Computer Sciences Commons^™

Autonomous Generation Of Ids Rules From Threat Intelligence, Azim Bazarov

Theses and Dissertations

Signature-based Intrusion Detection Systems (IDS) detect malicious activities by matching network or host activity against predefined rules. These rules are derived from Cyber Threat Intelligence (CTI), which includes attack signatures and behavioral patterns obtained through automated tools and manual threat analysis, such as sandboxing. The CTI is then transformed into actionable rules for the IDS engine, enabling real-time detection and prevention of threats. The constant evolution of cyber threats necessitates frequent rule updates, which delay deployment time and weaken overall security readiness. Recent advancements in autonomous agentic systems powered by Large Language Models (LLMs) offer the potential for automatic IDS …

Extreme Artifacts Removal With Curriculum Learning, Sushant Gautam

Theses and Dissertations

Restoring severely blurred images remains a significant challenge in computer vision, impacting applications in autonomous driving, medical imaging, and photography. This thesis introduces a novel training strategy based on curriculum learning to improve the robustness of deep learning models for extreme image deblurring. Unlike conventional approaches that train on only low to moderate blur levels, this method progressively increases the difficulty by introducing images with higher blur severity over time, allowing the model to adapt incrementally. Additionally, perceptual loss and hinge loss were integrated during training to enhance fine detail restoration and improve training stability. Various curriculum learning strategies were …

Topnet R1: A Multi-Stage Ai Framework For Topic Discovery In Scientific Abstracts, Md Elias Hossain

Theses and Dissertations

Scientific abstracts are rich sources of knowledge, yet extracting meaningful topics remains challenging due to limitations in existing topic modeling techniques. Traditional methods often struggle with interpretability, scalability, and contextual understanding. To overcome these issues, we introduce TopNet R1, a multi-stage ensemble framework that integrates traditional topic models with contextual embeddings from Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 4 (GPT-4) large language models (LLMs). Top- Net R1 operates in three phases: (1) topic generation using Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Latent Semantic Analysis (LSA), and Hierarchical Dirichlet Process (HDP); (2) LLM-assisted pattern recognition …

Unboxing The Black Box: Graph-Based Interpretability In Transformer Models, Ramak Nassiri

Theses and Dissertations

This thesis introduces a novel interpretability framework for transformer encoder models by hypothesizing that their internal embedding updates define a state transition system. We propose that transformers implicitly learn token-to-token influence dynamics, which can be analyzed using graph-theoretic methods. To validate this, we construct transition graphs from embedding changes and rank token importance using the PageRank algorithm. We develop two transformer models from scratch: a self-supervised model that incorporates serotype tokens to learn contextualized embeddings, and a supervised model initialized with these embeddings. Cosine similarity analysis of their token influence patterns reveals strong structural alignment, with values exceeding 93%. This …

Augmenting Healthcare Communication: Context-Aware Ai Frameworks For Clinical Decision Support And Automated Summarization, Subash Neupane

Theses and Dissertations

Healthcare communication is plagued by fragmented medical knowledge, patient misunderstanding of care plans, and clinician burnout from documentation burdens. These inefficiencies cost the U.S. healthcare system over $300 billion annually due to preventable nonadherence and administrative waste [47, 19]. While Artificial Intelligence (AI) technology like Large Language Models (LLMs) offer potential solutions, existing systems fail to deliver personalized, context-aware guidance or automate documentation without sacrificing accuracy. This dissertation addresses these gaps through three novel context-aware AI frameworks such as MedInsight, ClinicSum, and ClinicDuo. MedInsight leverages a multi-source context augmentation approach to synthesize patient centric medical responses by integrating Electronic Health …

Estimating Reliability Of Electric Vehicle Charging Ecosystem Using Principle Of Maximum Entropy, Himanshu Tripathi

Theses and Dissertations

This thesis addresses the challenge of estimating electric vehicle (EV) charging system reliability against unpredictable threats like cyberattacks and extreme weather, where traditional methods fail. We utilize the Principle of Maximum Entropy (PME), a statistical tool that provides unbiased risk estimates using limited information. Applied to the EV charging ecosystem, our case study shows how PME models stress factors to predict failures and optimize maintenance. This approach extends beyond EVs to other complex systems with scarce data, such as smart grids or healthcare devices. By linking uncertainty directly to reliability, PME offers a universal method to improve decision-making under unpredictable …

Bring Back The Blue-Book Exam: In An Age Of Ai, We Need To Return To Handwritten Assignments., Katie Day Good

University Faculty Publications and Creative Works

When ChatGPT was released three years ago, its ability to mimic human writing unsettled me. I’m a professor of communication; what did it mean that my students now had access to a machine that could communicate for them? My initial unease led to a half-joke with my colleagues. Universities could survive this threat, I ventured, but only if we reverted back to 19th-century teaching methods like Socratic dialogue, oral defenses, and lengthy essay exams. This once-laughable scenario is now a serious consideration for many faculty, including me.

Like many professors, I’ve recently abandoned take-home essays in favor of blue-book exams. …

Dynamic Mutational Profiling Of Binding Interactions And Allosteric Networks In Conformational Ensembles Of The Sars-Cov-2 Spike Protein Complexes With Classes Of Antibodies Targeting Cryptic Binding Sites: Confluence Of Binding And Allostery Determines Molecular Mechanisms And Hotspots Of Immune Escape, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The ongoing evolution of SARS-CoV-2 variants has underscored the need to understand not only the structural basis of antibody recognition but also the dynamic and allosteric mechanisms that could underlie complexity of broad and escape-resistant neutralization. In this study, we employed a multi-scale approach integrating structural analysis, hierarchical molecular simulations, mutational scanning and network-based allosteric modeling to dissect how Class 4 antibodies (represented by S2X35, 25F9, and SA55) and Class 5 antibodies (represented by S2H97, WRAIR-2063 and WRAIR-2134) can modulate conformational behavior, binding energetics, allosteric interactions and immune escape patterns of the SARS-CoV-2 spike protein. Using hierarchical simulations of the …

Antifungal Peptides From Casein Milk Of Etawa Crossbreed (Capra Hircus) As Biopreservation Agent For Bread And Molecular Docking Studies, Dian Riana Ningsih, Winarto Haryadi, Rachma Wikandari, Tri Joko Raharjo

Karbala International Journal of Modern Science

Bread without peptide fraction treatment started to grow mold on the fourth day with a total of 24x10⁷ fungi colonies/ml. Meanwhile, the addition of peptide fractions GMC5, GMC6, GMC7, and GMC8 effectively preserved bread for up to 4 days, indicated by no mold growth. The best treatment was the GMC6 peptide fraction on day 8 which grew the least amount of fungus at 7x10⁷ spores/ml. Peptide interaction with the CaATPase receptor Aspergillus sp. was carried out using HADDOCK 2.4. The 3D CaATPase structure prediction resulted in a model with good structural quality. This was supported by residues in …

A Deep Learning Framework With Explainable Ai For Atmospheric Blocking Detection And Interpretation, Devansh Khandelwal

Discovery Undergraduate Interdisciplinary Research Internship

Atmospheric blocking is a large-scale quasi-stationary phenomenon in mid-latitude circulation, characterized by persistent high-pressure systems that disrupt the typical west-to-east flow of the jet stream. These systems can cause extreme weather events—such as heatwaves, cold spells, or droughts—that persist for days or even weeks. This study proposes a deep learning framework to predict and interpret the occurrence of atmospheric blocking by integrating geophysical precursors such as geopotential height (Z500), stream function (SF200), and potential vorticity. These features, which are dynamically linked to blocking onset and persistence, serve as inputs to a Convolutional Neural Network model trained on the CESM Large …

Development And Validation Of Venous Thromboembolism-Bidirectional Encoder Representations From Transformers (Vte-Bert) Natural Language Processing Model, Omid Jafari, Shengling Ma, Barbara D Lam, Jun Y Jiang, Emily Zhou, Mrinal Ranjan, Justine Ryu, Raka Bandyo, Arash Maghsoudi, Bo Peng, Christopher I Amos, Abiodun Oluyomi, Nathanael R Fillmore, Jennifer La, Ang Li

Faculty, Staff and Students Publications

Background: Accurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) tool externally validated in representative patients is lacking.

Objectives: To train and validate an efficient NLP model to detect incident VTE event.

Methods: We designed a novel NLP platform, NLPMed, to assist thrombosis researchers with data preprocessing, phenotype annotation, language model finetuning, and NLP application. Using clinical notes, discharge summaries, and radiology reports from patients with cancer at 2 healthcare institutions, we finetuned Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) to develop VTE-BERT. The new model was trained to detect acute …

Crop Yield Prediction At Multiple Spatial Scales With Statistical Machine Learning, Vaibhav Charan, Pratishtha Poudel

Discovery Undergraduate Interdisciplinary Research Internship

Understanding and accurately predicting crop yield is becoming increasingly important today in the face of global food security challenges, and thus, the availability of standardized data and scalable models is the need of the hour. To support this, researchers have developed CY-Bench (Crop Yield Benchmark), a comprehensive dataset that helps forecast maize and wheat yields on a global scale. This research project primarily involved working with the CY-Bench dataset aiming to improve crop yield prediction through machine learning. Initially, papers explaining the CY-Bench dataset and other papers for agriculture modeling were studied and analyzed in detail. The research then progressed …

Imputation Via Domain Adaptation: Rethinking Variable Subset Forecasting From Knowledge Transfer, Runchang Liang, Qi Hao, Yue Gao, Kunpeng Liu, Lu Jiang, Pengyang Wang, Minghao Yin

Computer Science Faculty Publications and Presentations

Multivariate time series forecasting in practical deployment faces a critical challenge termed Variable Subset Forecasting (VSF), where certain variables accessible during training are entirely missing during inference. This creates a stark discrepancy between the training (source domain with full variables) and inference (target domain with partial variables) environments, disrupting cross-variable dependencies and fragmenting global temporal patterns. Existing imputation methods, limited to transferring local knowledge (e.g., temporal neighbors or pairwise correlations), fail to capture essential global dynamics, leading to severe performance degradation under distribution shifts. To address these challenges, we redefine VSF as a cross-domain knowledge transfer problem and propose VIDA, …

Systematic Review On The Technology’S Role In Supporting Lung Cancer Patients In The Treatment Journey, Safa Elkefi, Polly Wu, Roa Sabra, Steven Feiner, Lanyi Chen, Guy Hembroff, Alicia K. Matthews

Michigan Tech Publications

This systematic review examines the role of technology-based interventions in supporting lung cancer patients during their treatment. It identifies (1) the different technologies utilized, (2) their functions and benefits, and (3) the barriers encountered by patients. The authors searched six databases for literature examining the use of technology to support treatment among lung cancer patients. Twenty-three papers were included. We mapped each technology, telehealth platforms, online portals, and mobile apps, to specific treatment phases (pre-treatment, active treatment, post-treatment) and symptom domains (symptom management (N = 17), emotional distress (N = 12), and patient–provider communication (N = 7)). Our results demonstrate …

Exploitation For All: The Factors That Enable Pig Butchering Schemes To Weaponize Cybersecurity's Weakest Link, Melaney Freeman

Boise State Graduate Student Projects

This paper discusses pig butchering schemes and how they leverage human weaknesses through advanced social engineering and manipulation techniques that are enabled through the use of human trafficking, forced labor, and government corruption in Southeast Asia. Details regarding scammer exploitation and the formation of a victim-offender identity is presented, followed by the explanation of factors that allow organized crime groups to exist. Essential tactics used by scammers are examined to understand how they weaponize people's vulnerabilities for the duration of the scam to build a relationship with the victim and earn their trust in order to financially exhaust them through …

Ev Charging Management In A Real-Time Optimization Framework Considering Operational Constraints, Hilmi Cihan Güldorum, Ayşe Kübra Erenoğlu, İbrahim Şengör, Barry P. Hayes, Ozan Erdinç

Department of Computer Science Publications

The electrification of transportation plays a central role in the decarbonization of energy systems. Although electric vehicles (EVs) are expected to reduce energy related emissions, the increasing demand imposed by large scale EV adoption presents serious challenges for distribution systems (DSs), which were not originally designed to accommodate such loads. This study proposes a mixed integer quadratically constrained programming (MIQCP) framework to optimize the operation of an EV parking lot (EVPL) under DS constraints. The model compares three widely adopted objective functions: minimization of active power loss, charging cost, and uncontrolled charging impact, which is represented by minimizing the total …

Adaptive Quantum Gradient Descent For Training Novel Quantum Neural Network Classifiers, Irene Kahvazadeh

Waldo Library Student Exhibits

No abstract provided.

Short: Breaking The Charge: Exploiting State Manipulation In Ev Charging, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. However, the security of electric vehicle charging has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection for the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states of EV chargers …

Faithfulrag: Fact-Level Conflict Modeling For Context-Faithful Retrieval-Augmented Generation, Qinggang Zhang, Zhishang Xiang, Yilin Xiao, Le Wang, Junhui Li, Xinrun Wang, Jinsong Su

Research Collection School Of Computing and Information Systems

Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM’s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model’s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model’s parametric knowledge, which undermines the model’s internal knowledge structure …

Sifar: A Simple Faster Accelerated Variance‑Reduced Gradient Method, Zhize Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple faster accelerated gradient method called SIFAR for solving the finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex finite-sum problems, SIFAR improves previous state-of-the-art result given by Varag. In particular, for large-scale problems or the convergence error is not very small, SIFAR obtains the first optimal result O(n), matching the lower bound. ii) For strongly convex finite-sum problems, we also show that SIFAR can achieve the optimal convergence rate matching the lower bound. Besides, SIFAR enjoys a simpler loopless algorithmic structure while previous algorithms use …

Role Of Social Media Mindfulness In Combatting Fake News Propagation, Gaurav Bansal, Fiona Fui-Hoon Nah, Jason B. Thatcher

Research Collection School Of Computing and Information Systems

The dissemination of fake news by social media users is a key factor in the escalation of misinformation. Research suggests that social media networks are becoming increasingly homophilic, which leads to an overreliance on social media friends that contributes to the spread of fake news. However, little is known about how social media mindfulness can reduce the sharing of fake news. To investigate this research question, we conceptualized a social media mindfulness construct and developed the social media mindfulness scale. We also hypothesize that social media mindfulness lowers overreliance on friends’ knowledge, which increases skepticism about social media news that …

Zero-Shot Generalist Graph Anomaly Detection With Unified Neighborhood Prompts, Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight …

Achilles: A Formal Framework Of Leaking Secrets From Signature Schemes Via Rowhammer, Junkai Liang, Zhi Zhang, Xin Zhang, Qingni Sheng, Yansong Gao, Xinliang Yuan, Haiyang Xue, Pengfei Wu, Zhonghai. Wu

Research Collection School Of Computing and Information Systems

Signature schemes are a fundamental component of cyber-security infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole.In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that …

Complex System Governance And Cyber Operations, Willie Gernard Mccallister

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation examines the potential integration of Complex System Governance (CSG) within cybersecurity, emphasizing the development of a reference model for Cybersecurity Infrastructures. Traditional strategies for securing digital environments have struggled to address the intricate and dynamic layers inherent in modern cybersecurity systems. The purpose of this research is to explore the applicability of CSG as a framework to assess cybersecurity infrastructure using a case study research design. The research addresses two key questions: (1) How can the CSG reference model be adapted to explore cybersecurity infrastructure? (2) What results from CSG based exploration of cybersecurity infrastructure through a case …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

Optimizing Sensor Placement For Drone Detection According To A Grid Pattern, António Martinho Do Rosário Marçal

Masters Theses

In applications such as drone detection, it’s essential to place sensors efficiently, not only considering the cost of placement and operation, but also the maximization of the area covered.

This study presents an algorithmic approach to a placement strategy, which can be applied over any arbitrary area by defining the parameters of the grid according to which the sensors will be placed. The problem is framed as a multi-objective optimization task, considering trade-offs between sensor count and coverage.

One of the principal decision variables chosen is the shape of the cell blocks of the grid, for which two different values …

Traffic Prediction For Research And Education Networks: Anomaly-Aware Deep Learning And Benchmarking, Mohammad Arafath Uddin Shariff

School of Computing: Dissertations, Theses, and Student Research

Research and Education Networks (RENs) and High-Performance Computing (HPC) environments are critical infrastructures for modern scientific discovery, demanding sustained high-throughput and low-latency data transfers. Unlike commercial networks, RENs exhibit unique traffic characteristics, including predominant “elephant flows,” inherent burstiness, and complex temporal-spatial dynamics often decoupled from human-driven cycles. Traditional traffic forecasting methods, tailored for commercial Wide Area Networks (WANs), consistently fail to capture these distinct REN dynamics, leading to inefficient resource management and potential impediments to scientific progress.

This thesis addresses this critical gap by developing and validating a robust, scalable, and anomaly-aware traffic forecasting framework specifically tailored for REN/HPC networks. …

Bibliography For "Ai 2.0: Is Ai A Tool, A Threat, Or A Teammate?", Annikah Carpio, Sally Park, Melody Madrigal

Library Displays and Bibliographies

A bibliography created to support a display about AI 2.0 in August 2025 at the Leatherby Libraries at Chapman University.

Human-Ai-Collaboration-For-Coding, Siddhardha Ravi

Theses, Dissertations and Culminating Projects

AI-generated code, while rapidly producing functional solutions, often falls short in aspects like comprehensive error handling, robust documentation, and optimal architectural design, areas where human expertise excels. Conversely, humans can greatly benefit from AI's rapid code generation capabilities. This project proposes and evaluates "A Framework to Improve Code Quality by Utilizing Generative AI Coding Along With Human-Written Code", designed to create a synergy between AI and human intelligence for enhanced software development. Conducted over four weeks, the research leverages BigCodeBench as its core dataset to rigorously investigate how human intervention can improve AI-generated code quality, identify the most effective human-AI …

Artificial Intelligence Integration And Teachers' Self-Efficacy In Physics Classrooms, Fouad Yehya, Areej Elsayary, Ghadah Al Murshidi, Ahmed Al Zaabi

All Works

The United Arab Emirates (UAE), in its vision 2021 and the UAE centennial 2071 plan, highlights the essential role of artificial intelligence (AI) and technology in shaping a knowledge-based, future-ready society. This study explores the integration of AI in physics classrooms, focusing on secondary education in the UAE. It also investigates the perceptions and self-efficacy of physics teachers regarding the use of AI tools in classroom settings. A qualitative research design was employed to gather in-depth insights from 15 physics teachers across schools in Sharjah, assessing their confidence and readiness for AI integration through the lens of the attitude and …