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Support Systems, Capability Configurations, And Enabling Conditions For Circular Entrepreneurship, Israa Thiab May 2026

Support Systems, Capability Configurations, And Enabling Conditions For Circular Entrepreneurship, Israa Thiab

Theses

The transition to a circular economy requires entrepreneurs who can create value from waste, design regenerative business models, and navigate resource-constrained environments. Yet understanding what enables circular entrepreneurial success remains fragmented across the sustainability and entrepreneurship literatures. This dissertation examines how entrepreneurial support systems, capability configuration, and external enabling conditions shape circular venture development and performance through three complementary studies that together advance a unified framework of circular entrepreneurial effectiveness. Study 1 employs participatory action research with a donor-funded incubation program in Jordan, examining how stakeholder priorities reconfigure support systems for circular entrepreneurs. Analysis of two cohorts reveals that stakeholder-driven …

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An Integrated Framework For Information Security Risk Management: A Mixed-Methods Systematic Literature Review, Biju Varghese, Quang "Neo" Bui May 2026

An Integrated Framework For Information Security Risk Management: A Mixed-Methods Systematic Literature Review, Biju Varghese, Quang "Neo" Bui

Articles

Driven by escalating threats and regulatory scrutiny, information security risk management (ISRM) is now a strategic imperative for organizations across the industrial spectrum. Although prior literature reviews offer valuable insights into ISRM subdomains, they remain fragmented and overly reliant on manual methods, limiting their scope and scalability. Additionally, only limited reviews have been able to comprehensively synthesize ISRM literature across its three foundational pillars: processes, governance, and strategy. To address this gap, we conduct a large-scale systematic literature review of 623 ISRM-related articles using a mixed-methods approach which combines natural language processing techniques with thematic analysis. We identify 22 thematic …

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An Lstm-Based Framework For Predictive Power Quality Monitoring, Ian Aiken May 2026

An Lstm-Based Framework For Predictive Power Quality Monitoring, Ian Aiken

Theses

Rapid integration of photovoltaic (PV) systems introduces significant power quality challenges, including voltage sags and harmonic distortion, yet existing approaches either perform post-hoc classification or rely on computationally expensive meteorological forecasting unsuitable for real-time edge deployment. In this paper, a resource-efficient framework is developed for short-term power factor sag prediction using only on-line electrical measurements via a dual-track methodology. A regression model is first established to define the predictive limits of direct value estimation. The analysis of drop-event-specific metrics reveals that sensor features are lagging indicators incapable of regressing immediate drop magnitude, motivating a classification re- framing. A binary Long …

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Reconfigurable Dataflow For Efficient Matrix-Matrix Multiplication For Dnn Acceleration, Daniel Laudico May 2026

Reconfigurable Dataflow For Efficient Matrix-Matrix Multiplication For Dnn Acceleration, Daniel Laudico

Theses

General matrix-matrix multiplication (GeMM) is a principal computational bottleneck in modern deep learning workloads where performance can be heavily impeded by frequent memory accesses. This thesis introduces a multi-dataflow aware processing element (PE) designed to perform the multiply-accumulate (MAC) operations fundamental to GeMMs while utilizing a data flow that best fits the data provided. A salient feature of this architecture is its programmable flexibility, which enables the implementation and execution of different data flow strategies, including Output Stationary, Weight Stationary, Input Stationary, and Row Stationary. The flexible design supports both dense and sparse GeMM and General matrix-vector multiplication (GeMV). To …

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Material Selection In The Mountain West: A Multi-Criteria Decision Analysis (Mcda) Based Approach For Architects, Melissa Bach May 2026

Material Selection In The Mountain West: A Multi-Criteria Decision Analysis (Mcda) Based Approach For Architects, Melissa Bach

Theses

With modern advancements in technology and the expansion of a globally interconnected network, architects have access to a large range of building materials. Increasing public concern for the environment has prompted architects to prioritize materials that are more sustainable, but varying definitions of sustainability, coupled with unclear guidelines for material selection, repeatedly lead to uncertainty and decision-making challenges within the profession. To address this, a comprehensive methodology such as Multi-Criteria Decision Analysis (MCDA) is needed for the material selection processes. Furthermore, a gap in the literature demonstrates that the application of MCDA for material selection in the Mountain West region …

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The Future Of Airport Operating Models And Seamless Passenger Journeys: Strategic Readiness Implications For Dubai World Central (2035–2045), Mohammad Abdulla Alsuwaidi May 2026

The Future Of Airport Operating Models And Seamless Passenger Journeys: Strategic Readiness Implications For Dubai World Central (2035–2045), Mohammad Abdulla Alsuwaidi

Theses

Dubai World Central is being developed to support a major expansion of Dubai’s aviation capacity. Its planned scale raises questions beyond terminal construction and technology pro- curement because airlines, airport operators and government authorities must coordinate passenger processing while maintaining security, public confidence and operational continu- ity. This thesis examines how airport operating models and end-to-end passenger processing may evolve during 2035–2045 and assesses the strategic readiness implications for DWC. A qualitative, desk-based design was adopted using peer-reviewed research, official aviation publications, government documents, airport implementation evidence and selected industry reports. The analysis combined PEST horizon scanning, a technology-readiness assessment, …

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Energy Loss Hotspot Mapping In Power Distribution Networks, Abdulla Alhammadi May 2026

Energy Loss Hotspot Mapping In Power Distribution Networks, Abdulla Alhammadi

Theses

The electricity systems used in power distribution in the United States also consume a lot of electricity to warm up until it gets to homes, business or industries. These are technical losses which are caused by the physical resistance of the aging electrical equipment like transformers, conductors and distribution lines. With the aging of infrastructure, the resistance rises, and the amount of wasted energy increases. Although this is a large problem, most utility companies continue to use rough age estimations or customer complaints instead of using more direct and objective data-driven techniques to determine the concentration of the worst losses. …

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A Study Of Post -Quantum Cryptography Migration In An Emulated Enterprise Environment, Saquib Farooq Malik May 2026

A Study Of Post -Quantum Cryptography Migration In An Emulated Enterprise Environment, Saquib Farooq Malik

Theses

The imminent maturation of Cryptographically Relevant Quantum Computers (CRQCs) represents a fundamental threat to the digital security infrastructure, specifically targeting modern cryptographic standards such as AES, RSA and Elliptic Curve Cryptography (ECC). While Post -Quantum Cryptography (PQC) offers a mathematical defense, the transition is complicated by the “Harvest Now, Decrypt Later ” (HNDL) strategy, where encrypted data is intercepted today for future decryption. This thesis explores the critical necessity of an organized migration strategy to protect long-term data sensitivity against the rapid advancement of quantum capabilities and addresses the gaps in the current migration landscape: the absence of a standardized, …

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Quantifying Exterior Ecological Performance In Biomimicry, Bioclimatic, And Biophilic Architecture: A Comparative Framework Across Climatic Zones, Ruqayyah Siamwala May 2026

Quantifying Exterior Ecological Performance In Biomimicry, Bioclimatic, And Biophilic Architecture: A Comparative Framework Across Climatic Zones, Ruqayyah Siamwala

Theses

Historically, sustainability research in architecture has focused on interior comfort and operational energy efficiency, leaving the ecological performance of the immediate building façade relatively unexplored. By creating a comparison framework to assess the performance of biomimetic, bioclimatic, and biophilic architecture under external ecological conditions across four climatic zones—hot-humid, cold-humid, hot-dry, and cold-dry—this thesis closes that gap. Using quantitative data such as NDVI, land surface temperature, PM2.5, NO2, infiltration rates, and species count in addition to qualitative analysis of these three architectural strategies at the building-site interface, twelve case studies are evaluated against four ecological indicator groups: air quality, soil quality, …

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Kosovo Pisa Monitoring Via System Signals Benchmarking 2015/2018/2022, Peer Clusters, Early-Warning Screening, And Mixed-Methods Triangulation In Grade 10 Mathematics, Leon Cana May 2026

Kosovo Pisa Monitoring Via System Signals Benchmarking 2015/2018/2022, Peer Clusters, Early-Warning Screening, And Mixed-Methods Triangulation In Grade 10 Mathematics, Leon Cana

Theses

This thesis develops a practical monitoring approach for Kosovo’s PISA outcomes by linking country-level benchmarking of PISA 2015, 2018, and 2022 with measurable system signals and a focused school-level triangulation. Quantitatively, it benchmarks Kosovo against Western Balkans Six (WB6) and selected European peers through mathematics mean scores, below-Level-2 shares, and top-performing shares. It then uses k-means clustering to define empirical peer groups and tests an early-warning screening exercise in which 2015/2018 information is used to classify held-out 2022 high-risk profiles. Qualitatively, it triangulates the statistical signal patterns with policy document analysis and a low-burden Grade 10 mathematics micro-study in Prishtina …

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Designing A Uae-Centric Integrated Mobility System: Linking Transport,Traffic, And Urban Data For Smarter Travel, Alreem Jabir Alshamsi May 2026

Designing A Uae-Centric Integrated Mobility System: Linking Transport,Traffic, And Urban Data For Smarter Travel, Alreem Jabir Alshamsi

Theses

Urban mobility systems are central to smart city performance; however, in many rapidly developing cities, mobility services remain fragmented across agencies, platforms, and datasets. In the United Arab Emirates (UAE), cities such as Dubai and Abu Dhabi have made significant investments in digital infrastructure and intelligent transport solutions. Despite this progress, existing mobility applications continue to operate in functional silos, limiting real-time adaptability and cross-domain integration. Current platforms primarily focus on transport coordination and congestion avoidance, with limited integration of broader urban datasets such as building access schedules, environmental conditions, and localized service availability. This research addresses the absence of …

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Mathamr+: A Unified Graph Neural Network Framework For Multimodal Mathematical Information Retrieval, Jacob Yoon May 2026

Mathamr+: A Unified Graph Neural Network Framework For Multimodal Mathematical Information Retrieval, Jacob Yoon

Theses

Mathematical Information Retrieval (MIR) focuses on developing systems that enable users to search for and retrieve documents containing mathematical content. A key challenge in building effective math-aware retrieval systems lies in jointly modeling the symbolic and operational structure of mathematical expressions together with their surrounding linguistic context. Existing approaches often use linear token sequences, losing structural information, or rely on separate models for text and math, limiting their ability to capture cross-modal patterns and learn contextualized representations. This thesis proposes MathAMR+, a graph neural network-based retrieval framework that jointly models Abstract Meaning Representation (AMR) graphs, Operator Trees (OPTs), and Symbol …

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Ai In Action: Redefining Loan Default Prediction For The Digital Lending Era, Joe Vinson Ukken May 2026

Ai In Action: Redefining Loan Default Prediction For The Digital Lending Era, Joe Vinson Ukken

Theses

Credit risk assessment remains a very important part of financial institutions, particularly within the rapidly evolving digital lending environment. The research explores the effectiveness of five machine learning models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine—in predicting loan default using both financial indicators and categorical borrower attributes. The study is motivated by the growing availability of structured borrower data and the need to evaluate whether advanced machine learning algorithms can be implemented over conventional credit scoring methods. A publicly available L&T Vehicle Loan Default Prediction dataset comprising 233,154 borrower records and 41 structured attributes, the CRISP-DM …

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An Evidence-Based Ethical Risk Classification Framework (Ercf) For Ai Policies, Ahmad Almarri May 2026

An Evidence-Based Ethical Risk Classification Framework (Ercf) For Ai Policies, Ahmad Almarri

Theses

This thesis develops and evaluates an evidence-based Ethical Risk Classification Framework (ERCF) for analyzing substantive ethical engagement in AI policy documents. The study addresses the gap between high-level AI ethics principles and reproducible computational methods for comparative governance analysis. A secondary dataset of AI ethics policy recordswas used as the starting point. After retrieval, text extraction, and validation, a corpus of 44 policy documents was constructed from an initial 112 records (110 link-bearing entries). Supervised classification was implemented for four core dimensions: Accountability, Bias & Fairness, Privacy, and Transparency. A TF-IDF + Logistic Regression baseline was evaluated using stratified 5-fold …

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Tom: Toward A Design Language For Object Personality, Mike Minerva May 2026

Tom: Toward A Design Language For Object Personality, Mike Minerva

Theses

Design has been chasing seamless efficiency for decades, creating smoother interactions, more compliant objects, and experiences that disappear entirely. When an object is frictionless, it does not lack personality. It has a personality. One that is obedient, meaningless, and remarkably boring. An object that pushes back has boundaries, and that resistance comes from a position of caring. Objects have always been able to refuse. What is new is that the refusal can now come from inside the object, from memory, adaptive thinking and a disposition toward the person using it. Expressed consistently over time, that agency is perceived as personality. …

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Navigating The Polycrisis: A Systems-Foresight Approach To Career Guidance For Generation Alpha In Dubai, Sumaiya Abdul Samad Muhammed May 2026

Navigating The Polycrisis: A Systems-Foresight Approach To Career Guidance For Generation Alpha In Dubai, Sumaiya Abdul Samad Muhammed

Theses

The global labor market is navigating a ”polycrisis” of technological disruption and macroeconomic volatility, rendering linear career trajectories obsolete. Traditional K-12 career guidance relies on industrial-era ”predict-and-place” methodologies, which fail to equip Generation Alpha for an economy defined by structural job churn. This misalignment is especially critical in Dubai, where the ambitious mandates of the Dubai Economic Agenda (D33) and Education 33 (E33) require an agile, future-ready talent pipeline. This study aimed to design and validate a foresight-driven advisory framework to replace static vocational matching with continuous futures literacy. Employing the Systemic Foresight Methodology (SFM), the research diagnosed the structural …

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A Machine Learning Based Analysis Of Traffic Accident Severity Using Environmental, Temporal And Road Condition Factors, Khalid Alaleeli May 2026

A Machine Learning Based Analysis Of Traffic Accident Severity Using Environmental, Temporal And Road Condition Factors, Khalid Alaleeli

Theses

Traffic accidents on the road have been one of the most significant global issues as far as public safety is concerned, with the severity of crashes, as well as their fatalities, representing an undue burden on human and economic damages. The research constructs a machine learning-based model to examine and forecast the level of traffic accidents based on environmental and time, road and human variables. There were 798 valid observations and 14 variables discussing a dataset using the exploratory data analysis tool, supervised classification models (Decision Tree and Random Forest), and unsupervised K-Means clustering. The technique of feature engineering was …

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Bio-Based Materials In Adaptive Reuse: Assessing Durability, Disassembly, And Reuse Potential, Gloria Barry May 2026

Bio-Based Materials In Adaptive Reuse: Assessing Durability, Disassembly, And Reuse Potential, Gloria Barry

Theses

This study evaluates the performance of bio-based and conventional wall assemblies in relation to their ability to be disassembled and reused after exposure to environmental stress. While durability is often prioritized in construction, this research examines whether materials that resist change are also those most suitable for reuse. Four wall assembly types, two conventional (CMU with EPS insulation) and two bio-based (compressed earth blocks with hemp insulation), were constructed, each with two specimens to establish control and test conditions, using different connection strategies, including 1/2-inch mortared systems and a combination of thin mortar-slip joints with mechanical fasteners. Assemblies were subjected …

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Perceptual Evaluation Of Deep Learning Models For Hdr Reconstruction Under Severe Overexposure, Alireza Rabbanifar May 2026

Perceptual Evaluation Of Deep Learning Models For Hdr Reconstruction Under Severe Overexposure, Alireza Rabbanifar

Theses

Overexposure in imaging systems leads to irreversible loss of scene information due to sensor saturation, resulting in degraded perceptual quality and limited dynamic range. While recent deep learning approaches have shown promising results in correcting mild to moderate overexposure, their performance under severe overexposure and their perceptual quality in HDR image generation remain less understood. In this work, a comparative evaluation of four representative models—Multi-Scale Exposure Correction (MSEC), Illumination-Adaptive Transformer (IAT), LEDiff, and X2HDR—is conducted across multiple scenes and levels of overexposure, with particular emphasis on severe overexposure conditions. The evaluation focuses on the perceptual quality of the reconstructed HDR …

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Migration Of The Spirit, Yaqub Shabazz May 2026

Migration Of The Spirit, Yaqub Shabazz

Theses

Migration of the Spirit examines interior transition through sustained painting practice. While migration is often understood as geographic displacement, this thesis reframes it as an interior reorientation shaped by halted time, disciplined solitude, and the act of reclaiming one’s own narrative. Grounded in lived experience within institutional systems and sustained decades of teaching and studio practice, the work reflects a movement from externally imposed narratives toward self-authored clarity. The paintings are marked by architectural compression, repeated motifs, and restrained palettes dominated by sienna, umber, and muted blues. Figures emerge within densely structured spatial fields shaped by assertive linear frameworks that …

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From Centralized Forecasting To Distributed Intelligence: A Dual- Framework Approach To Dynamic Load Forecasting And Federated Learning In Smart Grids, Tousiya Nazir May 2026

From Centralized Forecasting To Distributed Intelligence: A Dual- Framework Approach To Dynamic Load Forecasting And Federated Learning In Smart Grids, Tousiya Nazir

Theses

With the growing shift of conventional power systems toward decentralized smart grid electrical systems, many key challenges have come into action. Data privacy, scalability, and real-time decision making are major concerns. A vast amount of sensitive data is generated due to the widespread adoption of distributed energy resources (DERs), electric vehicles (EVs), and other intelligent devices. Although this data is useful for grid improvement, it also raises concerns about privacy, security, and communication, which, in turn, make centralized machine learning unsuitable. Because of the aforementioned issues, this thesis presents a dual framework contribution. Firstly, it offers a systematic and comprehensive …

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Low-Pressure Air Blast Atomization Of Non-Newtonian Hydrogels For Spray-Based Coating Applications, Jerry Baklycki May 2026

Low-Pressure Air Blast Atomization Of Non-Newtonian Hydrogels For Spray-Based Coating Applications, Jerry Baklycki

Theses

The conventional spraying of gels has a hallmark characteristic of requiring high operating pressures to supply sufficient energy for atomization. As a result, much of the existing research on sprayable gels has focused on applications that allow for such high pressures (Mallory 2012, Saurabh, et al. 2022). A form of gel known as hydrogels represents a group of water-based gels that can be derived from natural or synthetic materials, presenting an alternative due to their unique rheological properties. These materials are typically shear-thinning and may exhibit thermoreversibility, both of which can significantly reduce effective viscosity under appropriate conditions. Independently or …

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Vibration Assisted Molten Metal Jetting Of Aluminum Alloys, Usama Rifat May 2026

Vibration Assisted Molten Metal Jetting Of Aluminum Alloys, Usama Rifat

Theses

Vibration has been used in different manufacturing processes such as casting, welding, and machining to reduce residual stress, gas porosity, and hot cracking, as well as to refine microstructure and improve strength. Vibration has likewise been coupled with additive manufacturing processes such as directed energy deposition and powder bed fusion to improve grain structure and mechanical properties. The molten metal jetting (MMJ) process, which ejects molten metal to 3d print metal parts, typically has a porosity of about 1%, which can increase further as the print bed temperature gets lower. The primary reasons behind this porosity are gas porosity and …

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The Next Generation Of Police Station Design: Integration Of Officer Wellness With Operational Demands, Kayla Zill May 2026

The Next Generation Of Police Station Design: Integration Of Officer Wellness With Operational Demands, Kayla Zill

Theses

The design of police stations has traditionally prioritized efficiency of operations and security, overlooking the wellness of officers. This research analyzes the integration of Trauma-informed Design (TiD) and WELL Building Standards into police stations design to create environments that aid officer well-being while maintaining operational functionality. Through literature review, including research of current police station design guidelines, trauma endured by officers, Trauma-Informed Design principles, WELL building standards, and understanding of the need for wellness to be incorporated into police station design. Officer interviews then further understanding of the needs of officers to create a scorecard and program guidelines to refocus …

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The Implications Of Expanding Solar Microgrids For Resilience In Puerto Rico, Adriana Dominguez Moret May 2026

The Implications Of Expanding Solar Microgrids For Resilience In Puerto Rico, Adriana Dominguez Moret

Theses

Puerto Rico’s energy landscape is defined by a cycle of continuous disaster recovery, exacerbated by its location in a high-risk natural disaster zone and a centralized grid prone to failure. The human cost of this vulnerability was exemplified by the 328-day power outage following Hurricane Maria, a period that rendered vital spaces unusable and highlighted the dire consequences of accelerated infrastructure aging and a heavy dependency on expensive fossil fuel imports. This thesis addresses these critical challenges by investigating the integration of community microgrid systems into the urban landscape as a strategic framework for power continuity. Using Casa Pueblo’s existing …

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Urban Illumination And Circadian Disruption: Examining Artificial Light At Night, Caitlin Mcmasters May 2026

Urban Illumination And Circadian Disruption: Examining Artificial Light At Night, Caitlin Mcmasters

Theses

Artificial light at night is increasingly prevalent in dense urban environments, yet it remains unclear how often typical city lighting conditions likely exceed recommended nighttime limits for residential circadian health. This thesis evaluates nighttime light exposure in two contrasting urban districts, Back Bay in Boston and Times Square in New York City, using satellite derived nighttime radiance, GIS based urban morphology, and documented lighting infrastructure characteristics. Rather than relying on calibrated photometric simulations, the study employs a qualitative, evidence based comparative approach to assess relative façade exposure and interpret conditions against established benchmarks, including the WELL Building Standard and ILP …

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From Village Core To Urban Impact: A Method For Assessing Community-Centered Design Across Scales, Jessica Randise May 2026

From Village Core To Urban Impact: A Method For Assessing Community-Centered Design Across Scales, Jessica Randise

Theses

By utilizing pioneered urban planning principles and design strategies that focus on creating human centric spaces, this research will contribute to a deeper understanding of intentional urbanism and the importance of place-based design. The built environment to which surround us plays a key role in impacting a community’s development and civic pride amongst its community members. By understanding and pinpointing the successful design strategies utilized in both the villages of Fairport and LeRoy, NY, other regionally similar places with lesser success can be revitalized. This will be accomplished through the understanding of key development categories across all three villages which …

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Adaptive Insider Threat Detection With Graph-Based Models, Rashed Hassan Nasser Alrazooqi May 2026

Adaptive Insider Threat Detection With Graph-Based Models, Rashed Hassan Nasser Alrazooqi

Theses

Insider threats represent one of the most challenging categories of cybersecurity risk facing modern organizations. Traditional detection approaches based on fixed rules, static thresholds, and signature databases are not effective for capturing the subtle and evolving nature of insider behaviour. This work proposes and implements a multi-stage unsupervised analytical framework for insider threat detection based on large-scale NetFlow telemetry. The framework operates through five interconnected stages: memory-efficient chunk-based streaming ingestion of raw NetFlow records, hourly temporal segmentation of host-level traffic statistics, composite anomaly scoring combining logarithmically scaled byte volume and flow frequency, weighted host-to-host communication graph construction with greedy modularity …

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Generative Ai To Support College Students During The Writing Process, Damionie Vernon May 2026

Generative Ai To Support College Students During The Writing Process, Damionie Vernon

Theses

The growing integration of Generative Artificial Intelligence (GAI) in higher education raises important questions about its role in supporting college students during the writing process. I used a mixed-methods approach, with a focus on quantitative questionnaires and qualitative written artifacts, to analyze patterns of GAI usage and perceptions by testing 34 college students. My thesis will explore these questions: (1) How do college students approach writing tasks in college?, (2) How do college students utilize generative AI to assist with writing tasks?, (3) What are the perceptions of college students on the use of generative AI to assist with writing …

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Towards Effective And Efficient Robot Learning From Demonstrations Via Human-Robot Interaction, Xiwen Dengxiong May 2026

Towards Effective And Efficient Robot Learning From Demonstrations Via Human-Robot Interaction, Xiwen Dengxiong

Theses

Robot learning from demonstrations is critical in industrial and service robotics applications, where robots must acquire diverse skills and adapt to human workflows in complex, unstructured environments without relying on exhaustive manual programming. Although robots have been widely deployed in industrial settings, acquiring new skills and adapting to novel tasks remain significant challenges due to (1) the large volume of demonstration data typically required, (2) the limited reusability of previously learned knowledge, and (3) the high cost of collecting high-quality demonstrations.  This dissertation aims to address key challenges in robot learning from demonstrations in complex and unstructured environments by making …

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