Digital Commons Network^™

Many-Body Physics Of Ultracold Alkaline-Earth Atoms With Su(N)-Symmetric Interactions, Eduardo Ibarra-García-Padilla, Sayan Choudhury

Faculty Research, Scholarly, and Creative Activity

Symmetries play a crucial role in understanding phases of matter and the transitions between them. Theoretical investigations of quantum models with SU(N) symmetry have provided important insights into many-body phenomena. However, these models have generally remained a theoretical idealization, since it is very difficult to exactly realize the SU(N) symmetry in conventional quantum materials for large N. Intriguingly however, in recent years, ultracold alkaline-earth-atom (AEA) quantum simulators have paved the path to realize SU(N)-symmetric many-body models, where N is tunable and can be as large as 10. This symmetry emerges due to the closed shell structure of AEAs, thereby leading …

Mobile Technology-Based Library Services In Academic Libraries Of Ghana, Silas Adjei, Patience Adetsi, Isaac Kojo Agyeman

Library Philosophy and Practice (e-journal)

In contemporary academic library settings, there's a growing need for libraries to offer access to their resources and services remotely and without boundaries. Mobile technologies (MT) have gained widespread acceptance among academic library stakeholders globally, as they are seen as effective communication tools for providing convenient library services to patrons. This study aimed to explore the potential benefits of adopting and implementing Mobile Technology-based Library Services in academic libraries in Ghana. Conducted as a descriptive survey using a quantitative approach, the research focused on two private universities in Ghana: Valley View University and Central University. The sample comprised 390 undergraduate …

What Knowledge Resources Do General Chemistry Students Use To Agree Or Disagree With Atomic Level Acid–Base Animations?, Resa M. Kelly, John H. Kim, Adrian Villalta-Cerdas, Sarah J.R. Hansen, Sevil Akaygun

Faculty Research, Scholarly, and Creative Activity

Atomic-level visualizations serve as pivotal instructional tools in chemistry education, providing insights into the particulate level of matter, a dimension fundamental, yet typically invisible, in the study of chemistry. This research investigates how students’ engagement with these visualizations influences their conceptual understanding of atomic-level phenomena. Specifically, it examines the knowledge resources used by college chemistry students (n = 15), both current and former General Chemistry students, to agree with visual representations when confronted with conflicting animations depicting atomic level acid–base neutralization reactions. Employing an epistemological framework, the study analyzed video recordings of students constructing and articulating their models and subsequently …

Application Of Information And Communication Technology In The Management Of Library In University Of Mkar, Mkar, Benue State, Joseph Lughlugh, Titilola Olaniyi Adegoke Dr, Saater Iorngulum Mr

Library Philosophy and Practice (e-journal)

The study examined application of information and communication technology (ICT) in the management of library in University of Mkar, Mkar, Benue State. Three research questions guided and directed the study. Descriptive survey design was adopted for the study. The population of the study comprised 29 library staff in the University of Mkar. The study adopted census sampling procedure thus, all the 29 library staff participated in the study. One instrument tagged “application of information and communication technology in the management of library questionnaire (AICTMULQ)” was used for data collection. The instrument was validated by experts. The reliability of the instrument …

“I Need This Person’S Support To Have A Career” The Material And Emotional Impacts Of Neoliberalism On Trans Collegians’ Classroom Experiences At A Public University, Justin A. Gutzwa, Robert A. Marx

Faculty Research, Scholarly, and Creative Activity

Neoliberal capitalism has undoubtedly impacted every sphere of public education in the United States. As faculty are pushed towards identity-neutral modalities of instruction, students who identify as transgender, non-binary, or other expansive gender identities (trans) are forced to reckon with implicit and explicit power dynamics in classrooms that prioritize White, cisheteropatriarchal modes of knowledge production in nuanced ways. This qualitative study explores the ways neoliberal capitalism impacts the curricular experiences of trans students by centering the power dynamics they encounter through interactions with their professors and their peers. Findings underscore the pernicious nature of the neoliberalization of higher education in …

Computing Education In African Countries: A Literature Review And Contextualised Learning Materials, Sally Hamouda, Linda Marshall, Kate Sanders, Ethel Tshukudu, Oluwatoyin Adelakun-Adeyemo, Brett A. Becker, Emma R. Dodoo, G. Ayorkor Korsah, Sandani Luvhengo, Oluwakemi Ola, Jack Parkinson, Ismaila Temitayo Sanusi

Faculty Research, Scholarly, and Creative Activity

This report begins with a literature review of computing education in Africa. We found a substantial body of work, scattered over more than 80 venues, which we have brought together here for the first time. Several important themes emerge in this dataset, including the need to contextualise computing education. In the second part of this report we investigate contextualisation further. We present a pilot study, grounded in the literature review, of the development of course materials, sample code, and programming assignments for introductory programming, contextualised for six African countries: Botswana, Egypt, Ghana, Nigeria, South Africa, and Zambia. We include the …

Spherical And Sessile Droplet Dynamics By Fluctuating Hydrodynamics, John B. Bell, Andrew Nonaka, Alejandro L. Garcia

Faculty Research, Scholarly, and Creative Activity

We simulate the mesoscopic dynamics of droplets formed by phase-separated fluids at nanometer scales where thermal fluctuations are significant. Both spherical droplets fully immersed in a second fluid and sessile droplets which are also in contact with a solid surface are studied. Our model combines a Cahn-Hilliard formulation with incompressible fluctuating hydrodynamics; for sessile droplets, the fluid-solid contact angle is specified as a boundary condition. Deterministic simulations with an applied body force are used to measure the droplets' mobility from which a diffusion coefficient is obtained using the Einstein relation. Stochastic simulations are independently used to obtain a diffusion coefficient …

Action Research As A Pathway To Discovery In The School Library, David Vickers Loertscher, Michelle Young

Learning Hub

No abstract provided.

Tsao, H.-S. Jacob, San Jose State University

Emeritus and Retired Faculty Biographies

University of California, Berkeley, Operations Research, Ph.D. 1984

University of Texas at Dallas, Mathematical Statistics, MS, 1979

National Chiao-Tung University, Taiwan, Applied Mathematics, BS, 1976

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 …

Augmenting Missing Sensor Data For Robust Human Activity Recognition, Suryakangeyan Kandasamy Gowdaman

Master's Projects

Applications of ubiquitous computing, including health monitoring, sports analytics, and ambient-assisted living, rely on Human Activity Recognition (HAR) using wearable sensors. However, model robustness is challenged by missing sensor values, class imbalance, inter-subject variability, and temporal noise. This work proposes a complete HAR pipeline that addresses these challenges through sampling, time-series augmentation, dynamic feature handling, and GAN-PCA-based imputation. Built on the DeepSense architecture, the model integrates convolutional feature extraction with bi-GRUs for temporal modeling. The system is evaluated using 5-fold cross-validation, subject-aware holdout, and LOSEO strategies on the Opportunity dataset. Results demonstrate consistent accuracy across folds and strong generalization to …

On The Adversarial Robustness Of Quantized Neural Networks Against Common Adversaries In Time-Series Forecasting, Maanak Arora

Master's Projects

Real-world edge applications now use modern machine learning models which require both resource efficiency and robustness against adversarial threats. Deep neural networks which include time series forecasting models still face risks from adversarial perturbations while quantization techniques used for memory and compute efficiency create unpredictable robustness challenges. This project investigates the adversarial resistance of Long Short-Term Memory (LSTM) models after applying post-training quantization at three different precision levels: 16-bit floating point (FP16), 8-bit integer (INT8) and custom 4-bit quantization. The Jena Climate dataset serves as our main benchmark for training a fullprecision LSTM model followed by multiple quantization strategies which …

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 …

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 …

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 …}

Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula

Master's Projects

Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior …

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 …

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. …

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 …

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 …

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 …

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 …

Performance Comparison Of Machine Learning Across Metal, Cuda, And Neuromorphic Frameworks, Ryan Saini

Master's Projects

Machine learning’s computational demands necessitate optimal performance and utilization. This research compares Apple Silicon M3 Pro with MPS, NVIDIA RTX 3070 GPU with CUDA, and neuromorphic computing for machine learning methods. We provide a cross-platform and cross-architecture performance analysis of machine learning methods to identify optimal configurations for training and inference scenarios. On traditional neural networks, Apple Silicon with MPS delivers superior energy efficiency at the cost of longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs. Convolutional spiking neural networks perform competitively on event-based data, particularly on …

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 …

Malware Opcode Embedding And Quality Assessment Of Generative Sample Embeddings, Atishay Jain

Master's Projects

Malware is software used to damage and disrupt computer systems with the intent to cause damage to the victim. Malware detection and classification into malware families is a crucial problem for cybersecurity researchers. One of the major bottlenecks in improving these systems is the shortage of good quality labeled malware data, especially for malware families with scarce samples. Researchers have utilized generative models to generate malware data to address this issue. Malware embeddings encode patterns within a malware file, which can be used to detect and classify malware. Recently, encouraging results have been obtained in generating malware embeddings using generative …

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 …

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 …