Digital Commons Network^™

Efficient Blending Of Large Language Models, Sandeep Chatterjee

Master’s Dissertations

Due to the limited capabilities of single Large Language Models (LLMs), multiple LLMs can be employed in tandem for better reliability of answers. Blending refers to combining the strengths of various LLMs to make use of their complementary capabilities for generating high-quality responses. It is a non-trivial problem, and the task becomes even more difficult when aiming for minimal latency and supervising the blending components. The standard framework, LLM- Blender, approaches this in three stages: response generation, candidate selection via ranking, and response fusion through summarization. However, this pipeline faces two critical limita- tions—high latency due to repeated ranking steps, …

Geometry Based Uav Trajectory Planning For Mixed User Traffic In Mm Wave Communication, Sk Abid Hasan

Master’s Dissertations

Unmanned aerial vehicle (UAV) assisted communication is a revolutionary technology that has been recently presented as a potential candidate for beyond fifth-generation millimeter wave (mmWave) communications. Although mmWaves can o↵er a notably high data rate, their high penetration and propagation losses mean that line of sight (LoS) is necessary for e↵ective communication. Due to the presence of obstacles and user mobility, UAV trajectory planning plays a crucial role in improving system performance. In this work, we propose a novel computational geometry-based trajectory planning scheme by considering the user mobility, the priority of the delay sensitive ultra-reliable low-latency communications (URLLC) and …

Word Level Attack For Text Ranking, Tanmay Karmakar

Master’s Dissertations

Neural Ranking Models (NRMs) have become state-of-the-art in information retrieval, demonstrating remarkable effectiveness across various search and ranking tasks. However, their increasing deployment in real-world systems raises critical concerns about their robustness and susceptibility to adversarial attacks. This project investigates the fragility of modern NRMs by proposing and evaluating a document perturbation method based on targeted, single-word perturbation. Our approach strategically identifies an influential word depending on the query to be substituted or added in the document. We have done experiments on benchmark datasets to assess the impact of these minimal perturbations on ranking performance. Our findings reveal that even …

Sla Evaluation And Composition In Reconfigurable Cloud-Based Services, Michael Iannelli

Dissertations, Theses, and Capstone Projects

Given the business model of offering data and computing services in a cloud setting, a major question arises: How do the services of one cloud provider compare to those of others? With the ubiquitous use of smartphones and tablets, the ability of a cloud provider to support QoS and client mobility becomes paramount. This research proposes a methodology for evaluating service-level agreements (SLAs) between cloud providers and their consumers, with a particular focus on dynamic SLA composition to adapt to changes in the application requirements and the external environment—such as traffic surges, security threats, or evolving business models.

In one …

Tracing My Roots: An Exploratory Data Visualization And Analysis Of Jewish Immigration And Assimilation To New York City, Jamie E. Gelberg

Dissertations, Theses, and Capstone Projects

As my Capstone, I explored the complex process of immigrant assimilation to New York City from the late 19th century and beyond through a personal lens, using my Ashkenazi Jewish family as a case study.

I outlined and analyzed relevant demographic data from the US Census Bureau, Berman Jewish DataBank, and other sources to understand New York City during this period and how Jewish immigrants fit into the story. I focused on my family history, immigration and settlement, social assimilation, and economic status. I also incorporated personal narratives from my family history from 3 generations. These narratives help provide context …

Computerized Diagnostic Decision Support Systems-Isabel Pro Versus Chatgpt-4 Part Ii, Joe M Bridges, Xiaoqian Jiang, Michael Ige, Oluwatoniloba Toyobo

Faculty, Staff and Student Publications

Objective: Does a Tree-of-Thought prompt and reconsideration of Isabel Pro's differential improve ChatGPT-4's accuracy; does increasing expert panel size improve ChatGPT-4's accuracy; does ChatGPT-4 produce consistent outputs in sequential requests; what is the frequency of fabricated references?

Materials and methods: Isabel Pro, a computerized diagnostic decision support system, and ChatGPT-4, a large language model. Using 201 cases from the New England Journal of Medicine, each system produced a differential diagnosis ranked by likelihood. Statistics were Mean Reciprocal Rank, Recall at Rank, Average Rank, Number of Correct Diagnoses, and Rank Improvement. For reproducibility, the study compared the initial expert panel run …

Alibaba: Building Advanced Intellectual Property Governance For E-Commerce Marketplaces, Liang Chen, Sin Mei Cheah, Can Huang, Guoqiao Liu

Asian Management Insights

How the global e-commerce powerhouse harnessed artificial intelligence (AI) to balance innovation and intellectual property (IP) rights protection.

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Exploiting Compiler-Introduced Vulnerabilities In C: A Cross-Compiler And Cross-Architecture Analysis Of Undefined Behavior, Erik Mccutchen

Master's Theses

Compilers are a critical component in generating secure software across engineering disciplines. However, languages like C that permit undefined behavior introduce a fundamental tension between the compiler’s interpretation of undefined behavior and the security of the generated code. This tension can result in security vulnerabilities that, from the programmer's perspective, are ``created'' by the compiler. The widespread use of these languages, combined with the complexity of modern optimizations and limited developer visibility into compiler behavior, makes these vulnerabilities both pervasive and difficult to detect.

Building on prior work, this thesis refines a dataset of C code snippets that exhibit Compiler-Introduced …

Enhancing Energy Consumption Forecasting For Electric Vehicle Charging Stations With Time Series Dense Encoder (Tide), Amril Nazir, Abdul Khalique Shaikh, Aftab Ahmed Khan, Abdul Salam Shah, Nadia Khalique

All Works

The increasing adoption of electric vehicles has led to the installation of charging stations in various locations in major cities worldwide. This study focuses on energy consumption forecasting for Boulder, Nevada, United States electric vehicle charging stations. Efficient management of energy resources at these charging points is crucial for optimizing resource utilization and reducing charging time. While existing literature has focused on energy consumption prediction in smart homes and grids, the significance of electric charging points in smart cities must be considered. The transformers have handled time series forecasting better with larger datasets like the Temporal Fusion Transformer and the …

Contract Quality Feature Extraction Using Llm, Aaron C. Washington

Theses and Dissertations

This study explored the potential insights generated from linguistic complexity measurements and large language model (LLM) based assessments on the quality of contract documents. By combining structured True/False prompts with log-probability analysis and ambiguity scoring, the study introduced novel contract-quality assessment methods. Results support a feature-driven approach to contract evaluation, one that offers automated, scalable insights for triaging risk and improving drafting practices. These assessment methods contribute to the growing field of legal natural language processing by offering modular tools for effective contract analysis.

Glacial Lakes Segmentation Using Multispectral Remote Sensing Data And Deep Learning Models, Debankan Das

Master’s Dissertations

The identification and delineation of glacial lakes through segmentation is crucial for tracking glacial changes and evaluating potential dangers from sudden flood events (GLOFs). These floods can severely impact populated areas and man-made structures downstream. Recent advances in high-quality satellite imagery have sparked increased attention toward using advanced machine learning methods, particularly deep learning, to enable precise and automated glacial lake detection. In this study, we explore the effectiveness of deep learning-based pointwise semantic segmentation for glacial lake mapping using multisource remote sensing imagery, including both optical and synthetic aperture radar (SAR) data. We experiment with a novel stack combination …

Digital Resurrection Of Thonis-Heracleion: Technological Advances In Underwater Archaeology And A Speculative Ai-Driven Reconstruction Methodology, James Hutson, Passent Chahine

Faculty Scholarship

This article synthesizes past archaeological research on the submerged Egyptian city of Thonis-Heracleion, critically reviewing excavations and technological interventions deployed since its rediscovery by Franck Goddio and the IEASM team. Situated approximately 10 meters beneath Aboukir Bay near Alexandria, the city represents a significant nexus of Greek and Egyptian cultural heritage, vividly documented in classical sources such as Herodotus and Strabo. Prior excavations have recovered temple complexes, colossal statues, ritual artifacts, and an extensive array of ancient shipwrecks, mapping only a fraction of the extensive site. These investigations utilized pioneering geophysical methods, including multibeam sonar, side-scan sonar, and photogrammetry, establishing …

Coda: A Digital System For Generation Of Piano Practice Exercises From Symbolic Music Notation, Annie Tang

Computer Science Senior Theses

Effective practice remains one of the greatest challenges in music education, yet cur- rent digital music tools primarily support only passage engagement or surface-level feedback, failing to provide proactive guidance for overcoming technical challenges within piano repertoire. This thesis presents Coda, a digital system that generates custom piano practice exercises from symbolic music notation based on established piano pedagogy principles that have historically been taught orally.

Unlike existing tools that only provide a viewable symbolic music notation display or only an interface to take notes and record a practice session, Coda uses rule- based algorithmic transformations rooted in pedagogical logic …

Thriving In The Age Of Ai: Navigating Ai Identity Threat Through Ai Job Crafting, Yuming He

Theses and Dissertations in Business Administration

As artificial intelligence (AI) technologies like GenAI tools increasingly reshape the workplace, employees increasingly face threats to their work identity. Grounded in the identity threat response model and job crafting theory, this study investigates how AI identity threat influences employee AI job crafting behaviors and how these behaviors, in turn, affect vitality and learning. Using survey data from 521 full-time employees who actively engage with AI tools, the results indicate that AI identity threat stimulates both AI approach job crafting and AI avoidance job crafting. AI approach crafting enhances both vitality and learning, while AI avoidance crafting only supports vitality. …

Ideal Query Expansion Using Reinforcement Learning, Madhuchchhanda Das

Master’s Dissertations

Information retrieval (IR) systems often struggle with short, ambiguous, or underspecified queries, leading to suboptimal document retrieval. Traditional query reformulation methods, such as those based on the Rocchio algorithm, rely on heuristic term selection and relevance feedback but typically apply fixed or manually tuned weights to expanded terms. This limits their adaptability and generalization across diverse query-document contexts. In this thesis, we propose a novel reinforcement learning (RL)-based framework to dynamically optimize term weighting in reformulated queries. We model the problem as a Markov Decision Process (MDP), where each state represents a query as a vector of term weights. An …

On The Deployment Of Ris-Mounted Uav Networks, Anupam Mondal

Master’s Dissertations

Reconfigurable intelligent surfaces (RIS) enable smart wireless environments by dynamically controlling signal propagation to enhance communication and localization. Unmanned aerial vehicles (UAVs) can act as flying base stations and thus, improve system performance by avoiding signal blockages. In this paper, we propose a gradient ascent and coordinate search based method to determine the optimal location for a system that consists of a UAV and a RIS, where the UAV serves cellular users (CUs) and the RIS serves device-to-device (D2D) pairs. In particular, by optimizing the net throughput for both the D2D pairs and the CUs, the suggested method establishes the …

Modeling And Verification Of Sigma Delta Neural Networks, Sirshendu Das

Master’s Dissertations

In the context of modern day embedded safety-critical systems and low-resource edge devices in particular, Sigma-Delta Neural Networks (SDNNs) offer a promising alternative to traditional Artificial Neural Networks (ANNs) by leveraging eventdriven, sparse computations inspired by biological neural processing. This energyefficient paradigm makes SDNNs well-suited for neuromorphic hardware and realtime applications, particularly in scenarios with temporal redundancy, such as video processing. However, as neural networks become integral to safety-critical systems, ensuring their robustness against adversarial perturbations is an absolute necessity. In this work, we propose an end-to-end framework for formal modeling and verification of SDNNs using Satisfiability Modulo Theory (SMT). …

Addressing Class Imbalance Problems To Improve Animal Detection Through Aerial Image Data, Suryang Koushal

Master’s Dissertations

Monitoring animal populations in wildlife reserves is essential for conservation, especially for endangered species, but manual censuses are costly, risky, and logistically challenging due to vast, inaccessible terrains. Unmanned Aerial Vehicles (UAVs) with digital cameras provide a safer, scalable solution for collecting aerial imagery to estimate animal populations. However, semi-automated processing of these images faces significant challenges due to class imbalance in datasets, including foreground-background disparities, where background terrain dominates over sparse animal instances, and inter-class imbalances from uneven species representation and varied visual appearances (e.g., species, sizes, fur patterns) against diverse backgrounds like deserts or forests. These imbalances hinder …

Energy-Efficient Uav Movement And User-Uav Association In Multi-Uav Networks, Subhadip Ghosh

Master’s Dissertations

These days, unmanned aerial vehicle (UAV)-based millimeter wave (mmWave) communication systems have drawn a lot of attention due to the increasing demand for faster data rates. Given the susceptibility of mmWave signals to obstacles and high propagation loss of mmWaves, ensuring line-of-sight (LoS) connectivity is critical for maintaining robust and efficient communication. Furthermore, UAVs have limited power resource and limited capacity in terms of number of users it can serve. Most significantly di↵erent users have di↵erent delay requirements and they keep moving while interacting with the UAVs. In this paper, first, we have provided an efficient solution for the optimal …

Enhancing Expressive Power Of Graph Neural Networks Using Geometric Transformations, Suranjan Dey

Master’s Dissertations

Graph Neural Networks (GNNs) are highly effective in many real-world tasks, such as molecular property prediction, modeling protein structures, analyzing user-item relationships, and making link predictions. What sets them apart is their ability to learn meaningful representations by capturing not just the features of individual nodes, but also the overall structure of the graph they belong to. This expressive strength allows GNNs to model complex relationships more accurately. In this work, we take a step further by introducing geometric transformations aimed at improving how GNNs handle spatial information. In particular, we focus on angular aggregation methods that maintain rotational consistency, …

Application Of Deep Learning In Analysis Of Stellar Spectra, Piyush Yayati

Master’s Dissertations

In recent years, the analysis of high-resolution stellar spectra has become increasingly important for estimating key stellar parameters such as effective temperature (Teff ), surface gravity (log g), metallicity ([M/H]), and rotational velocity (v sin i). Traditional methods often rely on manual calibration or spectrum synthesis, which can be time-consuming and error-prone, especially for M dwarfs whose spectra are dense with molecular features. In this study, we investigate the use of convolutional neural networks (CNNs) to automate the estimation of stellar parameters using synthetic and observed data.We adopt a StarNet-like CNN architecture trained on synthetic spectra generated from the PHOENIX-ACES …

Causal Explanations In Deep Learning Systems, Dhruv Vansraj Rathore

Master’s Dissertations

Deep learning models often deliver high predictive accuracy; however, their lack of interpretability can hinder their adoption in critical fields such as healthcare and finance. This thesis explores the concept of Intrinsic Causal Contribution (ICC), a novel method for explaining neural network predictions by quantifying each input feature’s intrinsic causal influence on the output, independent of correlated effects. ICC models the network as a Structural Causal Model and employs Causal Normalizing Flows to handle complex dependencies, with efficient estimation via the Jansen Estimator. Analysis on both synthetic and real data sets provides evidence that ICC produces faithful, interpretable attributions, often …

Universally Consistent Hyperbolic Deep Neural Networks, Sagar Ghosh

Master’s Dissertations

The ubiquitous pertinence of Deep Neural Networks has made it pivotal in modern Computer Science Applications, ranging from Computer Vision to Pattern Recognition and Machine Translation. Although these deep architectures are primarily based on Euclidean Spaces, Hyperbolic Neural Networks (HNN) gained traction in recent times to tackle more complex non-Euclidean data having inherent hierarchical structures. These HNN architectures have shown commendable improvements in test results on tree or graph-like data by exploiting the inherent exponential metric distances of hyperbolic spaces, making them more suitable to embed non-Euclidean data. Although HNNs surpass their conventional Euclidean counterparts by commendable margins, little to …

Linear Systems Over Pura Vida Neutrosophic Algebra, Rayyanu Abdullahi Muhammad, Abdulhadi Aminu

Neutrosophic Systems with Applications

Neutrosophic numbers offers a strong foundation for representing uncertainty, indeterminacy, and imprecision within mathematical systems. Pura Vida Neutrosophic Algebra (PVNA) expands upon max-plus algebra (also known as tropical algebra or path algebra) using neutrosophic numbers. In this study, we propose a novel extension of the Pura Vida Neutrosophic Algebra (PVNA) by formulating and analyzing linear systems within this algebraic context–an area that, to the best of our knowledge, has not been previously examined. Specifically, we introduce the concept of Neutrosophic Max-Plus Linear Systems, develop an algebraic methodology for their representation, and establish the necessary and sufficient conditions for the existence …

Evaluating Disaster Relief In Supply Chains Using A Neutrosophic Mcdm Approach, Nada A. Nabeeh

Neutrosophic Systems with Applications

Disaster-prone regions and affected areas encounter persistent challenges in maintaining supply chain continuity due to environmental uncertainties and infrastructure disruptions. Effective supply chain disaster management (SCDM) is essential for relief disaster disruptions, specifically in upstream processes and functions within the humanitarian supply chain. The integration of advanced technologies like the metaverse and Multiple-Criteria Decision-Making (MCDM) methods supports strategic planning and enhances resilience. This study presents a multi-criteria decision-making (MCDM) proposed approach for disaster relief evaluation in supply chain management. The proposed model integrates Interval-Valued Neutrosophic Numbers (IVNNs) to manage uncertainty and ambiguity inherent in disaster various criteria which are often …

Cutting Through The Infodemic Efficiently: News Claims Surveillance And Llm-Based Lightweight Fact Verification, Xuan Zhang

Dissertations and Theses Collection (Open Access)

In the context of the current infodemic, the rapid spread of misinformation poses a severe threat to social stability and public health. Recently, the rise of deep learning technologies has offered the potential for accelerating the development of automated misinformation detection and verification. However, current technological capabilities and computational resources often prove inadequate for the exhaustive scrutiny required, rendering the enhancement of processing efficiency a critical imperative. Given the vast amount of data on the internet, current technology and computational power often fall short in timely and accurate scrutiny of each piece of information, making the improvement of processing efficiency …

Rich Models And Methods For On-Demand Same Day Deliveries, Zhiqin Zhang

Dissertations and Theses Collection (Open Access)

Same-day delivery has brought numerous conveniences to people’s lives, but it has also presented challenges in terms of service management. To effectively optimize on-demand same-day delivery operations within urban logistics, intelligent decision-making strategies capable of adapting to rapidly changing circumstances are essential. Employing effective decisionmaking strategies that account for order allocation, route planning, courier scheduling, and other relevant factors, is pivotal in advancing logistics operations, enhancing efficiency, customer satisfaction, and resource utilization in the context of dynamic same-day delivery problems.

The focus of this thesis revolves around different emerging challenges presented by on-demand same-day delivery problems, with a particular emphasis …

Interactive Generative Modeling: A Pathway For Improved Simulation And Decision Making, Changyu Chen

Dissertations and Theses Collection (Open Access)

This dissertation presents Interactive Generative Modeling (IGM), a unified perspective that integrates interactive paradigm and generative modeling to advance the development of general-purpose intelligent systems. IGM is motivated by the observation that while reinforcement learning (RL) has mastered a wide range of complex simulated tasks, it struggles to generalize in high-dimensional, open-ended tasks. In contrast, generative models excel in such settings due to their expressivity and their ability to serve as powerful priors (e.g., LLMs pretrained on massive corpora). By bridging these two paradigms, IGM offers a promising path forward.

The first direction explored in this dissertation is IGM for …

Learning And Optimization Under Human-Centric Considerations, Qian Shao

Dissertations and Theses Collection (Open Access)

This dissertation investigates learning and optimization problems shaped by humancentric considerations, such as preferences, demonstrations, behavioral patterns, and resource constraints. As real-world decision-making increasingly involves interaction with human agents, data, and limitations, modeling these factors becomes critical for building practical, adaptive, and robust systems.

The research spans four domains. First, we study preference-aware delivery routing by learning implicit practitioner preferences and incorporating them into a hierarchical route optimization framework. Second, we develop imitation learning methods for cost-constrained settings, enabling agents to mimic expert behavior while respecting safety and resource limitations. Third,we explore early rumor detection in data-limited environments, integrating large …