Computer Sciences Commons^™

Gopher: Efficient Dynamic Graph Pattern Mining Via Dag-Driven Execution, Yi Zhang, Yu Huang, Chaoqiang Liu, Haifeng Liu, Juntao Chen, Jingrui Yuan, Jianhui Yue, Xiaofei Liao, Hai Jin, Jingling Xue

Michigan Tech Publications

Graph pattern mining is essential for analyzing dynamic networks, where graphs evolve over time. To accommodate these changes, existing solutions update match sets incrementally, avoiding the need to re-mine the entire graph and achieving significant performance improvements. However, these methods suffer from inefficiencies due to redundant set intersection operations across subgraph instances, causing performance degradation. In this paper, we propose Gopher, a DAG-driven dynamic graph pattern mining system that leverages computation locality for enhanced performance. Gopher uses DAGs to represent set operations, identifying and merging common subexpressions at compile time. This reduces redundant computations during runtime. To maximize performance, we …

Visualization For Formal Languages, Chris Pinto-Font, Andrew Bastien, Vincent Borrelli, Keegan Mcnear

Electrical Engineering and Computer Science Student Publications

The primary goal is student comprehension of complex DFA concepts.

This project is important for students and professors because being able to visualize Deterministic Finite Automota (DFA) and how it is created and expressed can lead to better understanding of how parsing and computation works.

Student Code Online Review And Evaluation 2.0, Dorothy Ammons, Shamik Bera, Patrick Kelly, Rakan Alsharif

Electrical Engineering and Computer Science Student Publications

The goal of this second rendition to S.C.O.R.E is to enhance the application by adding features such as:

• Cheat detections
• Roster imports
• Grade exports
• Custom rubrics

Java Oo Visualizer, Darian Dean, Ashley Mckim, Simon Gardling, Josh Kalinsky

Electrical Engineering and Computer Science Student Publications

Current Challenge: Understanding the execution of an object-oriented program is challenging for students beginning their undergraduate CS degree

Common Issue: Students learn syntax but fail to see the underlying behavior of object creation, reference assignment, and method calls

Unified Solution: bridge the gap between abstract concepts and concrete understanding by animating the process of object instantiation, reference assignment, and results of method

Fit Ar Navigation App (Fitarna), Vincenzo Barager, Dathan Dixon, Jacob Hall-Burns, Ethan Wadley

Electrical Engineering and Computer Science Student Publications

Navigation Complexity: Large-scale academic facilities like the Evans Library present significant wayfinding challenges for new students and visitors.

GPS Limitations: Standard satellite-based navigation fails indoors due to signal attenuation and lack of floor-level granularity.

The Solution: FITARNA leverages Augmented Reality to provide intuitive, real-time visual guidance, bridging the gap between digital maps and the physical environment.

Panther Shuttle App, Joey Hilte, Chase Monigle, Tony Arrington, Jonathan Suo

Electrical Engineering and Computer Science Student Publications

Florida Tech students rely on the campus shuttle system to travel between dorms, classrooms, and nearby housing areas. However, existing tools do not provide reliable real-time tracking or notifications about delays.

The Panther Shuttle App provides students with live shuttle locations, schedules, and alerts to improve campus transportation and reduce wait times.

Wallee. Personal Finance App, Emma Bahr, Matteo Caruso, Joshua Cajuste, Kyle Gibson

Electrical Engineering and Computer Science Student Publications

Traditional finance apps assume fixed monthly income. Fail users with variable income:

a. Students

b.Freelancers

c. Gig workers

Require constant manual updates. Provide low adaptability to real-life finances

Brainbench - Llm Evaluation, Daniella Seum, Orion Powers

Electrical Engineering and Computer Science Student Publications

As large language models become more widely used, there is a growing need for reliable and consistent methods to evaluate their performance. Existing benchmarks often lack transparency, consistency across runs, or the ability to handle varied answer formats. BrainBench was developed to address these gaps by providing an automated, reproducible evaluation framework that enables fair comparison of models and deeper insight into their strengths and limitations.

Dynamic-Query Robustness Of Ann Indexes Under Time-Indexed Drift, Stellamaris Nakacwa, Majid Shaalan

Harrisburg University Other Works

Approximate nearest-neighbor search is a central retrieval primitive in dense question-answering and retrieval-augmented generation systems. Existing ANN evaluation protocols typically measure recall, latency, throughput, and search-effort sensitivity under a fixed-query assumption: a query vector is submitted to an index, approximate neighbors are retrieved, and the result is compared with exact nearest-neighbour ground truth. This assumption is appropriate for conventional vector-search benchmarking, but it is less complete for multi-step, distributed, and agent-controlled retrieval pipelines in which the retrieval-facing query may be refined, recomputed, or displaced across execution steps. This paper introduces a time-driven dynamic query evaluation framework for ANN search. The …

The Role Of Human-Centered Artificial Intelligence In Crewed Mars Missions, Jordan Neuman

Student Publications and Presentations

The suggestion of implementing Artificial Intelligence (AI) in crewed missions to the Martian surface can revolutionize mission operations, psychological support, and ethical decision‑making under challenging conditions. This study is a structured, qualitative literature review of current AI research for the future of human space exploration. It uses publicly available peer‑reviewed journal articles and NASA/ESA technical reports on AI for Mars missions. Using Perplexity, the researcher first prompted the AI tool to identify recurring topics across these sources and generate themes. The researcher independently reviewed all sources to confirm and refine those themes. Three themes—mission autonomy, local resource utilization and life‑support, …

Reminders App Execution, Alexander Gardner

Harrisburg University Other Works

This poster highlights the creation of an Android mobile application for reminders. This app is titled It's Time, Remind! Developed with the Flutter SDK and SQLite for the local database. Firebase Authentication was utilized for user account registration, login/ logout, and profile management. Users can additionally choose to use the app without an account. Users are able to view, create, and delete reminders and notes. Reminders also notify at specified times.

Worldview-Bench: A Benchmark For Evaluating Global Cultural Perspectives In Large Language Models, Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir

All Works

Background: Large Language Models (LLMs) are predominantly trained and aligned in ways that reinforce Westerncentric epistemologies and socio-cultural norms, leading to cultural homogenization and limiting their ability to reflect global civilizational plurality. Existing benchmarking frameworks fail to adequately capture this bias, as they rely on rigid, closed-form assessments that overlook the complexity of cultural inclusivity. Objectives: To address this cultural bias problem, we introduce WorldView-Bench, a benchmark designed to evaluate Global Cultural Inclusivity (GCI) in LLMs by analyzing their ability to accommodate diverse worldviews. Methods: Our approach is grounded in the Multiplex Worldview proposed by Senturk et al., which distinguishes …

High Performance Ae-Cnn Model Enhanced Via Gaussian Augmentation For Rssi-Dependent Indoor Positioning In Wireless Sensor Networks, Kahlaa K. Al-Nassrawy, Ghaidaa A Al-Sultany

Karbala International Journal of Modern Science

Precise indoor positioning system remains a significant challenge within Wireless Sensor Networks (WSNs) due to the instability and noise sensitivity of Received Signal Strength Indicator (RSSI) data. This paper proposes an advanced hybrid deep learning framework designed for 2D indoor localization, utilizing RSSI measurements to predict human or object position. Furthermore, to enhance the robustness of the model against signal variance and promote its generalization capability under this uncertainty, a data augmentation strategy is applied using three methods: Gaussian Noise Injection (GNI), Gaussian Mixture Model (GMM), and Bayesian Gaussian Mixture Model (BGMM). The purpose of data augmentation is to simulate …

Analyzing Accessibility Issue Detection Differences Between Wave And Google Lighthouse Using A Controlled Wcag Violation Webpage, Saabiriin M. Abdi

Research & Creative Achievement Day

Automated accessibility evaluation tools, such as WAVE and Google Lighthouse are widely used to assess compliance with the Web Content Accessibility Guidelines (WCAG). However, prior studies indicate that these tools often disagree, vary in the success criteria they support, and differ in the type of issues they detect. Because most evaluations were conducted on real websites, where the precise number of accessibility violations is unknown. Without a ground-truth baseline, it is impossible to measure false negatives, false positives, or the accuracy of detection.

This project addresses that gap by creating a controlled HTML webpage containing 40 intentional WCAG 2.1 Level …

Grp-02-199 Topological Constraints For Protein Folding, Charles Fanning

C-Day Computing Showcase

We study whether topological loss-based constraints improve multidomain whole-chain protein structure prediction beyond the ColabFold baseline by better preserving the topologies of folded proteins. We benchmark against Wasserstein metrics with our own virtual persistence and RKHS semi-metric constraints as well as higher-order virtual persistence diagrams.

Uc-158-205 Scrappyfin — Ind, Simulated Digital Wallet &​ Fraud Detection Platform, Logan Davis, Dante Galvan, Zahaira Jordan, Avery Bracey, Chris Pham

C-Day Computing Showcase

ScrappyFin is an educational FinTech platform developed in partnership with The Home Depot to simulate a digital wallet system and demonstrate fraud detection techniques. Users can create virtual wallets and perform synthetic transactions in a controlled environment, enabling analysis of financial behavior without real risk. The platform combines rule-based logic with machine learning models to identify suspicious activity, such as unusual transaction amounts or patterns. An admin dashboard provides clear explanations for flagged transactions. ScrappyFin showcases how modern financial systems detect risk while serving as a practical learning tool for software engineering and data-driven applications.

Uc-152-211 Ccse Capstone Meeting Intelligence Platform, Calvin Crose, Noah Enyart, Marcus Johnson, Jaeden Jones, Jonah Smith

C-Day Computing Showcase

The CCSE Capstone Meeting Intelligence Platform is a web-based monitoring system designed to assist CCSE leadership in overseeing a large variety of capstone projects. Currently, the CCSE leadership handles the overseeing of projects manually, either with advisors sitting in on student-client meetings or by reviewing recordings, which can lead to problems where potential red flags are unidentified. To help manage this, the system processes Microsoft Teams meeting transcripts and analyzes them using a Large Language Model (LLM) combined with Retrieval Augmented Generation (RAG) techniques. It then will identify potential project risks like scope creep, conduct concerns, and deviations from project …

Uc-167-222 Secu Horizon, Julian Duarte, Chance Boecker, Adam Martin, Rylan Collins, Oliver Haggard

C-Day Computing Showcase

Secu Horizon is a fast-paced, stealth-based 3d action platformer, where you run around in a dystopian city. The main mechanic of the game revolves around a knife projectile that the player throws and teleports to. The game will have a strong mix of smooth platforming, soft stealth sections, and bullet time combat, as the player throws around the knife to defeat enemies. The feeling that this game will invoke is that of being an unstoppable rebel ninja, in the cold dead of the night.

Uc-151-197 Nest: An Ai-Powered Transition Navigator For Aging-Out Foster Youth In Georgia, Stephen Sookra, Tylin Delaney, Brenden Bryant

C-Day Computing Showcase

Each year, approximately 600–700 young people age out of the Georgia foster care system with no permanent family, no housing plan, and no clear guide beyond a 250‑page state transition PDF. The outcomes are severe: high rates of homelessness, low college completion, and unstable employment. Nest is an AI‑powered, mobile‑first web application that turns this overwhelming bureaucracy into a personalized 90‑day transition plan generated in under 60 seconds. Through a short conversational intake, the system collects a youth’s age, county, housing status, and education or work goals, then uses a deterministic rules engine to determine likely eligibility for key programs …

Uc-149-185 Ai-Assisted Media Organization And Intake System, Meilun Wu, Nevaeh Branham, Isaiah Higgins, Claude Kangni, Fatima Ahmed

C-Day Computing Showcase

This project includes the design and validation of a metadata-driven media intake and organization system implemented for the Office of the District Attorney, Cobb Judicial Circuit. The institution produces a considerable volume of photographs and video content via outreach programs and community interaction initiatives. Yet, the organization does not have an organized framework for managing media files, which results in inefficient file retrieval and data loss over time due to a lack of standardized organizational practices. In this regard, this project aims to develop a workflow-based system for the efficient organization of media files that will be powered by a …

Uc-139-175 Agentic Debugger And Documenter, Olaoluwa Omodemi, Jade Le, Preston Dietz

C-Day Computing Showcase

Modern software development teams routinely introduce subtle bugs — mutable default arguments, bare exception handlers, insecure eval()/exec() calls, resource leaks, hardcoded secrets — that escape manual review but accumulate into technical debt and security risk. Existing linters identify problems but leave remediation to the developer. This project investigates whether a coordinated multi-agent system, combining rule-based static analysis with generative LLM reasoning, can autonomously detect, fix, and document such issues with no developer involvement beyond providing the input file.

Ur-160-200 Tortured Artist, Caitlin Tigani, Ben Scholl, Adam Tucker, Anaiya Tucker

C-Day Computing Showcase

You are a photographer that wants to move out, so you take pictures of your house to give to your real- estate agent. However, as you are developing the photos you hear a noise that makes you turn on the lights, ruining your photos. Now you must retake the photos before morning, but something around the house has changed. Rooms are no longer in the right place, items are moved around, doors are locked, and an entity is watching you. Will you find the secrets within the puzzles or be left tortured?

Ur-147-188 Staged Multi-Modal Alzheimer Classification Using Uncertainty Quantification, Branden Chen, Ethan Litton, Long Doan

C-Day Computing Showcase

Diagnosing Alzheimer’s disease often depends on costly neuroimaging techniques such as MRIs and PET scans, which are not always accessible and can place a significant financial burden on healthcare systems. Existing clinical workflows lack a reliable way to determine which patients truly require these advanced tests, resulting in either unnecessary imaging or delayed and inaccurate diagnoses. To address this challenge, we propose the Uncertainty-Driven Dual-view (UDD) model, a multi-stage framework that integrates low-cost clinical and structural data with uncertainty-aware learning. The model first generates predictions using accessible data and quantifies its confidence, referring only high-uncertainty cases for further evaluation with …

Ur-084-219 Towards Bounding The Behavior Of Neural Networks, Emmanuel Nwankwo

C-Day Computing Showcase

Modern neural networks are typically considered black-box systems: while they are able to achieve state-of-the-art performance in many domains, it is difficult to elicit the reasons behind their decisions. From this, a sub-field of artificial intelligence called eXplainable Artificial Intelligence (XAI) arose to fill this gap. One approach to XAI is based on the symbolic compilation of a neural network's behavior to a logical formula. However, such approaches are limited in scalability, due to the fundamental difficulty of the problem. This research instead proposes an incremental and anytime approach to explaining the behavior of a neural network, for image recognition. …

Grp-125-144 Mapping The Affordances Of Human-Ai Interaction: A Large-Scale Text Mining And Statistical Analysis Of Llm Usage Patterns, Anil Vallepu

C-Day Computing Showcase

People increasingly communicate with AI for schoolwork, office tasks, and daily needs. This study investigates the affordances of human-AI interaction using modern text mining and statistical analysis on the WildChat-1M dataset of over 1.1 million real-world ChatGPT user conversation logs. We apply BERTopic to extract latent interaction topics, compute a probabilistic topic-document matrix P(T|D), and perform rigorous statistical testing including Welch’s T-test and ANOVA to compare affordance patterns between GPT-3.5 and GPT-4.0 Results reveal that creative writing, Coding, message drafting and many interesting topics are the dominant affordances, while a spatio-temporal trend analysis maps how interaction patterns evolve globally over …

Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu

C-Day Computing Showcase

NeuroVision presents a unified framework for mapping electroencephalography (EEG) signals to language and visual meaning. Extracting semantic information from EEG remains a fundamental challenge due to its low signal-to-noise ratio, high dimensionality, and inter-subject variability. To address these challenges, we propose a multimodal representation learning framework that aligns EEG signals with both textual and visual embeddings through temporal modeling, spatial brain-region decomposition, and contrastive learning. The framework integrates self-supervised pretraining with supervised multimodal alignment to learn robust and transferable representations. Experimental results demonstrate BLEU-1 of 0.1106 and ROUGE-1 of 0.1493 for EEG-to-text generation, alongside a 52% improvement in retrieval performance …

Grp-148-223 Uncertainty-Guided Conservative Propagation For Robust Coronary Artery Segmentation, Huan Huang, Chen Zhao

C-Day Computing Showcase

Coronary artery segmentation plays a key role in cardiovascular disease analysis, yet existing methods often produce fragmented and structurally inconsistent vessels in challenging regions. We propose an uncertainty-guided conservative propagation (UGCP) framework that improves segmentation reliability by allowing high-confidence regions to guide uncertain ones through controlled information propagation under a conservation principle. This mechanism enhances structural continuity while preventing unstable updates. Experiments on Coronary CT Angiography (CCTA) and Invasive Coronary Angiography (ICA) datasets demonstrate improved segmentation accuracy and topology preservation. Additional evaluations on other vascular datasets further suggest the generalizability of the proposed approach.

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg

C-Day Computing Showcase

This project evaluates automated SMS spam classification by comparing traditional machine learning against modern transformer architectures. We built a Bidirectional LSTM (BiLSTM) baseline using TF-IDF feature extraction and NearMiss-1 undersampling to handle severe class imbalances. We then compared this against a fine-tuned Hugging Face Sentence-BERT model. Preliminary results show Sentence-BERT significantly outperformed the BiLSTM baseline (99.01% vs. 95.65% accuracy). These findings demonstrate that transformer-based embeddings offer a highly accurate, scalable solution for spam mitigation without relying on aggressive data undersampling.

Gc-168-220 Active Directory To Cloud Security Data Pipeline, Michael Butler, Shahiba Shamshad, Mounia Touil, Koko Afantchao

C-Day Computing Showcase

This project builds an automated pipeline that extracts identity and asset data from on-prem Active Directory, stages it in Google BigQuery, and securely sends normalized data to Lucid through Google Cloud Run. Using PowerShell scripts, the system collects users, groups, computers, DNS, DHCP, and related metadata without changing the source environment. BigQuery serves as the staging layer for validation and processing, while Cloud Run transforms and transfers the latest data to Lucid for visualization. The goal is to provide a repeatable, traceable, and reliable workflow that improves visibility into identity relationships for security investigations, auditing, and validation in a controlled …

Grp-120-138 Wall-E: Wide-Area Aerial Live Learning For Emergency Disaster Evaluation, Shiva Shrestha

C-Day Computing Showcase

WALL-E is an AI-powered, custom-built quadcopter that surveys disaster zones in real time, classifying building damage and generating a GPS-tagged damage map with no internet required. The system uses an RGB camera for live AI inference and a FLIR thermal camera for additional situational awareness. A custom YOLO based model runs entirely onboard the Jetson Orin Nano, logging every detection via GPS for immediate command use. Future work will expand detection capabilities to include human presence identification.