Digital Commons Network^™

Leveraging Large Language Models For Career Mobility Analysis: A Study Of Gender, Race, And Job Change Using Us Online Resume Profiles, Palakorn Achananuparp, Ye Xu, Yao Lu, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges – such as missing demographic attributes, missing wage data, and noisy occupation labels – through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known …

Dual-Lora And Quality-Enhanced Pseudo Replay For Multimodal Continual Food Learning, Xinlan Wu, Bin Zhu, Feng Han, Pengkun Jiao, Jingling Chen

Research Collection School Of Computing and Information Systems

Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LMMs) in food analysis suffer from catastrophic forgetting when learning new tasks, requiring costly retraining from scratch. To address this, we propose a novel continual learning framework for multimodal food learning, integrating a Dual-LoRA architecture with Quality-Enhanced Pseudo Replay. We introduce two complementary low-rank adapters for each task: a specialized LoRA that learns task-specific knowledge with orthogonal constraints to previous tasks’ subspaces, and a cooperative LoRA that consolidates shared knowledge across tasks via pseudo replay. To improve the …

Design Principles For Customer-Engaging Digital Service Systems: An Action Research Study, Keng Leng 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.

Towards Secure Program Partitioning For Smart Contracts With Llm’S In-Context Learning, Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PARTITIONGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into critical (privileged) and normal codebases, guided by a few annotated sensitive data variables. We evaluated PARTITIONGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PARTITIONGPT successfully generates …

Editorial: Special Section On Challenges And Opportunities In Retrieval-Augmented Generation For Llms: Techniques, Trends, And Applications, Philip S. Yu, Haofen Wang, Feida Zhu

Research Collection School Of Computing and Information Systems

Retrieval-Augmented Generation (RAG) represents a transformative advancement for Large Language Models (LLMs) by integrating external knowledge to substantially improve accuracy and mitigate hallucinations. As a pivotal technology in the contemporary generative Artificial Intelligence (AI) landscape, RAG addresses fundamental challenges in knowledge-intensive tasks. This special issue serves as a dedicated platform to showcase these cutting-edge advancements. It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG.

Qualitative Study For Llm-Assisted Design Study Process: Strategies, Challenges, And Roles, Shaolun Ruan, Rui Sheng, Xiaolin Wen, Jiachen Wang, Tianyi Zhang, Yong Wang, Tim Dwyer, Jiannan Li

Research Collection School Of Computing and Information Systems

Design studies aim to develop visualization solutions for real-world problems across various application domains. Recently, the emergence of large language models (LLMs) has introduced new opportunities to enhance the design study process, providing capabilities such as creative problem-solving, data handling, and insightful analysis. However, despite their growing popularity, there remains a lack of systematic understanding of how LLMs can effectively assist researchers in visualization-specific design studies. In this paper, we conducted a rnulti-stage qualitative study to fill this gap, which involved 30 design study researchers from diverse backgrounds and expertise levels. Through in-depth interviews and carefully-designed questionnaires, we investigated strategies …

Artem: Enhancing Large Language Model Agents With Spatial-Temporal Episodic Memory, Cassandra Hui Ming Tan, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Current large language models (LLMs) exhibit significant deficiencies in episodic memory tasks including encoding, storing, and retrieving specific information from temporally dependent events over a long period of time. Recent approaches to handle memory tasks in LLMs, such as in-context learning, retrieval-augmented generation (RAG), and fine-tuning, may resolve the long-term retention issues, but are still inadequate to handle tasks requiring chronological awareness of the stored information. We introduce Agentic Retrieval with Temporal-Episodic Memory (ARTEM), a hybrid LLM-based agent architecture integrating LLMs with a self-organizing neural network named Spatial-Temporal Episodic Memory (STEM), designed to handle episodic memory tasks. Our approach employs …

Dystop: Dynamic Staleness Control And Topology Construction For Asynchronous Decentralized Federated Learning, Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao

Research Collection School Of Computing and Information Systems

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under heterogeneous and dynamic edge environments. Although a few recent asynchronous DFL (ADFL) mechanisms have been proposed to address these issues, they typically yield stale model aggregation and frequent model transmission, leading to degraded training performance …

Llamoco: Instruction Tuning Of Large Language Models For Optimization Code Generation, Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo, Jiacheng Chen, Yining Ma, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, combining the strength of large language models (LLMs) and Evolutionary Computation (EC) has shown promising results for addressing optimization problems. It typically involves either iterative next-step solution seeking or directly prompting LLMs to generate critical optimization codes. However, these methods often suffer from low computational efficiency, high sensitivity to prompt design, and a lack of domain-specific knowledge. We introduce LLaMoCo, the first instruction-tuning framework designed to adapt LLMs for solving optimization problems in a code-to-code manner. LLaMoCo features a comprehensive instruction set that includes code-style problem descriptions as input prompts and robust optimization codes from expert EC optimizers as …

Entropic Foundation Of Finance And Physics: Securities Price Dynamics And Quantum Theory, Mohammad Abedi

Electronic Theses & Dissertations (2024 - present)

In many scientific and financial contexts, we must reason and make predictions under conditions of incomplete information. This dissertation develops Entropic Dynamics (ED) as a unified framework for deriving dynamical laws directly from principles of inference. Within this approach, probability distributions represent states of knowledge, and their evolution is determined through entropy maximization subject to relevant constraints. This leads to a novel concept of entropic time and a formulation of dynamics as an inferential process. In this talk, I will present how ED provides a common foundation across multiple domains. In physics, quantum dynamics for particles and scalar fields in …

Artificial Intelligence, Fundamental Motives, And Evolutionary Mismatch, Amy J. Lim, Jose. C. Yong, Edison Sora Tan

Research Collection School of Social Sciences

In recent years, the intersection of artificial intelligence (AI) and psychology has garnered unprecedented attention, particularly following the advent of generative AI tools in 2022. These tools, capable of producing human-like text, images, and even deepening our understanding of cognitive processes, have not only captured the public imagination but also sparked new concerns and debates within the psychological community. While AI has been a subject of research for decades, the emergence of its generative capabilities has truly thrust AI into the spotlight. This article explores how these advancements are reshaping our understanding of human cognition and behavior, as well as …

Challenges And Best Practices For The Development Of Coastal Hazard Decision Support Tools, Noah Hallisey

Open Access Dissertations

There is growing interest in developing Decision Support Tools (DSTs) supporting emergency managers and other decision-makers during coastal storm events. Yet many tools fail to inform decision-making due to challenges associated with their development and transition into operational tools for real-time decision-making. This dissertation investigates these challenges and offers best practices for their development and implementation for real-world decision-making through a literature review, interviews with subject matter experts in the United States, and a real-world case study.

Manuscript 1 reviews existing published literature to understand whether and how tool developers are incorporating emergency managers in the development of DSTs and …

Synthesis And Characterization Of Succinate Dehydrogenase Inhibitors H2/Z14 And C6/Z96 In Order To Combat Small Cell Lung Cancer, Lauren M. Graves

Honors Undergraduate Theses

The use of ubiquinone inhibitors to combat cancer is a recent development in medicinal science and procedures to efficiently synthesize these drugs remain limited. C6/Z96 and H2/Z14, two succinate dehydrogenase inhibitors, have been previously tested in vitro against NSCLC with positive results. Their similarity to ubiquinone is what makes them a strong candidate for a succinate dehydrogenase inhibitor at the ubiquinone binding site. The heterocyclic scaffolds and substituents are proposed to bind strongly through a combination of electronics, hydrogen-bonding, and fit, allowing them to bind well and prove to be more favorable than ubiquinone when competing for the Q-site. By …

Advances In Active And Passive Integrated Photonic Devices On Thin-Film Lithium Niobate, Pooja S. Kulkarni

Graduate Studies Theses and Dissertations 2026

Thin-film lithium niobate (TFLN) has emerged as a powerful platform for integrated photonics, combining the exceptional electro-optic, nonlinear, and optical properties of bulk lithium niobate with the scalability and compactness of planar nanophotonic technologies. Building on these principles, advanced device architectures such as adiabatic dichroic filters have demonstrated exceptional spectral performance spanning over two octaves of bandwidth on the TFLN platform. Beyond reciprocal devices, it also offers promising pathways toward integrated nonreciprocal components. By leveraging broadband filtering structures and advanced nonlinear photonic design strategies, compact and monolithic optical isolators can be envisioned without relying on traditional magneto-optic materials. In the …

Student Familiarity With The Periodic Table Of The Elements: Results From Cued-Recall And Eye-Tracking Assessments On Memory, Victor A. Okuo

Theses and Dissertations--Chemistry

Learning element symbol–name associations and the spatial organization of elements on the periodic table is a foundational step in learning chemistry, supporting later understanding of chemical formulas, equations, bonding, and stoichiometry. Although students often rely on memorization strategies to learn periodic table content, this task is challenging due to the large number of elements and the apparent ambiguity in matching some element symbols to their names.

This study explores students’ recall of element names when given element symbols as cues and their knowledge of element locations on the periodic table when given element names as cues. The study also examines …

The Effects Of Problem-Based Learning On Mathematical Creativity And Self-Efficacy Of High School Students, Margaret Remus

Graduate Research Theses & Dissertations

Mathematical creativity is recognized as an important component of mathematics education; however, there is limited understanding of how instructional approaches can effectively support this skill among high school students, particularly through problem-based learning (PBL). This study examined the effects of problem-based learning on mathematical creativity among high school students.

This study employed a mixed-methods approach using an alternating treatment design with two groups of participants. Group 1 received the PBL intervention followed by a control condition, while Group 2 received the control condition followed by the PBL intervention. Quantitative data were collected through a mathematical creativity test measuring flexibility, fluency, …

Improving The Quality Of Physics Practice, Wyatt W. Thompson

All Graduate Theses, Dissertations, and Other Capstone Projects

This case study started with personal observations and experiences. As a lab teaching assistant, I observed on many occasions that students do not like their current homework platforms, often venting to me about their frustrations during lab time. As a student, I shared similar sentiments with them. To develop a possible solution to these observations, a theory of reasoning was chosen as the foundation. The heuristic-analytic theory of reasoning (HATR) suggests that people present an epistemic model (beliefs and ideas about a phenomenon) through a heuristic process (generation from memory) and engage in an analytic process to validate the epistemic …

An Analysis Of Slms For Machine Translation Of Healthcare Documents, Wyatt Clausen

All Graduate Theses, Dissertations, and Other Capstone Projects

The language barrier creates significant healthcare challenges for patients who struggle with English. While large language models (LLMs) have advanced machine translation, their high computational costs, environmental impact, and risk of data breaches have raised concerns. Small language models (SLMs) can address these concerns and be further adapted to specific tasks through parameter-efficient fine-tuning (PEFT) methods, including Quantized Low-Rank Adaptation (QLoRA). This study compares the Spanish-to-English translation quality of two 7-billion-parameter SLMs, Mistral and Llama 2 7B, in the medical domain, using BLEU, chrF, and COMET as evaluation metrics. It further investigates whether QLoRA fine-tuning and Retrieval-Augmented Generation (RAG) on …

A Comparative Analysis Of The Ecophysiology Between The Invasive Macroalgae Starry Stonewort And The Native Macroalgae Chara To Predict Invasion Potential, Thomas J. Zellmer

All Graduate Theses, Dissertations, and Other Capstone Projects

Starry stonewort (Nitellopsis obtusa) is an invasive macroalga from Eurasia. Currently, starry stonewort’s invasive range consists of the upper Midwest and Great Lakes region, including the Mississippi River. Starry stonewort poses recreational and ecological problems by forming dense canopies that extend through the water column. Starry stonewort occupies a late-season niche space with dense biomass occurring in late July and persisting well into autumn. There is a lack of information on the ecophysiology of starry stonewort. In this study, two populations of starry stonewort, Minnesota and New York, were analyzed to compare regional differences in ecophysiology, along with chara (native …

Statistics 103a Instructor Guide, Elizabeth R. Wentworth

Open Educational Resources

This set of slides contains reading, original videos, activities and instructions for instructors to run a complete 12 week course in any modality. These resources can be used to supplement in-person instruction or can be used for either a hybrid or asynchronous course.

Topic Modeling The Cuny Graduate Center's Dissertations And Theses, Michael Mandiberg

Open Educational Resources

This 4 week module is designed for Data Analysis, Data Visualization, and Digital Humanities courses at the MA/MS or advanced 400-level undergraduate level. It introduces students to textual analysis with topic modeling and requires a solid foundation in Python. The module uses Gensim and a Colab notebook to introduce a standard text analysis workflow used in Digital Humanities, archival research, and exploratory data analysis.

Students build a topic model describing 19,000 CUNY Graduate Center dissertations and theses. They work with an unexplored dataset to load and explore the data, prepare the corpus, train and evaluate a topic model, and interpret, …

Cross-Temporal Statistical Approaches For Evaluating Predictor-Outcome Relationships, Jeff Joseph

Dartmouth College Ph.D Dissertations

Central auditory function is linked with cognitive deficits, but few research projects use existing statistical approaches or develop new ones to forecast cognitive deficits using the results of central auditory tests. To address this limitation, we use a series of statistical learning frameworks for predicting a child’s cognitive abilities based on his/her central auditory performances and demographic factors. Two key challenges exist. First, children may start the study at a time when they are unable to perform the central auditory tests or cognitive tests. Second, cognitive performance is age-dependent, particularly in the early formative years of childhood and adolescence. To …

Application Of Diatom Indices To Monitor Environmental Shifts In The Downstream Tuntang River, Central Java, Mirza Hanif Al Falah, Tri Retnaningsih Soeprobowati, Hadiyanto -, Muhammad Isnaini Ibnu Sabil, Marta Wojewódka-Przybył, Łukasz Kruszewski, Paweł Rydelek

Applied Environmental Research

Diatoms are widely recognized as sensitive bioindicators for reconstructing past water quality and ecological changes in aquatic ecosystems. In this study, multiple diatom-based indices were applied to sediment cores collected from three downstream stations of the Tuntang River, Central Java, to infer long-term environmental change in a tropical river system. A total of 38 sediment subsamples were analyzed, with dominant taxa including Fragilaria crotonensis (2.4–18.6%), Nitzschia palea (3.1–22.4%), and Aulacoseira granulata (2.0–15.8%), indicating sustained nutrient enrichment. Diatom indices consistently classified conditions as mesotrophic to eutrophic throughout the sediment profiles. The occurrence of the marine diatom Cylindrotheca closterium (up to 6.3%) …

Water Resource Protection Principles And Strategies, Julia Peterson

UNH Cooperative Extension

No abstract provided.

Stormwater Best Management Practices, Kat Kelleher

UNH Cooperative Extension

No abstract provided.

A Proposed Tort To Address The Negligent Enablement Of Cloud Data Breaches, Michael L. Rustad

American University Business Law Review

[INTRODUCTION] The term “cloud computing” means the remote storage of software applications, tools, and data accessed through the internet. Cloud customers enter into subscription agreements with providers who give 24/7, on-demand, as-needed access to software, storage, and networking services owned and managed by providers through a web browser. “Many businesses are transitioning to the cloud for data storage, remote work, and collaboration.” Cloud providers operate their software as a software-as-a-service (“SaaS”) model, under which customers pay a subscription fee to access the software. Netflix and Amazon Prime Video are examples of subscription services that deliver television programs and videos through …

Machine Learning Prediction Of Federal Appellate Court Outcomes: A Multi-Circuit Analysis With Administrative Law Implications, Nicky Nuertey Apenahier

Dissertations and Theses

Federal appellate courts are the final arbiters in many cases, yet systematic machine learning analysis across all twelve circuits remains largely absent from the computational law literature. With courts of appeals deciding tens of thousands of cases annually and the Supreme Court reviewing only a fraction, understanding what predicts reversal outcomes has both theoretical importance and practical consequences for litigants, attorneys, and judicial administrators. This study addresses that gap using eleven years of federal appellate decisions from the Federal Judicial Center’s Integrated Database. A systematic comparison of twenty-five machine learning models, spanning five algorithms and five class-imbalance correction strategies, identifies …

Provoking Generative Ai Futures: Merging Theory And Praxis, Regina M. Luttrell Ph.D., Nick Bowman

Media Studies - All Scholarship

An accessible exploration of the myriad applications and challenges of generative AI for media and communication students, scholars, and practitioners alike.

The latest emergence of increasingly low-cost and scalable AI technologies presents a point of both celebration and concern for the contemporary media and information ecosystem. To this end, this edited volume gathers media and communications scholars and practitioners to engage in discussions and exchange ideas about current trends and developments in the field. Questions this volume asks include: What are the essentials of generative AI from a media and communication perspective? How has generative AI influenced research and scholarship? …

Data Repository For: Dorsey Et Al., 'Rapid Transient Uplift Driven By Active Slab Tear, Southern Calabria, Italy', Rebecca J. Dorsey, Nathan D. Brown, Sergio G. Longhitano, Marco Meschis, Domenico Chiarella, Charles P. Ogle

Earth & Environmental Sciences Datasets

Southern Italy preserves a well-studied record of deformation and uplift related to migrating oblique collision, trench retreat, and tearing of subducted ocean slabs. However, the surface expression of these processes is incompletely understood due to a lack of reliable ages for marine terraces >200 m above sea level (masl). Here we use luminescence methods to date shallow marine sands from terraces ~ 100 to 1,000 masl in southern Calabria and interpolate the results with regional surface mapping. Burial ages overlap across the full range of sampled elevations with a weighted mean of 118.8 ± 8.7 thousand years before present (ka). …

The Effects Of Financing Green And Brown Sectors: What Do Theories And Evidence Say?, Hao Liang, Maria Teresa Punzi

Research Collection Lee Kong Chian School Of Business

This paper critically examines the economic and welfare implications of financing green and brown sectors. Drawing on a comprehensive review of recent theoretical and empirical literature, we highlight that while conventional green finance—allocating capital toward environmentally friendly (“green”) sectors and away from carbon-intensive (“brown”) sectors—can promote decarbonization, it may also produce unintended externalities. In particular, it can inadvertently incentivize higher emissions from brown firms and contribute to economic disruption. Using a dynamic stochastic general equilibrium (DSGE) model, we demonstrate that lowering the cost of capital for green sectors leads to only modest reductions in emissions, whereas raising it for brown …