Digital Commons Network^™

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 …

Demystifying Memorization In Llm-Based Program Repair Via A General Hypothesis Testing Framework, Jiaolong Kong, Xiaofei Xie, Shangqing Liu

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have achieved remarkable success in various applications, particularly in code-related tasks such as code generation and program repair, setting new performance benchmarks. However, the extensive use of large training corpora raises concerns about whether these achievements stem from genuine understanding or mere memorization of training data—a question often overlooked in current research. This paper aims to study the memorization issue within LLM-based program repair by investigating whether the correct patches generated by LLMs are the result of memorization. The key challenge lies in the absence of ground truth for confirming memorization, leading to various ad-hoc methods …

Advancing Academic Advising With Knowledge Graphs: Integrating Machine Learning And Llms For Personalized Course Planning, Sara Alshamsi

Theses

Academic advising plays a critical role in helping students make informed decisions, improve academic performance, and successfully navigate their university journey. However, with increasing university enrollment, traditional advising methods often struggle to scale, leading to student frustration and overburdened advisors. Additionally, designing course offerings that match student demand is a complex and error-prone process involving multiple stakeholders. To address these challenges, this thesis proposes an automated, data-driven system for generating personalized academic plans for students. The primary aim of this thesis is to develop a system that reduces students’ dependency on advisors while simultaneously providing accurate estimates of course demand …

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 …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo

Research Collection School Of Computing and Information Systems

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading …

Specialization Or Diversification? Creators’ Strategies On User-Generated Content Platforms, Ziwei Ye

Theses and Dissertations in Business Administration

Recent advancements in digital platforms have reshaped content creation and distribution. User-generated content (UGC), created and shared by internet users, is transforming entertainment, communication, and information sharing. The rise of UGC has fueled the growth of the "creator economy"—an ecosystem of creators, users, and advertisers facilitated by platforms such as YouTube and TikTok. While prior research has primarily explored how UGC platforms incentivize content quantity and quality, this study advances the literature by examining how creators' content strategies influence consumer attention and how platform mechanisms shape this relationship, offering new insights into the interplay between creator behavior and platform design. …

Property Testing Ai: An Efficient Frontier, Paul Sopher Lintilhac

Dartmouth College Ph.D Dissertations

In this dissertation, we take a step towards addressing the major problem of a lack of standardized and rigorous approaches to testing and evaluation of AI systems. Taking inspiration from both the fields of Property Testing and Property Based Testing (for programs), we develop a novel taxonomy of partially overlapping classes of properties of AI systems, including simple properties, compound properties, higher order properties, data relation properties, and architecture-utility properties. We argue that this taxonomy categorizes a diverse set of AI traits -- including accuracy, fairness, robustness, monotonicity, point-wise and global privacy properties, sensitivity, and more -- according to the …

Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection Against Multipath Routing, Ziming Zhao, Zhaoxuan Li, Xiaofei Xie, Zhipeng Liu, Tingting Li, Jiongchi Yu, Fan Zhang, Binbin Chen

Research Collection School Of Computing and Information Systems

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multipath routing phenomenon that …

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 …

A Proposed Mathematical Framework For Fuzzy It Service Management (F-Itsm) And Neutrosophic It Service Management (N-Itsm), Takaaki Fujita

Neutrosophic Systems with Applications

Fuzzy sets, rough sets, hyperrough sets, intuitionistic fuzzy sets, neutrosophic sets, plithogenic sets , and other frameworks for handling uncertainty are under active research every day. These concepts can model a wide range of real-world phenomena and are frequently investigated to facilitate more efficient decision-making. IT Service Management is a systematic approach to designing, delivering, managing, and improving IT services in alignment with organizational objectives. In this paper, we explore the Mathematical Frameworks for Fuzzy IT Service Management (F-ITSM) and Neutrosophic IT Service Management (N-ITSM), which combine these uncertainty-based ideas with IT Service Management practices.

A Neutrosophic And Q-Rung Orthopair Fuzzy Sets Approach For Desertification Susceptibility Mapping: A Case Study In Matrouh, Egypt, Nabil M. Abdelaziz, Khalid A. Eldrandaly, Amira M. Fawzy, Gehan A. Fouad, Safa Al-Saeed

Neutrosophic Systems with Applications

This study introduces an innovative approach to desertification susceptibility mapping by integrating q-rung orthopair fuzzy sets (Q-ROFS) with a neutrosophic environment. Conducted in Matrouh, Egypt, the research quantifies desertification risk through advanced modeling techniques that address uncertainty and non-linearity in environmental data. The Q-ROFS framework enhances risk prediction by capturing complex relationships among desertification indicators. Neutrosophic logic, meanwhile, effectively addresses imprecision and ambiguity. The resulting susceptibility map clearly distinguishes between vulnerable and non-vulnerable regions, offering valuable guidance for policymakers and planners. The analysis revealed that approximately 79.98% of the study area falls under moderate susceptibility, 14.27% under high susceptibility, and …

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 …

A Critical Evaluation Of The Criticisms Against Neutrosophic Statistical Methods, Muhammad Aslam, Abdulrahman Alaita, Florentin Smarandache

Neutrosophic Systems with Applications

Neutrosophic statistical analysis has gained attention for incorporating the degree of indeterminacy when analyzing imprecise and interval data under uncertainty–-an aspect often overlooked by classical statistics, fuzzy statistical analysis, and interval statistics. Recently, critical discussions have emerged regarding the use and applications of neutrosophic statistics, with some questioning its usefulness and validity. In this paper, we present a critical assessment of the existing literature, focusing on areas where misunderstandings and misinterpretations of neutrosophic statistical methods have occurred. We also examine flawed comparisons made between the results of neutrosophic statistics and interval statistics. Furthermore, substantial issues have been identified in the …

Keep The Balance: A Parameter-Efficient Symmetrical Framework For Rgb+X Semantic Segmentation, Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu

Research Collection School Of Computing and Information Systems

Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations …

Advancing Fake News Detection With Graph Neural Network And Deep Learning, Haji Gul, Feras Al-Obeidat, Muhammad Wasim, Adnan Amin, Fernando Moreira

All Works

In the modern era of digital technology, the rapid distribution of news via social media platforms substantially contributes to the propagation of false information, presenting challenges in upholding the accuracy and reliability of information. This study presents an updated approach that utilizes graph neural networks (GNNs) alongside with advanced deep learning techniques to improve the identification of false information. In contrast to traditional approaches that primarily rely on analyzing text and assessing the credibility of sources, our methodology utilizes the structural information of news propagation networks. This allows for a detailed comprehension of the interconnections and patterns that are indicative …

Adapting Teaching And Learning With Existing Generative Ai By Higher Education Students: Comparative Study Of Zayed University And King Abdulaziz University, Dina Tbaishat, Ghada Amoudi, Maha Elfadel

All Works

This study examines the role of higher education students’ perceptions in adapting Generative AI (GenAI) tools for teaching and learning, with a particular focus on the factors that influence student satisfaction and engagement. A comparative approach is adopted, exploring student experiences at Zayed University (ZU) in the UAE and King Abdulaziz University (KAU) in Saudi Arabia. The principal variables of interest, including Expected Benefits (EB), University Support (US), Ethical Awareness (EA), and Technology Self-Efficacy (TSE), are examined, with particular attention to their direct and mediated influences through Behavioral Intention (BI) on student satisfaction (SS). Data were collected through surveys and …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Enriching Vision Representation By Deep Neural Networks And Self-Supervised Learning, Yucong Shen

Dissertations

Nowadays, more and more interesting computer vision tasks are tackled by deep learning approaches. However, the increasing model complexity imposes significant computational and storage costs. To address this challenge, this dissertation explores efficient deep learning techniques, proposing morphological layer, an efficient feature extraction layer. It achieves competitive image classification accuracy with significantly decreased model parameters. Another attempt at efficient deep learning is a proposed channel pruning approach that compresses deep neural networks by identifying and removing redundant channels using optimal transport theory. This approach achieves significant reductions in model size and computational cost while maintaining or even improving performance across …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

Plm-Dbps: Enhancing Plant Dna-Binding Protein Prediction By Integrating Sequence-Based And Structure-Aware Protein Language Models, Suresh Pokharel, Kepha Barasa, Pawel Pratyush, Dukka B. Kc

Michigan Tech Publications

DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through handcrafted features or sequence-based protein language models (PLMs), overlooking structural cues critical to protein function. In addition, most existing tools are trained for general DBP predictions, which are often not accurate for plant-specific DBPs due to the unique structural and functional properties of plant proteins. Our work introduces PLM-DBPs, a deep learning framework that integrates both sequence-based and structure-aware representations to enhance DBP prediction in plants. We evaluated several …

Photojournalism In The Age Of Deepfakes: The Role Of Media Literacy And Ethical Standards In Restoring Trust In Visual Reporting, Ionnnis Kontos, Katerina Chryssanthopoulou, Ioannis Galanopoulos-Papavasileiou

All Works

This article explores the impact of deepfake technology on photojournalism, highlighting its role in undermining trust in visual media. As deepfakes allow for the creation of highly realistic manipulated content, they pose significant challenges regarding the authenticity of journalistic imagery and erode the authority of visual truthfulness. The widespread use of deepfakes has led to a decline in public confidence in the credibility of news, raising concerns about the future of photojournalism in an era of digital deception. As a solution to regaining viewers’ trust, this article suggests a twofold approach: First, it emphasizes the importance of media literacy in …

Artificial Intelligence Use In Medical Education: Best Practices And Future Directions, Rasheed A. M. Thompson, Yash B. Shah, Francisco Aguirre, Courtney Stewart, Costas D. Lallas, Mihir S. Shah

Department of Urology Faculty Papers

PURPOSEOF REVIEW: This review examines the various ways artificial intelligence (AI) has been utilized in medical education (MedEd)and presents ideas that will ethically and effectively leverage AI in enhancing the learning experience of medical trainees.

RECENT FINDINGS: AI has improved accessibility to learning material in a manner that engages the wider population. It has utility as a reference tool and can assist academic writing by generating outlines, summaries and identifying relevant reference articles. As AI is increasingly integrated into MedEd and practice, its regulation should become a priority to prevent drawbacks to the education of trainees. By involving physicians in …

Some Studies On Information Set Decoding Algorithms And Universal Hash Functions, Sreyosi Bhattacharyya

Doctoral Theses

This thesis presents some studies on Information Set Decoding algorithms and Universal Hash Functions. In the context of Information Set Decoding (ISD) the thesis studies time/memory trade-off of ISD algorithms and in the context of universal hash functions, the thesis studies design and efficient implementations of polynomial hash functions defined over prime order fields. A cornerstone of ISD algorithms is the algorithm proposed by Stern and it introduced the meet-in-the-middle collision search approach to ISD algorithms. Though this algorithm is more efficient in terms of asymptotic time complex- ity than the preceding algorithms proposed by Prange, Lee and Brickell and …