Physical Sciences and Mathematics Commons^™

Integration Of Hybrid Quantum-Neuromorphic Ai With Cloud, Edge, And High-Performance Computing Environments, Arun B. Prasad, Ajay Prasad, Dineshkumar Rajendran, Anurag Tiwari, T. Akilan, Islombek Khushvaktov

Computer Science Faculty Publications

The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows …

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 …

Voxvista: Enhancing Screen Reading Experience For Online User Comments, Yash Prakash, Akshay Kolgar Nayak, Mohammed Shoaib Alyaan, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to …

Modeling Joint Visual Attention In Naturalistic Dyadic Interactions, Kuushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Joint visual attention (JVA) provides important insight into how individuals coordinate attention during social interaction. Egocentric eye tracking enables the study of JVA in natural, multi-user settings. This work presents a multi-stage framework to identify and analyze JVA using egocentric video and gaze data. The approach consists of three steps: spatiotemporal tube-based visual similarity, gaze-guided object detection, and attention pattern analysis using the ambient–focal coefficient K. Results show that object-focused collaborative activities exhibit high JVA, with object detection capturing higher joint attention than visual similarity, whereas conversation-based or independent activities show lower and more fragmented joint attention. Analysis of K …

Exploring Marshall–Olkin Models Through Bibliometric And Topic Modeling Approaches Uses Latent Dirichlet Allocation (1981-2025): A Study Based On Scopus Data, Humberto Llinás, Brian Llinás, Carlos López, Daniela Nuñez

Computer Science Faculty Publications

The Marshall–Olkin family of distributions has gained increasing attention in fields such as reliability engineering, survival analysis, financial risk modeling, and actuarial science because of its flexibility in modeling dependence among events and its wide range of extensions. Despite its growing relevance, a systematic understanding of how research on Marshall–Olkin models has evolved over time is still limited. This study addresses this gap by combining bibliometric techniques with topic modeling to analyze the structure and evolution of the scientific literature on Marshall–Olkin models. The analysis includes all 266 peer-reviewed publications on Marshall–Olkin models indexed in Scopus between 1981 and 2025. …

Exploring Large Language Models For Trustworthy Use: Insights From Research And Development, Sandeep Kalari, Sahithi Padidela, Vikas Ashok, Ravi Mukkamala

Computer Science Faculty Publications

Large Language Models (LLMs) are increasingly being adopted in a wide variety of domains, including sensitive domains such as healthcare and finance. However, persistent challenges such as unreliable data sources, privacy breaches, and hallucinated output continue to hinder their usage. We have experimented with several strategies to address these challenges. First, we developed BlockQwen, a blockchain-augmented framework that integrates decentralized trust validation, role-specific access control, and verifiable audit trails into the Qwen 2.5 LLM workflow. Second, we developed PrivAware, a multilayered privacy-enforcement framework, using a fine-tuned Flan-T5 model with self-attention masking, to safeguard data while maintaining high utility. Both systems …

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, 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 …

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 …

Attf-Gnn: An Attention-Based Multi-Omics Graph Neural Network With Modality Learning For Disease Subtyping, Sovon Chakraborty, Eleni Adam, Terry Stilwell, Harold Riethman, Desh Ranjan, Pratip Rana

Computer Science Faculty Publications

We propose AttF-GNN, an attention-based graph fusion strategy for diseases classification and subtyping. In multiomics analysis, not all types of molecular data are equally relevant for disease subtyping and considering all modalities equally may obscure discriminative signals and limit the effectiveness of predictive models by overlooking modality-specific contributions. Therefore, we design an attention-based multimodal GraphSAGE framework that can automatically emphasize the modalities providing the most relevant information for classification. At first, we have constructed three graphs using mRNA, RNA-seq and DNA methylation modalities, and train each omics with individual GraphSAGE encoders. Next, a unified intersection graph is formed using an …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

Deep Incomplete Multi-View Clustering Via Hierarchical Imputation And Alignment, Yiming Du, Ziyu Wang, Jian Li, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and compactness within clusters. To address these challenges, we propose DIMVC-HIA, a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components: (1) view-specific autoencoders for latent feature extraction, coupled with a view-shared clustering predictor to produce soft cluster assignments; (2) a hierarchical imputation module that first estimates missing cluster assignments based on cross-view contrastive similarity, and then reconstructs missing features using …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

A Survey Of Ai Methods For Geometry Preparation And Mesh Generation In Engineering Simulation, Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Franck Ledoux, Na Lei, Roshan Quadros, Navamita Ray, Nick Winovich, Yongjie Jessica Zhang

Computer Science Faculty Publications

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons …

Environmental Pesticide Exposure In The Etiology Of Pediatric Brain Tumors And Leukemia: A Scoping Review Of Epidemiological Studies, G. N. Vandesteeg, A. R. Russman, M. R. Sandbulte, E. G. Rogan, M. G. Rhodes

School of Natural Resources: Faculty Publications

No abstract provided.

Mapping Robusta Coffee (Coffea Canephora) Cropping Systems In Uganda: A Two-Step Pixel And Sub-Pixel Based Approach With Sentinel-2 Data, Getachew Kebede, Bester Tawona Mudereri, Onisimo Mutanga, Tobias Landmann, John Odindi, Natacha Motisi, Fabrice Pinard, Henri E.Z. Tonnang, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

Coffee is a highly valued commodity and a widely consumed beverage, playing an important role in global trade. However, coffee farming landscapes are increasing transitioning into smaller-scale agricultural setups. This transformation highlights the critical need for accurate classification and mapping of coffee cropping systems (CS), especially in countries like Uganda, where dense vegetation and complex terrain present substantial challenges to traditional land survey methods. Moreover, understanding the spatial distribution of Robusta coffee (Coffea canephora) CS is essential for developing site-specific management strategies, guiding extension services, and informing evidence-based policy decisions. To address this gap, the present study aimed to enhance …

Optimal Control Analysis Of Malaria Transmission In The Presence Of Insecticide Resistance And Climate Variability In Kenya, Lorna Chepkemoi, Titus Okello Orwa, Samuel Mwalili, Rachel Waema Mbogo, Steeven Belvinos Affognon, Daisy Salifu, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Malaria remains a major public health concern in Kenya, where changing climatic conditions, insecticide resistance, and mosquito behavioral adaptations continue to challenge control efforts. This study develops and analyzes a climate-sensitive malaria transmission model that incorporates mosquito behavior, insecticide resistance, and vector–human ecological dynamics to identify optimal control strategies for the Kenyan context. The model’s well-posedness was established, the basic reproduction number (R0) computed using the next-generation matrix method, and the stability of equilibrium points assessed. Spatial analysis of R0 was performed using temperature and rainfall raster data to map transmission risk across Kenya under varying insecticide use scenarios. Results …

Ai-Driven Real-Time Detection Of Zero-Day Browser Exploits Using Webassembly-Based Instrumentation, Temitope Damilola Elijah

College of Graduate Studies: Theses & Dissertations

The rapid evolution of web browsers into fully fledged application execution environments has significantly expanded their attack surface, making them prime targets for sophisticated zero-day exploits that evade traditional signature-based security mechanisms. To address this challenge, this research proposes an AI-driven framework for real-time detection and analysis of zero-day exploits in web browsers by integrating browser-level telemetry monitoring, unsupervised anomaly detection, and large language model–based threat interpretation. The framework introduces a lightweight WebAssembly telemetry agent embedded within the browser runtime to capture low-level execution behaviors, including WASM module instantiation, memory growth patterns, network interactions, and runtime API activity. These telemetry …

Assessing The Diversity And Composition Of Crayfish Assemblages In The Ogeechee River Basin, Reginald Turner

College of Graduate Studies: Theses & Dissertations

Crayfish assemblage composition in the southeastern United States is understudied relative to other aquatic taxa, such as aquatic insects and fishes, and the Coastal Plain watersheds of that region are particularly underrepresented in the contemporary literature on this topic. For example, although 38% of the crayfish species in Georgia are considered “species of greatest conservation need,” most of the distributional data used to make these designations are outdated, with some dating back over 50 years. This thesis sought to update our understanding of the contemporary distributions of crayfish species within the Ogeechee River Basin (ORB), a watershed in southeastern Georgia …

Swimming In Uncertainty: Filling Data Gaps And Providing An Educational Platform For Beach Water Quality At Tybee Island, Georgia, Lukas Roberson

College of Graduate Studies: Theses & Dissertations

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Swimming in beaches water contaminated with high levels of bacteria can make you sick. Current monitoring at the public beaches on Tybee Island consists of weekly monitoring and enumeration of fecal indicator bacteria that takes 24 hours for results. If the number of bacteria exceed regulatory limits, a public health advisory is issued, and affected waters are retested until …

Early Icu Physiological Subtyping From Time-Series Data: A Comparative Study Of Feature Complexity, Predictive Performance, And Model Interpretability, Rojeena Khadka

College of Graduate Studies: Theses & Dissertations

Intensive Care Unit (ICU) patients do not follow a single uniform physiological pattern. Patients admitted with the same diagnosis show different clinical trajectories over time making standardized classification and treatment approaches insufficient. The increasing availability of large-scale electronic health records in MIMIC-IV makes it possible to investigate such heterogeneity through data-driven approach that captures how physiology evolves during the early phase of ICU admission. This thesis compares two analytical pipelines designed to identify physiological subtypes from the first 48 hours of ICU time series data. This study then assesses how well these subtypes predict in-hospital mortality. The first approach, referred …

When The Gorenstein Projective Modules Are Precovering: A Survey On The Existence Of The Gorenstein Projective Precovers, Alexis L. Mcburney

College of Graduate Studies: Theses & Dissertations

This thesis surveys known results on the existence of Gorenstein projective precovers and studies conditions under which the class of Gorenstein projective modules is precovering. In classical homological algebra, projective resolutions are used to study modules, while relative homological algebra replaces projective modules with a different class that must be precovering for resolutions to exist. Gorenstein homological algebra extends these ideas using Gorenstein projective modules. We review key developments by Jorgensen, Murfet and Salarian, Estrada, Iacob, and Yeomans, and Cortés and Saroch. We discuss the strongest known existence result, showing that Gorenstein projective precovers exist under suitable conditions; in particular, …

Factorial Design: A New Look Through Overlap Measures, Sarah W. Liebenow

College of Graduate Studies: Theses & Dissertations

Traditional factorial analysis often relies on ANOVA, which assumes normality and equal variances. This thesis presents a nonparametric approach for assessing main and interaction effects in a 2 × 2 factorial design using the overlap coefficient, estimated through kernel density methods. A bootstrap procedure is used to approximate its sampling distribution for hypothesis testing. Simulation studies compare the overlap-based test with the ANOVA F-test, permutation F-test, and the Kruskal–Wallis test under heteroskedasticity and non-normal conditions. Results show that the overlap measure is highly sensitive to differences in spread and shape, detecting effects that traditional methods frequently miss.

Flexible Membrane Electrodes For Use In High-Capacity Lithium-Ion Batteries, David A. Denemark

College of Graduate Studies: Theses & Dissertations

Wearable electronic devices require high-capacity flexible batteries to improve user comfort and increase usage time per charge. In this thesis, flexible lithium-ion battery (LIB) electrodes are prepared using a novel and scalable phase inversion method, embedding one-dimensional SnO₂ nanowires and double-walled carbon nanotubes within a porous polyacrylonitrile asymmetric membrane. The optimized electrode delivered an initial specific discharge capacity of 915.93 mAh g⁻¹, approximately 2.5 times greater than conventional graphite anodes, with 61.99% capacity retention after 45 cycles. The incorporation of a carbon mesh substrate further enhanced both electrochemical and mechanical performance, achieving 89.46% capacity retention after 45 cycles and a …

First Record Of Arrhenophagus Chionaspidis Aurivillius (Hymenoptera: Encyrtidae) Parasitizing White Mango Scale In Kenya: Its Potential Distribution And Prospects In Biocontrol, Francis Obala, Abdelmutalab G.A. Azrag, Rehemah Gwokyalya, Shepard Ndlela, Ingo Grass, Georg Petschenka, Sunday Ekesi, Samira A. Mohamed

All Peer-Reviewed Publications

The white mango scale, Aulacaspis tubercularis Newstead (Hemiptera: Diaspididae), is one of the most destructive pests of mango worldwide. Its current management in most of the invaded areas largely depends on the intensive use of chemical insecticides. Biological control using parasitoids represents one of the most effective and environmentally sustainable management options. However, in Kenya, one of Africa's leading mango-producing countries, no effective parasitoid species associated with A. tubercularis had previously been recorded. This study aimed to identify encyrtid parasitoids associated with A. tubercularis in Kenya, and to assess how bioclimatic factors influence their occurrence and habitat suitability. Mango leaves …

Reinterpreting Stochastic Optimal Control Under Ecological Uncertainty: Inferring Decision Urgency From Vegetation Biomass Dynamics, Komi Mensah Agboka, Tobias Landmann, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

Stochastic optimal control provides a rigorous framework for systems subject to uncertainty, yet its operational use in ecological crisis contexts remains limited by interpretability. We reinterpreted a stochastic control formulation in which selected parameters emerged as indicators of decision urgency rather than normative preferences. Vegetation biomass was modeled as a stochastic stock subject to nonlinear loss driven by feeding pressure (e.g., desert locust activity) and multiplicative noise, with uncertainty represented by a time-varying volatility term that integrated extreme rainfall anomalies and conflict-related disruption. Rather than prescribing an optimal policy, we inverted the closed-form solution of the control problem to infer …

Impact Of Environmental Microparticles On Insect Olfaction, Steve B.S. Baleba, Danube K.N. Wandji, Yves H. Tchiechoua, Komi Mensah Agboka, Iman B. Hassaballa, Victor O. Omondi, Beatrice T. Nganso, Saliou Niassy, Souleymane Diallo, Merid N. Getahun

All Peer-Reviewed Publications

Terrestrial insects underpin key ecosystem services, including pollination, herbivory regulation, decomposition, nutrient cycling, and disease control. These functions depend on chemical communication that guides insects to food, mates, hosts, shelters, and oviposition sites while helping them avoid threats. Environmental microparticles, such as micro- and nanoplastics, tyre wear particles, soot, mineral dust, and agricultural residues, are now widespread across air, soil, vegetation, and indoor environments, exposing insects through contact, deposition, and ingestion. Growing evidence shows that these particles disrupt insect olfaction by adsorbing volatile compounds, blocking antennal sensilla, and interfering with receptor and neuronal processes. These disruptions impair foraging, mating, oviposition, …

Major And Institution Transfer Path Combinations And The Relaxed Lasso Method: Parsimonious Prediction Of Vertical Transfer Student Success, David Wutchiett, A. W. Logue, Martin Kurzweil, Colin Chellman

Publications and Research

The majors, institutions, and their combinations, which characterize transfer between higher education institutions relate to student success but introduce high-dimensionality and multicollinearity to statistical modeling. Relaxed lasso regression, a procedure blending regularized lasso models with unregularized refit models, selects variables and facilitates unbiased coefficient estimation in high-dimensional settings. Using a large sample of students transferring from associate to bachelor’s programs (n=28,166), we compared lasso and ridge regression parsimonious models predicting bachelor’s graduation and posttransfer GPA improvement, evaluating associations using unregularized parsimonious predictive models. Relaxed lasso parsimonious optimal models outperformed standard lasso and ridge regression models while including few of 702 …

The Colorado River: Searching For Solutions In The Face Of Uncertainty: Conference Summary, Emilia Endara, Douglas Kenney

Books, Reports, and Studies

The Colorado River is facing unprecedented change and uncertainty. Average annual flows continue to decline, and negotiations to craft post-2026 guidelines to manage the river have repeatedly bogged down in disagreements about delivery obligations, sharing shortages, and mandatory conservation. While some clarity may emerge in the coming months, the ability to sustain many existing municipal and agricultural water users is already being compromised. Meanwhile, there is growing recognition that a durable resolution must also address the water rights of sovereign Tribal Nations and better integrate environmental values into river management. All sovereigns, stakeholders, and sectors must work together now more …

Thirst For Data: State Responses To Data Center Water Use, Daniel Anderson, Sydney Stellato

Books, Reports, and Studies

No abstract provided.