Digital Commons Network^™

Automated Sponge Segmentation With Mask R-Cnn On Arms Plate Images For Reef Monitoring, Mrunali Abhijit Thokadiwala

Master's Projects

Climate change-driven ocean warming and acidification are disrupting the ecological balance of coral reefs. Notably, these oceanic conditions are undermining coral health and accelerating their decline which is favoring some sponge species in outcompeting them for dominance. Although functional, these altered reefs destabilize the reef architecture, hinder nutrient cycling, and support fewer marine species. Thus, monitoring the growth and abundance of various sponges and understanding their roles at different stages of ecological succession in coral reefs is vital. Autonomous reef monitoring structures (ARMS) are often used for this purpose, but manual taxonomic analysis using their images is time-consuming, inconsistent and …

Disease Diagnosis Using Rag Llm With Smart Prompt Engineering, Qadeerullah Syed

Master's Projects

Although recent trends indicate that LLMs outperform traditional methods in solving complex problems with enhanced reasoning, there has been barely any progress in replicating the quality of diagnoses like those of actual human doctors. The identification of an accurate diagnosis with thorough reasoning is still a significant challenge, even with advanced AI models. The process of performing accurate diagnosis remains challenging due to a lack of transparency in state-of-the-art models existing today, a lack of explanation in the diagnosis process, an emphasis on results rather than reasoning, and a lack of foundational knowledge in models, along with limited exploration of …

Rul Estimation Of N-Cmapss Turbofan Engines Using Deep Learning With Customized Penalty And Expanded Sensors, Prathmesh Pethkar

Master's Projects

Accurate prediction of Remaining Useful Life (RUL) for aircraft engines is important to enhance maintenance efficiency and flight safety. For this project, a solution to RUL prediction on NASA's N-CMAPSS data set, mimicking realistic engine degradation under simulated full-flight scenarios, is being proposed. For addressing the high-dimensional noisy sensor data challenge, a new feature engineering pipeline was utilized. Models trained on healthy data predict normal sensor behavior, and the discrepancy between these predictions—referred to as residual features—is a measure of degradation. To handle the size and computational demands of the dataset, training was conducted on Google Cloud Platform using GPU-supported …

Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu

Master's Projects

Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard …

Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy

Master's Projects

The interoperability of heterogeneous blockchain networks is the basis for the widespread application of blockchains in various fields. Cross-chain data oracles play a significant role in enabling distributed applications to exchange data and assets across different blockchains, thereby greatly enriching and expanding the application scenarios and use of blockchains. With the continuous advancement of blockchain technology, more and more researchers and industry participants have begun to focus on developing cross-chain data oracles. Current cross-chain data oracles face issues with trust, as they rely on centralized intermediaries or limited validator networks, increasing the risk of manipulation or single points of failure. …

Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei

Master's Projects

In recent years, neural network models have achieved phenomenal performance due to the increasing parameters and complexity of model architectures. However, these advancements come with high environmental costs as they require massive computational resources and consume substantial amounts of electricity, leading to high carbon emissions. Previous studies have demonstrated that substantial redundancies exist in large pre-trained models, and reducing these redundancies through compression would not compromise model performance. While these studies focused on retaining comparable model performance, the direct impact of compression on energy consumption when training models appears to have received little attention. By quantifying the energy usage associated …

Optimization Of Permutation Flowshop Scheduling Using An Island Genetic Algorithm For Makespan Minimization, Sahil Salim

Master's Projects

The Permutation Flowshop Scheduling Problem is a well-known NP-hard combinatorial optimization problem that involves the sequencing of n jobs across m machines in the same order to minimize the total makespan value. This project proposes a Heterogeneous Island Genetic Algorithm framework (HIGA). Each island represents a group of solutions that evolve in parallel using different initialization heuristics, crossover and mutation operators, and adaptive parameters. A dynamic, stagnation-based migration strategy is proposed to maintain targeted communication between the islands. The proposed HIGA approach was compared against the basic Standard Genetic Algorithm (SGA) and a more advanced Niche-based Genetic Algorithm (NEH-NGA) on …

Multimodal Deception Detection Via Audio-Text Fusion With Deep Learning And Asr, Xiangyi Li

Master's Projects

Emotion detection plays a crucial role in human-computer interaction, enabling machines to recognize and respond appropriately to human emotional states. This project explores a two-stage approach to emotion detection using multimodal data, first predicting dimensional values (Arousal, Valence, Dominance) from textual and audio inputs, then mapping these representations to discrete emotion categories. We compare this approach with direct categorical classification using transformer-based language models like BERT, RoBERTa, and DeBERTa for text processing, alongside various audio feature extraction methods, including MFCCs and spectrograms. Using the IEMOCAP dataset, we evaluate both approaches across text-only, audio-only, and multimodal configurations. Our findings reveal that …

Enhancing Recommender Systems Using Graph Neural Networks, Long Short-Term Memory And Textual Embeddings, Tianxiang Chen

Master's Projects

Recommender systems surround us. They shape what we watch, how we buy, and even what our future might look like next. The Amazon Review Dataset and Movielens, those two datasets will help this project explore how to improve recommender systems through the user’s preferences. Two methods were combined: sequence-based models and graph-based models. Sequence models, such as LSTMs and Transformers, look at the order of user actions to find patterns by their sequence. On the other hand, Graphbased models focus on relationships between users, items, and their attributes. Textual embeddings added depth and context. Both methods offer something special, according …

Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul

Master's Projects

Malware detection and classification remain critical challenges in cybersecurity, especially as malicious software becomes increasingly sophisticated and prevalent. While much of the work involving embeddings has traditionally relied on supervised learning approaches, there is significant potential in leveraging unsupervised learning techniques to discern hidden structures in malware data. By employing embedding techniques to convert malware samples into high-dimensional vector representations, we can capture the subtle and complex patterns inherent in malicious code without relying on pre-labeled data. This unsupervised approach helps categorize malware into predefined malware families, greatly aiding in developing cybersecurity solutions. In contrast to traditional supervised models that …

Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana

Master's Projects

Malware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein …

Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui

Master's Projects

This work explores securing and optimization of Transformer-based time series forecasting models. We employ several quantization techniques, including quantization-aware training (QAT), and tested the robustness of quantized models by adversarially attacking them. The preliminary results of this work, in our controlled setup, indicate that quantized models outperform the full precision model in terms of robustness against adversarial attacks. They achieved this robustness while showing a very minimal decrease in their forecasting performance.

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

Genegate: Genetic Gating In A Mixture-Of-Experts For Real-Time Multi-Objective Traffic Signal Control, Rashmi Vishwanath Bhat

Master's Projects

Urban traffic signal control often needs to juggle between competing goals. It needs to minimize delays, reduce emissions, prevent crashes, and prioritize emergency vehicles all while the demand is constantly fluctuating. Traditional fixed-time or statically blended policies cannot reallocate priorities quickly when conditions change. We introduce GeneGate, a mixture-of-experts framework that uses a lightweight genetic gate to fuse four specialist controllers (throughput, emissions, safety, emergency) and adjusts their weights in real time. A short offline genetic search produces a robust initial blend, and an online micro-evolution step refines it every few cycles based on live traffic feedback. GeneGate’s adaptive gating …

Abstractions Of The Game “Set”, Martin Grant

Master's Projects

Each card in the game of SET can be represented as a point in Z⁴₃, where Z₃ is the f ield of 3 elements; an in-game position without any SETs can be represented as a cap set. We find the largest cap sets in Zⁿ ₃ for n ≤ 4 and prove their uniqueness. Then, we provide more insight into Ellenberg and Gijswijt’s proof of upper bound for the maximum size of cap set in Fⁿ_q, where F_q is the field of q elements, which they find to be o(c^{n …}

Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh

Master's Projects

The use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models …

Homomorphically Encrypted Faceted Values, Tanmay Singal

Master's Projects

Faceted values prevent the implicit flow of sensitive information by controlling the visibility of program data. They achieve this by maintaining two facets for each variable: a public facet, which is observable, and a private facet, which remains hidden. Although this method secures the flow of sensitive data, it can be leaked if the server storing the faceted values is compromised. While faceted values may be encrypted on the server, doing so would necessitate that the private facets be briefly decrypted during execution to allow arithmetic operations to be performed on them, creating an attack vector for information to be …

The First Prison, William Gillis

Seattle Journal for Social Justice

No abstract provided.

Tedvil: Leveraging Transformer-Based Embeddings For Vulnerability Detection In Lifted Code, Gary Mccully, John Hastings, Shengjie Xu

Research & Publications

Ransomware and other malware inflict devastating financial and operational damage on organizations worldwide by exploiting deeply embedded, hard-to-detect vulnerabilities in their systems. Detecting these vulnerabilities in compiled code before malicious actors exploit them remains a critical challenge in cybersecurity. This research introduces TEDVIL (Transformer-based Embeddings for Discovering Vulnerabilities in Lifted Code), a novel framework which uses transformer-based embeddings to train neural networks to detect vulnerabilities in lifted code. The framework was implemented using bidirectional (BERT and RoBERTa) and unidirectional (GPT-1 and GPT-2) transformer-based models to generate embeddings for training Long Short-Term Memory (LSTM) neural networks to detect stack-based buffer overflows …

Pd Supporting Cs And Math Integration: Implications Of Teacher Interest And Confidence For Workshop Design, K. Ann Renninger, Ruth C. Elias , '20, Mariko J. Kamiya , '19, Jennifer N. Paige , '22, Raymond A. Youngblood , '21

Educational Studies Faculty Works

Background and Context: Integrating computer science (CS) and math in classrooms is an increasingly recognized way for schools to address national CS mandates. There is a need to understand how professional development (PD) can support teachers to integrate. Objective: We examined math teachers’ interest, and confidence, in math, CS, and student learning, in relation to their participation in professional development (PD) designed to support them to integrate CS with math in their classrooms. Method: We used mixed-methods to study participants in entry-level hybrid PD, and a sub-group of those participants who took the workshop a second time. Findings: Results showed …

Solar Flare Forecasting Multiple Ml And Curation Technique Study Hour-By-Hour Sharp Parameter Data Archive, Timothy S. Newman

Open Data

Hour-by-hour AR parameter data as a series of .csv files in a zip archive. Contains the "filtered" data described in the "Solar Flare Forecasting using Machine Learning (ML) and SDO/HMI Data: Multiple ML Model and Data Curation Technique Comparison Study" paper of Newman, Hall, Farris, Singh, Pogorelov, Benson, Raza, and Trital paper, 2025 submission date, for ApJS. Each file in the zip has data for one class of flares at a timepoint a certain number of hours in advance of flare onset. The first letter of such file names indicates flare class and the number before "hrs" in the title …

The Sustainability Of The Outdoor Recreation Industry: Studied Through Mountaineering In Teton County, Wy, Samantha Olstein

Student Research and Publications

No abstract provided.

Ai, Post-Truth Realities, And Thai Students’ Information-Seeking Behavior, Pavel Slutskiy

Manusya, Journal of Humanities

This paper investigates how ai challenges traditional verification, citation, and evaluation principles, thereby eroding truth. ai-generated content, lacking verifiable citations, operates on probabilistic models that present information as probable rather than factual. The widespread reliance on ai for information-seeking risks diminishing critical evaluation, blending fact with fiction, and compromising academic integrity. To explore these effects, a mixed-method study was conducted involving 240 Chulalongkorn University students who used various online resources like Google, Wikipedia, and ChatGPT to research Rudolf Carnap’s verificationism views. Their choices were analyzed to assess dependency patterns on these platforms. Further, a qualitative analysis compared ai-generated responses with …

Town Of Plainfield, New Hampshire Annual Report 2024, Annual Reports Of The Officers And Selectmen And The School District, Plainfield Town Representatives

Plainfield, NH Annual Reports

This is an annual report containing vital statistics for a town/city in the state of New Hampshire.

General Catalog 2025-2026, Utah State University

University Catalogs

This catalog contains information on the Academic Departments and Programs as well as Course Descriptions for the above school year at Utah State University.

Verification Of Real-Time Smoke Simulations: A Case Study On Early Summer North American Wildfire Events, Daniel Bonilla

Pitzer Senior Theses

As wildfire intensity and frequency escalate in times of unprecedented climate change and severe droughts, it is important to enhance forecasting models that accurately and efficiently predict wildfire smoke movement. NOAA’s experimental Rapid-Refresh Forecasting System with Smoke and Dust (RRFS-SD) aims to improve upon the High-Resolution Rapid Refresh- Smoke (HRRR-Smoke), by extending the domain to include all of North America and incorporating new developments in physics and wildfire parameterizations. Our work explores the process of adding satellite Aerosol Optical Depth (AOD) data, through AERONET, as an additional method of verification of the RRFS-SD experimental model. Using a case study on …

Optimizing Decision-Making In A Cerebral Palsy Model Using Reinforcement Learning, Richard Ampah

Pitzer Senior Theses

This study presents an original interdisciplinary investigation into how reinforcement learning (RL) can model motor and cognitive defects and potentially improve motor and cognitive functions in individuals with cerebral palsy (CP), a non-progressive neurological disorder that impairs movement and adaptability. Integrating computational neuroscience and machine learning, the research applies policy gradient methods and Markov Decision Processes (MDPs) to simulate adaptive learning in agents with and without CP-related constraints.

The central aim is to compare the cumulative rewards of optimal policies, derived from value iteration, and human-like learning policies using the REINFORCE algorithm, both with and without the Bellman baseline. The …

The Arts And Student Engagement: Correlational Insights For School Accountability Of Low Income, English Learners, And Foster Youth, Paul Treesuwan

CGU Theses & Dissertations

This quantitative study examined the relationship between students’ engagement in the arts and their engagement in both academic classes and the broader school community within a diverse, urban, public charter high school. Using survey data from 251 students in grades 9–12, analyses explored whether arts engagement was associated with academic and school-community engagement and whether these relationships differed across English Learner (EL), low-income (F/R), and foster-youth subgroups. Results revealed that arts engagement was moderately and positively correlated with both academic engagement and school-community engagement confirming consistent cross-domain associations. ANOVA analyses detected no statistically significant differences in engagement levels by EL, …

Paths To Mathematics Success: A Path Analysis Of Educational Expenditures To Math Outcomes Of Latine Students, Aldo Ramirez

CGU Theses & Dissertations

This quantitative critical study employed multiple linear path analysis models with difference scores to investigate the effects of funding adjustments on mathematics learning rates for Latine students. The theoretical framework integrated the Education Production Function with the QuantCrit frameworks to untangle the complex web of relationships associating educational funding adjustments and the academic outcomes of Latine students. The study specifically examined these relationships across U.S. districts stratified by poverty level (20%). To strengthen the causal inference and enhance control over confounding variables, the research design incorporated several key methodological features: (1) the use of difference scores to control for baseline …

Petroleum Reservoir Dynamics, Mehdi Zeidouni

E-Textbooks

The book is structured into nine chapters, each designed to build a comprehensive understanding of reservoir engineering principles. Chapter 1 introduces the discipline and its connections with other areas of petroleum engineering. Chapters 2 through 5 address steady-state flow and its various complexities: Darcy’s equation and its limitations (Chapter 2); flow geometries and skin factor concepts (Chapter 3); compressible oil and gas flow (Chapter 4); and non-horizontal flow using the flow potential concept (Chapter 5).

In Chapter 6, the pressure diffusivity equation is explored alongside key concepts of radius of investigation and average reservoir pressure. Chapter 7 delves into the …