Digital Commons Network^™

Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov

Master's Projects

Optical networks transport data encoded on light signals over optical fiber cables. Individual optical networks are managed by domain administrators, such as service providers, vendors, or regional bodies. Multidomain optical networking explores the possibility of enabling seamless data transmission across domain boundaries. In this project, we explore how we can perform network monitoring in a hierarchical multidomain optical network while preserving domain security and autonomy. This is done by allocating dedicated monitoring trails across various broker abstractions of our network topologies. We propose three heuristic functions that expedite trail selection. Of the three heuristics, our least monitored algorithm performs the …

Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms, Sujith Kakarlapudi

Master's Projects

Modern search engines and wiki platforms generate vast quantities of user inter-
/="/">action data such as page views, edits, clicks, and session events. This data must be stored and aggregated efficiently to enable scalable analytics and responsive querying. Yioop, an open-source search engine framework, serves as our primary case study, processing millions of such events to power its indexing and recommendation features. This report explores a shift from the conventional database storage based model to a log based model, in order to improve scalability and write efficiency. A size-limited, append-only logging facility was provided to log analytics events: the …

Optimizing Integrated Access And Backhaul Topology Using Monte Carlo Tree Search, Srimanth Reddy Ummenthala

Master's Projects

Integrated Access and Backhaul (IAB) plays a central role in enabling scalability and high-throughput in wireless networks, especially in areas where wired backhaul is not feasible. Optimizing the topology of IAB networks is a complex task, involving trade-offs between path loss, node capacity, and link quality. To address this, this project investigates a Monte Carlo Tree Search (MCTS)-based approach to improve overall network performance by maximizing the capacity of network, considering Signal-to-Noise Ratio (SNR) on wireless links. MCTS provides a guided search mechanism to construct topologies efficiently under the constraints and is evaluated against baseline topologies. Experimental results show that …

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 …

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 …

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. …

Transforming Chatbot Text: A Sequence-To-Sequence Approach To Human-Like Text Generation, Natesh Reddy

Master's Projects

With the advancements in Large Language Models (LLMs) such as ChatGPT, the boundary between text written by a human and that created by AI has become blurred. This poses a threat to systems designed to detect AI-generated content. In this work, we adopt a novel strategy to adversarially transform GPT-generated text using sequence-to-sequence (Seq2Seq) models, to make the text more human-like. We concentrate on improving GPT-generated sentences by including significant linguistic, structural, and semantic components that are typical of human-authored text. The goal is to then use this transformed data to train a more robust detection model. Our experiments show …

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 …

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.

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 …

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 …

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 …

Ai-Enabled Anticipatory Handover Predictions In 5g Networks, Ojas Ankush Naik

Master's Projects

As users move across network cells in 5G, it is critical to maintain seamless connectivity through efficient and fast handovers. However, as 5G networks have a very dense deployment of cells and higher carrier frequencies, handovers are often more frequent and challenging, leading to failures or the ping-pong effect. In this research, we are going to use Artificial Intelligence (AI) techniques to enable predicting handover(HO) events proactively as opposed to reactively, aiming to reduce HO failures and unnecessary handovers. We develop Long-Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models to forecast future signal measurements, predict handover trigger points, and compare …

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi

Master's Projects

This paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect …

Human Vs. Llm: Interpreting Emotion From Neutral Faces In Contextually Charged Backgrounds, Sai Mounika Peteti

Master's Projects

In this study, we investigate the intersection of cultural context, visual perception, and implicit bias in interpreting neutral human expressions. Specifically, we explore how emotionally charged backgrounds can shape viewers interpretations of neutral facial expressions. Using an experimental setup, participants are shown neutral human portraits paired with varying background types ranging from emotionally neutral or pleasant to evocative scenes. Participants are tasked with selecting which background best matches the emotional state of the person depicted in the portraits. Eye-tracking data is collected to analyze visual attention patterns and cognitive processing. In parallel, we also evaluate how large language models (LLMs) …

Why We Need To Fill In Our Mixed-Use Communities’ Business Vacancies And How We Can Do That, Victor Huynh

Master's Projects

This planning report provides an in-depth exploration as to why so many mixed use communities, including transit-oriented developments, in metropolitan regions across the country are facing challenges with filling in their retail business vacancies at the ground floor. It also provides a current snapshot of best practices to remedy this widespread barrier to cities functioning more effectively and sustainably. This report provides a combination of findings from a literature review which identifies the most common reasons and remedies for business vacancies, and a research design which identifies which urban planning or urban design characteristics correlate most frequently with case sites …

Librarians In U.S. Public Schools Without Other Kinds Of Specialist Educators: The Scale Of Opportunities For Instructional Leadership, Keith Curry Lance, Caitlin Gerrity

Learning Hub

This article explores the unique instructional leadership opportunities for school librarians in U.S. public schools that lack other specialist educators, such as technology specialists, instructional specialists, and instructional coaches. Drawing on data from the 2020–21 National Teacher and Principal Survey (NTPS), the study highlights the prevalence of schools with librarians operating without full-time or part-time technology specialists, subject-specific instructional specialists, or instructional coaches. The absence of these roles often places librarians in a central position to support both students and teachers in areas such as literacy development, STEM education, and technology integration. Additionally, the article examines the potential for librarians …

Archeota, Fall/Winter 2024, Sarah Miller, Kaitlyn O'Dell, Erica Leff, Peyton Walters, Joshua Insel

Archeota

Archeota is a platform for SJSU iSchool students to contribute to the archival conversation. It is written BY students, FOR students. It provides substantive content on archival concerns and issues and promotes professional development in the field of archival studies. Archeota upholds the core values of the archival profession.

Contents

A Tale of Two Film Archives: History and Impact of the British Film Institute and the Cinémathèque Française by Sarah Miller

Enhancing Archival Access with Sustainability: Insights from a Library Scholar Internship at Cal Poly Humboldt by Kaitlyn R. O’Dell

Farewell to Our Winter 2024 Graduate: Interviews with SAA Student …

Grounded In Experience: A Collaborative Approach To Resource Development For Adults With Ehlers-Danlos Syndrome, Morgan Kam

Doctoral Projects

Background: Ehlers-Danlos syndrome (EDS) is a complex connective tissue disorder that significantly affects daily occupations, participation, and quality of life, yet early support for individuals pursuing or newly receiving a diagnosis remains limited. Occupational therapy (OT) is uniquely positioned to address these challenges through interventions that promote self-management and functional engagement.

Purpose: This project aimed to develop an experience- and evidence-informed educational resource for individuals undergoing or newly receiving an EDS diagnosis, while increasing awareness of OT’s role in managing this chronic, multisystemic condition.

Methods: A mixed-methods approach integrated qualitative insights from interviews, online support groups, and practitioner feedback. A …

Family-Level Impacts Of Equine-Assisted Occupational Therapy For Children With Disabilities: An Exploratory Mixed-Methods Pilot Study, Emily Canigiula

Doctoral Projects

Equine-assisted occupational therapy (EAOT) has been shown to enhance functional, social, and quality-of-life outcomes for children with disabilities (McDaniel Peters & Wood, 2017; Prieto et al., 2022). While child-focused outcomes are well documented, significantly less is known about how EAOT participation may influence caregivers and the broader family unit, as existing research largely prioritizes individual outcomes over family experiences (Peters et al., 2017; Tan & Simmonds, 2018; Trzmiel et al., 2019; Xiao et al., 2023). Emerging qualitative studies suggest potential family-level impacts including family engagement, reduced parental stress, and enhanced feelings of pride, empowerment, and connection (Kalmback et al., 2020; …

Applying Occupational Empowerment Principles In A Life Skills Program For Unhoused Transitional-Aged Youth: Feasibility And Early Outcomes, Eriza Navarro

Doctoral Projects

The purpose of this occupational therapy doctoral capstone project was to address a gap in community-based services for unhoused transitional-aged youth (TAY) by promoting life skill acquisition and supporting participation in meaningful activities in order to enhance self-efficacy. This project aimed to design, implement, and evaluate a structured occupation-based life skills program that builds upon existing services at a local drop-in center. The program incorporated life skill education and activity-based, experiential interventions tailored to meet the needs and learning styles of the population. Methods included conducting a needs assessment, developing the program curriculum, and piloting the 6-week program consisting of …

Promoting Participation And Self-Efficacy Among Youth With Disabilities In Community-Based Vocational Education, Allison Hollender

Doctoral Projects

Occupational therapy, with its focus on participation, contextual adaptation, and strength-based practice, is uniquely suited to support the growth and inclusion of youth in transition (Angell et al., 2018; Cahill et al., 2020; Carroll et al., 2025). Research indicates that transitional-aged youth (TAY) with disabilities, ages 14-24, are less likely to graduate from high school, pursue postsecondary education, or live independently (BLS, 2024; Cheng & Shaewitz, 2022; Cook et al., 2015). These disparities are compounded by occupational deprivation, or limited opportunities for meaningful participation, which can negatively impact long-term self-efficacy and belonging (Arbesman & Logsdon, 2011; Cahill et al., 2020). …

Stream Response To A Dam Removal: Mill Creek, Davenport, Ca, Jessica J. Williamson

Master's Theses

Dams have many effects on stream morphology and ecology, including a loss of sediment supply downstream, reduced access to the entire stream for aquatic fauna, and a flooded area upstream of the dam. Dams are a hurdle for migrating organisms such as anadromous fish, which is a group of concern. An over 100-year-old dam on Mill Creek, a stream draining a coastal watershed in central California, was removed in the fall of 2021. The site is in the Santa Cruz Mountains, where endangered salmonids such as steelhead, Oncorhynchus mykiss, have historically lived. To study the change in the stream from …

Review Of From The Ruins Of Enlightenment: Beethoven And Schubert In Their Solitude By Richard Kramer, Sarah Waltz

The Beethoven Journal

No abstract provided.

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. …

Optogenetic Methods To Stimulate Gamma Motor Neuron Axons Ex Vivo, Apoorva Karekal, Remie Mandawe, Cameron Chun, Sai Kiran Byri, Danitza Cheline, Serena Ortiz, Shawn Hochman, Katherine A. Wilkinson

Faculty Research, Scholarly, and Creative Activity

It is challenging to stimulate gamma motor neurons, which are important regulators of muscle spindle afferent function, without also recruiting alpha motor neurons. Here, we test the feasibility of stimulating gamma motor neuron axons using optogenetics in two transgenic mouse lines. We used an ex vivo muscle–nerve preparation in adult mice to monitor muscle spindle afferent firing, which should increase in response to gamma motor neuron-induced lengthening of the sensory region of the muscle spindle. A force transducer measured alpha motor neuron-mediated twitch contractions. Blue LED light (470 nm; 1–5 mW) was delivered via a light guide to the sciatic …

Measurement Resolution Enhanced Coherence For Lattice Fermions, Hilary M. Hurst, Yik Haw Teoh, I. B. Spielman

Faculty Research, Scholarly, and Creative Activity

Weak measurement enables the extraction of targeted information from a quantum system while minimizing decoherence due to measurement backaction. However, in many-body quantum systems, backaction can have unexpected effects on wave-function collapse. We theoretically study a minimal many-particle model consisting of weakly measured noninteracting fermions in a one-dimensional lattice. Repeated measurement of the on-site occupation number with single-site resolution stochastically drives the system toward a Fock state, regardless of the initial state. This need not be the case for measurements that do not, even in principle, have single-site spatial resolution. We numerically show for systems with up to 16 sites …