Digital Commons Network^™

Adaptive Security Metric For Optimizing Post-Quantum Cryptography In Constrained Iot Devices, Aisha Nasser Ahmed

Theses

Quantum Computing poses real threat to Classical Public-Key Cryptography requiring the use of Post-Quantum Cryptography for all Internet of Things Devices. However, there are greater computational, memory and communication overheads in PQC algorithms that create additional burdens on resource constrained IoT devices. At this time, there are no standard measures for systems developers to determine optimal PQC settings for the various IoT Device Classes. This Thesis develops a new framework of metrics for determining the most suitable PQC settings based on Security Strength, Performance Indicators (Latency, Memory, Energy), Communication Overhead and Reliability for each IoT device class. The Research introduces …

Ai-Enabled Multi-Layer Security Operations: A Combined Siem, Ids, And Threat Intelligence Model For Adaptive Cyber Defense, Mohamad Khayat

Dissertations

This dissertation presents a comprehensive framework for the evolution of Security Operation Centers (SOCs) through the integration of advanced artificial intelligence (AI), blockchain, and optimization techniques. Motivated by the increasing complexity of cyber threats and the limitations of traditional reactive SOC strategies, this work begins with a systematic literature review that identifies critical gaps in current SOC operations. Based on these insights, a reference architecture is proposed to guide the integration of intelligent components into SOC environments. To address the challenge of secure and trustworthy information sharing, a blockchain-based threat intelligence platform is developed, leveraging Byzantine Fault Tolerance and Zero-Knowledge …

Enhancing Llm Code Generation: A Systematic Evaluation Of Multi-Agent Collaboration And Runtime Debugging For Improving Accuracy, Reliability, And Latency, Nazmus Ashrafi

Theses

The use of large language models (LLMs) for automated code generation has emerged as a significant focus within AI research. As these pretrained models continue to evolve, their ability to understand and generate complex code structures has opened up new possibilities for automating intricate programming tasks with greater accuracy. Although contemporary foundational models demonstrate promising results, researchers continue to explore optimal post-training strategies to enhance code quality. These include supervised fine-tuning, retrieval-augmented generation (RAG), debugging, and many others. In this thesis, I combine two such widely used post training approaches—namely (1) multi agent collaboration and (2) runtime execution of information-based …

A System Framework To Symbolically Explore Intel Tdx Module Execution, Pansilu Pitigalaarachchillage, Xuhua Ding

Research Collection School Of Computing and Information Systems

We present TDXplorer, the first dynamic symbolic analysis system for Intel's TDX Module, the software trusted computing base of TDX. Without using TDX hardware, an analyzer function on top of TDXplorer can not only apply dynamic analysis to control and instrument the TDX Module's execution, but also carry out symbolic execution for path exploration as well as security and functionality reasoning. The two types of analysis are seamlessly integrated in a way that symbolic execution is conducted directly upon the TDX Module's binary code and runtime states, which are shaped by using dynamic analysis techniques. We implement TDXplorer on Linux …

Contrastrepair: Enhancing Conversation-Based Automated Program Repair Via Contrastive Test Case Pairs, Jiaolong Kong, Xiaofei Xie, Mingfei Cheng, Shangqing Liu, Xiaoning Du, Qi Guo

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recentstrides in Large Language Models (LLM), such as ChatGPT, have yielded encouraging outcomes in APR,especially within the conversation-driven APR framework. Nevertheless, the efficacy of conversation-drivenAPR is contingent on the quality of the feedback information. In this article, we propose ContrastRepair, anovel conversation-based APR approach that augments conversation-driven APR by providing LLMs withcontrastive test pairs. A test pair consists of a failing test and a passing test, which offer contrastive feedback tothe LLM. Our key insight is to minimize the difference between the generated passing test and the …

Multi-Period Risk-Aware Procurement Optimization Under Covid-19 Disruption, Jonathan Chase, Hoong Chuin Lau, Jinfeng Yang, Lu Liu

Research Collection School Of Computing and Information Systems

Supply chain resilience has been a topic of active research in the operations research and AI communities for several years, but the COVID-19 pandemic threw the frailties of global supply chains into sharp relief. Disruptions and delays caused by fresh outbreaks leading to lockdowns, put severe strain on supply chains in many industries. In this work we develop lockdown-resilient procurement capabilities for a global technology company. First, through analysis of lockdown data from China we develop a logarithmic regression-based lockdown prediction method to complement a supplier risk metric for conventional risks. Second, we develop a multi-period stochastic optimization model that …

Classical Shadows With Improved Median-Of-Means Estimation, Winston Fu, Dax Enshan Koh, Siong Thye Goh, Jian Feng Kong

Research Collection School Of Computing and Information Systems

The classical shadows protocol, introduced by Huang et al (2020 Nat. Phys. 16 1050), makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation values of M observables with failure probability δ using only O ( log ⁡ ( M / δ ) ) measurements. In their analysis, Huang et al used loose constants in their asymptotic performance bounds for simplicity. However, the specific values of these constants can significantly affect the number of shots used in practical implementations. To address this, we studied a modified MoM estimator proposed by Minsker (2023 Proc. 36th Conf. on Learning Theory …

Cookingdiffusion: Cooking Procedural Image Generation With Stable Diffusion, Yuan Wang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Yi Tan, Xiang Wang

Research Collection School Of Computing and Information Systems

Recent advancements in text-to-image generation models have excelled in creating diverse and realistic images. This success extends to food imagery, where various conditional inputs like cooking styles, ingredients, and recipes are utilized. However, a yet-unexplored challenge is generating a sequence of procedural images based on cooking steps from a recipe. This could enhance the cooking experience with visual guidance and possibly lead to an intelligent cooking simulation system. To fill this gap, we introduce a novel task called cooking procedural image generation. This task is inherently demanding, as it strives to create photo-realistic images that align with cooking steps while …

Rethinking Teaching Evaluation Reports: Designing Ai-Transformed Student Feedback For Instructor Engagement, Ruoxi Shang, Keri Mallari, Au Wei Bin Yeong, Ken Yasuhara, Anthony Tang, Gary Hsieh

Research Collection School Of Computing and Information Systems

Student feedback is critical for improving teaching, yet instructors often avoid reading evaluations due to emotional burden and information overload. We present a systematic exploration of how language models can distill and transform student evaluations into adaptive, actionable insights. Through a systematic design space exploration combining 4 feedback strategies (removing harmful content, paraphrasing criticism, sandwiching negatives, adding constructive suggestions) with 4 presentation formats (themes, cards, letters, chatbots), we created six AI-augmented prototypes of teaching evaluations. Interviews with 16 post-secondary instructors revealed that effective use of AI in feedback processing should: (1) support action formation through focused views and divergent thinking, …

Polymind: Parallel Visual Diagramming With Large Language Models To Support Prewriting Through Microtasks, Qian Wan, Jiannan Li, Huanchen Wang, Zhicong Lu

Research Collection School Of Computing and Information Systems

Prewriting is the process of generating and organising ideas before a first draft. It consists of a combination of informal, iterative, and semi-structured strategies such as visual diagramming, which poses a challenge for collaborating with large language models (LLMs) in a turn-taking conversational manner. We present Polymind, a visual diagramming tool that leverages multiple LLM-powered agents to support prewriting. The system features a parallel collaboration workflow in place of the turn-taking conversational interactions. It defines multiple ''microtasks'' to simulate group collaboration scenarios such as collaborative writing and group brainstorming. Instead of repetitively prompting a chatbot for various purposes, Polymind enables …

Ivycross: A Privacy-Preserving And Concurrency Control Framework For Blockchain Interoperability, Ming Li, Jian Weng, Jia-Si Weng, Yi Li, Yongdong Wu, Dingcheng Li, Guowen Xu, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Interoperability is a fundamental challenge for long-envisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves low-cost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design …

Polyqent: A Polynomial Quantified Entailment Solver, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Milad Saadat, Maximilian Seeliger, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Polynomial quantified entailments with existentially and universally quantified variables arise in many problems of verification and program analysis. We present PolyQEnt which is a tool for solving polynomial quantified entailments in which variables on both sides of the implication are real valued or unbounded integers. Our tool provides a unified framework for polynomial quantified entailment problems that arise in several papers in the literature. Our experimental evaluation over a wide range of benchmarks shows the applicability of the tool as well as its benefits as opposed to simply using existing SMT solvers to solve such constraints.

Better Digital Contracts With Prosocial Friction-In-Design, Brett Frischmann, Moshe Y. Vardi

Faculty Publications

Contract law is supposed to enable people to reach genuine agreements and cooperate. If this ideal was ever a reality, the rise of mass market contracts and boil­erplate rendered it pure fiction. Modern consumer contracts are incomprehensible to most people. No one reads them anyway.

Digital contracting involves design features that amplify traditional boilerplate harms and create others. For example, digital contracting is too cheap; low marginal costs lead to overexpansion in scale and scope. To make matters worse, the loss of autonomy from repeat engagement with digital contracting systems is pernicious. People become increasingly predictable and programmable as digital …

A Comprehensive Pipeline For Autonomous Surface Vehicle Navigation In Challenging Aquatic Environments, Mingi Jeong

Dartmouth College Ph.D Dissertations

The primary objective of this thesis is to develop and validate innovative and robust navigation methods for autonomous surface vehicles (ASVs) in challenging scenarios. These efforts aim to establish a complete autonomy pipeline for robotic decision-making systems, enabling high-level tasks such as environmental monitoring and autonomous transportation with broader impacts. The ocean economy contributes over 1.5 trillion USD annually, supporting diverse cultures and economies through tourism, fisheries, shipping, and renewable energy. The global marine industry handles over 90% of the world’s cargo transportation, underscoring its critical importance. Despite this significance, current maritime navigation relies heavily on human decision-making, which is …

Topoimages: Incorporating Local Topology Encoding Into Deep Learning Models For Medical Image Classification, Pengfei Gu, Hongxiao Wang, Yejia Zhang, Huimin Li, Chaoli Wang, Danny Z. Chen

Computer Science Faculty Publications

Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). Despite remarkable successes of numerous image processing methods that rely on appearance information, these methods often lack sensitivity to topological structures when used in general deep learning (DL) frameworks. In this paper, we introduce a new general approach, called TopoImages (for Topology Images), which computes a new representation of input images by encoding local topology of patches. In TopoImages, we leverage persistent homology (PH) to encode geometric and topological features inherent in image patches. Our main objective is …

Explainabledetector: Exploring Transformer-Based Language Modeling Approach For Sms Spam Detection With Explainability Analysis, Mohammad Amaz Uddin, Muhammad Nazrul Islam, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

Short Message Service (SMS) is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages - commonly known as SMS spam. With the rapid adoption of smartphones and increased Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have recognized the critical role SMS plays in today's modern communication, making it a prime target for abuse. As cybersecurity threats continue to evolve, the volume of SMS spam has increased substantially in recent years. Moreover, the unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult …

Juxtaposing Approaches To Risk-Based Ai Governance In Different ‘Rights’ Contexts: A Comparative Analysis Between Singapore And The Eu, Jane Loo, Mark Findlay

Research Collection Yong Pung How School Of Law

Comparative analysis of European and certain Asian approaches to governance often degenerates into simplistic dichotomies based on universal human rights assumptions. This chapter rejects such dualities, ill-informed by theory and historical reflection. The emerging argument is founded on a historical realist approach to theorising difference. Assisted by Polanyi’s double movement, the detailed substantive comparison is preceded by considerations of how recent trends in governing AI have uniformly adopted a countermovement against the dis-embedding of data and technology from the social leading to a risk/responsibility paradigm. From here, a more nuanced reflection of AI governance approaches in the EU and Singapore …

Building Career Pathways: Investigating The Potential For A Cooperative Education Program For Graduate Itm Students At Central Washington University, Liam J. E Holland

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Susan Rivera, PhD

This research examines the feasibility of introducing a cooperative education (co-op) program at the master’s level in the Central Washington University (CWU) ITM Department. Methods included a review of existing co-op programs, analysis of interviews with past co-op students, and an examination of survey data on student interest in graduate-level cooperative education. The study aims to identify key elements of effective co-op programs and assess demand for such programs. The analysis of current co-op models offers insights into program structure, industry engagement, and student success. The 2019 NACE Internship & Co-op Survey found that 50.2% …

Cwu's Electronic Waste Management: A Success Story In Sustainable Practice, Srijita Gurung

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Susan Rivera, PhD; Kevin Lomax

Purpose: This research examines how Central Washington University (CWU) manages electronic waste (e-waste) to promote economic, environmental, and equitable sustainability practices. In February 2024, Computer Support Services (CSS) started a computer reconciliation project, which is vital due to the Windows 10 operating system going end of life after October 14, 2025, and no longer receiving software support. This project tracks and documents the life of electronic assets, with a focus on the role of Surplus and IT Asset Disposal (ITAD) companies. Method: The primary research methods involve interviews, and online research of active …

Optimizing Cybersecurity Through Ai Predictive Analytics And Human Expertise, Cathy Mae C. Dutong

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

As cybersecurity threats evolve in complexity and scale, the reliance on artificial intelligence (AI) has become increasingly prevalent across both public and private sectors. This study examines the dual role of AI-driven predictive analytics in strengthening organizational cybersecurity, while addressing the ongoing need for human oversight. Through a mixed-method approach, combining survey data from cybersecurity professionals with an extensive literature review, this research analyzes AI's capacity to detect emerging threats, the systemic challenges associated with AI integration, and the indispensable role of human expertise in interpreting AI outputs. Findings indicate that while AI enhances proactive …

Global Trends In Ai-Driven Product Development: A Cross-Country Analysis, Shilpa Dhananjayan

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

Artificial Intelligence (AI) is transforming industries and accelerating global innovation, yet its benefits remain unevenly distributed. A nation’s AI readiness—its capacity to adopt and implement AI technologies—plays a crucial role in economic growth and technological advancement. Key determinants of AI readiness include digital infrastructure, data accessibility, government policies, research and development (R&D) investment, and workforce development. This study examines the relationship between AI readiness, AI adoption, innovation, R&D investment of a nation, Digital Infrastructure Index (DII) and Human Capital Index (HCI) using a Random Forest regression model. Findings reveal a strong correlation between AI adoption …

Innovation Within Sustainable Technologies: The Future Of Energy Conserving Hardware In Data Centers, Brooke Cruz

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Susan Rivera, PhD

The US is confronting a growing energy demand crisis, with analysts projecting a 2.4% rise in data center energy consumption by 2030. Data centers are major energy consumers, with primary usage concentrated in IT equipment and extensive cooling systems. While Power Usage Effectiveness (PUE) remains the leading metric for assessing energy efficiency, it is limited to assessing total facility energy and IT energy use without accounting for environmental and social impacts. Supplementary metrics of data center energy consumption include Water Usage Effectiveness (WUE) and Carbon Usage Effectiveness (CUE), which measure water use and carbon emissions, …

Fundamentals Of The New Neutrosophic Matrices, Adebisi Sunday Adesina, Ogunmuyiwa Sodiq Damilola

Neutrosophic Systems with Applications

The New Neutrosophic Matrices provides a mathematical extension of classical and fuzzy matrix theory that incorporates the element of indeterminacy alongside truth and falsity. Neutrosophic logic, pioneered by FlorentinSmarandache, provides a richer framework for dealing with uncertainty and vagueness in real-world data. This study explores the definitions, classifications, and algebraic operations onneutrosophic matrices, including addition, multiplication, scalar operations, and the formation of identities.A comparative analysis is presented to highlight the distinctions between classical, fuzzy, and neutrosophic matrices. From the concepts, potential applications could be proposed most especially, for more problem solving as well as for future exploration.Findingsaffirmthat neutrosophic matrices offer …

Lens: Lightweight And Explainable Llm-Based Apt Detection At The Edge For 6g Security, Suhib Bani Melhem, Muhammed Golec, Abdulmalik Alwarafy, Yaser Khamayseh

All Works

Expected to be deployed in the early 2030s, sixth-generation (6G) wireless networks, with their high speed and integration with cutting-edge technology such as intelligent edge computing, expand the attack surface and face serious cyber threat risks such as Advanced Persistent Threats (APTs). This type of cyber attack can imitate benign network traffic and operate for long periods of time without being detected by traditional detection systems. This paper introduces LENS, a lightweight and explainable LLM-based network security framework designed to address this cybersecurity threat for 6G environments. LENS uses a fine-tuned DistilBERT model to convert raw network streams into natural …

Solution Of Fractional Order Diffusion Equations With Clique Neural Network, Merve Zeynep Kaya, Mesut Karabacak, Ercan Çelik

Mathematical Modelling and Numerical Simulation with Applications

In this paper, the clique artificial neural network method is used to solve the fractional diffusion equation, which is a subclass of partial differential equations. The clique neural network architecture is constructed using input, hidden, and output layers. Several degrees of clique polynomials were used as activation functions, and the output layer was obtained by multiplying them with weight coefficients. Subsequently, the optimization equation was derived, and the exact solution, numerical solution, and error function graphs were obtained using a specialized algorithm. Analysis of the results demonstrates that the clique artificial neural network method provides quicker and more accurate results …

Synergistic Modeling Of Hydrogel Gelation Via Time-Delay Dynamics And Machine Learning Algorithms, Mine Babaoglu, Dipesh ., Pankaj Kumar, Jagjit Singh Dhatterwal, Mansoor Alsulami

Mathematical Modelling and Numerical Simulation with Applications

This paper presents an integrated framework in which delay differential equation (DDE) modeling and machine learning (ML) approaches are coupled to study hydrogel formation kinetics, with emphasis on delayed crosslinker addition. Conventional mechanistic models disclose many physical and kinetic complexities of reacting mixtures; they seldom depict the nonlinear and time-evolving complexities inherent in developing polymer networks. To address this, a mathematical model is developed that examines how the insertion of crosslinkers affects system stability and equilibrium. Analytical and numerical results show that delays nearing critical levels cause bifurcation behavior with substantial implications on gelation kinetics. Sophisticated machine learning systems, including …

Drawing On Uncertainty Methodologies Of Neutrosophic Hypersoft Sets In Cognitive Computing-Driven Healthcare Systems, Mona Mohamed, Nurhan Alaa

Neutrosophic Systems with Applications

A new paradigm called cognitive computing simulates human reasoning and decision-making through integrating advanced techniques such as artificial intelligence (AI) and natural language processing (NLP). Cognitive computing systems, in contrast to traditional systems, can handle both structured and unstructured data, adjust to new information, and offer context-sensitive insights. This study examines how cognitive computing improves decision-making, personalization, and human-machine collaboration in various fields. Cognitive computing in the healthcare sector processes clinical notes, imaging data, and electronic health records to help physicians with diagnosis, treatment planning, and patient engagement. This study examines key applications, including their role in diagnostic support, where …

Neutrosophic Set Model For Controlling Electronic Waste Requirements Management Policies To Minimize Ecological Impact And Improving Resilience And Sustainability, Mohamed Abouhawwash, Nitin Mittal, Sudeep Tanwar

Neutrosophic Systems with Applications

The growing issue of electronic waste (e-waste) necessitates management approaches that promote sustainability and resilience while reducing environmental effects, particularly considering global disruptions and pressure on manufacturers to implement extended producer responsibility laws. There is a research gap in our knowledge of the link between sustainability and resilience since most of the literature currently available on e-waste management focuses on either operational efficiency or sustainability. This study proposes multi-criteria decision making (MCDM) methodology for controlling electronic waste requirements management policies to minimize ecological impact and improving resilience and sustainability. We use the EDAS methodology to rank the alternatives. The criteria …

Application Of Dematel Based On Bipolar Neutrosophic Sets For Sustainable Agriculture Practices, Lazim Abdullah, Nor Liyana Amalini Binti Mohd Kamal

Neutrosophic Systems with Applications

The development of natural capital is a fundamental objective within sustainable agricultural systems, where the optimization of both crop and livestock production is vital to addressing global food demands. Despite this imperative, major agricultural sectors such as paddy and rubber production, often fall short of satisfying consumption needs. This study aims to identify and prioritize the most influential criteria for sustainable agriculture using the Bipolar Neutrosophic Set-based Decision-Making Trial and Evaluation Laboratory (BNS-DEMATEL) method. Expert evaluations were elicited from five agricultural specialists using linguistic assessments to analyze the performance and interdependencies among sustainability criteria. Computational analyses were conducted using MATLAB …

Evaluating And Ranking Genai Chatbots Under Uncertainty: A Type-2 Neutrosophic Rancom–Marcos Mcdm Framework, Hend Ahmed, Abduallah Gamal

Neutrosophic Systems with Applications

Owing to integrate GenAI chatbots to enhance productivity across various tasks, this research presents T2NN-RANCOM-MARCOS multi-attribute decision-making model, which employs Type-2 Neutrosophic Number (T2NN) to handle uncertain data, the RANCOM method, distinguished by its easy, highly repeatable, less time consuming, more appropriate to deal with problems exceeds 5 criteria with expert errors to assign subjective weights to criteria and MARCOS method to evaluate and rank eight GenAI chatbots against six main criteria are included 23 sub-criteria: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, Trust and Confidence and Economic are primarily derived from QUEST evaluation …