Digital Commons Network^™

Classification And Analysis Of Families Of Graphs With Focus On Nordhaus-Gaddum Bounds, Sibi Pranav Muthuprakash

Theses and Dissertations

Nordhaus-Gaddum type problems focus on bounding characteristics of graph and its complement. There are certain families of graphs which yield optimal upper bounds on various Nordhaus-Gaddum characteristics of graph. In this paper, we will talk about bounding combinatorial algebraic connectivity, normalized algebraic connectivity, the Cheeger constant, and the isoperimetric number of a graph and its complement. We will identify some optimal or near optimal families of graphs and conduct an analysis on these families that will help to bound these characteristics. We will also prove and conjecture some bounds that can help in formulating stronger bounds on these characteristics of …

Flowdb: A Complete Method For Extracting Queries From Functional Programs, Koby J. Lewis

Theses and Dissertations

Programmers working on database-backed applications frequently face trade-offs between designing maintainable software and efficient software due to the overhead of querying a database. We demonstrate FlowDB, an analysis for database-backed programs that can aid in program transformations that allow a program to be written in a maintainable style while still being performant. FlowDB employs demand-driven control flow analysis (DCFA) to produce a set of query sites that may be evaluated during the execution of a program. This set of query sites may then be used by a developer or compiler to cache needed results.

Ai-Enhanced Structured Literacy Intervention For Secondary Students: A Case Study Of Science Of Reading, Jennifer Bird

Teaching & Learning Faculty Publications

This study examines the effectiveness of Lexia PowerUp, an AI-powered literacy program, for sixth-grade students requiring Tier 3 reading intervention. Seven sixth-grade students (six boys, one girl; five African American, two Caucasian; all qualifying for free/reduced lunch) participated in a six-month intervention combining 50 minutes of daily small-group instruction with individualized Lexia PowerUp usage. Researchers measured progress through Achieve 3000 Lexile assessments and Lexia PowerUp performance data across three skill strands: Word Study, Grammar, and Comprehension. All participants demonstrated Lexile level improvements from beginning-of-year to mid-year assessments, though students remained below sixth-grade benchmarks (925-1070L). Analysis of Lexia PowerUp progression showed …

Autoregressive Temporal Modeling For Advanced Tracking-By-Diffusion, Pha Nguyen, Rishi Madhok, Bhiksha Raj, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Object tracking is a widely studied computer vision task with video and instance analysis applications. While paradigms such as tracking-by-regression,-detection,-attention have advanced the field, generative modeling offers new potential. Although some studies explore the generative process in instance-based understanding tasks, they rely on prediction refinement in the coordinate space rather than the visual domain. Instead, this paper presents Tracking-by-Diffusion, a novel paradigm for object tracking in video, leveraging visual generative models via the perspective of autoregressive models. This paradigm demonstrates broad applicability across point, box, and mask modalities while uniquely enabling textual guidance. We present DIFTracker, a framework that utilizes …

The Effect Of An Invasive Species (Lantana Camara) And A Fast-Growing Herb (Acanthus Pubescens) On The Plant And Animal Communities In Kibale National Park, Uganda, Kathleen Bufka

Theses and Dissertations

Fast-growing plant species, like Lantana camara and Acanthus pubescens, could threaten biodiversity in Kibale National Park, Uganda. I surveyed plants and wildlife in areas where these plants were frequently encountered compared to those areas where they are infrequent. I found mixed results based on plant traits and the animals considered. My findings support management efforts on these two species to prevent forest degradation.

Investigating The Influence Of Experimental Parameters On The Formation Of Gold Nanoparticles: A Green Synthesis And Design Of Experiments Approach, Isaac Opeyemi Subuloye

Master's Theses

This thesis examines the green synthesis of gold nanoparticles using catechin, a naturally occurring plant-derived compound, as both a reducing and capping agent. The synthesis process was guided by a structured Design of Experiments (DOE) methodology to ensure systematic investigation and optimization. A two-phase experimental approach was adopted. First, a full factorial screening design was used to evaluate the effects of catechin concentration, NaOH concentration, and pre-reaction time. This was followed by an optimization phase using a face-centered composite design to identify conditions that produce nanoparticles with ideal characteristics. The nanoparticles were analyzed using UV-visible spectroscopy and dynamic light scattering …

Integrated Sustainability: Unlocking Environmental Potential In Electric Vehicles And Solar Panels, Zachary C. Anderson

Honors Theses

This thesis investigates the environmental, economic, and individual level factors influencing consumer adoption of electric vehicles and solar panels. Through a comprehensive scoping review and case-based analysis, it identifies key incentives and barriers related to purchase intentions for each alternative energy technology. Central factors influencing adoption include cost-related concerns, individual purchasing power, demographic effects, and policy impact. These findings suggest that targeted interventions – specifically those used to amplify incentives and mitigate barriers – can positively impact individual consumer purchase intention, including through bundling options. Highlighting the importance of integrated sustainability, this study explored how electric vehicles and solar panels …

The 6th International Workshop On Talent And Management Computing (Tmc 2025), Hengshu Zhu, Yong Ge, Hui Xiong, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing …

A Review: The Beauty Of Serendipity Between Integrated Circuit Security And Artificial Intelligence, Chen Dong, Decheng Qiu, Bolun Li, Yang Yang, Chenxi Lyu, Dong Cheng, Hao Zhang, Zhenyi. Chen

Research Collection School Of Computing and Information Systems

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and …

Solving Two-Stage Stochastic Integer Programs Via Representation Learning, Yaoxin Wu, Zhiguang Cao, Wen Song, Yingqian Zhang

Research Collection School Of Computing and Information Systems

Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph convolutional network (GCN) based VAE embeds scenarios into a low-dimensional latent space, conditioned on the deterministic context of each instance. With the latent representations of stochastic scenarios, we perform two auxiliary tasks: objective prediction and scenario contrast, which predict scenario objective values and the similarities between them, respectively. These tasks further integrate objective information into the representations through gradient backpropagation. Experiments show that the learned scenario representations can …

Evowiki: Evaluating Llms On Evolving Knowledge, Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Research Collection School Of Computing and Information Systems

Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as contamination. In this paper, we introduce EvoWiki, an evolving dataset designed to reflect knowledge evolution by categorizing information into stable, evolved, and uncharted states. EvoWiki is fully auto-updated, enabling precise evaluation of continuously changing knowledge and newly released LLMs. Through experiments with Retrieval-Augmented Generation (RAG) and Continual Learning (CL), we evaluate how effectively LLMs adapt …

Bhvit: Binarized Hybrid Vision Transformer, Tian Gao, Yu Zhang, Zhiyuan Zhang, Huajun Liu, Kaijie Yin, Chengzhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

Model binarization has made significant progress in enabling real-time and energy-efficient computation for con-volutional neural networks (CNN), offering a potential solution to the deployment challenges faced by Vision Transformers (ViTs) on edge devices. However, due to the structural differences between CNN and Transformer architectures, simply applying binary CNN strategies to the ViT models will lead to a significant performance drop. To tackle this challenge, we propose BHViT, a binarization-friendly hybrid ViT architecture and its full binarization model with the guidance of three important observations. Initially, BHViT utilizes the local information interaction and hierarchical feature aggregation technique from coarse to fine …

Quantizing Text-Attributed Graphs For Semantic-Structural Integration, Jianyuan Bo, Hao Wu, Yuan Fang

Research Collection School Of Computing and Information Systems

Text-attributed graphs (TAGs) have emerged as a powerful representation for modeling complex relationships across diverse domains. With the rise of large language models (LLMs), there is growing interest in leveraging their capabilities for graph learning. However, current approaches face significant challenges in embedding structural information into LLM-compatible formats, requiring either computationally expensive alignment mechanisms or manual graph verbalization techniques that often lose critical structural details. Moreover, these methods typically require labeled data from source domains for effective transfer learning, significantly constraining their adaptability. We propose STAG, a novel self-supervised framework that directly quantizes graph structural information into discrete tokens using …

Akma+: Security And Privacy-Enhanced And Standard-Compatible Akma For 5g Communication, Guomin Yang, Guomin Yang, Yingjiu Li, Minming Huang, Zilin Shen, Imtiaz Karim, Ralf Sasse, David Basin, Elisa Bertino, Jian Weng, Hwee Hwa Pang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

The Authentication and Key Management for Applications (AKMA) protocol is a fundamental building block for security and privacy of 5G cellular networks. Therefore, it is critical that the protocol is free of vulnerabilities that can be exploited by attackers. Unfortunately, based on a detailed analysis of AKMA, we show that AKMA has several vulnerabilities that may lead to security and privacy breaches.We define AKMA+, an enhanced protocol for 5G communication that protects against security and privacy breaches while maintaining compatibility with existing standards. AKMA+ includes countermeasures for protecting communication between the user equipment (UE) and application functions (AFs) from attackers, …

Colloquial Singaporean English Style Transfer With Fine-Grained Explainable Control, Jinggui Liang, Dung Vo, Yap Hong Xian, Hai Leong Chieu, Kian Ming A. Chai, Jing Jiang, Lizi Liao

Research Collection School Of Computing and Information Systems

Colloquial Singaporean English (Singlish) is an informal English marked by a unique blend of languages reflecting Singapore’s multicultural identity. Style transfer between Singlish and Standard (formal) English is vital for various applications, yet existing methods often lack explainability and fine-grained control. To fill this gap, we contribute in two key ways. First, we construct a large, high-quality dataset of formal and informal sentences, annotated across six linguistic aspects—Syntax, Lexical Borrowing, Pragmatics, Prosody/Phonology, Emoticons/Punctuation, and Code-Switching—with detailed explanations. Starting with manually annotated cases, we scaled the dataset to 140K with ensured quality. Second, inspired by the “Society of Mind” theory, we …

Connecting Giants: Synergistic Knowledge Transfer Of Large Multimodal Models For Few-Shot Learning, Hao Tang, Shengfeng He, Jing Qin

Research Collection School Of Computing and Information Systems

Few-shot learning (FSL) addresses the challenge of classifying novel classes with limited training samples. While some methods leverage semantic knowledge from smaller-scale models to mitigate data scarcity, these approaches often introduce noise and bias due to the data's inherent simplicity. In this paper, we propose a novel framework, Synergistic Knowledge Transfer (SYNTRANS), which effectively transfers diverse and complementary knowledge from large multimodal models to empower the off-the-shelf few-shot learner. Specifically, SYNTRANS employs CLIP as a robust teacher and uses a few-shot vision encoder as a weak student, distilling semantic-aligned visual knowledge via an unsupervised proxy task. Subsequently, a training-free synergistic …

Comparison Of Nutrient Concentrations And Primary Production Indicators Across Plume Fronts, Taylor Dishon

Electronic Theses and Dissertations

Coastal buoyant plumes carry low-salinity waters containing particulate and dissolved terrestrial materials with an abundance of nutrients into the coastal ocean, including inorganic nitrogen and phosphorous. Plume fronts act as transport barriers created from the convergence of a fresher plume with an older, saltier plume or with ambient waters, leading to potential accumulation of phytoplankton and nutrients. This accumulation creates an optimal environment for primary production. The primary goals of this study were: to investigate the distribution of and potential relationships between nutrient concentrations and chlorophyll a across plume fronts of the Winyah Bay River Plume, and examine potential relationships …

A Multi-Isotope Investigation Of Mammoth Diet, Habitat, And Movement Patterns In The Central Great Plains, U.S.A., Jacob Mccloskey

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

During the Last Glacial Maximum (LGM), the Great Plains in Nebraska consisted of wooded parklands and grasslands, whereas spruce (Picea) forests, parklands, and steppe grasslands dominated east of the Missouri River. I analyzed bulk carbon (δ¹³C), oxygen (δ¹⁸O), and strontium (⁸⁷Sr/⁸⁶Sr) isotopes from tooth enamel in isolated mammoth (Mammuthus) molars to compare diet and movement patterns in two late Pleistocene assemblages: one in Nebraska (n = 24), and one in western Iowa and Missouri (n = 17). Furthermore, I identified a morphometric subset of molars as …

Online Prediction Of Streaming Data, Aleena Chanda

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

We present two new approaches for point prediction with streaming data based on a) the Count-Min sketch and b) Gaussian Process Priors with random bias. The methods are intended for the most general case where no true model can be usefully formulated for the data stream. In statistical contexts, this is often called the M open problem class. For the Count Min Sketch method we show that the predicted distribution function ^F converges to F under the assumption that the data consists of i.i.d samples from a fixed distribution function F. To implement the Gaussian Process Prior methods, we used …

Resilience Of Interdependent Transportation And Healthcare Systems: A Simulation-Driven Framework Incorporating Social Equity And Facility Optimization, S. Yasaman Ahmadi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The overarching goal of this dissertation is to develop an integrated, simulation-driven framework to assess and enhance the resilience of interdependent transportation and healthcare systems before, during, and after natural hazards. By examining how spatial disruptions, social vulnerability, operational constraints, and infrastructure interdependencies affect access to and functionality of hospitals, this research offers a multifaceted evaluation of system performance under stress. This dissertation examines the impact of natural hazards, particularly floods, on the interdependent transportation and healthcare systems. The proposed approach combines and advances GIS-based network analysis, graph-theoretical modeling, and discrete-event simulation to evaluate system performance under different scenarios. The …

Predicting Music Origin With Deep Learning, Fruzsina Ladanyi

Electronic Theses, Projects, and Dissertations

This project explores the usage of a late fusion deep learning architecture to predict the geographic origin of music. Mel-Frequency Cepstral Coefficients (MFCCs) and the language of the music sample are used as features. MFCCs were extracted from audio files to capture sound features. The language was identified using OpenAI’s Whisper model to provide additional context. A late fusion neural network architecture combining Long Short-Term Memory (LSTM) layers for sequential MFCC input and dense layers for non-sequential language features were employed to support both classification and regression tasks. The classification model achieved an accuracy of 33.03% across 56 countries or …

Fact-Checker: A Web Application For Leveraging Large Language Models For Fact-Checking Youtube Videos, Andrew R. Craig

Electronic Theses, Projects, and Dissertations

Fact-Checker is a web application that allows users to fact-check YouTube videos. It feeds YouTube’s closed captioning transcript to a large language model (LLM) to extract claims. It then uses multiple LLMs, such as Gemini, Llama, and Claude, to verify these claims. The modular design makes it easy to change to a different LLM or model if needed. The application is built using Python for access to Application Programming Interfaces (APIs) and Streamlit as the front-end framework. The utilization of Docker and Dockerfiles enables easy distribution and deployment. It enables the application to be deployed on almost any hardware platform …

Guided Modes Through The Dual Perspectives Of Geometrical Optics And Wave Equation, Mason Spears

Mathematics and Statistics Undergraduate Departmental Honors Theses

In an optical waveguide, only finitely many electric field intensity profiles travel without loss, and these are aptly named guided modes. This paper uses fundamental concepts from the two paradigms of interpreting light, geometrical and physical optics, to derive guided modes axiomatically. First, basic principles of geometrical optics are demonstrated in a slab waveguide to explain characteristics of optical waveguides such as total internal reflection and numerical aperture. Dispersion curves are assembled from the same principles and used to show that only finitely many guided modes exist for a given waveguide. Next, Maxwell’s equations are used to derive the famous …

What's The Value Of A State? Accounting For The Ecosystem Services And Natural Capital Of Oregon, Kathryn Liebrecht

University Honors Theses

Ecosystem services are vital to human welfare and society, but often are overlooked when it comes to planning and setting policy. By neglecting to incorporate the benefits received from natural resources, policy makers may unknowingly be making decisions that harm the long term sustainability and use of these services. To highlight the importance of Oregon's ecosystems, the present study aims to provide a full accounting of the ecosystem services and natural capital of the state. Using data from the Institute for Natural Resources' updated Oregon statewide habitat map, Oregon's ecosystems were matched to biome categories from the Ecosystem Services Valuation …

An Exploration Of The Structure Of The Linear Algebraic Model Of Color, Isaac Martin

University Honors Theses

This thesis surveys the mathematical grounding of linear algebraic models of color. It aims to build from the ground up the framework by which additive color is broadly understood in the digital age. Primarily building on the work of Jozef Cohen, Eric Dubois, David H. Krantz, and Günter Wyszecki, it aims to chart the construction of a model of color that underpins most modern understandings of color. While the construction is certainly established in colorimetric circles, the construction is, in the thesis author's opinion, either obtuse or non-rigorous. Ideally, this thesis serves to make the construction accessible to an audience …

A Focus On Student Education: Determining Student Attitudes Towards Transparent Autograding And Developing Artificially Intelligent Tools To Help Students Succeed, Andra Rice

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis is composed of two parts both relating to helping students succeed. First, the focus is on determining how we can help students feel more comfortable using an AI tool that can provide them immediate feedback. Second, machine learning algorithms are explored in relation to tracking student tasks to encourage healthy study habits.

The development of effective autograders is key for scaling assessment and feedback. While AI based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students. Our research tested the effect …

Short: Breaking The Charge: Exploiting State Manipulation In Ev Charging, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. However, the security of electric vehicle charging has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection for the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states of EV chargers …

Gcot: Chain-Of-Thought Prompt Learning For Graphs, Xingtong Yu, Chang Zhou, Zhongwei Kuai, Xinming Zhang, Yuan Fang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we design CoT prompting for graphs to guide graph models to learn step by step? On one hand, unlike natural languages, graphs are non-linear and characterized by complex topological structures. On the other hand, many graphs lack textual data, making it difficult to formulate language-based CoT prompting. %Therefore we cannot directly adopt the CoT prompting methods used in the language domain. In this work, we propose the first CoT prompt learning framework …

Ai-Assisted Triage And Decision Support Of Head And Neck Cancer Screening And Diagnosis In Low-Resourced Settings, Min Hun Lee, Sean Shao Wei Lam, Shaun Xin Hong Liew, Michael Dorosan, Nicholas Graves, Jonas Karlström, Hiang Khoon Tan, Walter Tsong Lee

Research Collection School Of Computing and Information Systems

The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites.In this project, our international and interdisciplinary team will collaborate with …

Faithfulrag: Fact-Level Conflict Modeling For Context-Faithful Retrieval-Augmented Generation, Qinggang Zhang, Zhishang Xiang, Yilin Xiao, Le Wang, Junhui Li, Xinrun Wang, Jinsong Su

Research Collection School Of Computing and Information Systems

Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM’s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model’s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model’s parametric knowledge, which undermines the model’s internal knowledge structure …