Computer Engineering Commons^™

Using Ali-4 Z-Implication In Controller Design, Shamil Azer Ahmadov

Chemical Technology, Control and Management

One of the widely used theories in the information processing is Professor Zadeh's fuzzy logic theory. Fuzzy implications form the basis of this theory. When processing information using fuzzy implication, the chosen judgment method and the type of implication affect the result. Referring to the review of the relevant literature on fuzzy implications, it can be noted that there are still unresolved problems and issues. For example, fuzzy implications cannot be used in processing imperfect information or information based on probabilistic and fuzzy uncertainty. Existing fuzzy implications face limitations in practical applications. Fuzzy implications only take into account inaccuracy, but …

Shelter Portal: Qr-Based Service Tracking For Low-Barrier Shelters, Kate Steele, Colton Knopik, Michael Fischer

2026 Symposium

Shelter Portal is a web-based service tracking application developed for low-barrier shelters, including Catholic Charities’ House of Charity and Rising Strong programs. Many shelters still rely on manual headcounts and estimated meal totals, which are labor-intensive, error-prone, and insufficient for tracking individual service use over time. This limits operational visibility and makes it difficult to generate reliable reports, identify usage trends, and support external reporting requirements. Shelter Portal addresses this problem by providing a more accurate and privacy-conscious way to document shelter services.

The system was designed as a kiosk and web-based platform that uses scannable QR code cards to …

Sbw 3.0: A Blockchain-Enabled Framework For Secure And Efficient Information Management In Web 3.0, Md Arif Hassan, Bui Duc Manh, Cong T. Nguyen, Chi-Hieu Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Nguyen Van Huynh, Dusit Niyato

Staff Scholarship - Australia & Dubai

In this paper, we propose an effective blockchain-enabled information management framework, named Smart Blockchain-based Web 3.0 (SBW 3.0). Our framework aims to handle information within Web 3.0 efficiently, enhance data security and privacy, create new revenue streams, and encourage users to contribute valuable information to websites. To this end, SBW 3.0 employs blockchain technology and smart contracts to manage the decentralized data collection in Web 3.0. Moreover, we introduce a robust consensus mechanism grounded in Delegated Proof-of-Stake (DPoS) to reward user contributions. Furthermore, we develop a non-cooperative game model to examine user behavior in this context and conduct thorough analysis …

Escaping Isolation: An Analysis Of Virtual Machine And Container Breakout Vulnerabilities, Felix Iov

Cybersecurity Undergraduate Research Showcase

Cloud computing providers rely on multi-tenant architectures to maximize resource efficiency. This infrastructure depends on virtualization, which provides isolation between clients. This comes primarily in the form of Virtual Machines (VMs) and Containers. However, “breakout attacks” or “escapes” are a critical threat where attackers bypass these isolation layers to gain unauthorized access to the host system and neighboring environments. This paper surveys virtualization escape threats and analyzes three case studies: a runc container escape (Leaky Vessels), a VMware ESXi VM escape (VSOCKPuppet), and an NVIDIA GPU container escape (NVIDIAScape). Each demonstrates different attack surfaces, including file descriptor misuse, kernel driver …

Reducing Systemic Bias In Behavioral Targeting Using Explainable Ai: The Harmonia Complex Systems Approach, Ruchira Deokar, Preethi Nanjundan, Jossy P. George, Carlos Gershenson

Northeast Journal of Complex Systems (NEJCS)

Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered …

Generalized Planning Using Language Models And Its Applications, Vishal Pallagani

Theses and Dissertations

Planning is a fundamental capability for intelligent systems, yet classical planners remain difcult to scale and generalize across domains due to their reliance on hand- engineered models, brittle search procedures, and limited adaptability. These limita- tions constrain real-world applications that require fexible reasoning, robust decision- making, and the ability to operate across diverse environments.

Large language models have emerged as powerful learners supported by their ar- chitectures and large-scale training data, demonstrating strong capabilities in tasks such as question answering, code generation, summarization, and reasoning. Moti- vated by their broad generalization abilities, I investigate whether language models can be systematically …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …

Move Fast And Don’T Break Things: Collaborative Privacy Governance In Higher Education, Tyler Schroder, Chad Fenner

Research & Publications

Student-developed applications increasingly replicate or replace official university platforms, often prioritizing speed over security and privacy. This “shadow IT” ecosystem emerges from gaps in institutional tools and is amplified by AI-assisted development, which can introduce insecure defaults. These informal systems risk exposing FERPA‑protected or sensitive institutional data, as seen in student‑built directory and club‑information apps that redistributed restricted information more permissively than intended. While most universities lack clear governance mechanisms for student developers, Yale’s structured, student‑specific data‑use policy offers a notable model. This paper examines these risks and proposes a collaborative, API‑first framework that supports innovation while enforcing privacy, security, …

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

Evaluating Regularized Logistic Regression And K-Nn On Mnist Under Increasing Random Missingness, Daniel Markwei

Data Science and Data Mining

This paper investigates the effect of random missingness on the performance of regularized multinomial logistic regression and the k-nearest neighbors (k-NN) classifier for handwritten digit recognition on the MNIST dataset. In particular, we study L1-regularized (LASSO) logistic regression and L2-regularized (Ridge) logistic regression alongside k-NN. Varying percentages of random missingness were introduced into the original dataset, and each model was evaluated in terms of its classification performance. The results show that random missingness degrades the performance of all three classifiers. Overall, k-NN consistently achieves higher accuracy than both L1- and L2-regularized logistic regression across all missingness levels; however, its performance …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Toward Transparent Bureaucracy: Nlp-Based Document Classification And Power Dynamics In The Srikandi System, Zulfatun Sofiyani, Suprayitno Suprayitno, Faisal Fahmi, Mega Putri Mahadewi

Proceedings from the Document Academy

As the Indonesian government advances digital document management through the SRIKANDI system, challenges persist regarding fragmented and subjective classification practices. This study proposes the integration of Natural Language Processing (NLP)-based classification within SRIKANDI to enhance consistency, transparency, and accountability in document management. Framed by an interdisciplinary theoretical foundation, the study synthesizes Michael Buckland’s document theory, viewing documents as dynamic social evidence, with Michel Foucault’s theory of power, highlighting classification as an exercise of institutional authority, and NLP methodologies that enable automated, content-driven categorization. The study positions documents as both technological artifacts and political constructs, whose classification practices simultaneously structure meaning …

Ai Applications In Assessing Risk For Periodontal Disease: A Systematic Review, Grant O. Korte, Claudia M. Tellez Freitas

Annual Research Symposium

Objectives:

The goal of this systematic review is to examine the impact of using artificial intelligence (AI) to predict a patient’s risk level for periodontal disease by analyzing proven systemic health diseases linked to periodontitis. This is being done by primarily focusing on prevention and early diagnosis using machine learning programs that have been proven effective in other fields of periodontitis research.

Methods:

To conduct this study, a comprehensive literature review of journals published after 2020 was performed through four databases: PubMed, Scopus, Web of Science and Dentistry and Oral Science Source. The search was completed in adherence …

Artificial Intelligence, Society 5.0 And Smart City Adaptation Initiatives For Businesses: An Integrated Approach, Ines A. M. Gila, Fernando A. F. Ferreira, Neuza C. M. Q. F. Ferreira, Florentin Smarandache, Momtaj Khanam, Tugrul Unsal Daim

Engineering and Technology Management Faculty Publications and Presentations

The mass migration of human populations to urban areas has resulted in unprecedented challenges for city services. To address and find solutions for these emerging issues, decision-makers must embrace the smart city and Society 5.0 paradigms, which comprehensively tackle various dimensions of the problem and ensure adaptability to evolving citizen needs. Central to the success of these paradigms is technology, particularly artificial intelligence (AI). AI’s transformative capabilities enable the expansion of services, automation of tasks, efficient operationalization and processing vast amounts of data to address urban challenges, aligning with several sustainable development goals (SDGs) such as sustainable cities and communities …

Implementation Of Zero Trust Architecture On Local Server Management In Educational Institutions, Joko Purwanto, Safar Dwi Kurniawan

Journal of Strategic and Global Studies

Educational institutions are facing increasingly complex cyber threats, particularly as they continue to rely on on-premises servers with limited cybersecurity resources. Zero Trust Architecture (ZTA) offers a security model that rejects implicit trust and requires strict verification of each access request. This study aims to examine the applicability of ZTA in managing local servers within educational institutions through a qualitative literature review approach. Relevant literature from 2014 to 2025 was analyzed using thematic synthesis to identify recurring concepts, strategies, and gaps. The results show that ZTA, when integrated with Identity and Access Management (IAM), Security Information and Event Management (SIEM), …

A Modular Llm Approach To Argument Extraction In Philosophical Texts, Nate Miller

Masters Theses

Engaging with philosophical works is a rewarding but demanding task that challenges both human readers and computational systems designed to extract arguments from dense philosophical reasoning, and although large language models (LLMs) have made substantial progress in argument extraction, the most advanced models are often costly to run. As a result, there is growing interest in determining if multi-agent pipelines that divide a task into smaller stages can reduce cost while maintaining or improving performance.

This study investigates a modular multi-agent approach for extracting arguments from philosophical texts using LLMs, and compares its performance, cost, and runtime to both single-agent …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Julio Villalba-Moreno, Alexander Grinshpun, Freddy Gabbay, Luke Seed, Rui Duarte, Manuel Lopez, Oscar Alonso, Robert Owen

Electrical & Computer Engineering Faculty Research

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …

When Disasters Trigger Cyber Vulnerabilities: Mapping Physical-Digital Interdependencies In Critical Infrastructure Systems, Dikshya Panta, Sicheng Wang, Aditya Sapkota, Prakash Ranganathan

Faculty Publications

Critical infrastructure (CI) systems such as power, water, communications, and emergency services are increasingly exposed to compound risks in which natural disasters and cyber incidents interact and amplify one another. Traditional risk assessments often isolate physical and digital threats, overlooking the cascading dependencies that emerge when operational stress, emergency reconfiguration, and adversarial exploitation coincide. This study conducts a 2019–2025 scoping review and introduces a Geographic Information System (GIS) driven six-stage disaster cyber compounding framework that characterizes, maps, and operationalizes compound risk across interdependent CI sectors. The framework integrates a common operating picture, analytic situational understanding, exposure mapping, threat-fingerprint encoding, detection …

Pengujian High Fidelity Prototipe Media Pembelajaran Interaktif Bahasa Inggris Dengan Gamifikasi Menggunakan User Experience Questionare, Adhitya Prastyadi, Meizano Muhammad Ardhi, Daniel Rinaldi, Margaretha Karolina Sagala

Jurnal Sosial Humaniora Terapan

Abstract

Testing a Hi-Fi Prototype of Interactive English Learning Media with Gamification Using UEQ

This study aims to test and evaluate the User Experience (UX) quality of a High-Fidelity prototype for interactive English learning media integrating gamification elements. The research is motivated by the low English proficiency and motivation among Indonesian students, and the potential of digital media and gamification to enhance learning engagement. Inspired by the success of the Duolingo application, this prototype was designed to create an enjoyable and effective learning experience. A quantitative research methodology was employed, involving high school students as participants selected via purposive sampling. …

Comparative Evaluation Of Deep Learning Models: Resnet18, Minivgg, And Yolov8 For Five-Class Blood Cell Classification, Keita Sakurai

Master's Theses or Doctor of Nursing Practice

Accurate classification of blood cell types is a critical task in automated hematological analysis. This study presents a comparative evaluation of three deep learning architectures, ResNet18, MiniVGG, and YOLOv8, for five-class blood cell image classification. To ensure a fair comparison, all models were trained under standardized conditions, including a consistent 90:10 training–validation split, controlled dataset size, and fixed training epochs. ResNet18 was trained to establish a baseline using residual learning. MiniVGG employed a compact VGG-inspired design with regularization to balance efficiency and accuracy, while YOLOv8 leveraged a lightweight, pretrained classification backbone with integrated data augmentation. Experimental results demonstrate a clear …

Ai-Driven Network Orchestration: Adaptive Routing For Federated Learning Over Software-Defined Hybrid Wans, Osama Abu Hamdan

Computer Science and Engineering Dissertations - Archive

Modern wide-area networks increasingly adopt hybrid architectures that combine high-capacity wired backbones with flexible wireless links to extend connectivity to remote and underserved locations. However, the bandwidth variability inherent in wireless segments creates routing challenges that traditional protocols, designed for static link capacities, cannot adequately address. Simultaneously, Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm in which geographically dispersed clients collaboratively train shared models without exchanging raw data. When deployed over wide-area networks, FL training is severely bottlenecked by communication overhead, particularly in cross-silo settings where model payloads reach hundreds of megabytes and synchronous aggregation protocols …

Scaling Llm Inference: From Novel Attention Mechanisms To Efficient Kv Cache Management, Weishu Deng

Computer Science and Engineering Dissertations

The rapid scaling of artificial intelligence workloads has shifted the dominant performance bottleneck of modern computing systems from compute to memory. Graph neural networks (GNNs) issue increasingly irregular memory accesses, while large language models (LLMs) issue increasingly large ones; in both cases, the relative scaling of memory bandwidth and capacity continues to lag behind the scaling of compute. Consequently, naively executing these workloads on commodity GPUs results in stalled streaming multiprocessors, exhausted high-bandwidth memory (HBM), and serving stacks that incur PCIe transfers on the critical path. This dissertation argues that the efficient scaling of attention-based AI workloads requires the joint …

Virtualized And Distributed Neighborhood Data Centers, Benjamin T. Niccum

Computer Science and Engineering Theses

This thesis evaluates whether PCIe-fabric-based resource pooling can support a decentralized neighborhood micro-data-center model under real implementation constraints. The work combines architecture design, prototype deployment, performance benchmarking, and security assessment. Results show strong prototype-scale feasibility with low-latency and high-throughput behavior, while also identifying deployment-blocking security gaps and operational maturity requirements. The thesis contributes an evidence-traceable path from concept validation to deployment-grade roadmap planning.

Advancing Ethical Innovation In Human–Ai Collaboration: Trust And Legitimacy In Technology-Mediated Teams, Sanket Ramchandra Patole

Human Resource Development Theses and Dissertations

Human Resource Development (HRD) confronts a central paradox in the digital age. The technologies designed to broaden access to learning and collaboration often reproduce the same social hierarchies that HRD seeks to challenge. Artificial intelligence (AI), digital collaboration platforms, and algorithmic management are widespread features of organizational life. Yet these systems do not generate inclusive outcomes for all workers. Women of Color (WoC), situated at the intersection of racialized, gendered, and technological structures, experience digital transformation as both possibility and constraint. They encounter opaque decision systems, diminished authority in virtual teams, and digitally-mediated microaggressions, while also developing new capacities for …

Beyond Permissions: A Privacy And Security Analysis Of Sensor And Network Activity On Xr Devices Using Xrmonitor, Gayatri Sravya Siripurapu

Computer Science and Engineering Theses - Archive

Standalone virtual reality headsets integrate multiple sensors and cameras whose runtime behavior is not transparently documented, creating potential privacy and security risks that are difficult to audit using existing tools. The relationship between software feature activation and the underlying hardware engaged at runtime remains opaque, limiting the ability of users and researchers to assess what data is being collected and when.

This thesis presents XRMonitor, a unified framework for monitoring and analyzing sensor activation behavior and network traffic on Meta Quest devices. The framework is motivated by the lack of transparency in how standalone XR devices engage their hardware sensors …

End-To-End Development And Experimental Validation Of A 1/10-Scale Autonomous Vehicle, Rikkin Pankaj Panchal

Electrical Engineering Theses

Autonomous vehicle development demands vast resources, making scaled down platforms a critical alternative for solving core algorithmic challenges. The primary contribution of this thesis is the end to end development and validation of a complete real time autonomous driving pipeline deployed on a one tenth scale vehicle. To streamline platform development, an AI assisted annotation framework automates dataset generation, significantly reducing manual labor while improving training data quality. The system perception stack features a reinforcement learning guided online multi camera calibration framework that enables adaptive surround view stitching without the need for offline recalibration. This is paired with robust lane …

Towards Application-Driven Optimal Memory And Storage Management, Venkata Naga Prajwal Challa

Computer Science and Engineering Dissertations

Modern computing systems increasingly run on diverse hardware platforms and support applications with widely different access patterns, performance goals, and data lifecycles. In this setting, traditional one-size-fits-all approaches to memory and storage management are often inefficient because they apply fixed policies regardless of application behavior, workload context, or hardware asymmetry. Such generic designs can lead to unnecessary data movement, wasted bandwidth, excessive rewriting, poor resource utilization, and degraded user-perceived performance. This dissertation is motivated by the view that optimal memory and storage management should be application-driven: instead of treating all data uniformly, systems should adapt their decisions to how applications …

Analyzing Network Traffic And Data Exfiltration Via Smb In Post-Vm Escape Scenarios, Noah M. Disanza

Williams Honors College, Honors Research Projects

Virtual machines (VMs) play a crucial role in modern IT infrastructure environments by providing isolation and enhanced security, among other things, for both personal and corporate systems. VMs are heavily rely upon to safely test malware, manage infrastructure, and reduce risk to host systems. This reliance is so substantial that the idea of reducing risk to the host system is believed to be erasing risk entirely. However, this mindset has shown to be challenged time and time again by the emergence of exploits known as virtual machine escapes. These exploits allow malicious actors to break out of the virtualized environment …