint, and energy consumption (via VDD_IN telemetry). Experimental results demonstrate that YOLOv8-Nano and YOLOv9-Tiny achieve leading accuracy across datasets, while YOLOv6-Nano and YOLOv10-Nano offer favorable trade-offs in speed and memory. TensorRT optimization yields 3×–5× speedup over PyTorch. Energy draw ranges from 7.6 W to 10.4 W, depending on the model and configuration. Pareto frontiers reveal that no single model dominates across all objectives. The study highlights the importance of multi-objective benchmarking in edge AI and provides practical guidance for selecting detectors based on deployment-specific constraints. The findings support theory-driven and application-oriented advancement of efficient detection architectures for real-time, resource-aware computer vision systems