Digital Commons Network^™

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.

Maple: Malware Analysis Through Projection Of Low-Dimensional Embeddings, Quang Duy Tran

Master's Projects

Machine learning has become a popular and powerful tool for malware analysis and detection. With the rise in popularity of natural language processing (NLP) techniques, researchers can now extract contextual embeddings from malware opcode sequences, enabling the capability to analyze hidden malware patterns and advanced code obfuscation strategies. However, unlike malware binaries, which can be directly visualized as images, these embeddings exist in high-dimensional spaces, making it difficult to observe their global patterns or spatial structures. In this paper, we propose a framework for visualizing malware embeddings in lower-dimensional space using various dimensionality reduction techniques. Our approach converts malware binaries …

Framework For Identity Privacy Through Gender Based Skeletonization, Harrison Hwang

Master's Projects

The protection of one’s privacy and sensitive information is becoming increasingly difficult in the modern age full of surveillance and data collection. Through the use of image based object detection machine learning models trained for human and facial recognition, people can be identified and tracked to a terrifyingly accurate degree. On the other hand, the information present in surveillance media can play a key role in security and law enforcement. This presents a problem of how to preserve key information without compromising the privacy of any individuals present in the video. In this research project, Computer Vision techniques and a …

Enhancing Code Review Automation With Large Language Models Using Qlora Fine-Tuning And Rags, Sumukh Naveen Aradhya

Master's Projects

In this technological era where Artificial Intelligence and Machine Learning are revolutionizing various domains, Large Language Models (LLMs) are emerging as a very powerful tool. In the software development lifecycle, it is extremely important to have reliable code reviews to ensure security and maintain code quality. This project aims to survey various existing methodologies to aid creation of efficient code review automation agents and also research on ways to make this process more efficient. Parameter Efficient Fine-Tuning (PEFT) methodologies such as LoRA and QLoRA have been explored with an additional focus on a hybrid model that combines adaptive QLoRA with …

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 …

Strengthening The Preservation Ecosystem For Below-Market-Rate Housing In Santa Cruz, Bennett Williamson

Master's Projects

In Santa Cruz, California, policies and political movements of the last fifty years have both prevented sprawl and stymied the construction of new infill housing. As a result, today Santa Cruz is one of the most expensive housing markets in the nation, and the lack of available affordable housing has led to incredibly high rent burdens for the majority of the population, putting residents at risk of displacement through landlord action or market forces. This report assesses the efficacy of preserving existing affordable housing as an anti-displacement strategy in Santa Cruz. It uses key stakeholder interviews with local housing practitioners …

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 …

Advanced Knowledge Extraction With Biomedical Data Using Llms, Akshat Krishna

Master's Projects

The rapid growth of biomedical research has led to an overwhelming volume of unstructured textual data in the scientific literature. This has necessitated the development of an automated approach for knowledge extraction and integration. In

this project, we present a comprehensive pipeline for constructing a unified biomed- ical knowledge graph by combining two well-known datasets: CHEMPROT [1],

which captures chemical–protein interactions, and EU-ADR [2], which annotates drug–gene–disease relationships. In order to identify important biomedical entities and interactions from CHEMPROT dataset, we perform Named Entity Recognition (NER) and relation Extraction (RE) using state-of-the-art biomedical models like BioBERT [3], BioGPT [4] and …

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 …}

Coral Vision – Crustose Coralline Algae Detection With Computer Vision, Ryan Tseng

Master's Projects

Crustose coralline algae (CCA) are a group of red algae that are vital contributors to the health of coral reef ecosystems. Monitoring CCA abundance can serve as an indicator for coral reef health and improve reef conservation efforts. Autonomous Reef Monitoring Structures (ARMS) are artificial structures that can be deployed into coral reef ecosystems and retrieved to gather ecological data without harming reef structures. Traditional methods of calculating CCA abundance require manual analysis and are labor-intensive. Recent developments in computer vision and deep learning technology have provided an avenue to fully automate this task. This research aims to train a …

Living In The Hyphen: Creating Puentes Through Encuentros And Pláticas With Latinx Cross-Cultural Kids, Jennifer Jane Daby

Dissertations

Latinx cross-cultural kids, or children of immigrants, are often faced with a feeling of not belonging to mainstream American culture nor their parents’ home culture(s) of origin. As such, they are viewed through a deficit lens and forced to negotiate their sense of identity and belonging through language and culture to fit in. By embracing the concept of educación, using culturally responsive methods of encuentros and pláticas with cross-cultural kids and their families, this study intends to find ways to foster a sense of belonging for this population in schools through participants’ testimonies. It aims to find ways to bridge …

Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre

Master's Projects

Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …

Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula

Master's Projects

Graph neural networks (GNNs) have emerged as a powerful paradigm for collaborative filtering. However, they often fall short in fully leveraging side textual content, resulting in suboptimal recommendations. To address this limitation, we explore the synergy between GNNs and deep contextual embeddings of item descriptions, aiming to enhance recommendation quality on the Amazon-Books dataset. We propose SemanticGraphRec, which combines GNNs with Large Language Models (LLMs) to leverage both collaborative filtering and textual item content. Experimental results demonstrate that incorporating semantic item embeddings produced by fine-tuning LLMs consistently improves performance. Our approach enhances recommendation relevance in sparse data scenarios by leveraging …

Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana

Master's Projects

Exponential growth in cloud computing has brought enormous changes in data storage and processing, but also raised several questions on the security, privacy, and efficient storage of data. This report provides a dual-focused approach toward solving these challenges. First, we try to build an application securely and efficiently using data deduplication and Proxy Re-Encryption for optimization of storage and enabling secure data sharing. Deduplication ensures that redundant data is removed before encryption for maximum efficiency in storage, while PRE enables the safe sharing of encrypted data by re-encrypting the keys for specified recipients without the leakage of sensitive information. We …

Rift - Reddit Information Falsity Tagger, Parth Joshi

Master's Projects

Social media platforms such as Reddit are widely used for sharing and consuming information. User-generated content poses a great risk for misinformation creation and dissemination on these platforms. “Fake news”, as it is commonly referred to, has far-reaching social implications, swaying public perception, making political viewpoints more radical, and adversely impacting health decisions. The covariable features that come with fake news make it even harder to detect because it is presented in the form of text, images, videos, and even social interactions. This paper describes a novel method for detecting fake news on Reddit: RIFT, short for Reddit Information Falsity …

Application Of Advanced Convolutional Neural Network With Robust Hashing On Obfuscated Image Based Malware Dataset, Sanket Shekhar Kulkarni

Master's Projects

This project report provides in-depth details on the creation of a malware classification system that makes use of Convolutional Neural Networks (CNNs) that have been strengthened by data set obfuscation and strong hashing. We test many CNN architectures, including MobileNet, ResNet, and DenseNet, using rigorous hashing and obfuscation techniques on datasets. The entire pipeline is described in this research, which ranges from the gathering and preprocessing of data sets to the application of novel hashing techniques that boost overall accuracy in classification and increase resilience against malicious attacks. Parallel to this, we show that dataset obfuscation adds an additional level …

Malware Generation And Classification Using Pixelcnn, Mounika Krishna Teja Karumudi

Master's Projects

Malware poses a serious threat to both data privacy and system security. With the wide variety of malware families and the surge in cyber-attacks, the accurate classification of malware is crucial for building effective detection and prevention systems. In recent years, deep learning (DL) methods in computer vision have shown promise in classifying malware by converting malware files into visual representations and applying DL algorithms to classify the resulting images. Among the different approaches to malware family classification, image-based methods have gained significant interest. This research focuses on leveraging DL techniques for image-based classification of malware. The success of identifying …

Enhancing Robustness Of Cnn Model For Malware Detection Using Gan-Based Data Augmentation And Transfer Learning, Milind Anand Pathak

Master's Projects

Malware classification is a critical component in the field of cybersecurity. Accurate identification of a malware family can enable timely threat detection and response. In this thesis, we propose a robust image-based malware classification pipeline using Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), with a focus on improving performance for underrepresented malware families. We train a baseline CNN model on the Malimg dataset across 25 malware families, but observe misclassifications in classes with limited data and overlapping visual features. To address this, we apply targeted augmentations and generate class-specific synthetic data using StyleGAN2-ADA. A CNN trained on the …

Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks, Soham Choudhury

Master's Projects

This paper presents a novel approach for optimizing network monitoring in optical communication systems using Reinforcement Learning (RL). Assuming a multi-domain architecture with limited domain visibility, we simulate multiple optical connections using an optical communications simulation software, GNPy, obtaining key network metrics to model the system. We developed two RL agents: the first agent selects near-optimal monitoring paths based on network states, and the second agent dynamically adapts its selected paths in response to state changes, such as fiber failures or issues with ROADMs. This adaptive approach allows for continuous improvement of network monitoring, ensuring resilience and efficient fault detection. …

Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari

Master's Projects

The widespread use of multiple social media platforms has amplified the expression of public opinions over the Internet in languages such as English, Hindi and Spanish. With the aid of technological advancements in machine learning, we can analyze opinions posted on the Internet and gauge public sentiments. There are organizations and businesses that are interested in the evaluation of these sentiments as these type of data can generally be used to obtain the opinion of a product, restaurant, a candidate, etc. In this study, we perform a comparative analysis of three popular ensemble learning methodologies (Boosting, Bagging and Stacking) based …

Nontraditional Students Enrolling In Online Education: Assessing Outcomes And Barriers To Success, Kua Vang

Master's Projects

Online education has significantly reshaped how colleges and universities define and evaluate academic achievement. In March 2020 during the height of the Covid-19 pandemic, the U.S. government enforced social distancing regulations that affected operational continuity for higher education institutions. The need to transition from traditional classroom instruction to online was unavoidable. Institutions were not prepared, however, and their shortcomings in providing high-quality online learning for students were exposed. The inadequacy can be due to higher education institutions decade-long mindset of seeing online education as a less credible alternative. As a result, institutions are more eager than ever before to invest …

Characterizing Somatic Variants In Nanopore Data With Machine Learning, Shwethal Sayeeram Trikannad

Master's Projects

Oxford Nanopore Technology (ONT) is a popular long-read sequencer in genomics. However, its high base-calling error rate produces several sequencing artifacts. Detection of somatic variants in ONT sequenced tumor-normal samples remains challenging due to low frequencies. In this study, machine learning was applied to a dataset created by benchmarking ClairS output against HCC1395 and colo829 truth sets to classify variants and artifacts. Relevant features were engineered from sequence context and variant site characteristics to model artifact profiles. HistGradientBoostingClassifier achieved 0.876950 accuracy, outperforming all other models. Variant quality was the top predictor with an aggregate accuracy of over 85%. This work …

Managing Compliance: The Role Of Leadership Styles In Information Security Practices In Nigerian Public University Libraries” Insights From North-East Nigeria, Musa Giade Ya'u Musagiade, Gaberial O. Alegbeleye, Madukoma Ezinwanyi

Library Philosophy and Practice (e-journal)

Background: Information security compliance comprises adhering to established policies, protocols and to ensure absolute security. Despite the importance of security compliance in academic libraries, evidence from previous studies reported a low level of information security compliance among personnel in public university libraries in North-East Nigeria. However, proper leadership styles influence information security compliance leading to absolute protection of information systems. So, this study explored the influences of leadership styles in security compliance in public university libraries in North-East, Nigeria.

Method: This study adopted a survey research design. The population consisted of 618 library personnel from the 14 public university libraries …

Working With Similarities And Differences: Relational Processes In Transdisciplinary Qualitative Research With Diverse Teams, Michael S. Dao, Soma De Bourbon, Melissa Mcclure Fuller, Miranda Worthen

Faculty Research, Scholarly, and Creative Activity

Transdisciplinary research and research teams are becoming increasingly valued in academic spaces. The potential of transdisciplinary research is that the diversity of thought and experience can create robust research processes from project inception, methodological protocol, data collection, data analysis and reporting output. Yet, transdisciplinary research teams can also bring about complications pertaining to conflicting epistemological perspectives, areas of expertise, and objectives for applied impact. In noting the benefits and downsides of transdisciplinary research, this article aims to detail how a transdisciplinary research team navigated a qualitative and participatory longitudinal research project. Drawing from a larger community-based participatory research project, the …

Advancing The Environmental Dna And Rna Toolkit For Aquatic Ecosystem Monitoring And Management, Xavier Pochon, Holly A. Bowers, Anastasija Zaiko, Susanna A. Wood

Faculty Research, Scholarly, and Creative Activity

The application of environmental DNA (eDNA) and RNA (eRNA) technologies to aquatic ecosystem monitoring and management has increased rapidly in the last decade. These methods are providing many new and exciting opportunities for enhanced biodiversity assessment, ecological health evaluation, and species detection. This special issue of PeerJ Life and Environment brings together 20 innovative studies that collectively advance the eDNA toolkit. Four key themes are covered: (i) Methodological advancements, (ii) Ecological health assessments and biomonitoring, (iii) Species detection, and (iv) Application and management. The studies cover a suite of topics including; optimizing sample collection, developing species-specific assays, evaluating bioindicator species, …

Could Password Sharing Entitle You To Monitor Your Partner's Social Media Accounts?, Irum Saeed Abbasi, David Khabaz, Maryam Abbasi, Greg Feist

Faculty Research, Scholarly, and Creative Activity

Social media offers a plethora of strangers cum friends whose photo shopped images seem idealistic and more appealing than one's primary partner. Due to the physical absence and lack of non-verbal cues, online communications can quickly become aggressive and emotionally intimate. Emotional intimacy between online friends is considered a betrayal in a dyadic relationship. To protect mates from external relationship threats, romantic partners may request and/or coerce their significant other for social media account passwords. In a sample of 299 adults (women = 246) between18 and 72 years (M = 21.12, SD = 5.39), we explored the participants’ attitudes toward …

Layover Start Timing Predicts Layover Sleep Quantity And Timing On Long-Range And Ultra-Long-Range Trips, Michael J. Rempe, Ian Rasmussen, Kevin Gregory, Cheyenne Johnson, Matthew Hsin, Erin Flynn-Evans, Amanda Lamp, Cassie J. Hilditch

Faculty Research, Scholarly, and Creative Activity

Study Objectives: Airline transport pilot sleep during layover is an important factor for alertness on subsequent flights. Assessing pilots' sleep on layover is an important first step in helping them obtain the most recuperative sleep possible on layover. Here, we investigate the quantity and timing of sleep during layovers and determine predictors for layover sleep. Methods: Sleep was assessed in 256 pilots flying a total of 473 long-range (LR; flight time 12-16 hours) or ultra-long-range (ULR; flight time > 16 hours) trips. Sleep was assessed using actigraphy. We employed linear mixed-effects models with layover sleep characteristics as the outcomes. The predictor …

Label-Free Visualization And Segmentation Of Endothelial Cell Mitochondria Using Holotomographic Microscopy And U-Net, Raul Michael, Tallah Modirzadeh, Tahir Bachar Issa, Patrick Jurney

Faculty Research, Scholarly, and Creative Activity

Understanding the physiological processes underlying cardiovascular disease (CVD) requires examination of endothelial cell (EC) mitochondrial networks, because mitochondrial function and adenosine triphosphate production are crucial in EC metabolism, and consequently influence CVD progression. Although current biochemical assays and immunofluorescence microscopy can reveal how mitochondrial function influences cellular metabolism, they cannot achieve live observation and tracking changes in mitochondrial networks through fusion and fission events. Holotomographic microscopy (HTM) has emerged as a promising technique for real-time, label-free visualization of ECs and their organelles, such as mitochondria. This nondestructive, noninterfering live cell imaging method offers unprecedented opportunities to observe mitochondrial network dynamics. …

Smithian Merchant Towns And Good Government In The Wealth Of Nations, John B. Estill

Faculty Research, Scholarly, and Creative Activity

Commentators on Adam Smith’s Inquiry into the Nature and Causes of the Wealth of Nations often characterize Smith as a proponent of government in multiple areas, including security in person, property, contract, and some public goods. However, Smith understood government more expansively than people today. In volume I, book III, chapters I–IV, Smith describes the evolution of merchant towns in England that led to “good governance” that dismantled the feudal system. This limited government not only included the security provided by the formal laws and institutions. It also aligned the informal elements of individual civic ethics and self-reliance with the …

“They're Like Slash”: Multimodality And Embodied Agency In Students' Critical Engagements With Texts, María José Aragón, Meghan Corella, Nora W. Lang

Faculty Research, Scholarly, and Creative Activity

Despite recent calls to more fully incorporate multimodal perspectives into literacies research, there is still limited scholarship examining how students critically engage in reading activities by drawing on embodied practices. Racially and linguistically minoritized students are particularly disadvantaged by dominant logocentric and developmentalist approaches, which privilege oral and written discourse and often position these students as less capable of performing complex literacy practices. Drawing from three independent ethnographic studies, our multimodal interactional analysis examines how students of a range of ages and raciolinguistic backgrounds use embodied actions and other semiotic resources to agentively navigate text, task, and ideological constraints in …