Digital Commons Network^™

Digital Presence In Live Hybrid Performance, Luke Cargill

Dartmouth College Master’s Theses

This thesis examines how digital presence, the sense that a digital performer is socially and performatively "there," is designed and tested in live hybrid performance. Drawing on four practice-based projects (Vicarious, Voltage, Vicarious: Encore Edition, and SUPER BLOOM), the first phase identifies a recurring but empirically untested claim: that digital presence depends on interactivity and co-presence with live performers, though these factors were always entangled in practice.

The second phase tests this claim directly. A new mini-performance, featuring the first fully AI-driven digital character in this line of work, was produced for controlled comparison. Using a 3 (intro type: AI-driven, …

From Disruption To Replacement: The 2026 Cae Cybersecurity Community Symposium And The Emerging Federal-Academic Compact For An Ai Workforce, Sunday Oludare Ogunlana

Journal of Cybersecurity Education, Research and Practice

The cybersecurity workforce gap in the United States is estimated at several hundred thousand unfilled positions, and the rapid integration of artificial intelligence into adversary tradecraft and federal cyber operations is widening that gap qualitatively as well as quantitatively, threatening national security and the operational readiness of graduates entering the field. This perspective article synthesizes the principal arguments advanced by five federal and academic speakers at the 2026 CAE Cybersecurity Community Symposium, using verbatim session transcripts, a structured thematic extraction process, and triangulation against published workforce policy and peer-reviewed literature. Findings document a unified speaker thesis that artificial intelligence now …

Computer Organization And Assembly Language Programming, Chenxi Wang Phd, Muhammad Rashed Phd

Mavs Open Press Open Educational Resources

Computer Organization and Assembly Language Programming is an open textbook written for CSE 2312 students at The University of Texas at Arlington and for anyone who wants to see clearly how high-level code becomes machine operations. The book takes the position that assembly is not a historical curiosity but a working tool: it is where system programming, embedded development, performance tuning, and real debugging skill begin.

Across sixteen chapters, this textbook builds from number systems and base conversion through ALU operations, status flags, and shift operations, then into ARMv7 assembly syntax, the load and store architecture, endianness, addressing modes, branch …

Bridging Content, Practice, And Pedagogy In K–12 Computing Education: A Review Of Computer Science In K–12: An A To Z Handbook On Teaching Programming, Matthew Priem

Essays in Education

This book review examines Computer Science in K-12: An A to Z Handbook on Teaching Programming, a resource designed to support K-12 computer science educators. Bringing together contributions from forty authors, the book integrates content knowledge, computational practices, and pedagogical strategies for teaching programming across grade levels. The review highlights the text’s emphasis on addressing student misconceptions, connecting research to practice, and balancing teacher guidance with student autonomy. Although the alphabetical organization may not always reflect pedagogical progression, the modular structure allows for flexible use. Overall, the handbook is a valuable professional resource for both aspiring and practicing computer …

Lightweight End-To-End Cryptographic Framework With Semantic Qos For Ar-Based Telesurgery, Pavan Kumar Satram

Masters Theses

This thesis presents the design, implementation, and evaluation of a lightweight end-to-end cryptographic framework integrated with a semantic quality-of-service classification system for augmented reality based telesurgery. Telesurgery can deliver expert surgical care to underserved populations, but adoption has been limited by unresolved cybersecurity, network performance, and resilience challenges. The core tension is that strong encryption adds latency that may exceed the clinical safety threshold, while unencrypted systems remain vulnerable to attacks that could endanger patients during live procedures.

The framework addresses this tension through a dual-edge security middlebox that performs per-flow encryption using semantically selected ciphers: AES-128-GCM for latency-critical haptic …

The Honored Children Of Adam: A Muslim Reading Of Magnifica Humanitas In The Age Of Artificial Intelligence, Iqbal Akhtar

The Journal of Social Encounters

No abstract provided.

Magnifica Humanitas And The Obsolescence Of Just War, Noreen Herzfeld

The Journal of Social Encounters

No abstract provided.

Artificial Intelligence And The Rediscovery Of The Human: An Islamic Reflection On Magnifica Humanitas, Mazhar Ali Bari, Biliana Popova

The Journal of Social Encounters

No abstract provided.

Palmnet: Confidence-Calibrated Edge-Cloud Ai For Field Diagnosis Of Date Palm Diseases, Muntadher Kareem, Raed Majeed

Karbala International Journal of Modern Science

Date palm (Phoenix dactylifera L.) is a cornerstone crop for Iraq and the wider MENA region, yet reliable in-field diagnosis of leaf disorders remains slow, labour-intensive, and constrained by a limited pool of agronomists. This paper presents PalmNet, a full-stack diagnostic system that classifies nine leaf conditions through a calibrated edge-cloud framework. The system is developed and evaluated on a public dataset of 3,089 field images spanning the nine classes, using a 70/15/15 stratified split. A ShuffleNetV2 student network, distilled from a ConvNeXt-Tiny teacher, is deployed on two complementary edge endpoints: a Raspberry Pi Zero 2 W field station …

Proactive Deep Q-Learning Approach For Anomaly Detection In Iot Idss, Hawraa A. Habeeb, Mehdi E. Manaa

Journal of Intelligent Informatics, Networking, and Cybersecurity

Breach rates and unparalleled vulnerabilities are a constant feature of the cyber landscape these days, and the increasing complexity of the proliferation of Internet of Things (IoT) nodes is to be expected. With these challenges, the conventional intrusion detection systems (IDS) are proven to be unable to deal with the extensive and varied data streams. Such systems can be fundamentally attributed to the classical nature of these systems, which are lacking in flexibility to analyze traffic in real-time and thus have no proactive capabilities of identifying patterns of unknown attacks. Considering these technical barriers, in this paper, an offensive-defensive system …

Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz

Discovery Day - Daytona Beach

This project explores advanced image analysis techniques to assess the microstructure of highly strained austenitic stainless steel. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract precise measurements for grain size from scanning electron microscopy (SEM) images. These metrics will be examined against the computed grain sizes of the sample from electron backscatter diffraction measurements. By automating the extraction of grain size measurements from SEM images, this study contributes to steamlining the quality assurance/ quality control of industrially processed materials.

Geometry-Conditioned Adversarial Defense For Sar Automatic Target Recognition Via Regime-Specialist Classification Heads, Skyler Fabre

Discovery Day - Daytona Beach

This project, titled Geometry-Conditioned Adversarial Defense for SAR Automatic Target Recognition via Regime-Specialist Classification Heads, addresses the critical vulnerability of deep neural networks deployed in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems to adversarial perturbations. This is where imperceptible pixel-level modifications cause confident misclassification, posing serious risks in defense and aerospace applications. The objective is to develop and evaluate RegimeResNet, a geometry-conditioned classification architecture that exploits sensor metadata unique to SAR collection systems. Rather than treating all images uniformly, RegimeResNet partitions the SAR capture space into nine geometric regimes defined by depression angle and target azimuth angle extracted …

Energy-Aware Bimodal Contact Detection For Leg Odometry, Emre Girgin

Discovery Day - Daytona Beach

Autonomous exploration of extraterrestrial environments using legged robots requires robust GNSS-free 3D state estimation. Standard leg odometry relies on Zero-Velocity Updates (ZUPT), which assume a grounded foot remains completely stationary. This assumption consistently fails on deformable granular terrain due to unobservable slippage, rapidly degrading state estimation. To mitigate this critical failure mode, we propose a dual contact-detection framework designed to robustly gate an Error-State Extended Kalman Filter (ESEKF) tracking pose, velocity, and IMU biases.   The architecture isolates physical load and kinematics by modeling contact detection as two independent parallel Hidden Markov Models (HMMs). The Load HMM processes Ground Reaction Forces, …

Interplanetary Trajectory Optimization With Reinforcement Learning, Shiloh Cuffe

Discovery Day - Daytona Beach

This project investigates the application of reinforcement learning (RL) to optimize low-thrust interplanetary trajectory design, focusing on the Earth-Venus transfer leg of the BepiColombo mission. Traditional trajectory optimization methods, such as patched conics and genetic algorithms, often require simplifying assumptions or complex optimization schemes. This work formulates the trajectory design problem as an optimal control problem (OCP) within a Markov Decision Process (MDP) framework, enabling an RL agent to learn efficient transfer strategies under realistic spacecraft constraints. The objective is to develop an autonomous guidance approach capable of replicating or improving upon established mission designs. The spacecraft is modeled as …

Cars Imass - Comparing Llm Vs Human Operator Effectiveness In Multi-Agent Swarm Coordination, Gatlin Nelson

Discovery Day - Daytona Beach

Title: Dual-Perspective Risk Analysis for Human-LLM Decision Comparison in UAV Swarm Navigation   Unmanned aerial vehicle (UAV) swarms operating in low-altitude wireless network environments encounter localized disruptions that degrade positioning and navigation metrics. These disruptions are modeled as geographic failure zones with defined boundaries. A UAV discovers a zone by entering it and observing degraded performance on its onboard systems. This work assumes that affected UAVs can autonomously retreat to safety using onboard sensors and focuses on the subsequent rerouting decision. Once recovered, the system generates candidate repositioning points surrounding the vehicle, each scored using Conditional Value-at-Risk (CVaR). A human operator …

A Survey On Machine Learning Applications For Operating System Fingerprinting, Siri Siqveland

Discovery Day - Daytona Beach

In the modern age of computers and interconnected networks, cybersecurity and cyber-attackers are evolving in tandem to exploit each other’s vulnerabilities. One technique used by both parties is Operating System Fingerprinting (OSF): with the knowledge of what Operating System a target system is running, innate vulnerabilities can be identified and patched or exploited. Historically, OSF utilizes two main methods: passive and active—the former trades accuracy with undetectability while the latter is generally more detectable but more accurate. However, recent work has combined OSF with Machine Learning (ML) to improve accurate identification. The work presented here is a survey for the …

Stress-Triggered Automation Reliance, Jazmin Elek

Discovery Day - Daytona Beach

Automation is widely used in complex systems and includes any process that replaces human motor, sensory, or cognitive functions with machines or computers (Norman, 1996). As automation becomes more common, understanding how humans trust and interact with these systems is critical. Trust can be measured by whether users override automation or blindly follow its prompts (Norman, 1996).   Artificial intelligence (AI) introduces additional complexity by enabling systems to learn patterns from data it generates. AI performs tasks with the ability to learn from experience (NASA, 2024). AI builds internal databases that can mimic human-like responses (Norman, 1996). However, AI systems can …

Measuring The Tokenization Premium: A Cost Audit For Underserved Language Communities, Avijit Roy, Proma Roy, Hrishitva Patel

Publications and Research

Large language models are increasingly deployed as general-purpose educational and technical assistance systems, but their basic infrastructure does not treat languages equally. One underexamined source of disparity is tokenization: semantically equivalent content can require substantially different token counts across languages, affecting API cost, latency, and usable context length before a model is even invoked. We introduce the Tokenization Equity Audit (TEA), a reproducible benchmark for measuring tokenization premiums in technical tutoring content. TEA evaluates three widely used tokenizers, GPT-4o’s o200k base, Qwen2.5-7B, and Mistral-7B, on a 120-item Python debugging corpus translated from English into Bengali, Hindi, Arabic, Tamil, and Yoruba. …

Enabling Asl Digital Communication Under Poor Internet Access, Swann Thantsin

Student Theses

Video conferencing degrades asymmetrically. When bandwidth falls, a hearing caller loses picture quality and keeps the conversation; a deaf and hard of hearing signer, whose language is carried entirely in the visual modality, loses the conversation. This thesis asks whether signed video reduced to the rates at which commercial platforms fail can be reconstructed at the receiver well enough to keep signing legible. A twostage reduction pipeline crops to the signer and transmits the face and hands at higher fidelity than their surroundings, achieving a reduction of approximately 99%; reconstruction uses a recurrent bottleneck mixer architecture, trained both conventionally and …

Adaptive Phishing Url Detection Using Hybrid Fuzzy C-Means Clustering And Xgboost, Muntadher Mohammed Kareem, Rawaa I. Farhan

Karbala International Journal of Modern Science

Phishing attacks continue to evolve in sophistication, rendering static detection methods increasingly ineffective. Existing URL-based approaches suffer from limited adaptability to emerging phishing patterns, mislabeled training data, and insufficient validation protocols. This paper proposes a hybrid phishing URL detection system that integrates Fuzzy C-Means (FCM) clustering with XGBoost classification, enhanced by a novel Micro Adaptive Feature Extractor (MAFE). The system employs a multi-stage pipeline: feature engineering generating 36 statistical and interaction features, MAFE producing 15 adaptive features through class-aware dynamic weighting, micro-pattern detection, and entropy analysis, and FCM with K=2 clusters providing soft membership features to XGBoost. A two-pass confidence-based …

Wip: Developing A Generative Ai Autoethnography Assistant, Jyoti Suhag, Jennifer Drewyor, Kathryn Bugbee, Michelle Jarvie-Eggart, Lynn Albers, Leo Ureel

Michigan Tech Publications

Background: Generative AI (genAI) is transforming educational research, offering new possi-bilities for conducting interviews while local sandboxing minimizes data privacy risks and hallucinations. Purpose: This work-in-progress presents the AI Autoethnography Assistant, a project investigating how large language models (LLMs) can support autoethnographic interview design and execution. Approach: Using prompt engineering grounded in Interpretative Phenomenological Anal-ysis, paraphrasing techniques, and structured follow-up questions, we developed protocols that incorporate po-sitionality and prompt reflection on origin stories and pivotal life moments. Outcomes: Conversations conclude at the user’s discretion, with the AI generating a thematic summary. We tested refined prompts across four platforms (Gemini, Claude, …

Modeling And Simulation Of Solar Battery Charge Controller Using Adaptive Particle Swarm Optimization Mppt Algorithm, Mustafa Sacid Endiz, Göksel Gökkus

Mathematical Modelling and Numerical Simulation with Applications

Implementing an effective Maximum Power Point Tracking method is crucial for optimizing solar energy harvesting against environmental fluctuations like solar radiation and temperature. This paper introduces a novel approach for modeling and simulating a solar battery charge controller, using a modified Particle Swarm Optimization algorithm. The power stage of the system is based on a SEPIC converter, which is employed to manage the power conversion and improve the energy transfer to the battery. The developed circuit model is evaluated under various radiation levels at a constant temperature, as well as under different temperature levels at a constant radiation. Simulations are …

Rockin’ Rover On The Rainbow Road, Michael Kolta, Lawrence Burgee, Ying Yuan

Transformations

This paper presents a progressive series of age-appropriate lesson plans for grades K-12 that all use the same interdisciplinary activity to educate students about Science, Technology, Engineering, Art, and Mathematics (STEAM) simultaneously. Technology from Texas Instruments (TI) was employed including a TI Nspire graphing calculator that can run Python programs, a TI Innovator Hub, and a TI Rover. The TI Rover is a small, robotic car that has sensors and is controlled by the calculator via the Hub hardware interface. A Python program was developed that uses the color sensor in the Rover to detect the color on colored paper …

Real Bullets, Plastic Guns: Evaluating The Strength Of 3-D Printed Gun Parts, Maria Latenia Mayol

Student Theses

Privately made firearms (PMFs), often referred to as “ghost guns,” are firearms manufactured or assembled by individuals rather than federally licensed manufacturers. Although the terms are frequently used interchangeably, “ghost gun” more specifically describes an unserialized firearm, whereas PMFs include a broader range of firearms produced through nontraditional manufacturing methods. PMFs may be entirely 3-D printed, assembled from partially completed firearm kits, or constructed by integrating additively manufactured components with commercially manufactured firearm parts. The increasing accessibility of additive manufacturing and widespread dissemination of computer-aided design files have raised concerns about concealment, regulation, and forensic evasion, particularly when factory-manufactured components …

Closing The Awareness–Behavior Gap: A Role-Based Phishing Training Framework For Higher Education, Ranylene O. Olaybal, Ryan A. Olaybal

Journal of Cybersecurity Education, Research and Practice

Phishing remains one of the most persistent cybersecurity threats facing higher education institutions, where diverse user populations and highly connected digital environments increase exposure to social engineering attacks. Although cybersecurity awareness initiatives are widely implemented, high awareness does not always translate into secure behavior. This study examined phishing awareness, phishing-related practices, phishing susceptibility, and phishing experiences among college students, teaching faculty, and administrative staff in a private higher education institution in the Philippines. Using a quantitative cross-sectional design, data were collected from 553 respondents through a validated survey instrument and analyzed using descriptive statistics, one-way analysis of variance, Tukey's honestly …

Clinical Utility Of An Fda-Authorized Artificial Intelligence Imaging Platform In Interstitial Lung Disease Diagnosis, Arjun Prakash Tambe, Ryan Boente, Gautam George, Fayez Kheir, Omid Tahamtani Omran, Kavitha Selvan

Division of Pulmonary, Allergy, and Critical Care Medicine Faculty Papers

Background/Objectives: The diagnosis of interstitial lung disease (ILD) is challenging and frequently delayed. Clinically accessible and minimally invasive diagnostic tools are needed to expedite the diagnosis of ILD while minimizing risk to patients. Fibresolve is an imaging artificial intelligence (AI) tool recently approved by the Food and Drug Administration (FDA) for use in ILD diagnosis and made available to clinicians. The objective of this study was to describe its utility in clinical practice. Methods: We conducted a prospective, observational study of patients across the United States (US) in whom Fibresolve was utilized during routine clinical practice between July 2024 and …

Painting A Scene: 3d Painterly Rendering From Curves To Rebelle, William M. Luttrell

All Theses

Stylized 3D rendering has seen much development and success over the past few years. From Spider-Man: Across the Spider-Verse to The Bad Guys, many studios have developed tools to incorporate stylistic elements from graphic novels, comic books, watercolor paintings, and more into their productions. This stylization process incorporates the pacing, visual style, and themes from the source medium into the animated work, allowing a much greater freedom of expression for artists and directors.

Inspired by these films as well as the needs of the short film Kate Shelley and the Bridge of Darkness currently in production, This paper presents …

Causal Discovery In Photospheric Magnetic Field Time Series For Interpretable Solar Flare Prediction, Nathan W. Nelson

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are capable of damaging many valuable resources, including satellites, power grids, and even human lives. Being able to predict solar flares can allow for proactive measures to be taken that can prevent that damage. Many new deep learning methods for predicting solar flares have shown promise in this task, but the decisions they make are harder to explain to humans. This makes understanding why these models make mistakes difficult, which in turn makes fixing and maintaining them more challenging. We test a recent deep learning method that helps discover relationships between different measurements of the Sun as they …

Differential Effects Of Generative Artificial Intelligence On Formative And Summative Assessment Performance: A Quasi-Experimental Longitudinal Study, Md Istiak Morsalin

Master's Theses

Generative artificial intelligence has unsettled a core assumption of course assessment: that scores on unsupervised work reflect what students can do unassisted. This study tests whether the widespread availability of ChatGPT altered student performance differently on formative versus summative assessments in Business Analytics and Foundations, a required undergraduate quantitative-methods course taught by one instructor across nine cohorts. In a quasi-experimental longitudinal design, the Fall~2022 cohort ($n = 90$), the last to finish before ChatGPT's public release, serves as a control against eight post-ChatGPT cohorts spanning Spring~2023 through Summer~2025 ($N = 646$). Outcomes were drawn from McGraw-Hill Connect records using matched …

Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo

Research Collection School Of Computing and Information Systems

The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks from code generation to program repair, producing a massive volume of software artifacts. This surge in automated creation has exposed a critical bottleneck: the lack of scalable and reliable methods to evaluate the quality of these outputs. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. This approach leverages …