Digital Commons Network^™

Ai, The Liberal Arts, And Indigenous Languages: Forming Code Into Language, Christina Graebner

Summer Research Showcase

During the Summer, Spanish Professor Adam Coon and I worked on creating an annotated biography on AI and Indigenous languages for the Digital Well at the UMN Morris Library. Through this project, we have dived into conversations and research focusing on using AI as a translator. In recent years, the conversation around AI has created a surge of studies and research around the relationship between Indigenous languages and artificial intelligence. AI will only continue to expand, and it creates new ways to open communication but creates new ethical guidelines needed to be followed. Our project gathers research articles, podcasts, and …

Self Efficacy And Instructional Support Predict Cyber Deception Acceptance In Ics And Ot Cybersecurity, Daniel Ward

Journal of Cybersecurity Education, Research and Practice

Cyber deception can produce high-confidence evidence of unauthorized activity in industrial control systems (ICS) and operational technology (OT), but practitioners must consider the technology useful, safe, understandable, and supported before they will use it. This study reports a secondary quantitative analysis of a deidentified survey of United States-based ICS and OT professionals to determine whether psychological and instructional factors predict adoption readiness and effective utilization beyond education, experience, and sector. Hierarchical ordinary least squares regression with HC3 robust standard errors was conducted on 262 complete cases. The demographics-only model was not significant and explained 2.8 percent of outcome variance. Adding …

Artificial Intelligence And Social Equities: Navigating The Intersectionalities In A Digital Age (Editorial), Daisuke Akiba, Julie Albright

Publications and Research

This editorial article introduces and synthesizes the Special Issue, “Artificial intelligence and social equities: navigating the intersectionalities in a digital age,” which examines how AI systems intersect with race, ethnicity, and interconnected identity dimensions across global contexts. The eight contributions span healthcare, digital media, higher education, organizational communication, and speculative futures, addressing anti-racist psychiatric algorithms, AI-generated visual disinformation, epistemic injustice between the Global North and South, algorithmically mediated rural–urban divides, culturally untranslated technology transfer, accessibility auditing across the AI lifecycle, and the tension between mechanical objectivity and empathic understanding. Read together, they show that AI is neither inherently …

A Vision For The Future Of Academic Publishing In Sports Analytics, Ryan Elmore, B. Baumer, Brian Macdonald, Gregory J. Matthews, Michael E. Schuckers

Statistical and Data Sciences: Faculty Publications

This article introduces the Journal of Statistics and Data Science in Sports (JSDSS), a Diamond Open Access, peer-reviewed journal. The journal is founded on three core principles. First, our commitment to open access is absolute. Second, reproducibility is critical and fundamental to the journal. Third, we believe sport is a rich and underutilized laboratory for statistical and data science innovation. The aim of the Journal of Statistics and Data Science in Sports is to provide an outlet for original, rigorous, practical, state-of-the-art, reproducible, and peer-reviewed analysis of sports data as well as the data science tools (software, applications, data, etc.) …

Personal Authenticity For Engagement And Transfer In Introductory Cybersecurity Education, Daniel T. Hickey, Ronald J. Kantor

Journal of Cybersecurity Education, Research and Practice

Abstract—This conceptual/theoretical paper explores how personal authenticity might promote generative learning in introductory cybersecurity courses. Generative learning transfers confidently to future educational, professional, personal, and testing situations. This cycle of design-based research addresses the concern that more typical professionally authentic contexts (e.g., hospitals, banks, etc.) may be alien and overwhelming to many students, particularly those in introductory courses and/or from non-professional families and communities. If so, this leads to “inert” knowledge that does not transfer. Personal authenticity is rooted in expansive framing, a modern theory of learning transfer. We reframe expansive framing as personal authenticity to make it …

Nasa’S Ecostress Satellite Reveals Widespread Midday Depression In Ecosystem Evapotranspiration, Jingyi Bu, Jingfeng Xiao, Joshua B. Fisher, Yiqi Luo

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Plants often exhibit a midday depression in water use (i.e., transpiration), reflecting a constraint on their ability to sustain maximum water transport, which may occur at the cost of reduced photosynthesis. Eddy covariance observations and geostationary satellites cannot quantify this widespread phenomenon globally while resolving fine-scale spatial variability. Using evapotranspiration measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and machine learning, we quantify the global distribution of midday depression in evapotranspiration. Midday depression primarily occurs during peak-growing seasons in temperate zones and dry periods in the tropics, with a morning shift of the peak evapotranspiration time …

Interpretable Multimodal Learning For Integrating Neuroimaging And Genetic Data In Alzheimer’S Disease, Kun Zhao, Siyuan Dai, Yingying Zhang, Guodong Liu, Pengfei Gu, Chenghua Lin, Paul M. Thompson, Alex D. Leow, Heng Huang, Haoteng Tang

Computer Science Faculty Publications

Introduction: Early detection of Alzheimer's disease (AD) requires models that combine brain structure changes with genetic risk, but existing methods struggle to align these different data types.

Methods: We present R-GenIMA, an interpretable multimodal large language model that pairs a region-of-interest vision transformer with genetic prompting to jointly analyze structural MRI and single nucleotide polymorphisms (SNPs). Each brain region becomes a visual token and SNP profiles are encoded as structured text, letting the model link regional atrophy to genetic factors through cross-modal attention. Tested on the ADNI cohort, R-GenIMA performs well in classifying four groups: normal cognition, subjective memory concerns, …

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, …

Large Models Empowering Cybersecurity: Opportunities And Challenges, Zhuofeng He, Dongbin Hu, Yige Yuan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Cybersecurity serves as a critical pillar for national security and social stability. Large models in cybersecurity are emerging as key enablers for the intelligent transformation of cyber offense and defense systems. As one of the most advanced core technologies in artificial intelligence, large models are introducing new research directions and application paradigms in the cybersecurity domain. This study systematically reviews the current landscape of cybersecurity-oriented large model applications and products, and explores their deployment scenarios in practice. It further analyzes the development trends in model capabilities, industry ecosystems, and trustworthiness, while identifying major practical challenges such as data privacy protection, …

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 …

Trust, Delegation, And Alignment In Human-Ai Decision Making, Erik O. Kimbrough, Brennan Mcdavid, Diba Vazirian

ESI Working Papers

This paper studies delegation to artificial intelligence in a setting where human principals retain the consequences of delegated choices. Participants wrote prompts instructing ChatGPT-4o mini how to choose on their behalf in three canonical economic domains: risky choice, intertemporal choice, and social allocation. We then elicited the compensation participants required to let the AI’s choices count for payment and compared participants’ own choices to choices generated from their prompts. The design produces two central empirical objects: a revealed measure of reluctance to delegate, captured by willingness to accept compensation for AI delegation, and a behavioral measure of alignment, captured by …

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 …

The Self-Aware Room: A Framework For Operational Self-Awareness In Evolvable Blended Environments, David B. Smith

Publications and Research

The Self-Aware Room (SAR) is a room-scale research environment developed within the larger Balanced Blended Space and Blended Reality Performance System research trajectory. Rather than treating the room as a conventional “smart” environment composed of fixed automation technologies, SAR approaches it as an evolvable blended environment made from physical, virtual, conceptual, sensory, computational, and performative relationships. Its defining feature is not any particular sensor, model, or output device, but the set of transformations through which physical activity becomes structured observation, bounded representation, interpreted state, governed decision, and mediated response.

This paper develops the conceptual and methodological foundations of SAR as …

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 …

Leo’S Choices, Katherine G. Schmidt

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.

Humanity’S Grandeur In The Balance: Theological Reflections On Magnifica Humanitas, William L. Portier

The Journal of Social Encounters

No abstract provided.

Riyadh Charter On Artificial Intelligence For The Islamic World, - Islamic World Educational, Scientific And Cultural Organization (Icesco), - Saudi Data And Artificial Intelligence Authority (Sdaia)

The Journal of Social Encounters

The Riyadh Charter on Artificial Intelligence for the Islamic World was launched by the Islamic World Educational, Scientific and Cultural Organization (ICESCO) and the Saudi Data and Artificial Intelligence Authority (SDAIA) in September 2024, in collaboration with the Saudi National Commission for Education, Culture, and Science, during the third Global AI Summit (GAIN Summit) in Riyadh.

The Charter has been widely recognized and endorsed by Islamic scholars and policymakers. During the 26th Session of the Council of the International Islamic Fiqh Academy, it was highlighted as a moral and strategic compass for AI technologies, addressing the limitations of existing international …

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 …

Lightweight Uav-To-Uav Detection And Tracking For Advanced Air Mobility Applications, Taylor Hostetter

Discovery Day - Daytona Beach

Lightweight UAV-to-UAV Detection and Tracking for Advanced Air Mobility Applications addresses the significant challenge of reliable UAV-to-UAV detection on resource-constrained platforms, particularly within Advanced Air Mobility (AAM) environments where dense, low-altitude airspace requires robust detect-and-avoid capabilities. This work presents the development and experimental evaluation of a lightweight detection and tracking framework for autonomous detect-and-avoid applications. The approach is designed to support real-time onboard operation in multi-vehicle environments characteristic of emerging AAM systems. The proposed framework integrates optical and LiDAR sensing with a low-complexity machine learning decision-support layer that reduces false detections without replacing the underlying control-oriented detection pipeline. This design …

Designing Process-Focused Feedback For College Writers Using Genai, Beata Blood, Maissane Aik, Zoey Zaldivar

Discovery Day - Daytona Beach

As generative AI tools like ChatGPT become more common in higher education, writing instructors face the challenge of guiding students toward effective and ethical use, particularly in asynchronous environments where immediate feedback is limited. This presentation reports on an exploratory study that addresses this challenge by shifting attention from AI’s outputs to students’ moment-by-moment writing processes. Grounded in applied linguistics approaches to writing research and process-tracing methods, the project employed case studies with both expert and novice users of GenAI. Expert participants, including academics and industry professionals, completed writing tasks while integrating AI into their workflows. Their sessions were recorded …

Small Uas Detection: Threat Intelligence & Risk Management Project, Tyler Johnson

Discovery Day - Daytona Beach

The TRANSPORTATION SECURITY ADMINISTRATION / FEDERAL AIR MARSHAL SUAS DETECTION: THREAT INTELLIGENCE & RISK MANAGEMENT PROJECT addresses the emerging safety and security challenges posed by the rapid growth of small Unmanned Aircraft Systems (sUAS) in complex airspace environments. This study analyzed 92 days of sensor-captured Remote Identification (RID) data collected near Fort Lauderdale-Hollywood International Airport (FLL) to assess operational behaviors, aviation risk, and ground risk associated with drone activity. The primary objective of this research is to identify patterns of unauthorized or hazardous sUAS operations to enhance situational awareness and inform actionable risk-mitigation strategies. The analysis identified 335 flights from …

How Ai Influences The Design Process Of Unmanned Underwater Vehicles’ (Uuvs) 3d Sonar System, Eden Tsouklaris, Abriella Smith, Brianna Broderick, Carissa Aumack, Victoria Cornaro

Discovery Day - Daytona Beach

With the exponential growth of Artificial Intelligence (AI), user interface (UI) designers have explored using AI to shorten design time. This study assessed the effectiveness of UIs designed with AI programs versus manual methods for an Unmanned Underwater Vehicle (UUV) control system. Participants were tasked with designing an interface that would allow submarine operators to monitor and coordinate three UUVs repairing a severed underwater communication cable at a depth of 2,000 meters. The scenario presented several operational challenges (zero visibility, sonar-only perception, data latency, and potential system degradation), requiring participants' designs to maintain spatial awareness and support remote repair tasks. …

Humans Vs. Ai: Comparing Approaches To Disaster Response Interface Design, Kelly Nguyen, Olivia Hartmann, Kailey Hrbek, Madeline Nees, Gabrielle Roth, Emily Silliman

Discovery Day - Daytona Beach

Amphibious emergency support operations involve rapidly changing information, high stress, and significant cognitive demands, which can make decision-making and situation awareness more difficult for operators. When interfaces are poorly designed, they can contribute to issues such as alarm flooding, confusion from incomplete information, and delayed responses, all of which increase operational risk during time-critical disaster situations. This study explores whether using generative AI to assist with interface design will improve performance (output quality and effort) and usability compared to a manual sketch mock-up. Participants were asked to design a dashboard interface to support disaster relief operations following a Category 5 …

Navigating Enhanced Exploration Assistance (Nexa), Azhari Abbas, Caleb Fakunle, Ryan Powell, Donovan Livingston

Discovery Day - Daytona Beach

NEXA is an artificial intelligence software platform developed to enhance residential security and property monitoring through seamless integration with autonomous drone systems. This research application of advanced AI in surveillance aims to create a standalone solution capable of real-time threat detection and intelligent alert management. By processing visual and sensory data, NEXA facilitates autonomous drone operation with minimal human intervention. Secure communication channels ensure that instant alerts are delivered to property owners and, potentially, law enforcement, improving response times in security incidents, search-and-rescue operations, and perimeter surveillance. Additionally, NEXA is capable of interfacing with commercially available drone platforms and presents …

Helio: Heliophysics Enhanced Learning For Intelligent Orbits, Kylie Nager, James Kirk

Discovery Day - Daytona Beach

HELIO: Heliophysics Enhanced Learning for Intelligent Orbits   Satellite constellations operating in near-Earth space are increasingly vulnerable to space weather disturbances, such as solar flares, coronal mass ejections (CMEs), and high-speed solar wind streams, which degrade communications, destabilize attitude control, and accelerate orbital decay. These disturbances directly threaten mission continuity, constellation availability, and space asset survivability. Current protective approaches rely primarily on ground-based alerts and lack integration with broader space domain awareness, which results in programmed reactive protocols that are often initiated too late to prevent performance degradation and asset loss. The HELIO project addresses this gap by turning space-weather forecasts …

Feasibility Of High-Throughput Onboard Ai For Mars Rovers Under Solar Constraints, Aashman Gupta

Discovery Day - Daytona Beach

This project evaluates the feasibility of sustained onboard AI autonomy for a solar-powered Mars rover by directly linking solar energy availability to achievable compute performance. While Mars solar irradiance and edge computing performance have been studied independently, no unified framework currently couples surface power generation to autonomy throughput in an experimentally validated manner. The project will begin with a simulation of solar power generation for a 1 m² rover-mounted array across a Martian sol, accounting for seasonal variation, dust opacity, and array configuration (fixed versus sun-tracking). The resulting power profile will then be coupled to representative compute platforms running autonomy …

Phaëthon System, Brady Roudabush, Lauren Gallo, Emelia Thompson, Jacob Woods

Discovery Day - Daytona Beach

Phaëthon System is the project name for the Search and Rescue Drone Initiative. This initiative will improve the current search and rescue drone industry by introducing new techniques to get through dense forest canopies and other places where an overhead view is not useful. The Phaëthon System uses a swarm of drones that can penetrate under the tree canopy to map and search with the utmost efficiency and safety for rescuers. A command drone is launched to survey the overall search area, and set up a communications and data link. The next component is then released, which is a swarm …

Investigating The Spatial Scales Of Ionospheric Irregularities Using Wavelet Analysis, Nash Mcleod

Discovery Day - Daytona Beach

Investigating the Spatial Scales of Ionospheric Irregularities Using Wavelet Analysis:   Ionospheric radio wave scintillation arises from plasma density irregularities in Earth’s ionosphere. Consequently, rapid fluctuations occur in the phase and amplitude of Global Navigation Satellite System (GNSS) signals and can impact communication and navigation systems. These irregularities span from a wide range of spatial and temporal scales and evolve dynamically under the influence of magnetosphere-ionosphere (MI) processes. We investigate phase and amplitude scintillation events using Continuous Wavelet Transform (CWT) to study the spatial evolution of ionospheric irregularities. These irregularities are thought to be formed via different plasma mechanisms such as …