Digital Commons Network^™

Grp-0100-193 Are Tgnn-Based Intrusion Detection Results Trustworthy? A Dataset Audit And Evaluation Framework, Faysal Chowdhoury, Sait Suer, Yinning Zhang

C-Day Computing Showcase

Temporal Graph Neural Networks (TGNNs) have reported near-perfect accuracy in Network Intrusion Detection (NID). However, this research reveals these results are often artifacts of dataset flaws rather than genuine model capability. Through a systematic audit of five benchmark datasets, we identify critical issues: node identity leakage, feature extraction artifacts, train/test contamination, and temporal sparsity. We demonstrate that models often learn to recognize specific attacker IP addresses instead of generalizing attack behavior. We propose a standardized evaluation framework featuring leakage-aware relabeling and attack-aware chronological splitting to provide a more reliable basis for future TGNN-NID research.

Grp-09-169 Using Logic To Explain And Formally Verify The Behavior Of Relu Neural Networks, Nguyen Thi Binh Nguyen

C-Day Computing Showcase

Homeschooling in the United States has expanded rapidly, reaching about 3.4 million students (6.26% of K-12 school-age population) in 2024-2025, with accelerated growth following COVID-19. Understanding the reasons behind these decisions is important for informing education policy, resource allocation, and the design of schooling systems that better meet families’ needs. Most of the research on homeschooling relies on statistical methods such as logistic regression or probit models to identify significant factors associated with homeschooling decisions. In this work, we approach this research question from a different perspective by leveraging Explainable AI techniques to provide deeper insights into the homeschooling decisions.

Grp-165-217 Evaluation Of Multi-Platform Simulation Environments For Diverse Robotic Manipulation Tasks, Zhiguo Liu

C-Day Computing Showcase

Robotic development often requires transitioning between different simulation environments to meet specific task requirements. This project presents a comparative evaluation of four major simulation platforms—Gazebo, MuJoCo, CoppeliaSim, and Isaac Sim—through the successful reproduction of diverse manipulation tasks. By implementing system integration, dual-arm coordination, sequential logic, and reinforcement learning across these engines, this study identifies the functional strengths and practical engineering constraints of each environment. The results provide a qualitative guide for selecting simulation tools based on task-specific needs, such as middleware compatibility versus physical fidelity.

Uc-097-186 Nudox - Compiler Based Information Retrieval, Mikita Slabysh

C-Day Computing Showcase

Nudox is a language-agnostic, version-aware documentation and search platform backed by compiler-level analysis. By lowering source code to intermediate representations, Nudox extracts structural metadata, like function signatures, types, and modules independent of the source language. At the core of the platform is a custom search engine built around versioned knowledge: queries resolve to graph nodes and expand outward along structural edges using semantic heuristics, surfacing contextually relevant symbols rather than flat text matches. The result is a canonical, automatically generated source of truth that tracks how a codebase evolves across commits.

Uc-115-161 Wayward Stray: Selix, Arly Tinoco, Tyler Ercole, Ivy Stansel, Austin Lothman, Jeremi Charland-Martin

C-Day Computing Showcase

Wayward Stray:Selix is a 3rd person platformer which places importance on exploration and discovery. Players will take the role of Selix as they explore an arid desert, fighting off enemies and discovering items hidden around the map, which reveal more about the game world and its characters. Selix, a young dragon, is exiled from the only home he’s known, forced into a strange land in search of a new place to call his own. Along the way, he finds a companion, a small dove that aids and guides his way. Exploring these uncharted areas, Selix discovers there’s more to the …

Uc-123-140 P15-T2 Boating Safety Game Us Army Corps | Boating Mvp, Maryam Hamza, Saleh Hamza, Will Vanwinkle, Trevor Caffrey, Tobi Akinsunmi

C-Day Computing Showcase

This project is an interactive 2D educational boating safety game developed in Unity to teach students essential water navigation and life jacket safety practices in an engaging and immersive format. Designed in collaboration with a real-world sponsor, the game simulates a dynamic boating environment where players navigate obstacles, identify hazards, and make safety decisions under time constraints. The experience integrates instructional modules, guided character narration, and a final quiz phase that reinforces knowledge through immediate feedback, scoring, and achievement-based rewards. Players learn critical concepts such as proper life jacket fit, safe boating procedures, hazard identification, and shallow water awareness. The …

Uc-128-145 Ksu Esports Discord Server Bot​, Austin Gammill, Foster Thomas, Lam Truong, Jeffrey Olubajo, Ismail Ahmed

C-Day Computing Showcase

The KSU Esports program has requested us to develop further upon the Discord Server Bot that they are currently using. This prior implementation was developed by Capstone students last year. Our project’s goal was to build upon their work, polish existing features, commands, UI/UX, and fix known bugs. We have worked on improving the bot’s matchmaking algorithms, tournament seeding logic, API integration, database persistence layer, stability, and statistics tracking. Furthermore, we have developed the UI/UX to be more user-friendly, added support for additional games, and overhauled the bot’s database logic.

Uc-138-166 The Allies Connect Platform: Improving Access To Community Resources Through Technology​ ​, Sarah Holland, Neha Anand, Aldrick Andoh, Yacine Diop, Alex Rogers

C-Day Computing Showcase

Finding help shouldn’t be difficult, but for many people, it is. Important information about food, shelter, and local support is often scattered across different websites, social media pages, and documents, making it hard to find what’s needed, especially in urgent situations. The Allies Connect platform was created to bring that information into one place. It is a centralized, mobile-friendly platform that allows users to: • Search for resources • Register for events • Connect with nonprofits At the same time, the platform also provides organizations with simple tools to keep their information accurate and up to date. By focusing on …

Uc-143-178 Georgia Laws Of Life Crm Implemenetation, Nicholas Sternon, Josh Flores, Jessica Scales, Opurbo Bhuiyan, Shiv Patel

C-Day Computing Showcase

This project focuses on implementing a Customer Relationship Management (CRM) system for Georgia Laws of Life using the Little Green Light (LGL) platform. The organization previously relied on spreadsheets, which caused issues such as duplicate records, inefficient reporting, and difficulty managing relationships. To address this, the team analyzed existing workflows and developed a structured data model. The system was configured, and sample data including constituents, donations, schools, and contracts was successfully imported to validate the design. The results show that the CRM system improves data organization, enhances relationship tracking, and provides a more efficient and scalable solution for managing organizational …

Uc-162-194 Smart Soil Analyzer, Samuel Florez Garcia, Edward Johnson, Aaron Gamino, Tassha Burton, Wyatt Kinney

C-Day Computing Showcase

The Smart Soil Analyzer is a machine learning-based application designed to maximize agricultural efficiency and sustainability. Our team developed a predictive system using a K-Nearest Neighbors (KNN) classifier trained on a comprehensive crop recommendation dataset. The tool allows users to input key environmental and soil metrics, including Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, pH levels, and rainfall. By processing these variables, the model accurately predicts the most suitable crop for the specific land conditions. This solution provides farmers with data-driven insights to optimize yields, reduce fertilizer waste, and combat soil degradation through precise crop matching.

Uc-164-215 Hootnest: Ai-Powered Ksu Student Assistant, Tabitha Washington, Aspen Steele

C-Day Computing Showcase

HootNest helps prospective Kennesaw State students get clear, reliable answers about college life. It is designed for students who may not have easy access to counselors, mentors, or campus visits. The chatbot allows students to ask the chatbot anything they need to know.

Ur-133-165 Quantum Machine Learning For Science And Engineering, Barclay Barnes, Anna Zharikov, Meriem Hamzi

C-Day Computing Showcase

Quantum machine learning (QML) has emerged as a promising method for overcoming the computational limitations of classical machine learning when analyzing large and complex data sets. This project investigates the application of QML algorithms to real-world science and engineering problems, with a focus on civil and environmental engineering datasets. We develop and evaluate a Python-based system, implemented in Google Colab, that integrates multiple quantum computing frameworks, including PennyLane, TensorFlow Quantum, and Qiskit, to implement and compare several QML models against their classical counterparts. The proposed system explores a range of algorithms such as Quantum Neural Networks, Quantum Support Vector Machines, …

Ur-171-118 Aidflow: A Predictive Financial Aid Transparency System For Students, Chaathurya Nakkana

C-Day Computing Showcase

Students frequently experience delays and confusion regarding financial aid refunds due to unclear system statuses and lack of communication. This project introduces AidFlow, a predictive financial aid transparency system that translates complex financial data into clear explanations, predicts refund timelines, and provides actionable guidance. A rule-based model and system pipeline were developed to simulate real-world scenarios and improve student understanding and decision-making.

Ex-116-209 The Understudy – A 2.5d Turn-Based Story Game​, Ara Randolph, Rin Egl, Cayden Herrington, Jonah Swerdlow, Carter Griffin, Amaya Cruz

C-Day Computing Showcase

“The Understudy” is a whimsy-filled 2.5D turn-based theatrical adventure where you play as the last-minute understudy, who has been suddenly thrust into the spotlight after the lead mysteriously vanishes right before showtime. Armed with nothing but masks (comedic, dramatic, and tragic) and a script you definitely didn’t not rehearse enough, you fight your way through a cast of dramatic acting troupe members, ranging from a painfully shy tree to a snarky jester ex to a pompous king who’s very sure you don’t belong on his stage. Swap masks to change your combat style, solve dialogue puzzles, and prove that even …

Gc-119-134 Pipeline For Vr Embodied Lecture Authoring And Ai Gesture Refinement, Rishi Kiran Aiyatham Prabakar

C-Day Computing Showcase

While Virtual Reality (VR) offers immersive educational opportunities, its pedagogical success relies heavily on a genuine sense of "instructor presence". This project presents a hybrid pipeline that automatically refines presenter 3D avatar gestures using semantic AI. Our non-VR recording system captures high-fidelity facial tracking and MediaPipe for upper-body pose estimation via standard RGB video. For emotion recognition, a local Large Language Model analyzes audio transcripts to generate a timestamped emphasis track. This semantic engine, intelligently exaggerating gestures during critical lecture moments. The captured motion and AI-enhanced gestures are synthesized and replayed on a virtual lecturer within an VR environment for …

Gc-126-148 Allies Connect- Georgia's Nonprofit And Volunteer Coordination Platform, Molly Calhoun, Takeshia Banks, David Castro, Ryan Hanrahan, Tarik Davis

C-Day Computing Showcase

Georgia's nonprofit services face an issue of discoverability. While many nonprofits have the resources to help their community members succeed, they have trouble actually connecting to members of the community that need their support. Connecting with these resources is challenging for community members because their avenues of communication are spread across the internet. Some have their own websites, some have a Facebook page where they post events, some rely on word of mouth and fliers, and others rely on phone chains to keep their community members informed. This means that community members seeking support need to be able to access …

Gc-130-160 C-Day Explorer: A Domain-Aware Platform For Discovering And Extending Ksu Student Projects, Rohan Jonnalagadda, Sanketh Chapaneri

C-Day Computing Showcase

C-Day showcases some of the strongest computing projects at KSU, but once each event ends, past work becomes scattered across semester pages, posters, PDFs, and videos, making it difficult to see long-term trends or build on prior ideas. C-Day Explorer addresses this gap with a centralized, domain-aware web platform that aggregates project records from 21 semesters of C-Day archives, KSU Digital Commons, winner pages, and YouTube presentation videos. The system organizes 1,286 projects into 11 computing domains with high abstract coverage, poster and video links, and similarity-based connections that help users quickly find related work and promising directions for extension. …

Grm-083-218 Wise: Whitebox Importance-Based Subnetwork Extraction And The Privacy-Preserving Properties Of Model Compression, Mason Pederson

C-Day Computing Showcase

WISE (Whitebox Importance-based Subnetwork Extraction) is a structured compression algorithm which extracts task-specific subnetworks by instrumenting a pretrained networks with learned gates on transformer components and optimizing on task loss and L0 sparsity regularization. WISE maintains high task performance at high sparsity levels (81-88% accuracy at 85%) where other SOTA methods collapse to near random chance. We present the first evaluation of model compression along privacy dimensions: attribute inference resistance, training data memorization, and extraction attack vulnerability. Structured compression via learned gates produces subnetworks with favorable privacy-utility balance without any explicit privacy mechanism. WISE masks also transfer to fresh models …

Grm-153-198 Safecircle: Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd, Awan-Ur- Rahman, Soarov Borty, Gowtham Ankolu

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) are irreversible and degenerative neurological conditions that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

Grm-156-153 Finding Top-K Assignments For Multi-Hypothesis Tracking, Tyler Hood, Rakshak Gurung

C-Day Computing Showcase

Multi-Hypothesis Tracking (MHT) is a framework for solving the data association problem in multi-target tracking by maintaining multiple possible assignments between observations and targets over time. Rather than committing to a single solution, MHT explores a set of competing hypotheses, allowing it to handle noise, missed detections, and ambiguous measurements. In practical systems such as radar, LiDAR, and vision-based tracking, MHT is commonly implemented using algorithms like Murty’s algorithm to generate multiple high-quality assignment solutions from the Hungarian algorithm. In this work, we instead propose an assignment-tree-based approach, where hypotheses are incrementally constructed and prioritized using a structured search strategy. …

Grm-157-180 Precision Engineering: Using Ai To Design Nanoparticles That Target Malignant Cells, Rakshak Gurung, Nino Tkabladze

C-Day Computing Showcase

The challenge of predicting nanoparticle distribution remain a significant hurdle in nanomedicine. This research presents a computational framework for the inverse design of nanoparticles, utilizing ML models to optimize drug delivery systems for tumor targeting. By analyzing the relationship between nanoparticle compositions and biological accumulation, the model identifies optimal configurations to maximize therapeutic efficacy. The results demonstrate that AI-driven inverse design can significantly streamline the development of precision nanocarriers, reducing the need for exhaustive experimental trials.

Temperature-Induced Uncertainty In Fixed-Context Retrieval-Augmented Generation, Steven Zeng, Murat Kuzlu

Cybersecurity Undergraduate Research Showcase

This study examines how decoding temperature affects output uncertainty in a fixed-context retrieval-augmented generation (RAG) system. We define uncertainty as the semantic dispersion among repeated answers under the same fixed retrieved context, with greater dispersion interpreted as higher uncertainty. To isolate this answer-generation variability from retrieval drift, each question was paired with a fixed retrieved context, and repeated generations differed only in temperature. The experiment used nine questions drawn from a machine-learning textbook corpus, with three questions each at easy, moderate, and hard difficulty. Each question was evaluated at five temperatures (0.0, 0.25, 0.5, 0.75, and 1.0) over 30 iterations, …

Histopathology Image Classification Using Machine Learning, Mohammed H. Alali

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Histopathology image classification is a critical component of cancer diagnosis. However, the gigapixel scale of Whole-Slide Images (WSIs) and the high variability in tissue staining and scanner quality across medical centers present significant computational challenges. This dissertation proposes a comprehensive machine learning framework to address these challenges, bridging the gap between theoretical models and practical clinical deployment.

First, to manage the massive dimensionality and noise inherent in WSIs, this research develops a robust feature extraction methodology. The pipeline implements a stringent tile filtering technique to eliminate physical artifacts and resolve severe class imbalances. It integrates ConvNeXt alongside an attention-based pooling …

Spatial Computing With The Apple Vision Pro In Minimally Invasive Procedure Simulation: A Randomized Crossover Feasibility Study, Sydney Cooper, Aaron Kyle Jones, Rahul Anil Sheth, Koustav Pal, Bruno Odisio, Mark Blaylock, Shelita Kimble, Justin Bird, David Rice, Daniel Shoenthal, Emil Patel, Vipin Kamath, Sanjay Gupta, Jeffrey Siewerdsen, Joshua Kuban

Advances in Cancer Education and Quality Improvement

Purpose: This study aimed to evaluate the feasibility of wearing the Apple Vision Pro (AVP), a mixed-reality headset that integrates augmented and virtual reality, while performing minimally invasive procedures. While studies have demonstrated that spatial computing technology can improve surgical precision and reduce the risks of surgical complications, to our knowledge, no studies have specifically addressed the impact of the AVP on task performance during simulated image-guided procedures.

Materials and Methods: Thirteen diagnostic and interventional radiology residents performed image-guided central venous catheter placement, thoracentesis, and paracentesis on simulation models. Each participant completed a non-timed practice followed by the procedures once …

Inc3vits Model: A Hybrid Architecture To Accelerate And Reduce Complexity For The Deepvariant Model For Variant Calling, Mustafa Al-Saffar, Sura Z. Al Rashid

Karbala International Journal of Modern Science

Deep learning has revolutionized genomic variant calling, yet the computational cost of current systems continues to limit scalability. We present a controlled efficiency study of DeepVariant-style pileup architectures under identical training and inference conditions, comparing architectural downsizing with a hybrid CNN–local attention design. Inc3ViTs pairs a streamlined InceptionV3 stem with a lightweight local attention head based on patch tokenization and windowed self-attention, enabling a direct comparison with a CNN-only reduced Inception baseline. Across whole-genome and whole-exome short-read datasets, Inc3ViTs reduces training time by ~40–50% and reduces inference runtime relative to the original DeepVariant. The CNN-only baseline indicates that most speedups …

Modelling Method Of Unmanned Vehicle Dynamics Based On Neural Network, Jun Wang, Min Liu, Xiaochuan Zhang, Yishan Ding, Juhui Feng, Ye Zhuang

Journal of System Simulation

Abstract: To address the challenges of high data acquisition costs of test data on dynamic characteristics between tires and soft terrain and low speed of numerical calculation for unmanned vehicles in complex terrestrial environments, a modeling method of unmanned vehicle dynamics based on a neural network was proposed. Tire-terrain contact dynamics models were built by using discrete element method (DEM) simulations for tire-terrain contact and experimental data, thereby creating a dataset of tire contact forces for various tire materials in terrestrial environments. The neural network was applied to regressively learn the dataset, and a nonlinear neural network tire model was …

Method For Testing And Evaluating Intelligence Level Of Virtual Forces Based On Operational Experiments, Dayong Liu, Zhiming Dong, Weidong Zhang, Wenjun Zhang, Jiancheng Gao

Journal of System Simulation

Abstract: The intelligence level of virtual forces is a key factor affecting the credibility and effectiveness of tactical confrontation simulations. To address the current lack of a testing and evaluation system, a method for testing and evaluating the intelligence level of virtual forces based on operational experiments is proposed. Guided by operational experiment theory, the method stimulates the intelligent behavior of virtual forces by constructing dynamic confrontation environments, and collects, calculates, analyzes, and evaluates their intelligence performance data according to a systematic process. The overall architecture, logical functional modules, and basic evaluation process of the method are designed. A "4M" …

Research On Control Of Coaxial Dual-Rotor Unmanned Aerial Vehicle Based On Improved Reaching Law, Xinhang Chen, Xiaodong Ling, Chengchang Lang, Shijun Zheng, Yiqi Tang

Journal of System Simulation

Abstract: To address the issues of significant chattering, slow convergence speed, and large overshoot in the control system of a coaxial dual-rotor unmanned aerial vehicle, a control method based on an improved double-power and hyperbolic function integral sliding mode reaching law was proposed. A novel reaching law was designed to achieve fast convergence when the system state is far from the sliding surface and smooth transition when approaching the sliding surface, thereby enhancing the overall convergence speed of the system and ensuring that the system reaches the sliding surface within a finite time. The saturation characteristic of the hyperbolic function …

Designing, Integrating, And Evaluating Blockchain Assignments In The Undergraduate Computer Science Program, Karol Lejmbach

Dissertations (1934 -)

Blockchains as a technology rely heavily on a wide range of computer science topics. These topics include: algorithms, data structures, network communication, cryptography and many more. These topics align nicely with topics often covered in undergraduate computer science course. Blockchain technology offers an interesting opportunity for computer science educa- tors. Blockchains rely heavily on a wide range of computer science topics. These topics include: cryptography, networking, data structures, and algorithms. Many of these top- ics are explored in undergraduate computer science programs either in core or elective classes. This offers an opportunity for teaching students about emerging technologies, such as …

Model-Based System Verification: Theoretical Framework, Key Technologies, And Future Prospects, Bo Sun, Yi Ren, Silin Wang, Qi Liu, Zhidong Li

Journal of System Simulation

Abstract: Traditional system verification methods face significant challenges in terms of efficiency, coverage, and traceability. To address these issues, this paper introduced model-based system verification (MBSV), which deeply integrated verification activities within the model-based systems engineering model system and evolution process. It presented the foundational logic of MBSV and proposed a multiview unified verification modeling strategy based on system modeling language (SysML), integrating requirements, structure, behavior, and constraints. The paper discussed the algorithms for selecting representative paths and reducing equivalent classes to enhance verification efficiency, the principles of test path search, as well as the intelligent path search mechanism based …