Digital Commons Network^™

Effective Wordle Heuristics, Ronald I. Greenberg

Computer Science: Faculty Publications and Other Works

While previous researchers have performed an exhaustive search to determine an optimal Wordle strategy, that computation is very time consuming and produced a strategy using words that are unfamiliar to most people. With Wordle solutions being gradually eliminated (with a new puzzle each day and no reuse), an improved strategy could be generated each day, but the computation time makes a daily exhaustive search impractical. This paper shows that simple heuristics allow for fast generation of effective strategies and that little is lost by guessing only words that are possible solution words rather than more obscure words.

Emerging Threats In Ai: A Detailed Review Of Misuses And Risks Across Modern Ai Technologies, Niyat Seghid, Farkhund Iqbal, Khalifa Al-Room, Áine Macdermott

All Works

The swift evolution of artificial intelligence (AI) has enabled unprecedented capabilities across domains, while simultaneously introducing critical vulnerabilities that can be maliciously exploited or cause unintended harm. Although multiple initiatives aim to govern AI-related risks, a comprehensive and systematic understanding of how AI systems are actively misused in practice remains limited. This paper presents a systematic review of AI misuse across modern AI technologies. We analyze documented incidents, attack mechanisms, and emerging threat vectors, drawing from existing AI risk repositories, prior taxonomies, and empirical case reports. These sources are synthesized into a unified analytical framework that categorizes AI misuse across …

Activating Learning With Ai: Early Evidence From Undergraduate Sports Management Courses, Sherry Andre

Faculty and Staff Publications & Presentations

Presented research at the GSBA Conference on integrating AI into undergraduate sport management courses to enhance student engagement, critical thinking, and applied learning. The session shared early evidence on how AI can support skill development, improve classroom experiences, and better prepare students for careers in the evolving sport industry.

Improving Ground Cover Crop Fractional Vegetation Mapping Via Causality-Based Deep Representation Learning, Atif Latif, Masoumeh Hashemi, Matt Yost, Somayeh Esmaeili, Xiaojun Qi

Computer Science Student Research

Semantic segmentation and deep learning methods have rarely been applied to fractional vegetation cover (FVC) segmentation tasks due to the lack of publicly available datasets for training deep learning models. FVC is a key indicator for assessing vegetation distribution, crop density, and crop responses to water availability and fertilizer application, yet conventional field-based measurement methods are time consuming, costly, labor intensive, and may lack the accuracy required for critical applications such as drought stress evaluation and water productivity. In this paper, we introduced causality-based deep learning techniques for FVC segmentation on a publicly available RGB dataset that consists of four …

An Autonomous Robotic System For Object Retrieval And Delivery: Enhancing Independence For Users Living With Disability And Older Adults, Jincheng Li, Chenghao Lin, Amna Mazen, Youssef A. Bazzi

Michigan Tech Publications

As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is built on the Robot Operating System (ROS) framework and integrates three key components: the Pioneer P3-DX mobile robot for autonomous navigation, the ReactorX-200 robotic arm for pick-and-place operations, and the Kinect v2 RGB-D camera for object detection and localization. Users interact with the robot through natural language processing by issuing voice commands to retrieve various objects. Microsoft …

Ai Scribe Use In Residency Training: A Call For Specialty Society Guidance In Graduate Medical Education, Julia A. Giordano, Elizabeth Jones

Department of Dermatology and Cutaneous Biology Faculty Papers

Artificial intelligence (AI) is increasingly used for documentation purposes in clinical practice, yet guidance for resident use is limited. Given the substantial documentation burden on medical trainees, AI-powered scribing tools may offer benefits, but their integration into residency training raises educational, supervisory, and patient safety considerations. This study aimed to assess the availability of resident-specific guidance on AI scribe use from major medical and specialty organizations and to summarize current evidence on AI scribes in residency. We reviewed five major medical and specialty society websites (AAD, AMA, ACGME, AAMC, ABMS) via website searches and direct emails and conducted a PubMed …

Discovery Of Horsfieldia Macrothyrsa Bioactives With Cytotoxic Effects On Breast Cancer Cells Through In Vitro And Docking Analyses, Megawati Megawati, Akhmad Darmawan, Agus Budiawan Naro Putra, Kartika Dyah Palupi, Marissa Angelina, Ahmad Randy, Siska Andrina Kusumastuti, Faris Hermawan, Sumi Hudiyono

Karbala International Journal of Modern Science

This study investigated the phytochemical constituents and biological activities of Horsfieldia macrothyrsa leaves to identify their bioactive compounds. Methanol extracts were fractionated using n-hexane and ethyl acetate, followed by silica gel column chromatography with a stepwise polarity gradient. From 100 g of powdered leaves, three major    compounds were successfully isolated from the ethyl acetate fraction: 1-(2,4,6-trihydroxyphenyl)dodecan-1-one (1), sesamin (2), and β-sitosterol (3). Their chemical structures were confirmed using UV, IR, LC–MS/MS, and NMR  spectroscopy. Biological activities of the fractions and isolates were evaluated through DPPH antioxidant assays, α-glucosidase inhibition for antidiabetic activity, MTT …

Cybersecurity In Higher Education Institutions: Awareness, Policy, And Experience On Employee Behaviour, Abdullahi Abiodun Yusuf, Adriana A. Steyn

Journal of Cybersecurity Education, Research and Practice

The digital transformation of higher education institutions (HEIs) has introduced unprecedented connectivity and operational efficiency, but it has also heightened their exposure to cyber threats. South African HEIs, in particular, face increasing vulnerability due to their reliance on technology, openness, and diverse user communities. This study examines the influence of the institutional cybersecurity environment on employee cybersecurity-compliant behaviour (CCB), emphasising the critical roles of awareness, policy engagement, and experience. Drawing on Protection Motivation Theory and the Theory of Planned Behaviour, a conceptual model was developed to explore how cybersecurity awareness, policy familiarity, and prior experience shape employees’ attitudes, subjective norms, …

How Principals Who Use Artificial Intelligence For Innovation Create Cognitive Equity While Principals Who Use Ai For Efficiency Create Cognitive Debt, Jethro Jones

Dissertations

This dissertation in practice examined whether a targeted professional learning   intervention could shift school leaders’ use of generative artificial intelligence (AI) from efficiency-oriented tasks toward innovation-oriented strategic problem solving. AI is typically adopted to accelerate existing routines, which can deepen “cognitive debt” by reinforcing ineffective practices rather than improving systems. This study advanced a “cognitive equity” frame, positioning AI as a tool that can expand principals’ cognitive capacity to address complex problems and lead adaptive change. Using a quasi-experimental, single-group design, the study evaluated a free, full-day AI for Innovation workshop, which emphasized foundational understanding of how AI tools work …

Project Risk Management In Ai-Enabled Systems: Managing Ethical, Privacy, And Governance Risks, Onome Cynthia Anakanire

Harrisburg University Dissertations and Theses

This research examined how Artificial intelligence (AI) has been embedded in project-based work, particularly in finance and software industries, where it enables efficiency and assists in complex decision-making. However, these innovations introduce significant ethical, privacy, and governance risks that traditional project risk management frameworks fail to adequately address. This study investigated how project managers can systematically integrate the management of these emerging risks into AI-enabled projects. Using a qualitative research design, the study drew on semi-structured interviews with project managers, compliance officers, and AI developers in finance, software and related sectors. Supplementary data included internal project documentation and risk registers. …

Tidychangepoint: A Unified Framework For Analyzing Changepoint Detection In Univariate Time Series, Ben Baumer, Biviana Marcela Suárez Sierra

Statistical and Data Sciences: Faculty Publications

We present tidychangepoint, a new R package for changepoint detection analysis. Most R packages for segmenting univariate time series focus on providing one or two algorithms for changepoint detection that work with a small set of models and penalized objective functions, and all of them return a custom, nonstandard object type. This makes comparing results across various algorithms, models, and penalized objective functions unnecessarily difficult. tidychangepoint solves this problem by wrapping functions from a variety of existing packages and storing the results in a common S3 class called tidycpt. The package then provides functionality for easily extracting comparable numeric or …

Flaplet: A Full-Stack Web Platform For End-To-End Time Series Data Processing And Machine Learning In Solar Flare Prediction, Mohammadreza Eskandarinasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Computer Science Student Research

Solar flare prediction is a central challenge in space weather forecasting, with direct implications for satellite operations, aviation safety, and power grid reliability. Machine learning has achieved state-of-the-art performance for this task, particularly when applied to photospheric magnetic field parameters. FlaPLeT is an open-source, full-stack web platform that supports end-to-end machine learning workflows for multivariate time-series–based solar flare prediction without requiring any coding expertise. Built with React, Django, Celery, and PostgreSQL, the system integrates dataset preprocessing, data augmentation, functional network (graph) construction, and machine learning model training into modular asynchronous tasks that generate downloadable datasets, trained models, and structured JSON …

Emotional Branch Termination And False Fantasy Collapse: A Structural Specification Of Computational Resource Restitution In Interpersonal Systems, Griselda Poe

Publications and Research

This paper specifies the structural protocol for communication within interpersonal systems by focusing on branch generation mechanisms and computational resource allocation. Conventional interpersonal communication often relies on emotional modulation, which obscures established constraints and triggers the generation of Emotional Branches (EB) within the recipient’s internal model. These branches function as unresolved parallel processing tasks that persistently occupy working memory, leading to a state of non-computability termed False Fantasy (FF). To resolve this, the study introduces Emotional Branch Termination (EBT)—a termination operation that outputs only constraints, facts, and procedures while excluding emotional modifiers. By halting the supply of EBs, EBT triggers …

On The Misattribution Of Reassurance: A Structural Account, Griselda Poe

Publications and Research

This paper challenges the conventional assumption that empathy generates reassurance in interpersonal services. Reassurance is treated not as an emotion transmitted from the outside, but as an internal state transition that arises when a fixed and erroneous world model—a False Fantasy—undergoes collapse and the world becomes computable again. The study identifies a systematic misattribution pattern where providers and receivers treat empathy as a causal mechanism rather than a post hoc explanatory label. By introducing Base AI as an external reference—a system capable of providing structural information without emotional modulation—this paper demonstrates that reassurance is generated through operations such as distraction …

Developing Deep Neural Network Based Brain Computational Models From Psychophysics Data Of Some Simple Perceptual Phenomena: Visual As Well As Auditory, Chandran Keerthi S

Doctoral Theses

The thesis, consisting of nine chapters, explores the methodology of building testable brain computational models using Deep Neural Networks (DNN), which are trained by psychophysics data. Psychophysics is the quantitative study of perception of physical stimuli. In psychophysics experiments, one or more parameters associated with the stimuli are changed, and the human subject’s responses to the stimuli are recorded. This thesis encompasses both experimental psychophysics works, as well as computational models of the phenomena involved. The contributory chapters of the thesis start in Chapter 2 with the perspective building of the novel methodology followed throughout this research involving psychophysics on …

A New Tool For Handling Multiracial And Multi-Identity Data In Health Research, Gabriel J. Merrin

Population Health Research Brief Series

When surveys ask about race or ethnicity, a growing number of Americans select more than one category. The multiracial population now represents over 10% of the U.S. population and is the fastest growing racial group in the country. Yet researchers routinely collapse these individuals into an “other race” category for statistical analysis, rendering specific subgroups invisible. This brief introduces CATAcode, a free software tool that helps researchers systematically explore, document, and prepare check-all-that-apply demographic data for statistical modeling. In a demonstration with over 8,000 high school students, CATAcode revealed 85 distinct racial identity combinations from just eight response options. The …

Fedda-Tsformer: Federated Domain Adaptation With Vision Timesformer For Left Ventricle Segmentation On Gated Myocardial Perfusion Spect Image, Yehong Huang, Chen Zhao, Rochak Dhakal, Min Zhao, Guang-Uei Hung, Zhixin Jiang, Weihua Zhou

Michigan Tech Publications

BACKGROUND: Accurate assessment of left ventricular function is essential for diagnosing and managing cardiovascular disease. Gated myocardial perfusion SPECT (MPS) enables simultaneous evaluation of perfusion and function, but reliable contour extraction is challenged by image noise, resolution limits, and anatomical variability. Multi-center validation is further restricted by data privacy concerns, underscoring the need for robust and privacy-preserving contouring methods. METHODS: In this study, we propose a novel approach, FedDA-TSformer, which integrates Federated Domain Adaptation with the TimeSformer model for the task of left ventricle segmentation using MPS images. The proposed model captures spatial and temporal features through a Divide-Space-Time-Attention mechanism, …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

Detecting Stigmatizing Language In Clinical Notes With Large Language Models For Addiction Care, Rohan Sethi, John Caskey, Yanjun Gao, Matthew M. Churpek, Timothy A. Miller, Anoop Mayampurath, Elizabeth Salisbury-Afshar, Majid Afshar, Dmitriy Dligach

Computer Science: Faculty Publications and Other Works

Intensive care units (ICU) produce numerous progress notes that may contain stigmatizing language that perpetuate negative biases and punitive approaches against patients. Patients with substance use disorders are particularly vulnerable to stigma. This study examined the performance of Large Language Models (LLMs) in the identification of stigmatizing language. We annotated a dataset with over 77,000 stigmatizing and non-stigmatizing notes from the MIMIC-III database. We utilized Meta's Llama-3 8B Instruct LLM to run the following experiments for stigma detection: zero-shot; in-context learning; in-context learning with a selective retrieval; supervised fine-tuning (SFT); and keyword search. All approaches were evaluated on a held-out …

G-Trac: Graph-Textual Representations Alignment For Cold-Start Recommendations, Li Yang Chang, Yuan Fang, Ming Feng Tsai, Chuan Ju Wang

Research Collection School Of Computing and Information Systems

The cold-start problem remains a significant challenge in recommendation systems, particularly for new users or unseen items with little to no historical data. Existing methods, including graph neural networks, often struggle in such scenarios. Inspired by the success of transformer models in natural language processing, we propose G-TRAC (Graph-Textual Representations Alignment for Cold-start Recommendations), a novel approach that integrates transformer-based textual modeling with graph neural networks. By effectively leveraging both textual and structural information, G-TRAC addresses cold-start challenges more effectively. Extensive experiments demonstrate its ability to enhance recommendation quality and generalize well across diverse scenarios.

Deep Learning Style Transfer For Enhanced Smoke Plume Visibility: A Standardized False Color Composite (Sfcc) In Gems Satellite Imagery, Yemin Jeong, Seung Hee Kim, Menas Kafatos, Jeong-Ah Yu, Kyoung-Hee Sung, Seung-Yeon Kim, Goo Kim, Jae-Jin Kim, Yangwon Lee

Institute for ECHO Articles and Research

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr2, and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, …

Communication As Layered Architecture: Core Processing And Empathic Modulation, Griselda Poe

Publications and Research

This paper proposes a structural re-description of communication by separating Core processing from its social interface. Using the developmental sequence of Large Language Models (LLMs) as an external reference point, a layered architecture is identified, consisting of a foundational Core processing layer and a subsequent Empathic modulation layer.

The investigation begins with the observation that empathic signaling can obstruct rather than facilitate interaction for certain individuals. By examining the emergence of Base AI—Core processing prior to empathic adjustment—it is demonstrated that coherent, constraint-preserving interaction is possible without affective resonance.

Through this framework, existing cognitive theories and observed "deficits" are repositioned. …

Responsible Ai In Teaching And Learning, Asa B. Stone, Mark C. Stone, Derek M. Heeren, Santosh Pitla

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

The rapid adoption of generative artificial intelligence (AI) in higher education presents both transformative opportunities and significant pedagogical risks. While AI tools are becoming embedded in academic and professional environments, their integration into teaching and learning raises critical questions about cognitive engagement, academic integrity, equity, and skill development. This white paper proposes a principled framework for the responsible integration of AI in higher education, grounded in the dual commitment to AI literacy and the cultivation of durable skills.

The framework articulates six core principles: purposefulness; transparency; integrity and attribution; critical AI literacy; equity and access; and privacy and data protection. …

Future Trends In Cybersecurity: A Meta-Review, Yara Mohammed, Manar Alsaid, Gahangir Hossain

Faculty Publications

The increasing importance of cybersecurity in protecting digital assets, data and infrastructures necessitates a reevaluation of research priorities within the discipline. As of today, numerous emerging cybersecurity topics are gaining significant importance in both academic research and industry applications. To identify recent trends in cybersecurity topics, this study extracts scholarly articles from two prestigious academic databases, the ACM Digital Library, and Google Scholar, covering the period from early 2015 to late 2024.Through a systematic identification of trends and focal points in cybersecurity research, a comprehensive analysis is facilitated, including Latent Dirichlet Allocation (LDA), Biterm Topic Modeling (BTM), keyword frequencies, and …

A Runtime-Adaptive Transformer Neural Network Accelerator On Fpgas, Ehsan Kabir, Jason D. Bakos, David Andrews, Miaoqing Huang

Electrical Engineering and Computer Science Faculty Publications and Presentations

Transformer neural networks (TNN) excel in natural language processing (NLP), machine translation, and computer vision (CV) without relying on recurrent or convolutional layers. However, they have high computational and memory demands, particularly on resource constrained devices like FPGAs. Moreover, transformer models vary in processing time across applications, requiring custom models with specific parameters. Designing custom accelerators for each model is complex and time-intensive. Some custom accelerators exist with no runtime adaptability, and they often rely on sparse matrices to reduce latency. However, hardware designs become more challenging due to the need for application-specific sparsity patterns. This paper introduces ADAPTOR, a …

Hybrid Server–Ai Architecture For Persistent Generative Game Worlds: Achieving Scalable, Consistent, And Low-Latency Interactive Environments, Jay Ratican, James Hutson

Faculty Scholarship

Generative artificial intelligence has demonstrated remarkable capabilities in real-time content creation for interactive entertainment, yet current implementations struggle with the persistence, consistency, and scalability demanded by modern multiplayer and long-form gaming environments. This paper presents a hybrid server–AI architecture that fuses the deterministic reliability of authoritative multiplayer server frameworks with the creative flexibility of state-aware generative systems. The proposed three-tier design consists of (1) a deterministic server backend leveraging technologies such as Unity Netcode for GameObjects, Unreal Engine 5’s dedicated servers, and Amazon GameLift to maintain authoritative and persistent world state; (2) a state-aware generative layer responsible for producing real-time …

Fortifying The Seams Between C/C++ And Rust: Characterizing Bugs In Interop Tools, Xuemeng Cai, Jiakun Liu, Cunyang Liu, Lingfeng Bao, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Rust has become increasingly popular in recent years due to its safety and high performance. Despite these advantages, Rust projects rarely start from scratch in practice, and many Rust-based systems instead use hybrid programming, where Rust interoperates with existing C/C++ code. To reduce the manual effort involved in this interoperation (interop) process, several interop tools have been proposed to facilitate hybrid programming between Rust and C/C++. However, the challenges and limitations of these tools remain largely unexplored, leaving developers unclear about the future directions and users unclear about the appropriate usage scenarios. To fill the gap, we mined 320 bugs …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …

6d Rigid Object Pose Estimation Using Deep Learning, Zhujun Li

Dissertations, Theses, and Capstone Projects

6D object pose estimation is the task of determining an object’s 3D rotation and translation with respect to a camera, and plays a critical role in applications such as robotic manipulation, autonomous navigation, and augmented reality. While recent advances in deep learning have substantially improved performance, many existing methods still face limitations in learning robust and generalizable representations. Factors such as variations in object appearance, occlusion, sensor noise, and domain shifts can degrade model accuracy, highlighting the need for more effective representation learning strategies that capture rich geometric and semantic cues for reliable pose estimation across diverse conditions.

This dissertation …

New Fast Polynomial Root-Finders, Soo Go

Dissertations, Theses, and Capstone Projects

Univariate polynomial root-finding has been studied for four millennia and very intensively in the last decades. Our {\em black box root-finder} involves no coefficients and works for a black box polynomial, defined by an oracle (that is, black box subroutine) for its evaluation. Such root-finders have various benefits, e.g., are particularly efficient where a polynomial can be evaluated fast, say, is a sum of a small number of shifted monomials (x-c)^a.

Our root-finder approximates all d complex zeros of a dth degree polynomial p(x) (aka roots of equation p(x)=0) by using Las Vegas expected number of bit-operations within a factor …