Computer Sciences Commons^™

Reliable Retrieval-Augmented Feature Generation With Large Language Model Reasoning, Jinghan Zhang, Xinhao Zhang, Fengran Mo, Dakshak Keerthi Chandra, Yu-Zhong Chen, Fei Xie, Kunpeng Liu

Computer Science Faculty Publications and Presentations

Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is to expand the current feature space using existing features and enriching the informational content. However, generating new, interpretable features usually requires domain-specific knowledge on top of the existing features. In this paper, we introduce a Retrieval-Augmented Feature Generation method, RAFG, to generate useful and explainable features specific to domain classification tasks. To increase the interpretability of the generated features, we conduct knowledge retrieval among the existing features in the domain to identify potential feature associations. These associations are expected …

Olaf: Towards Robust Llm-Based Annotation Framework In Empirical Software Engineering, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the Operationalization for LLM-based Annotation Framework (OLAF), a conceptual framework that organizes key constructs: reliability, calibration, drift, consensus, aggregation, and transparency. The paper …

A Large Language Model-Based Analysis Of Vulnerability Discovery In Windows Software, Puya Pakshad, Samson Quaye, Jamal Al-Karaki, Marwan Omar, Maurice E. Dawson

All Works

Source code security auditing is essential before software release in order to identify programming faults that may lead to vulnerabilities and functional failures. In this paper, we present a structured security assessment of the Windows App SDK by integrating multiple static analysis tools with a context-aware and disagreement-aware Large Language Model (LLM) interpretation layer. Although static analyzers are effective in reporting potential weaknesses, their raw outputs often contain redundant alerts, limited contextual explanation, and inconsistent severity assignments. To address these limitations, the proposed LLM-based interpretation layer normalizes and de-duplicates alerts, filters context-limited or nonactionable warnings, and refines severity prioritization under …

Deployment-Aware Deep Learning For Computer Vision: Efficient Architectures From 3d Segmentation To Mixed Reality, Bahar Uddin Mahmud

Dissertations

Deep learning has become the dominant approach for solving vision-centric problems; however, its successful deployment in real-world applications remains limited by high computational cost, data dependency, and insufficient integration with practical and human-centered environments. While state-of-the art deep learning models often achieve impressive performance in controlled settings, they frequently fail to generalize or operate efficiently under deployment constraints such as limited resources, complex data modalities, and real-time interaction requirements. These limitations motivate the need for a deployment-oriented deep learning framework that balances accuracy, efficiency, and practical usability.

This dissertation investigates the design and deployment of efficient deep learning architectures for …

Breadquest: Enhancing Roguelike Accessibility Through Procedural Generation And Thematic Design, Hahns Pena

Computer Science and Software Engineering

BreadQuest is a top-down roguelike dungeon crawler with a whimsical dessert theme that aims to make the genre more accessible while preserving strategic depth and replayability. Players explore procedurally generated dungeons, fight pastry-themed enemies, and collect bakery-inspired items that support a flavor-elemental combat system, with each run offering unique layouts, encounters, and rewards. Built in Unity with a modular, data-driven architecture, the game uses procedural generation techniques like Binary Space Partitioning, Voronoi diagrams, and Perlin noise to create varied and replayable levels. The project emphasizes approachable gameplay, cultural dessert inspiration, and replayability, with success evaluated through playtesting and player feedback.

Real-Time Simulation Of Bio-Luminescent Light Propagation Using Compute Shaders Within Unreal Engine, Jaden D. Halevi

Computer Science Senior Theses

Presented in this paper is a GPU-native approach to interactive fluid simulation within Unreal Engine 5. The system, BioFluidSim, implements an incompressible Navier-Stokes solver using Unreal’s Niagara Grid2D compute shader pipeline, with a modular biological emission output stage parameterized from experimentally measured Lingulodinium polyedrum bioluminescence behavior. The system is evaluated against FluidNinja Live, a commercially available fragment shader fluid implementation, as a performance baseline. Beyond performance, BioFluidSim offers greater physical fidelity than the fragment shader baseline. Helmholtz–Hodge pressure projection enforces a divergence-free velocity field at runtime, a physical constraint approximated but not enforced by fragment shader approaches. The biological emission …

A Systematic Review Of Intrusion Detection Systems For Internet Of Medical Things: Performance, Efficiency, Explainability, And Generalization, Oswald Adohinzin, Youssef Harrath

Research & Publications

The Internet of Medical Things (IoMT) has transformed health care delivery through medical devices, remote patient monitoring, and real-time clinical decision support. However, the proliferation of IoMT devices introduces security vulnerabilities that put patient safety and data privacy at risk. Intrusion Detection Systems (IDS) have emerged as essential components for protecting IoMT networks from cyberattacks. This article presents a systematic review of IoMT-IDS research, analyzing 53 high-quality papers published between 2020 and 2025, identified through database searches spanning 2016–2025 across IEEE Xplore, Springer, ScienceDirect, and ACM Digital Library. We organize the literature through a comprehensive taxonomy spanning classical machine learning …

High-Resolution Queries, Low-Resolution Context: Scaling Vision Transformers With Asymmetric Spatial Reduction, James M. Dwyer

Master's Theses

Vision Transformers (ViTs) have demonstrated great performance on image classifica tion benchmarks, however, the quadratic complexity of the self-attention mechanism with respect to sequence length limits their scalability to higher resolution inputs. The attention score matrix grows as O(N2) in both compute and memory, where N is the number of patch tokens, making ViTs computationally expensive and memory intensive for applications that require real-time inference or operate under resource constraints.

This thesis investigates whether the key and value sequences of the self-attention mechanism can be compressed using the local spatial structure of the image — while keeping queries at full …

Scorespeak: An Agentic System For Natural Language Control Of Musical Scores, Nathan S. Lim

Master's Theses

With the recent popularization of large language models (LLMs), natural language has become one of the most accessible and powerful ways for people to interact with creative tools. Although they have become common in mainstream domains like image and audio editing, there is currently no robust AI-based system that can reliably turn free-form language into edits for symbolic musical scores. This gap represents a missed opportunity to improve human workflows for creating and editing sheet music, but it is also a fundamental limitation for other agentic music systems; without a robust mechanism for translating free-form language into structured scores, AI …

An Evaluation Of Road Network Structure As A Predictor Of Traffic Volume, Colin M. Mcdonald

Master's Theses

This thesis evaluates the relationships between various graph theory metrics and taxi traffic volume for the cities of San Francisco, California and Porto, Portugal. We also evaluate a modified betweenness centrality metric which incorporates the count of distinct origin-destination pairs from the taxi data as the weight function. This thesis extends a paper by Pengyao Ye, Bo Wu, and Wenbo Fan by reducing circularity through a temporal train-test split and by comparing both line-graph and primal-graph formulations of betweenness centrality.

We found that past traffic volume is almost perfectly correlated with future traffic volume and that the modified betweenness centrality …

2026 Cyber-Resilient Health Care Workshop Report, Malcolm Schongalla, Sergey Bratus

Computer Science Technical Reports

The ISTS and the Dartmouth College Cybersecurity Cluster hosted the successful, inaugural Cyber-Resilient Health Care (CRHC) Workshop, March 5th & 6th, 2026. The event theme was "Innovation and Implementation," in response to the need to shift from reactive to proactive resiliency measures in the healthcare sector. Approximately 30 experts in clinical health care, cybersecurity, medical technology, policy, and innovation met to discuss solution-focused innovations addressing hard, cyber-related problems in health care. The agenda featured keynotes, an expert panel, innovation pitches, small group discussions, and a tabletop infrastructure disaster exercise. Participants gained insights into the obstacles and solutions involved in supporting …

(R2133) Analysis Of A Bulk Queue With Balking, Adaptive Overloading Service, Multiple Vacation, Inspection And Rework, S. Karpagam, R. Lokesh

Applications and Applied Mathematics: An International Journal (AAM)

We consider a queueing system that utilizes a single server to manage product flow through bulk and overload service modes, depending on queue length. Balking occurs when the queue length reaches ‘N’. After completion of service, products undergo quality inspection, with defective products sent for rework based on specific probabilities. If the product is non-defective, the server continues processing until the queue length exceeds a certain threshold; beyond this point, the server is either routed back to bulk service or directed to an overload service. Also, the server goes into a vacation mode when the queue size is below a …

3dcotton, Md Ahmed Al Muzaddid, William J. Beksi

Agriculture - Archive

3DCotton is an image dataset consisting of 8 cotton plants recorded at the Texas A&M University Research Farm. The images were captured using an Apple iPhone at a resolution of 1040x1920 pixels. Approximately 150 images per plant were taken from a distance of 1 m by recording multiple viewpoints. These images can be utilized for developing 3D reconstruction methods.

Ms110 Syllabus: Introduction To Computers, Information Systems, And Artificial Intelligence, Wei Zhang

Management Science and Information Systems Faculty Publication Series

This is a syllabus for Professor Wei Zhang's MS110: Introduction to Computers, Information Systems and Artificial Intelligence Course within UMass Boston's College of Management. This is an Open Educational Resource and can be remixed, copied, redistributed, altered and reused as long as permission is given to the original creator.

Shared Language For Responsible Ai Integration, Asa B. Stone, Mark C. Stone, Alisha Bevins, Jean Claude Niyomugabo, Irene Magara, Jacob Abaare, Derek M. Heeren, Mubarak Abu Zouriq

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

As AI rapidly reshapes how we work and learn, employers increasingly seek graduates who can think before they prompt, exercising judgment under pressure rather than merely producing output. Yet students are praised for AI use in one course and penalized for it in the next, and faculty are left to lead responsibly on shifting ground, with no shared language to guide them.

This paper introduces the PRAIRIE Framework for AI Integration, a shift from reactive gatekeeping toward proactive stewardship. It emerged from a qualitative sentiment analysis of three communities (students, faculty, and industry partners) whose concerns converged on one need: …

What Collaboration Means To Me: From Coordination To Collective Action: Cclp And Cyclops As Infrastructure For Collaborative Collection Stewardship, Boaz Nadav-Manes

Collaborative Librarianship

Academic libraries face growing pressure to manage complex collections under significant resource constraints. While consortial collaboration has long been recognized as a strategic response, existing models remain limited, often failing to influence the full collections lifecycle. This article examines how the Collaborative Collections Lifecycle Project (CCLP) and the Cyclops platform together offer a new model for moving beyond coordination toward genuine collective action in collection stewardship. CCLP provides a community-governed framework structuring collaboration from selection and acquisition through preservation and deaccessioning, while Cyclops complements it with an open, analytics-driven infrastructure that aggregates data across institutions, enabling cross-institutional analysis and translating …

Muon Lifetime: Theory, Experiment, And Simulation, Soren Agustin Munoz

Physics

This senior project investigates the muon lifetime through three complementary approaches: theoretical calculation, laboratory measurement, and computational simulation. The theoretical component develops the necessary background from relativistic field theory to the effective weak interaction, culminating in the leading-order Fermi-theory prediction of τ ≈ 2.2 μs, which explains why the muon lifetime lies on the microsecond scale.

The experimental component measures the lifetime of stopped cosmic-ray muons using a plastic scintillator, photomultiplier tube, and analog timing electronics. A binned Poisson likelihood fit to the primary 15-day acquisition run τ = 2.17^+0.03_-0.09 μs, consistent with the accepted value within the …

Hydpn: A Hybrid Deep Reinforcement Learning, Programming, And Neighborhood Operations Framework For Integrated Scheduling On Parallel Batch Processing Machines, Yuqi Wang, He Luo, Guoqiang Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Batch processing machines (BPMs) are widely used in industries such as semiconductors, metal processing, and healthcare, where jobs are processed in batches. As production, inventory, and distribution become increasingly integrated to improve efficiency, research on their joint scheduling in parallel BPM environments remains scarce. This paper addresses the integrated scheduling problem in parallel BPMs, involving production, inventory, and distribution stages, with the objective of minimizing total costs. A unified cost-based model is first formulated, applicable to both in-facility and external distribution scenarios. A hybrid algorithm framework, HyDPN, combining deep reinforcement learning, dynamic programming, and neighborhood operations is proposed. Extensive experiments …

On-The-Fly Generation-Quality Enhancement Of Deep Code Models Via Model Collaboration, Weifeng Sun, Naiqi Huang, Meng Yan, Zhongxin Liu, Hongyan Li, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

The growing prominence of deep code models in automating software engineering tasks is undeniable. However, their deployment encounters significant challenges in on-the-fly performance enhancement, which refers to dynamically improving the performance of deep code models during real-time execution. Conventional techniques, such as retraining or fine-tuning, are effective in controlled pre-deployment scenarios but fall short when adapting to on-the-fly adjustments post-deployment. CodeDenoise, a notable on-the-fly performance enhancement technology, leverages uncertainty-based methods to identify misclassified inputs and applies an input modification strategy to rectify classification errors. While effective for classification tasks, this approach is inapplicable to generative tasks due to two key …

Interfold: Learning Interpretable Diffusion Manifolds Beyond Binary Samples, Alexander Vincent Lewi, Rainer Tan, Shengfeng He

Research Collection School Of Computing and Information Systems

We propose InterFold, a framework for learning and applying interpretable semantic manifolds in latent diffusion models, without requiring binary or paired supervision. Existing methods for semantic editing either rely on limited paired data or uncover only coarse, unsupervised directions that fail to capture user-specific, fine-grained attributes. InterFold addresses these limitations by learning a target attribute manifold in the H-space of diffusion models using only a set of positive, unlabeled examples. To edit a new image, InterFold projects its H-space representation toward this learned manifold through test-time optimization, enabling precise, identity-preserving modifications of complex, non-binary concepts. To make these edits effective …

Videocreator: An Agentic System For Multi-Turn Video Production, Zhengyang Liang, Yan Shu, Cathal Gurrin, Nicu Sebe, Lizi Liao

Research Collection School Of Computing and Information Systems

Recent advances in video generation models enable visually compelling single clips. However, real-world video creation is inherently continuous and iterative: creators refine content over multiple rounds while maintaining narrative, style, and entity consistency. Existing standalone generators are largely stateless and lack memory of previously generated segments, making it difficult to produce a coherent and consistent video project. To address this gap, we present VideoCreator, a unified video agent that integrates generation and understanding with a project-level memory system. VideoCreator leverages understanding capabilities to perform fine-grained analysis of newly produced content and uses persistent memory to retain and reuse prior context …

Sok: Understanding Zkvm: From Research To Practice, Guomin Yang, Yunbo Yang, Yuejia Cheng, Haibo Tang, Bingsheng Zhang, Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge virtual machine (zkVM) is a powerful infrastructure for proving the correctness of a program execution with a succinct proof, attracting significant interest from researchers, developers, and users. It has been widely used in applications such as blockchain rollups, privacy-preserving machine learning, and off-chain computation. As the field grows, a wide range of zkVMs have been proposed. However, they adopt different choices in instruction formats, trace layouts, and proving backends, which results in a highly heterogeneous design landscape and makes it difficult to understand the relations among these systems.To bridge this gap, we provide a comprehensive study of zkVMs that …

Vehicle-Based Multi-Services For Future Smart Cities, Hao Sun, Jinhua Zhao, Hai Yang, Shenhao Wang, Hamsa Balakrishnan, Thomas W. Malone, Hai Wang

Research Collection School Of Computing and Information Systems

Vehicles are crucial for sustaining socioeconomic activity and improving quality of life in modern cities by offering diverse services. These include passenger mobility, goods delivery, information acquisition, and acting as mobile servers such as food trucks and mobile lockers. At the same time, they also contribute to traffic congestion and air pollution. This tension fosters the rise of urban resource-conserving and sustainable service solutions. In this article, we introduce the concept of “Vehicle-Based Multi-Services” (VeMuS), in which a single vehicle offers multiple services simultaneously. Drawing on practical use cases, we examine service classification and integration for vehicles and the potential …

Esg Reporting Lifecycle Management With Large Language Models And Ai Agents, Thong Hoang, Mykhailo Klymenko, Xiwei Xu, Shidong Pan, Yi Ding, Xushuo Tang, Zhengyi Yang, Jieke Shi, David Lo

Research Collection School Of Computing and Information Systems

Environmental, Social, and Governance (ESG) standards have been increasingly adopted by organizations to demonstrate accountability towards ethical, social, and sustainability goals. However, generating ESG reports that align with these standards remains challenging due to unstructured data formats, inconsistent terminology, and complex requirements. Existing ESG lifecycles provide guidance for structuring ESG reports but lack the automation, adaptability, and continuous feedback mechanisms needed to address these challenges. To bridge this gap, we introduce an agentic ESG lifecycle framework that systematically integrates the ESG stages of identification, measurement, reporting, engagement, and improvement. In this framework, multiple AI agents extract ESG information, verify ESG …

Ai, Medicine, And Social Determinants Of Health Data, Ryan Doyloo, Nicholson Price

Articles

This article examines how medical AI systems are incorporating SDoH data and the governance challenges that follow. The authors show that while SDoH integration can enhance clinical workflows and predictive accuracy — potentially improving outcomes for underserved populations — it also introduces acute risks of proxy discrimination, where facially neutral variables replicate protected characteristics. Surveying U.S., EU, and international frameworks, the authors argue that existing regimes lack clear ex ante guidance to distinguish beneficial from harmful uses of SDoH data. In response, they advance post-market monitoring as a pragmatic and scalable pathway: generating real-world, SDoH-stratified evidence that can support enforcement, …

Emotional Support Through Ai: Venting To Artificial Intelligence Or A Perceived Human May Offer Comparable Emotional Well-Being Benefits, Meilan Hu, Jerlyn Q. H. Ho, Claire Ng, Shermaine S. M. Wong, Andree Hartanto

Research Collection School of Social Sciences

Artificial Intelligence (AI) chatbots are increasingly being explored as sources of informal emotional support, with emerging evidence suggesting that venting to these systems can reduce negative affect. Yet, it remains unclear whether such benefits depend on the responder's perceived identity. Given that emotional relief from venting often hinges on perceived authenticity and emotional validation, this study investigates whether the emotional well-being benefits of venting differ when users believe they are interacting with an AI chatbot versus a human, even when responses are content-matched. In a pre-registered experiment ( N = 279), participants were randomly assigned to either an AI-assisted venting …

Pso-Style Social Influence In An Ant Colony Algorithm For Continuous-Domain Optimization, Ashraf M. Abdelbar, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

ACOR is a well-established Ant Colony Optimization (ACO) algorithm for continuous-domain optimization. In this paper, we propose an extension (which we call ACOR∗) in which several fundamental modifications are made to ACOR's solution construction process, including the incorporation of a social influence mechanism borrowed from Particle Swarm Optimization (PSO). Our modifications to the ACOR algorithm are intended to promote search diversity and combat premature convergence. We experimentally evaluate our proposal in the context of training feedforward neural networks for classification using 65 widely used datasets from the University of California Irvine (UCI) repository, as well as the optimization of several …

Cloud Tank: Virtual Reality Performance Instrument For Embodied Audiovisual Remixing, Asya Ulger

Computer Science Senior Theses

Traditionally, live audiovisual performances are made behind a desk using a laptop and an audio controller. This setup restricts the performer’s movements and separates the artist from their work. Moreover, it narrows what counts as the performance to the output alone — the audience is preconditioned to treat the projected image and sound as the whole event, and not the artist producing it. Cloud Tank challenges this by positioning the performer’s body as the instrument, using virtual reality. As the performer, I mix spatialized audio and drive beat-aligned, reactive video layers through hand movement and hand-pose recognition. The audiovisual output …

Ai Interview Helper: A Tool For Assisting Search And Rescue Long-Profile Interviews, Dylan P. Starink

Master's Theses

In Search and Rescue (SAR) operations, time pressure and limited interviewer experience can lead to missed opportunities when interviewing a missing person’s friends and family. This thesis presents a real-time, end-to-end system that provides context-aware follow-up question suggestions as interviews unfold. Leveraging large language models (LLMs) and agentic design patterns, the system is intended to support interviewers by helping them identify relevant follow-up questions and pursue potentially overlooked lines of inquiry.

The system was evaluated through three mock interviews with two SAR interviewer participants across two events. Given the limited sample size, the results provide early insights into the feasibility …

Conello Vendor Marketplace Capstone: A Review On The Capstone Process And Computer Science Degree, Levi Hauck

University Honors Theses

This Capstone Review Thesis discusses the current structure of the Computer Science Degree path and Computer Science Capstone at PSU. This thesis reviews the Conello Vendor Marketplace Capstone, a project designed to help address the issue of teambuilding in the workplace. It points out ways in which teamwork and organization is an underdeveloped skill in Computer Science. During the Capstone project, the project management method of Agile development was recommended and used. However, it became clear that the team, leader included, had a gap in knowledge and skills to be successful, as well as a lack of experience in team …