Digital Commons Network^™

Thermal Vacuum Technical Report, Taiye Ibukun Fatima Ougundare, Ryan Bade

Senior Project

This system enables ground-based testing of components prior to space deployment. It replicates key Low-Earth Orbit (LEO) conditions, including both pressure and temperature. The chamber reduces pressure from atmospheric conditions (~760 Torr) to ~7.6 × 10⁻⁶ Torr. The thermal vacuum system achieves temperatures ranging from −60 °C to +125 °C for realistic environmental simulation.

Skyforge: Payload Isolation, Denzel Ezekiel, Brayden Gogis

Senior Project

As humanity looks to expand its reach into space, there is an increasing need for large space structures. The existence of these structures would enable large-scale infrastructure for further exploration and long-term habitation of humans beyond earth. However, creating such large structures in space is extremely challenging. Extreme environmental conditions (temperature, vacuum, microgravity) make construction and material handling difficult. Manual construction is dangerous and expensive; it is unrealistic for astronauts to complete this work. The “SkyForge” project, inspired by “Skyworker” (2003), attempts to make large space structure construction possible. SkyForge is envisioned as a fully autonomous construction robot designed for …

Time-Correlated Photons From Spontaneous Parametric Down-Conversion (Spdc), Aaron Dubois , ‘26, Tanvir Islam , ‘26, Prashan Thapa Magar , ‘26

Senior Theses, Projects, and Awards

Time-correlated photons can be used to demonstrate emerging technical applications such as ghost imaging, and they can demonstrate quantum-based phenomena in order to interest undergraduate students in engineering applications involving optics and quantum mechanics. In this work, we use spontaneous parametric down-conversion to generate time-correlated photons. To achieve this, we simulated laboratory parameters with MATLAB and designed optical and electrical setups. Specifically, we used a 405 nm diode laser, various common optical components, and a nonlinear crystal designed to generate time-correlated photons, and we used single-photon detectors, time-to-amplitude converters, custom flash-conversion analog-to-digital converters, a Teensy 4.1 microcontroller, and the Arduino …

The Psychology Behind Ai-Generated Phishing And Social Engineering Attacks, A’Shya Reynolds

School of Cybersecurity Master's Level Projects and Papers

Cybercrime has evolved significantly with the integration of artificial intelligence (AI), transforming traditional phishing and social engineering attacks into highly sophisticated and personalized threats. While early phishing attempts relied on generic messaging and low success rates, modern AI-driven attacks leverage advanced data analytics, natural language processing, and behavioral prediction to manipulate victims more effectively.

This research examines how cybercriminals utilize AI to enhance psychological manipulation techniques in phishing and social engineering attacks, increasing victim susceptibility. Drawing from interdisciplinary literature in cybersecurity and psychology, this study explores key psychological mechanisms, including cognitive biases, emotional triggers, and decision-making processes that influence victim …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

Altered Pruning In A Generative Neural Network, Sara Momen

Honors Theses

This study examines whether altered pruning in a generative neural network leads to more restricted or repetitive outputs, using Autism Spectrum Disorder (ASD) as a cautious computational analogy rather than as a biological equivalence. A compact denoising diffusion probabilistic model (DDPM) with a U-Net backbone was trained on CIFAR-10, and pruning percentage was treated as the primary structural variable. Instead of relying on a single assumed baseline, the final implemented study evaluated 0%, 10%, 15%, 20%, 35%, and 50% pruning under controlled conditions: the same dataset, the same epoch budget, the same random seed, and 1,000 generated samples per condition. …

Ai-Powered Knowledge Management Systems Across Industries: A Systematic Review Of Applications, Implementation Barriers, And Ethical Challenges, Edmund Evangelista, Ghazala Rizvi

All Works

This systematic literature review (SLR) evaluates the existing literature on the benefits, implementation challenges, and ethical concerns associated with Artificial Intelligence (AI)-driven Knowledge Management Systems (KMS) across industries. The SLR followed PRISMA guidelines to identify studies from Scopus, Web of Science, JSTOR, and Google Scholar, using inclusion and exclusion criteria. Critical Appraisal Skills Programme (CASP) checklists were used to assess methodological quality and risk of bias in the included studies, and a structured narrative synthesis was employed to synthesize the findings. The review of 21 articles reveals benefits like improved knowledge capture and creation, storage, retrieval, personalization, and efficient dissemination, …

Preserving The Past, Innovating The Future: Integrating Metaverse, Blockchain, And Generative Ai For Tourism And Cultural Heritage Preservation, Mousa Al-Kfairy, Amna Ahmed Aaber Ahmed Alqubaisi, Omar Alfandi

All Works

The preservation and accessibility of cultural heritage are seriously threatened by urbanization, mass tourism, neglect, and natural disasters. This study utilizes cutting-edge technologies to address these issues by presenting a novel framework that combines generative AI, blockchain, and the metaverse. The Metaverse provides immersive virtual experiences that lessen the physical strain on delicate locations by enabling the creation of lifelike digital replicas of cultural heritage sites. By creating immutable records, blockchain technology ensures the legitimacy, ownership, and traceability of digital assets, enabling open access and NFT monetization. Rebuilding lost or damaged artifacts, creating lifelike 3D models, and customizing user interactions …

Trash Bot!, Owen Hoffman , ‘26, Ryder Maston , ‘26, Jack Nicholson , ‘26

Senior Theses, Projects, and Awards

Trash Bot! is the prototype of a smart and mobile waste receptacle for Swarthmore’s waste management system. With Swarthmore College committing to embed zero waste into campus culture and achieve a 90% diversion rate by 2035, flexible waste systems must be developed and improved. Trash Bot! seeks to improve the diversion rate and sort accuracy on campus by reducing the human error present in the current waste sorting process. Trash Bot! was also designed to seamlessly integrate with a classroom environment, maintaining safety standards and avoiding collisions. Trash Bot! uses computer vision coupled with a trained classifier algorithm to achieve …

Impact Of In Situ Observations From Three Torus Cases On Model Analyses And Forecasts Of Severe Convection, Robert M. Szot

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

The quality of forecasts of severe deep convection in high-resolution numerical weather prediction is dependent on the representativeness of the model initial conditions. This representativeness may be negatively impacted by limited observation availability and by the presence of mesoscale heterogeneities that were not detected by conventional observations assimilated into convection-allowing models. By assimilating storm-scale observations from the Targeted Observation by Radars and Uncrewed Aircraft Systems (UAS) of Supercells (TORUS) campaign into an ensemble styled after the Warn-on-Forecast System, this study aims to investigate if data from field work platforms can improve the quality of initial conditions in the ensemble, potentially …

Freedom Readers And Sustainable Development Goal 4: Advancing Literacy In Low-Income Communities Of South Carolina, Madison Hayes

Goal 4: Quality Education

No abstract provided.

Generative Ai In Enterprises: Optimizing Applications With Large Language Models, Donghao Huang

Dissertations and Theses Collection (Open Access)

This dissertation investigates how to deploy Large Language Models (LLMs) effectively in enterprise settings, where accuracy, reliability, cost, privacy, and operational constraints often matter more than benchmark performance alone. Drawing on seventeen peer-reviewed publications (eleven published and six accepted for publication), the work develops and validates optimization strategies across three connected themes: retrieval-augmented generation (RAG), agentic AI for workflow automation, and deployment guidelines for real-world enterprise environments.

First, we study RAG optimization through systematic evaluation of open and proprietary models, highlighting conditions under which efficient open-weight models can match or exceed proprietary alternatives. To address a pervasive failure mode in …

Math 242: Elements Of Probability And Statistics (Syllabus), Ahmad Almomani Ph.D.

School of Arts & Sciences

This syllabus was developed by SUNY Geneseo Professor Ahmad Almomani during the spring 2026 semester.

Course Objectives:

Upon successful completion of Math 242, R/Elements of Probability and Statistics, a student will be able to: 

• Organize, present and interpret statistical data, both numerically and graphically; 
• Use various methods to compute the probabilities of events; 
• Analyze and interpret statistical data using appropriate probability distributions, e.g. binomial and normal. 
• Apply central limit theorem to describe inferences; 
• Construct and interpret confidence intervals to estimate means, standard deviations and proportions for populations; 
• Perform parameter testing techniques, including single and multi-sample tests for means, standard deviations …

Statistical Investigation Project, Ahmad Almomani Ph.D.

School of Arts & Sciences

The assignment was developed by SUNY Geneseo Professor Ahmad Almomani for the course, MATH 242: Elements of Probability and Statistics in the spring 2026 semester.

The objective for this assignment is that students will apply statistical methods to analyze real-world data and communicate meaningful conclusions.

Hypersiniel: Guaranteed Output Delivery Comes (Almost) Free In Private Delegation Of Zksnarks, Yunbo Yang, Yuejia Cheng, Junkai Liang, Kailun Wang, Xuanming Liu, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Meng Hao, Guomin Yang, Deng, Robert H., Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness.Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to …

Bridging Draft Policy Misalignment: Group Tree Optimization For Speculative Decoding, Shijing Hu, Jingyang Li, Zhihui Lu, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet existing training objectives optimize only a single greedy draft path, while decoding follows a tree policy that re-ranks and verifies multiple branches. This draft policy misalignment limits achievable speedups. We introduce Group Tree Optimization (GTO), which aligns training with the decoding-time tree policy through two components: (i) Draft Tree Reward, a sampling-free objective equal to the expected acceptance length of the draft tree under the target model, directly measuring decoding performance; (ii) Group-based Draft Policy …

Do Purpleair Monitors Democratize Data For The Environment?, Christopher Rick, Kyla Lorden

Student Publications

PurpleAir monitors may serve a vital role in marginalized or under-monitored communities nationwide. These low-cost monitors—roughly $300—give communities the ability to monitor their air quality at local geographies in ways that are infeasible for expensive, regulatory-grade air monitors. They also have the potential to aid community advocacy efforts around environmental and transportation policy by giving community members literal ownership over the monitoring, including precise locations, and philosophical ownership over the advocacy efforts. To determine the potential democratization of air quality data by PurpleAir monitors in socially disadvantaged communities, we analyze the sociodemographic characteristics of neighborhoods with PurpleAir monitors. We find …

Ai-Assisted 3d Pre-Visualization: Streamlining Animatics For Filmmakers, Nishit Thapa

Honors Theses

Pre-visualization is a core part of film pre-production. Creators use it to test blocking, lighting, camera movement, and spatial relationships before committing to a shot. Despite this, three-dimensional pre-visualization remains out of reach for many entry-level filmmakers, as the software is expensive and takes substantial time to learn.

Recent AI developments have produced generative systems capable of creating highly re-alistic imagery and video from text prompts. These systems open new possibilities for visual sto-rytelling but are not built for the iterative, hands-on process that pre-visualization requires, where creators must adjust scene parameters, camera logic, and spatial configurations throughout.

This thesis …

The Eye: A Science-Fiction Screenplay Examining Space-Time, Meteorology, And Human Connection, Ella K. Lukowiak

Senior Theses

This thesis explores the nature of time, meteorology, and human interconnection to the universe through the lens of a science-fiction screenplay. The creative project spans a three-act, detailed outline, and a screenplay draft of Act I. It is based upon extensive scientific research regarding hurricane development, the inner workings of organizations such as the National Weather Service, Einstein’s Theory of Relativity, and the space-time continuum. I will delve into the various factors that shaped my science-fiction premise, including pivotal films such as Christopher Nolan’s INTERSTELLAR (2014) and Denis Villeneuve’s ARRIVAL (2016). Both of these filmmakers integrated complex science-fiction concepts into …

Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen …

Visual Loop: Bridging The Cognitive Gap In Software Development Through Visual-Ai Collaboration, Luis Filipe Fernandes Gomes, Xin Zhou, David Lo, Rui Abreu

Research Collection School Of Computing and Information Systems

Software development remains predominantly text-centric, despite decades of evidence showing that developers think and communicate visually. While sketches and diagrams externalize developers’ mental models, they remain disconnected from source code and quickly become outdated. Recent advances in foundation models, capable of both code and visual reasoning, create an opportunity to unify these representations. In this vision paper, we introduce Visual Loop, a continuous visual development environment that keeps code and informal sketches in bidirectional synchronization. Our prototype connects a code editor with a tablet-based visualization workspace, allowing developers to explore, annotate, and modify systems through freehand sketches interpreted by multimodal …

Can Financial Literacy Enhance Esg Disclosures?, Xiaoran Jia, Kiridaran Kanagaretnam, Kiat Bee Jimmy Lee, Chee Yeow Lim

Research Collection School Of Accountancy

Using an international sample of firms and two country-level measures of financial literacy, we find robust evidence of a positive relation between financial literacy and firms' Environmental, Social and Governance (ESG) disclosures. In cross-sectional analyses, we find that the effect of financial literacy in enhancing ESG disclosures is more prominent in poorer information environments, weaker legal institutions and weaker ESG reporting environments. Lastly, we find that financial literacy also enhances ESG performance. Our study contributes to and extends the literature by providing strong evidence that citizens' financial literacy enhances firms' ESG disclosure and the associated ESG performance.

On Autopilot? An Empirical Study Of Human-Ai Teaming And Review Practices In Open Source, Haoyu Gao, Peerachai Banyongrakkul, Hao Guan, Mansooreh Zahedi, Christoph Treude

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) increasingly automate software engineering tasks. While recent studies highlight the accelerated adoption of “AI as a teammate” in Open Source Software (OSS), developer interaction patterns remain under-explored. In this work, we investigated project-level guidelines and developers’ interactions with AI-assisted pull requests (PRs) by expanding the AIDev dataset to include finer-grained contributor code ownership and a comparative baseline of human-created PRs. We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership. Despite this, the majority of repositories lack guidelines for AI-coding agent usage. Notably, we observed a distinct interaction pattern: AI-co-authored PRs …

Weakly Supervised Video Anomaly Detection And Localization With Spatio-Temporal Prompts, Peng Wu, Xuerong Zhou, Guansong Pang, Zhiwei Yang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing works typically involve extracting global features from full-resolution video frames and training frame-level classifiers to detect anomalies in the temporal dimension. However, most anomalous events tend to occur in localized spatial regions rather than the entire video frames, which implies existing frame-level feature based works may be misled by the dominant background information and lack the interpretation of the detected anomalies. To address this dilemma, this paper introduces a novel method called STPrompt that learns spatio-temporal prompt embeddings …

Patchgpt: Multi-Agent Patch Backporting Without Model Fine-Tuning, Ye Liu, Ruidong Han, Chengyan Ma, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Patch backporting is crucial and prevalent in the maintenance of modern open-source software such as Linux kernels and forked repositories. However, porting patches across program versions remains a challenging problem due to the complexity of synergizing diverse patches with divergent program versions. In this paper, we propose PatchGPT, an agentic patch backporting framework for fine-grained patch generation. PatchGPT encompasses three agents: Miner for decomposing a sequence of atomic change steps as the original patch plan, Adapter for adapting the patch plan, and Executor for executing the adapted patch plan according to predefined change semantics. We conduct experiments on the PPatHF’s …

Fully Decentralized Hierarchical Federated Learning At The Edge With Post-Quantum Secure Communication, Tariq Qayyum

Dissertations

Federated learning (FL) enables collaborative model training without centralizing raw data, but deploying FL at scale in real edge environments remains challenging because iterative training and aggregation must operate over heterogeneous, resource-constrained, and often mobile devices with time-varying connectivity. Conventional hierarchical federated learning (HFL) partially mitigates communication cost by introducing fog/edge aggregation, yet many designs retain cloud-based global aggregation and cloud-centric coordination. This places wide-area network latency on the critical path of every training round, creates a single point of failure, and limits responsiveness as model sizes and federation scale grow. Moreover, moving coordination and aggregation closer to the edge …

Intelligent Deep Learning-Based Sign Language Translation System, Nada Rasem Shahin

Dissertations

The Deaf and Hard of Hearing (DHH) community uses sign language as a primary means of communication. However, the shortage of sign language interpreters and the existence of hundreds of sign languages limit accessibility and inclusion. Sign Language Machine Translation (SLMT) systems present a promising solution for bridging the communication gap between the DHH and the hearing individuals, supporting inclusive societies. In smart cities, such systems play an essential role in improving the quality of life on a community level. In particular, as the population’s well-being is critical, developing intelligent assistive technologies, such as SLMT systems, is necessary to provide …

Application Of The Tridiagonal Representation Approach And The J- Matrix Method Of Scattering In Theoretical Physics, Tunde Joseph Osunmusanmi

Dissertations

This dissertation is about the application of the Tridiagonal Representation Approach (TRA) in handling linear phenomenons, and for the first time, the J-matrix method of scattering in handling nonlinear phenomenons. The TRA is an algebraic method for solving linear ordinary differential equations of the second order. The advantage of the method in being algebraic is reinforced by the analytic power of orthogonal polynomials and special functions. On the computational side, it is favored as being reliant on powerful numerical techniques that deal with tridiagonal matrices such as Gauss quadrature and continued fraction. In the method, the solution of the differential …

Draft Final 2025 Bpsou Subdrain Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Pushing High-Performance Private Inference Towards Resource-Constrained Edge Clients, Xiangrui Xu

Computer Science Theses & Dissertations

The widespread adoption of Machine Learning as a Service (MLaaS) has enabled resource constrained edge clients, such as mobile and IoT devices, to leverage powerful deep learning mod els hosted on the cloud. However, this paradigm introduces critical privacy challenges regarding the client’s sensitive input data and the server’s proprietary model parameters. While cryptographic techniques like Homomorphic Encryption (HE) and Multi-Party Computation (MPC) enable Private Inference (PI), existing frameworks impose prohibitive computational and communication overheads that render them impractical for edge deployment. This dissertation introduces three novel frameworks—SPOT, LUTless, and PrivShap—to systematically address the efficiency bottlenecks of PI in edge …