Digital Commons Network^™

From Morphology To Machine Learning And Genomics: Understanding Phenotypic Variation In Wild Ducks, Sara Gonzalez

Open Access Theses & Dissertations

Understanding the genetic underpinning and distribution of phenotypic variation within and between divergent groups is core towards shedding light into how populations diverge and adapt, as well as how hybridization breaks or builds on these scenarios; and thus, central to evolutionary biology. In wild organisms, however, quantifying and linking phenotypic traits to underlying genetic processes, like mutation, gene expression, epigenetics and allele interactions, remains challenging. This difficulty arises from the complex interplay between morphology, environment, and gene regulation, as well as the logistical barriers of collecting and standardizing large-scale data across individuals and populations. As a result, researchers are increasingly …

Software Developer Job Satisfaction: Interpretable Machine Learning Insights From The Stack Overflow Developer Survey, Reagan E. Hoopes

All Graduate Theses and Dissertations, Fall 2023 to Present

Many researchers have investigated the factors influencing software developer workplace outcomes, such as job satisfaction, due to the central role of the tech industry in the global economy and the specialized expertise of software developers. Past research has often relied on small surveys and traditional analysis methods, with limited use of modern machine learning techniques. This study introduces an efficient and scalable approach to analyzing software developer job satisfaction using interpretable machine learning.

We use data from the 2019 and 2024 Stack Overflow Developer Surveys, an annual survey of software developers worldwide that encompasses a broad range of topics, including …

Data-Driven Streamflow Forecasting In The Upper Colorado River Basin Using Spatio-Temporal Graph Networks, Akhila Akkala

All Graduate Theses and Dissertations, Fall 2023 to Present

Forecasting river flow is essential for managing water supplies, reducing flood risk, and supporting healthy ecosystems. In the Upper Colorado River Basin, much of the yearly water comes from melting snow. However, many traditional models struggle to capture how snowpack and river flow interact, especially across such a large and complex region.

This study uses a modern machine learning approach called a Spatio-Temporal Graph Neural Network (STGNN) to improve streamflow prediction. The model uses Snow Water Equivalent (SWE)—a measure of how much water is stored in the snowpack—along with river flow data. By treating each river gauge as part of …

Artificial Intelligence For Reliability: Predictive Health Maintenance And Geolocation In Gps-Denied Environments, Rafael Toche Pizano

Graduate Theses and Dissertations

In this dissertation, we explore the potential of machine learning and deep learning techniques to enhance the performance and robustness of applications across two major domains. By addressing the challenges within these fields, we demonstrate that we can leverage learning algorithms to obtain substantial improvements in accuracy and robustness. First, we tackle a problem in the field of predictive health maintenance. We propose a novel auto encoder and neural network based methodology to predict failure times in complex aviation systems to learn to distinguish between normal and abnormal operational behavior, and use this information to inform the neural network to …

Privacy Protection In Cloud-Based Biometric Systems, Yatish Reddy Dubasi

Graduate Theses and Dissertations

The widespread adoption of server-based biometric authentication systems, often hosted in the cloud, has introduced significant privacy risks. While these systems offer convenience, they require storing sensitive biometric templates on remote servers, creating a high-value target for adversaries. Unlike passwords, compromised biometric data is immutable and cannot be reissued, leading to an irreversible loss of privacy. This threat is exacerbated by template inversion attacks, which can reconstruct a user's original biometric trait (e.g., a face image) from its stored feature vector. This dissertation addresses these critical privacy challenges by designing, implementing, and evaluating a suite of novel frameworks for privacy-preserving …

Towards Vision-Brain Understanding At Scales: From Classical To Quantum Machine Learning Approaches, Xuan-Bac Nguyen

Graduate Theses and Dissertations

In recent years, large-scale learning approaches such as unsupervised and self-supervised learning have revolutionized artificial intelligence. These methods enable machines to learn high-level representations without explicit human supervision, achieving remarkable success across vision, language, and multimodal tasks. However, such advances come at a cost—they rely on massive datasets, billions of parameters, and extensive computational resources. Despite these achievements, artificial systems still fall short of the remarkable learning efficiency of the human brain, which can infer, adapt, and generalize from limited experiences. This gap motivates a deeper exploration of how biological intelligence acquires knowledge and how these principles can inspire the …

Contextual Embedding Using Machine Learning For Cybersecurity: Access Control And Application, Thanh Bui

Graduate Theses and Dissertations

Access control is a well-established challenge in cybersecurity, with significant research focused on enhancing system autonomy and accuracy across various scenarios. Access control rules can be designed based on users’ roles, attributes, or relationships requesting access to specific resources. However, despite their benefits, these models still require human oversight. As systems expand and grow, it becomes increasingly complex for administrators to maintain precise access control rules, often necessitating extensive system updates or even a complete overhaul. This dissertation introduces a novel approach that leverages contextual embedding for user information to enable the system to autonomously authorize user requests for resources. …

When The Grid Goes Dark: A Digital Forensics Study Of Industrial Control System Cyberattacks, Katie Kettler

Graduate Theses and Dissertations

Industrial Control Systems (ICS) and Operational Technology (OT) maintain the grid, ensure water safety, and keep transportation running. Because they influence nearly every aspect of daily life, these systems have become prime targets for cyberattacks. The need for this research arises from the fact that when ICS and OT systems are compromised, the consequences go beyond data loss, and they can directly disrupt communities and endanger public safety. This thesis introduces digital forensics fundamentals and explains how investigations in ICS environments differ from those in traditional IT environments. This work then examines major attacks, including Stuxnet, the Ukrainian Grid Attacks …

Multi-Hop Hybrid Graph Neural Network, James Arthur

Open Access Theses & Dissertations

Graph-structured data appear across diverse domains, such as social networks, citation graphs, biological systems, and knowledge bases. Graph Neural Networks (GNNs) have emerged as a powerful framework for learning on such data, yet existing architectures face significant challenges. Graph Convolutional Networks (GCNs) suffer from over-smoothing as depth increases, Graph Attention Networks (GATs) introduce computational and statistical instabilities, and naïve multi-hop propagation inflates memory and computation while failing to adapt to topology. These limitations motivate the development of a new framework that is both expressive and scalable. This dissertation proposes the Multi-Hop Hybrid Graph Neural Network (MHHGNN), a novel architecture that …

Microgrid Assessment And Ml-Based Power System Faults Detection Leveraging Real-Time Co-Simulation, Diego Normando Gandara Mendez

Open Access Theses & Dissertations

The rapid growth of distributed energy resources (DERs) and the increasing reliance on data-driven decision making have reshaped the operational challenges of modern electric power systems. As microgrids become more prominent in distribution networks, utilities require methods that unify planning, control, and real-time situational awareness to ensure resilient operation under faulted or uncertain conditions. The goal of this MSEE thesis is to design and validate a latency-aware ML framework for rapid, reliable fault detection in distribution grids. To achieve the goal of the thesis, there are three specific objectives. Objective 1 evaluates optimized microgrid configurations under varying DER levels and …

Lamda: A Longitudinal Android Malware Dataset For Benchmarking Concept Drift Detection And Adaptation, Md Ahsanul Haque

Open Access Theses & Dissertations

Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem, adversarial development of new malware families, and the continuous emergence of both benign and malicious applications. Prior studies have shown that such concept drift—distributional shifts in benign and malicious samples—leads to significant degradation in detection performance over time. Despite the practical importance of this issue, existing datasets are often outdated and limited in temporal scope, diversity of malware families, and sample scale, making them insufficient for …

Phishibl: A Systematic Evaluation Of Instance-Based Learning Model For Predicting Phishing Susceptibility, Shova Kuikel

Open Access Theses & Dissertations

Despite enormous efforts to develop defenses against phishing attacks, humans still struggle to detect phishing emails given the constantly evolving attacker strategies. This thesis aims to test the predictive capabilities of a cognitive model that represents the individual susceptibility to phishing emails. While training programs aim to raise awareness, most remain outdated and ineffective against evolving attack strategies. Recent advances in Machine Learning, Artificial Intelligence, and Large Language Models (LLMs) offer new defenses, yet understanding human decision processes remains crucial, as effective systems must emulate how people evaluate unfamiliar emails based on prior experience. This research introduces a cognitive model …

Hubert-Based Models And Evaluation Strategies For Pragmatically-Faithful Speech To Speech Translation, Javier Vazquez

Open Access Theses & Dissertations

Pragmatic fidelity in speech-to-speech translation (S2ST) has largely been understudied, leading to communication tools inadequate to support non-superficial dialog. We aim to improve pragmatic faithfulness in English-Spanish translation through the development of machine learning models that are able to predict a corresponding pragmatic representation in the other language. To evaluate performance, we developed a pipeline that utilizes a recently-developed pragmatic similarity evaluation metric to compare models. Further, we developed models that exploit HuBERT features as these have been found suitable for various prosody and pragmatics related tasks. Our models outperformed human and state-of-the-art predictions, albeit the methodology being limited to …

Uncertainty Estimation For Graph-Based Learning In Digital Pathology, Saba Heidari Gheshlaghi, Nasim Yahyasoltani, Masoud Ganji

Computer Science Faculty Research and Publications

High-resolution digital scans of pathology slides, known as whole slide images (WSIs), have detailed spatial and contextual information for diagnosing cancer. However, the classification performance of WSIs by deep learning models is typically compromised by data with a different distribution, known as out-of-distribution (OOD), resulting in unreliable predictions. Therefore, having a reliable predictive uncertainty estimation is crucial for clinical adoption. This article comprehensively studies graph-based uncertainty estimation for WSI classification using two cutting-edge graph neural network (GNN) architectures: 1) graph attention networks (GAT); and 2) GraphSAGE. In this work, we introduce the first unified multihead GNN framework that leverages GraphSAGE …

Reverse (Bio)Engineering: A Machine Learning Approach To Optimize Baseball Pitcher Health And Performance, Robert C. Moore

All Dissertations

Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. Analysts routinely use ball flight data to identify the attributes of top performing pitchers, finding that the best pitchers throw with optimal combinations of release speed and spin to precise locations. However, for certain pitchers, the throwing motion required to produce optimal ball flight places exceedingly high biomechanical load on the elbow, and consequently injury rates continue to rise. This …

Digital Reflections: Evaluating Body Dissatisfaction In Xr Through Eye- And Body-Tracked Virtual Humans, Deyrel Diaz

All Dissertations

In an era where digital and physical realities increasingly intertwine, the perception of body image is undergoing a significant transformation. Traditional understandings of body dissatisfaction, long studied in relation to psychological distress and eating disorders, are now being reshaped by technologies such as Virtual Reality (VR), Augmented Reality (AR), and Artificially Intelligent (AI)- generated media. These technologies have introduced novel ways of experiencing and interacting with the human form, raising critical questions about their impact on self-perception and internalization of beauty standards.

As virtual representations become more prevalent in entertainment, social media, and interactive platforms, it is becoming more crucial …

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

All Dissertations

The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

A Study Of Configuration Management Database (Cmdb) Adoption In It Service Management (Itsm) Implementations Within Nj Community Colleges, Fredrick Dande

All-Inclusive List of Electronic Theses and Dissertations

This study examines the adoption of Configuration Management Databases (CMDBs) in IT Service Management (ITSM) implementations within New Jersey (NJ) community colleges. Despite the well-documented benefits of CMDBs—such as faster issue resolution, improved compliance, and greater visibility across IT infrastructures—implementation success rates remain low. As technology continues to enhance production capabilities and expand access to information, the need for centralized configuration visibility has become critical. A CMDB provides a single system of record for IT assets and services, helping organizations manage outages, assess changes, maintain compliance, and improve asset tracking. This research used an online survey to collect data from …

Contx: Scene Context Prediction Via Context Bank And Layout Perception, Jingxin Liang, Yangyang Xu, Haorui Song, Yuan Lu, Yuhui Deng, Yiyi Long, Yan Huang, Shengxin Liu, Jianbo Jiao, Shengfeng He

Research Collection School Of Computing and Information Systems

Scene context prediction, which seeks to infer unknown contextual information from isolated object properties, currently faces limitations due to predominant reliance on pixel-wise supervision that overlooks real-world context priors. To address this, we present ContX, a context-prior-driven, coarse-to-fine model. ContX distinctively integrates explicit linguistic-contextual knowledge in two key ways. First, it proposes a linguistic guided context bank, leveraging linguistic-statistical contextual data to guide the rationality of segmentation shapes and foster meaningful inter-class contextual interactions. Second, ContX augments contextual comprehension by correlating layouts with linguistic descriptions, enhancing layout perception through a multi-modal strategy. Comprehensive experiments demonstrate ContX's superiority and versatility, outperforming …

Backdoorllm: A Comprehensive Benchmark For Backdoor Attacks And Defenses On Large Language Models, Yige Li, Hanxun Huang, Yunhan Zhao, Xingjun Ma, Jun Sun

Research Collection School Of Computing and Information Systems

Generative large language models (LLMs) have achieved state-of-the-art results on a wide range of tasks, yet they remain susceptible to backdoor attacks: carefully crafted triggers in the input can manipulate the model to produce adversaryspecified outputs. While prior research has predominantly focused on backdoor risks in vision and classification settings, the vulnerability of LLMs in open-ended text generation remains underexplored. To fill this gap, we introduce BackdoorLLM1 , the first comprehensive benchmark for systematically evaluating backdoor threats in text-generation LLMs. BackdoorLLM provides: (i) a unified repository of benchmarks with a standardized training and evaluation pipeline; (ii) a diverse suite of …

Griffin: Effective Token Alignment For Faster Speculative Decoding, Shijing Hu, Jingyang Li, Xingyu Xie, Zhihui Lu, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates inference in large language models (LLMs) by generating multiple draft tokens simultaneously. However, existing methods often struggle with token misalignment between the training and decoding phases, limiting their performance. To address this, we propose GRIFFIN, a novel framework that incorporates a token-alignable training strategy and a token-alignable draft model to mitigate misalignment. The training strategy employs a loss masking mechanism to exclude highly misaligned tokens during training, preventing them from negatively impacting the draft model’s optimization. The token-alignable draft model introduces input tokens to correct inconsistencies in generated features. Experiments on LLaMA, Vicuna, Qwen and Mixtral models …

Large Language Models As End-To-End Combinatorial Optimization Solvers, Xia Jiang, Yaoxin Wu, Minshuo Li, Zhiguang Cao, Yingqian Zhang

Research Collection School Of Computing and Information Systems

Combinatorial optimization (CO) problems, central to decision-making scenarios like logistics and manufacturing, are traditionally solved using problem-specific algorithms requiring significant domain expertise. While large language models (LLMs) have shown promise in automating CO problem solving, existing approaches rely on intermediate steps such as code generation or solver invocation, limiting their generality and accessibility. This paper introduces a novel framework that empowers LLMs to serve as end-to-end CO solvers by directly mapping natural language problem descriptions to solutions. We propose a two-stage training strategy: supervised fine-tuning (SFT) imparts LLMs with solution generation patterns from domain-specific solvers, while a feasibility-and-optimality-aware reinforcement learning …

Agentguard: An Active Threat Discovery System For Package Confusion Using Multi-Agent Collaboration, Wei Ma, Yu Li, Zhi Chen, Ye Liu, Lingxiao Jiang, Qiang Hu, Junyi Tao

Research Collection School Of Computing and Information Systems

The proliferation of open-source software (OSS) has made software supply chains prime targets for attacks like Package Confusion, where adversaries publish malicious packages with names deceptively similar to legitimate ones. Existing detection methods often rely on simple lexical similarity or passive analysis of known package pairs, struggle with high false positive rates (FPR), fail to proactively identify emerging threats, and are vulnerable to adversarial evasion. To overcome these limitations, we introduce AgentGuard, a novel framework for proactive, single-input package confusion detection. AgentGuard employs a multi-agent architecture that autonomously discovers potential confusion targets using fine-tuned word embedding model to hybird semantic …

Accuracy-Enabling Differential Privacy-Preserving Truth Discovery, Man Zhang, Xinghua Li, Yinbin Miao, Bin Luo, Siqi Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Perturbation-based privacy-preserving truth discovery requires the Service Provider (SP) to calculate the truthful aggregation result from perturbed data of the Data Sources (DSs), which inevitably damages the aggregation accuracy due to perturbation noise added in the data. Thus, the existing works attempt to relieve the perturbation errors by reducing noise amounts or adjusting aggregation weights of DSs. However, the former sacrifices DSs’ privacy preservation and the latter has the limited accuracy recovery performance. Aiming at it, we propose an accuracy-enabling differential privacy-preserving truth discovery consisting of an independence-guaranteed data perturbation module and a progressive-private noise elimination module. Specifically, in the …

Fl-Cdf: Collaborative Defense Framework For Backdoor Mitigation In Federated Learning, Haiyan Zhang, Xinghua Li, Yinbin Miao, Shunjie Yuan, Mengyao Zhu, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed nature. Existing unilateral defense mechanisms often fail against persistent attack strategies, primarily due to their limited perspectives. To address the challenge of model misclassification on the server side caused by overlooked model similarity drift, and gradient misjudgment on the client side caused by semantic learning imbalances across classes, this paper proposes a collaborative defense framework for federated learning, termed FL-CDF. FL-CDF establishes an end-to-end defense through a bidirectional client-server collaboration mechanism. Specifically: (1) On the client side, an adversarial perturbation-based malicious neuron detection module is introduced. This module …

Navigating Ai-Nature Frictions: Autonomous Vehicle Testing And Nature-Based Constraints, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

In cities, the application of Artificial Intelligence (AI) is being directed towards transforming different aspects of urban life. These applications take material form in urban spaces, with autonomous vehicles (AVs) providing a prominent example. AI systems rely on large volumes of data on their surroundings to refine the algorithms and enhance the accuracy of prediction for operational efficiency and safety. However, such algorithmic learning and execution can present challenges when dealing with the unpredictable, complex, and dynamic aspects of urban spaces. Nature is a paradigmatic example of such unpredictability, because natural phenomena usually defy consistent patterns and precise data-based modelling. …

Iostom: Offline Imitation Learning From Observations Via State Transition Occupancy Matching, Quang Anh Pham, Brahmanage Janaka Chathuranga Thilakarathna, Tien Mai, Akshat Kumar

Research Collection School Of Computing and Information Systems

Offline Learning from Observations (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability—especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, …

No Experts, No Problem: Avoidance Learning From Bad Demonstrations, Minh Huy Hoang, Tien Mai, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

This paper addresses the problem of learning avoidance behavior within the context of offline imitation learning. In contrast to conventional methodologies that prioritize the replication of expert or near-expert demonstrations, our work investigates a setting where expert (or desirable) data is absent, and the objective is to learn to eschew undesirable actions by leveraging demonstrations of such behavior (i.e., learning from negative examples).To address this challenge, we propose a novel training objective grounded in the maximum entropy principle. We further characterize the fundamental properties of this objective function, reformulating the learning process as a cooperative inverse Q-learning task. Moreover, we …

Design Principles For Customer‑Engaging Digital Service Systems: An Action Research Study, Keng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Precision-Weighted Federated Learning, Jonatan Reyes, Lisa Di Jorio, Cecile Low-Kam, Marta Kersten-Oertel

Computer Science Faculty Publications

Federated learning (FL) using the federated averaging (FedAvg) algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that FedAvg underestimates the full extent of heterogeneity of data when the aggregation is performed. We propose Precision-Weighted Federated Learning (PW) a novel algorithm that takes into account the second raw moment (uncentered variance) of the stochastic gradient when computing the weighted average of the parameters of independent models trained in a FL setting. With PW, we address the communication and statistical challenges …