Computer Engineering Commons^™

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

Implications Of Quantum Computing For Enterprise Cybersecurity And Data Integrity, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Quantum computing represents a paradigm shift in computational capabilities that poses both unprecedented threats and opportunities for enterprise cybersecurity. This research examines the implications of quantum computing advancement on current cryptographic systems, data protection mechanisms, and organizational security frameworks. Through analysis of quantum computing developments from 2019-2024 and surveys of 280 cybersecurity professionals across various industries, this study identifies critical vulnerabilities in existing encryption standards and explores emerging quantum-resistant solutions. The findings reveal that approximately 78% of enterprises remain unprepared for quantum threats, with current RSA and ECC encryption systems facing potential compromise within the next 10-15 years. The research …

Unsupervised Transfer Learning And Its Applications In Human Pose Estimation, Qucheng Peng

Graduate Studies Theses and Dissertations 2026

This dissertation advances unsupervised transfer learning by addressing critical constraints in data privacy, intellectual property protection, and deployment in unseen environments. We propose novel frameworks for three progressively restrictive settings: Black-Box Domain Adaptation (BBDA), Source-Free Domain Adaptation (SFDA), Domain Generalization (DG), and Lifelong Domain Adaptation (LLDA) for 3D human pose estimation (HPE). To address BBDA, where only model outputs are accessible, we introduce RAIN (Regularization on Input and Network). This framework utilizes Phase MixUp for frequency-domain input regularization and Subnetwork Distillation to combat source overfitting.

Extending these privacy-preserving paradigms to spatial tasks, we present the first SFDA framework for HPE. …

From Recommender To Actor: The Normative Boundary When Rag Tools Become Tool-Calling Agents, Md Tahmid Rashid

Faculty Publications - Information Technology

Retrieval-Augmented Generation (RAG) systems increasingly operate not only as tools for retrieving and synthesizing information, but also as agents that can invoke external functions, modify digital environments, and execute tasks across software systems. This development raises a specific normative problem: the point at which a model’s output ceases to be merely informational and becomes an executable intervention in the world. Building on existing work in Responsible AI, accountability, and human oversight, this paper argues that tool-calling architectures place particular pressure on these frameworks because they can fuse retrieval, reasoning, and action within a single operational pipeline. To clarify this transition, …

Comprehensive Performance Evaluation Of Devops Infrastructure Under Dynamic Workloads, Abdulrazaq Mamud

College of Graduate Studies: Theses & Dissertations

This research aims to investigate performance optimization and reliability issues related to cloud-based computing environments through an analysis of three key infrastructure components: virtualized CPU resource management, distributed API rate limiting, and web server deployment architectures. This research combines machine learning and system experimentation as a way of exploring the impact of infrastructure-level behaviors on overall system performance and scalability. The first component of the research focuses on analyzing CPU Fragmentation in Virtualized Environments, where unbalanced workload allocation on Virtual CPU Cores causes increased tail latency, resulting in Service Level Agreement violations. Metrics are analyzed using the Random Forest classifier …

Formalizing Asymmetric Control-Telemetry Separation In Distributed Industrial Control Systems, Andrew Manison

College of Graduate Studies: Theses & Dissertations

Distributed industrial control systems often place control and telemetry traffic on the same communication substrate even though the two workloads impose different requirements. Control paths need bounded request-response latency and predictable acknowledgement semantics, whereas telemetry paths benefit from scalable publish-subscribe fanout and tolerance for consumer-side delay. This thesis argues that, for the tested class of mixed workloads on shared commodity infrastructure, these communication roles should be separated architecturally rather than forced through a single protocol. To evaluate that claim, the thesis formalizes an asymmetric control- telemetry pattern and instantiates it in the Asymtra framework using gRPC for synchronous control and …

Personality Exceptions: Enhancing Character In Narrative Planners With Internal Memory, Elinor Rubin-Mcgregor

University of Kentucky Doctoral Dissertations

Stories and narratives are powerful tools for forging connections between people, as they induce emotions shared through their audience and help build empathy and understanding through sharing unique perspectives. Interactive stories can do all this and more, as they can be used to better educate and engage their audience. But in such stories, the characters must have consistent personality and human-like emotions in order for the audience to relate to them. Current implementations of personality in interactive narratives either portrays characters with static personality that feel less realistic, or else require a great deal of additional work from the author …

Machine Learning Acceleration Of Micro-Credentialing, Knowledge Gap Remediation, And Micro-Recruiting Of Stem Assessments: From Learner Data To Hardware, Paul Amoruso

Graduate Studies Theses and Dissertations 2026

The growing demand for a skilled STEM workforce calls for adaptive, data-driven education that personalizes learning while aligning competencies with industry needs. This work introduces a multi-layered framework that integrates artificial intelligence (AI), data mining and hardware-aware optimization to support this goal. At its core, the AchieveUp system applies generative AI-assisted skill tagging to digitized assessments, enabling multi-semester tracking of student progress. Analysis across 14 semesters shows that underperforming students were found to benefit most from attending score clarification sessions, a relationship validated by an R² value of 0.83, indicating strong predictive validity between attendance and skill acquisition. To remediate …

Designing Narrative-Based Ai Assistance For Sensemaking In Collaborative Environments: Case Studies In Education And Dementia Care, Dylan Edward Moore

Dartmouth College Ph.D Dissertations

This thesis addresses a gap in the human-computer interaction literature regarding the design, development, and evaluation of narrative-based AI assistance for collaborative, complex problem solving. I explore this design space through three case studies across the domains of education and dementia care. This work encompasses multi-year industry partnerships and longitudinal fieldwork, user-centered design, dataset curation, model training, and system evaluation.

Specifically, the first case study considers a story-based web platform for teaching AI literacy through peer-generated, personalized narrative scaffolding. Learners on the platform showed significant knowledge gains and other learning-related outcomes. To describe the novel design of this system, I …

Deceptive Design And Disability: How Dark Patterns Affect Users With Visual Impairments, Konstantin Metz

Graduate Studies Theses and Dissertations 2026

Dark patterns are deceptive interface designs that steer users toward unintended actions, often at financial or privacy cost. This thesis examines how those that are vision impaired are at greater risk of these dark patterns given their visual acuity deficit. We conducted semi structured interviews with nine participants across common contexts including e-commerce checkout, subscription and cancellation flows, software download pages, and mobile permission prompts. The data show recurring exposure to dark patterns such as hidden or de-emphasized fees, pre-selected add-ons, deceptive advertising and fake download buttons along with unequal consent paths, repeated permission nagging, and cancellation friction. Participants reported …

Watch Your Step, Mind The Crowd: Socially Compliant Navigation In Unstructured Environments, Jack Vice

Graduate Studies Theses and Dissertations 2026

Autonomous mobile robots increasingly operate in complex real-world environments characterized by uneven terrain, dynamic obstacles, and human presence. This dissertation addresses the fundamental challenge of developing adaptive navigation strategies that enable robots to safely and effectively traverse unstructured outdoor environments including construction sites, agricultural fields, and disaster areas while maintaining social compliance around people. We advance this capability through five complementary contributions that collectively target dynamic, unstructured, human-occupied environments. First, we demonstrate evolutionary optimization of legged mobility for uneven terrain. Second, we propose using a spatiotemporal autoencoder to learn the temporal dynamics of moving people and objects. Third, we introduce …

Memory-Efficient Acceleration For Emerging Applications Via Hardware/Software Co-Design, Shilin Tian

Graduate Studies Theses and Dissertations 2026

Emerging artificial-intelligence and data-intensive scientific workloads increasingly face a memory wall: irregular access patterns and large intermediate data volumes make data movement, rather than arithmetic, the primary constraint on performance and energy efficiency. This dissertation develops a memory-centric hardware/software co-design methodology that jointly reshapes algorithms, architectures, and dataflows to retain frequently reused data on chip. The methodology is demonstrated through three accelerators and an RTL design tool. VITA replaces multi-head attention in vision-transformer-based 3D human mesh recovery with hardware-friendly average pooling and maps the resulting operators to a reconfigurable datapath, achieving 5.05-fold and 69.12-fold speedups over a state-of-the-art GPU and …

Non-Invasive Digital Restoration Of Damaged Photographic Film Negatives, Ankan Bhattacharyya

University of Kentucky Doctoral Dissertations

Physical restoration of damaged photographic film causes more damage. Also, existing non-invasive digital restoration of film negatives does not produce print-quality optical images. Instead, they produce X-ray projections, which are not the same as optical projections. This thesis addresses these problems and establishes a framework that can digitally restore old, damaged films without the need to open them physically. Virtual Unwrapping is an existing pipeline that has proven itself over the last couple of decades to work on unopenable papyrus scrolls, like the Herculaneum Scrolls. This thesis utilizes the concept of virtual unwrapping to restore damaged photographic film negatives. Due …

A Dataset-Centric Analysis Of Evaluation Protocols In Face Morph Attack Detection, Sheerko R. Hmasalah, Abdulbasit K. Al-Talabani, Aras T. Asaad

Polytechnic Journal

Face morph attack detection (MAD) is often evaluated using datasets collected from diverse sources, created for different purposes, and acquired under varying conditions. This heterogeneity raises questions about how evaluation protocols and dataset characteristics influence reported performance. This work adopts a dataset-centric perspective to analyze the effects of evaluation protocol and dataset composition, including visual quality and morph-to-genuine imbalance, on MAD performance. Experiments are conducted on eight datasets and four morphing techniques using a fixed pipeline evaluated across six backbones, including Vision Transformer (ViT) and five CNN architectures, and ten evaluation scenarios covering homogeneous and heterogeneous cross-morph generalization. Performance is …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

Performance Analysis Of Sparse Neural Networks In Brain Abnormality Detection, Megan Danh

Honors Undergraduate Theses

Neuroimages have held the capability of revealing to medical professionals patterns for brain abnormalities since their development. However, more recently, these professionals and researchers are looking to use neural networks to identify these brain abnormalities through neuroimages for early detection that would allow more effective treatment. Neuroimage datasets, specifically functional magnetic resonance imaging (fMRI), are extremely large in size. This would result in their processing and training to be computationally expensive, even with smaller neural networks. Fortunately, recent pruning methods have recently emerged, where network weights and neurons are pruned to reduce computational cost without compromising too much accuracy. By …

Lidar-Based Framework For Detecting Suspicious Human Activities, Ahd Aljumah, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

This study explores the development of Human Activity Recognition (HAR) systems capable of identifying suspicious activities to enhance security in public spaces. We propose an innovative solution that integrates LiDAR sensors with deep learning technologies. Our method employs advanced models operating on LiDAR point cloud, PV-RCNN for human detection, and LidarGait++ for classifying activities into categories such as standing or walking (non-suspicious) and sneaking or fighting (suspicious). Due to the scarcity of suitable real-world datasets for training such systems, we utilize a 3D simulation tool, Blender, to create realistic environments and generate labeled point cloud data. This synthetic dataset allows …

Probing Proficiency-Related Neural Representations With Pca And Ica, Onila R. Narayana Mudalige Don

Graduate Student Theses, Dissertations, & Professional Papers

How proficiency-related information is represented in task fMRI depends not only on the data themselves, but on the representational lens used to summarize them. This thesis examines second-language (L2) proficiency as a problem of representational organization rather than as a simple classification exercise. Using task fMRI from adult language learners performing semantic animacy judgments in their native language (L1) and second language (L2), I derive shared low-dimensional network representations with principal component analysis (PCA) and independent component analysis (ICA), then evaluate those representations under matched leakage-controlled decoding pipelines.

Across analyses, the central comparison is between representational frameworks rather than between …

Effect Of Local Steel Slag Incorporation On The Mechanical Properties Of Concrete, Ahmed Safaa Tariq Karmah, Wissam Khadum Alsaraj, Luma A. Zghair

Al-Esraa University College Journal for Engineering Sciences

The increasing interest in the production of concrete through the utilization of inexpensive materials has caused a rise in the number of studies concerning the use of materials with cementitious properties, such as Silica Fume, Slag, Fly Ash and other pozzolanic materials. Moreover, the increasing amounts of Steel Slag being produced from local factories that are being deposited without an available method of disposal provides an opportunity to harness the Slag in the production of an inexpensive, and possibly better concrete. Slag has been used as both an addition and substitution of the cement and fine aggregate. The mixtures included …

Decoding Climate Change: A Comprehensive Statistical Insight Into Temperature Anomalies In Iraq And Its Neighboring Countries, Jameel T. Al-Naffakha, Mohammed R. Al-Qassabanda, Israa Jafar

Al-Esraa University College Journal for Engineering Sciences

This study applies advanced statistical techniques, including the Mann-Kendall Trend Test and ARIMA forecasting, to validate and predict temperature anomalies across Iraq and its neighboring countries (2014–2024). Findings confirm a statistically significant warming trend (p < 0.00000005) across all nations, with Syria (1.02°C) and Turkey (0.97°C) experiencing the highest anomalies. GIS-based spatial analysis highlights regional disparities, identifying climate-vulnerable zones. The persistent rise in temperature anomalies correlates with worsening water scarcity, desertification, and extreme weather events, including prolonged droughts and heatwaves. Iraq’s peak anomaly (1.48°C) has exacerbated heat stress, agricultural decline, and reduced river inflows, while Kuwait and Saudi Arabia struggle to maintain critical infrastructure under record-breaking temperatures exceeding 50°C. These climate shifts pose severe risks to water availability, food security, and energy demand, necessitating urgent policy interventions. Key recommendations include enhanced water resource management, climate-adaptive agriculture, and renewable energy expansion. If left unaddressed, rising temperatures could destabilize the region, increasing socio-economic vulnerabilities and escalating resource conflicts. This study underscores the need for cross-border collaboration and sustainable adaptation policies to mitigate the long-term impacts of climate change in the Middle East.