Lost In Instructions: Study Of Blind Users' Experiences With Diy Manuals And Ai-Rewritten Instructions For Assembly, Operation, And Troubleshooting Of Tangible Products,
2026
Stony Brook University
Lost In Instructions: Study Of Blind Users' Experiences With Diy Manuals And Ai-Rewritten Instructions For Assembly, Operation, And Troubleshooting Of Tangible Products, Monalika Padma Reddy, Aruna Balasubramanian, Jiawei Zhou, Xiaojun Bi, Iv Ramakrishnan, Vikas Ashok
Computer Science Faculty Publications
AI tools like ChatGPT and Be-My-AI are increasingly being used by blind individuals. Although prior work has explored their use in some Do-It-Yourself (DIY) tasks by blind individuals, little is known about how they use these tools and the available product-manual resources to assemble, operate, and troubleshoot physical/tangible products – tasks requiring spatial reasoning, structural understanding, and precise execution. We address this knowledge gap via an interview study and a usability study with blind participants, investigating how they leverage AI tools and product manuals for DIY tasks with physical products. Findings show that manuals are essential resources, but product-manual instructions …
Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences,
2026
Michigan State University
Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee
Computer Science Faculty Publications
Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …
Shaping The Future: Emerging Technologies And Their Role In Industry 4.0 And Beyond,
2026
Old Dominion University
Shaping The Future: Emerging Technologies And Their Role In Industry 4.0 And Beyond, Liuliu Qin
Information Technology & Decision Sciences Faculty Publications
This paper provides a comprehensive review of emerging technologies driving the transition from Industry 4.0 to Industry 5.0. It examines the foundational concepts and pillars of Industry 4.0 and explores the transformative roles of Artificial Intelligence (AI), Extended Reality (XR), Collaborative Cobots (Cobots), Brain–Computer Interfaces (BCIs), quantum technologies, and next-generation connectivity (5G/6G). By integrating technological, human-centric, and sustainability perspectives, the study outlines how these emerging technologies reshape industrial systems and enable intelligent, adaptive, and inclusive futures.
Trustworthy Multimodal Ai For Medical Imaging: Enhancing Diagnosis, Reasoning, And Human-Agent Interaction In Extended Reality,
2026
University of Texas at Arlington
Trustworthy Multimodal Ai For Medical Imaging: Enhancing Diagnosis, Reasoning, And Human-Agent Interaction In Extended Reality, Jai Prakash Veerla
Computer Science and Engineering Dissertations
The transition from traditional microscopy to digital pathology has digitized diagnostic data, yet clinical workflows remain constrained by two-dimensional screens and passive, opaque analysis tools that fail to capture the spatial complexity of biological systems. While Foundation Models now promise to reason across histology and genomics, a critical disconnect persists between the richness of this data and the limited cognitive bandwidth of clinicians, who currently lack the immersive interfaces and trustworthy agents necessary to utilize it effectively. This dissertation presents a unified framework for "Embodied Agentic AI," establishing a pipeline that augments physician capabilities through immersive visualization, robust security, and …
Contextual Scaffolding And Self-Efficacy: Supporting Computer Skill Development Among Blind Learners In India,
2026
Old Dominion University
Contextual Scaffolding And Self-Efficacy: Supporting Computer Skill Development Among Blind Learners In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok
Computer Science Faculty Publications
Inclusive computer literacy education efforts, broadening the participation of blind or visually impaired (BVI) individuals, have gained traction in recent years. Existing literature investigating these efforts primarily draws evidence from affluent Global North contexts, where accessibility resources and legal frameworks are relatively more mature. Little is known about the in-situ teaching and learning challenges faced by trainers and BVI students, respectively, in resource-constrained, multicultural Global South countries like India. To address this knowledge gap, we conducted a four-month contextual inquiry at two computer training centers catering to 94 BVI students in India. We notably observed a rigid, experience-driven training environment …
A Novel Lightweight Framework For Low-Light Image Enhancement Via Gaussian Denoising And Clahe,
2026
Fort Hays State University
A Novel Lightweight Framework For Low-Light Image Enhancement Via Gaussian Denoising And Clahe, Daniel Oluwaseun Adesoji
Master's Theses or Doctor of Nursing Practice
Low-light image enhancement is a major challenge in digital imaging, especially in medical imaging, surveillance, and autonomous vision systems. Images captured under poor illumination often appear dark, noisy, and low in contrast, which makes it hard to observe important details. Traditional enhancement methods can improve brightness but usually introduce artifacts or increase noise. Although deep learning methods have shown strong performance, they usually require large datasets, and high computational resources. This creates a need for simpler and more efficient enhancement techniques. This study proposes a lightweight framework that incorporates Gaussian denoising with Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance …
From Physical Correlation To Emotional Connection: The Role Of Passive Haptics On Empathy In Virtual Reality,
2026
Dartmouth College
From Physical Correlation To Emotional Connection: The Role Of Passive Haptics On Empathy In Virtual Reality, Jemely Robles
Dartmouth College Master’s Theses
Virtual reality is increasingly explored as a tool for cultivating empathy, and haptic feedback as a tool for enhancing immersion. This paper investigates the effects of combining the two. Fifty-two participants experienced a custom-built VR scene in which a character was shown packing up a room. Participants were assigned to either a haptic condition, receiving passive haptic feedback corresponding to the character's actions, or a non-haptic control condition that did not receive any haptic input. Trait empathy was measured beforehand, and state empathy and engagement were measured after the experience. Thematic analysis was conducted on post-study interviews, and headset recordings …
Learning Design To Advance Human-Ai Collaboration In K-12 Education,
2026
Old Dominion University
Learning Design To Advance Human-Ai Collaboration In K-12 Education, Wing Sha Chan, Jinhee Kim, Seongryeong Yu, Rita Kay Detrick
STEMPS Faculty Publications
This chapter explores key components for designing effective Human-AI Collaboration (HAC) in K–12 education, addressing the current lack of theoretical and conceptual frameworks for structuring and implementing HAC in teaching and learning. It examines four essential areas: curriculum design, student and teacher–AI interaction, learning environments, and the evolution of HAC over time. The chapter introduces the concept of HAC in K–12 contexts, highlighting how humans and AI can leverage each other's strengths through co-evolutionary processes that foster mutual learning and collaboration. It reviews current HAC practices in schools and discusses their contributions to both teaching and learning. Finally, it presents …
Impacts Of Segmenting Principle On Learner Performance And Attitude In A 3d Environment: A Mixed-Method Multiple Case Study,
2026
Old Dominion University
Impacts Of Segmenting Principle On Learner Performance And Attitude In A 3d Environment: A Mixed-Method Multiple Case Study, Kristin Herman, Mohan Yang, Jim Shifflet, Noah Glaser
STEMPS Faculty Publications
This study presents a conceptual replication of Moreno’s (Appl Cogn Psychol 21:765–781. 10.1002/acp.1348, 2007) study on the benefits of adhering to the segmentation principle when utilizing multimedia learning objects. Furthermore, this study expands upon the original by taking place in a low-immersive virtual reality environment, allowing for further understanding on the extent to which multimedia principles are still relevant. Both a synchronous and an asynchronous case are presented. Results indicate benefits for both cases in far transfer of learning. Furthermore, synchronous learners indicated a significant reduction in cognitive load and increased overall attitudes towards learning due to segmented instruction.
Wearable Sensor-Based Phase Segmentation Analysis Of Front Crawl Swimming: A Scoping Review,
2026
Oakland University
Wearable Sensor-Based Phase Segmentation Analysis Of Front Crawl Swimming: A Scoping Review, Jonathan Simoes, Samuel Aylward, Daniel Hamze, Daniel James Goble, Daniel M. Russell, Joshua Haworth
Exercise Science Faculty Publications
Front crawl swimming stroke phase segmentation has historically relied on video analysis, but the development of wearable sensor technology has created new opportunities for automated phase segmentation. This scoping review mapped the available evidence on wearable sensor-based stroke phase segmentation methods in front crawl swimming, following PRISMA-ScR guidelines. A systematic search of SPORTDiscus, Web of Science, and IEEE Xplore conducted from January to June 2026, identified 15 eligible peer-reviewed studies published between 2000 and 2024. The review revealed an emerging field of research that has converged methodologically around inertial measurement units (IMUs) and the Chollet phase segmentation framework while remaining …
Error-Driven Density Control For Compact Gaussian Splatting Under Sparse Supervision,
2026
Wilfrid Laurier University
Error-Driven Density Control For Compact Gaussian Splatting Under Sparse Supervision, Abdelrhman Elrawy
Theses and Dissertations (Comprehensive)
This thesis studies efficiency and stability challenges in Gaussian-splatting-based reconstruction under sparse supervision. In few-shot novel view synthesis, standard 3D Gaussian Splatting (3DGS) can overfit the limited training views and grow an unnecessarily large number of primitives due to limitations in its Adaptive Density Control (ADC) mechanism. This thesis introduces an error-driven reformulation of ADC that triggers densification using opacity gradients as a lightweight proxy for rendering error, and shows that such aggressive densification must be paired with delayed and conservative pruning to prevent destructive create--destroy cycles. When combined with depth-based geometric regularization, the resulting framework produces substantially more compact …
Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis,
2026
University of Stavanger
Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette
Engineering Technology Faculty Publications
In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs' consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs' responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model's output. The LLM-CXR model is selected as the medical …
A Web-Based Wizard-Of-Oz Platform For Collaborative And Reproducible Human-Robot Interaction Research,
2026
Bucknell University
A Web-Based Wizard-Of-Oz Platform For Collaborative And Reproducible Human-Robot Interaction Research, Sean O'Connor
Honors Theses
The Wizard-of-Oz (WoZ) technique is widely used in Human-Robot Interaction (HRI) research, but two persistent problems limit its effectiveness: existing tools impose technical barriers that exclude non-engineering domain experts (the Accessibility Problem), and the fragmented landscape of robot-specific implementations makes interaction scripts difficult to port across platforms (the Reproducibility Problem- concerning execution consistency and portability, not third-party replication). Through a literature review, I identified three design principles to address both: a hierarchical specification model, an event-driven execution model, and a plugin architecture that decouples experiment logic from robot-specific implementations. I realized these principles in HRIStudio, an open-source, web-based platform providing …
Geovig And Purevig: Geometry-Aware Architectures For Efficient Computer Vision,
2026
Wilfrid Laurier University
Geovig And Purevig: Geometry-Aware Architectures For Efficient Computer Vision, Omar Ismail
Theses and Dissertations (Comprehensive)
Deploying deep learning models for medical image analysis on mobile devices requires a balance between inference latency, memory footprint, and delineating anatomical boundaries with high accuracy. While Convolutional Neural Networks (CNNs) and mobile Vision Transformers (ViTs) offer efficiency, they often struggle to model the irregular, non-local geometric structures inherent in biological tissues without incurring prohibitive computational costs. In this thesis, we introduce GeoViG (Geometric Vision Graph), an architecture that bridges the gap between efficient grid-based processing and explicit Geometric Deep Learning. GeoViG introduces a novel transition from high-resolution pixel grids to low-resolution dynamic graphs via a SpreadEdgePool operator, a geometry-aware …
Visual Analytics For Interpretable Quantum Computing,
2026
Singapore Management University
Visual Analytics For Interpretable Quantum Computing, Shaolun Ruan
Dissertations and Theses Collection (Open Access)
Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and …
Eeg And Imu Gait Signal Processing: A Comparative Assessment Of The "Reza" Exponential Filter And Classic Filters,
2026
Macon & Joan Brock Virginia Health Sciences at Old Dominion University
Eeg And Imu Gait Signal Processing: A Comparative Assessment Of The "Reza" Exponential Filter And Classic Filters, Reza Pousti, Daniel M. Russell, Derek C. Monroe, Christopher K. Rhea
Rehabilitation Sciences Faculty Publications
Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential "Reza" filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain-body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5-50 Hz, while IMU axes were averaged and band-pass filtered at 0.5-5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5-50 Hz; IMU:0.5-5 …
Examining Inclusive Computing Education For Blind Students In India,
2026
Old Dominion University
Examining Inclusive Computing Education For Blind Students In India, Akshay Kolgar Nayak, Yash Prakash, Md Javedul Ferdous, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok
Computer Science Faculty Publications
The growing demand for computer professionals, driven by the expanding Information Technology industry, has led to numerous inclusive computing education efforts. These efforts have even included blind or visually-impaired (BVI) students, who are being increasingly encouraged to pursue education and a career in computing, despite the visually-oriented nature of the discipline. Extant literature has predominantly focused on identifying and addressing the accessibility barriers faced by BVI students to promote more inclusive learning environments. While few studies have also investigated the accessibility of computing education from the perspectives of BVI learners and instructors, these have been primarily situated in the Global …
Finding The Signal In The Noise: An Exploratory Study On Assessing The Effectiveness Of Ai And Accessibility Forums For Blind Users' Support Needs,
2026
Stony Brook University
Finding The Signal In The Noise: An Exploratory Study On Assessing The Effectiveness Of Ai And Accessibility Forums For Blind Users' Support Needs, Satwik Ram Kodandaram, Jiawei Zhou, Xiaojun Bi, Iv Ramakrishnan, Vikas Ashok
Computer Science Faculty Publications
Accessibility forums and, more recently, generative AI tools have become vital resources for blind users seeking solutions to computer-interaction issues and learning about new assistive technologies, screen reader features, tutorials, and software updates. Understanding user experiences with these resources is essential for identifying and addressing persistent support gaps. Towards this, we interviewed 14 blind users who regularly engage with forums and GenAI tools. Findings revealed that forums often overwhelm users with multiple overlapping topics, redundant or irrelevant content, and fragmented responses that must be mentally pieced together, increasing cognitive load. GenAI tools, while offering more direct assistance, introduce new barriers …
A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification,
2026
Old Dominion University
A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana
Computer Science Faculty Publications
Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …
Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis,
2026
Old Dominion University
Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna
Computer Science Faculty Publications
With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …
