Computer Sciences Commons^™

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, …

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.

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.

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 …

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-177-226 Scrapper Kinetics Llc, Mikhail Rudenko, Andrew Torimoto, Jimmy Lock, Marco Cheng

C-Day Computing Showcase

Scrapper Kinetics LLC is a multiplayer and multimodal physics puzzle game. Where players get to choose between playing in VR or Desktop mode, and then, with up to 7 friends (8 players total), try to make a profit in the harsh dead space hulks they have been hired to scrap. We made the game as a test to see how easy it is to have completely different devices interact in the same play space.

Ur-166-204 Multi-User Stem Learning Experience Powered By Llm Conversational Agents, Devon Haynes, Chance Boecker, Oliver Haggard

C-Day Computing Showcase

While Extended Reality (XR) provides experiential and interactive foundations for STEM education, current storytelling and narrative-driven applications often lack responsive nonplayer characters (NPCs), limiting interactive potential through pre-scripted stories. Additionally, despite the growth of Large Language Model (LLM) integration in XR, limited research explores the combined use of multi-user XR systems and conversational Artificial Intelligence (AI) to facilitate real-time, adaptive instruction. This project seeks to address these gaps by 1) Developing a narrative-driven STEM learning XR prototype that incorporates synchronous multi-user interaction and an embedded LLM-driven conversational agent and 2) Exploring the effectiveness of combining these technologies to improve learning …

Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak

C-Day Computing Showcase

This project addresses the urgent need for transparent chatbot safety evaluations amid rising concerns about AI-facilitated self-harm. Using public social media datasets, we simulate two tasks: (1) detecting suicidal ideation via emotion-based risk scoring, and (2) stress-testing a support-style chatbot against 888 high-risk prompts, including euphemisms and “for a story” framing. A multi-label classifier trained on GoEmotions feeds emotion profiles into a logistic regression model to generate suicidality risk scores. These scores guide a local chatbot built with Ollama’s llama3, which analyzes user messages and steers responses toward safe, empathetic behavior. Evaluation shows ~90% of replies were safe or supportive. …

Gc-113-125 Carter’S Lake Visitor Center Boating Safety Game, Hunter Blake, Chancelor Brown, Lauren Robbins, Will Bryant, Kendrick Bryant

C-Day Computing Showcase

The Boating Safety Game is an educational, kiosk-based touchscreen game created for the U.S. Army Corps of Engineers and the Carters Lake Visitor Center. It is designed to improve the knowledge and engagement of boating safety concepts for visitors, particularly for students and youth. The project was developed using multiple game scenarios meant to reinforce safe boating practices through tutorial scenes, top down navigation, life jacket and required item selection tasks, and player motivation through quizzes, feedback, scores, and a star ranking system. The game’s design emphasizes accessibility and retention through simple touchscreen interaction, guided instruction, and repeated feedback on …

Gc-141-167 Smart Resume Screening & Interview Preparation Assistant, Loreli Olien, Tomas King, Hanzhi Chen, Kawanda Gray, Brittany Frazier

C-Day Computing Showcase

The hiring process often relies on manual resume review and keyword matching, which can lead to inconsistent and biased candidate evaluations. This project introduces a Smart Resume Screening and Interview Preparation Assistant designed to improve transparency and consistency in early-stage candidate evaluation. The system allows recruiters to upload resumes and job descriptions, then uses embedding-based semantic matching and large language models to assess candidate alignment across skills, experience, education, and projects. The application generates structured rankings, explainable insights, and tailored interview questions. This project focuses on developing a functional prototype that demonstrates how AI can enhance decision support while maintaining …

Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad

C-Day Computing Showcase

This research investigates whether uninformed search (BFS) or informed search (A*) is more effective when combined with a Genetic Algorithm for maze path planning. We design and implement four algorithms: baseline BFS and A*, hybrid GA+A*, and hybrid GA(BFS+A*). Our findings show that while A* alone performs optimally, integrating it with GA can produce alternative quality solutions, though with computational trade-offs. The study demonstrates that GA+A* provides the best balance between solution quality and runtime efficiency.

Gc-178-191 Communication App: Ai-Assisted Aac Platform​, Alex Wills, Maryam Koya

C-Day Computing Showcase

The Communication App is an accessibility-focused mobile application designed to support individuals with speech impairments, strong or unrecognized accents, and neurodivergent communication needs. The system leverages AI assisted speech-to-text (STT) and text-to-speech (TTS) technologies to enable real-time and seamless communication between users.This project aims to bridge communication gaps by providing a customizable, adaptive platform that learns user speech patterns over time. The application integrates cloud-based services, secure communication protocols, and an intuitive user interface to ensure usability, performance, and accessibility.

Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre

C-Day Computing Showcase

Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address …

Grm-012-173 Can You Trust Ai Code? Understanding And Detecting Breaking Changes Using Llms, K M Ferdous, Kowshik Chowdhury

C-Day Computing Showcase

AI-generated code is increasingly prevalent in software engineering practices, yet its reliability in preserving backward compatibility remains underexplored. This paper presents a unified study of (i) how often AI-generated code introduces breaking changes and (ii) whether large language models (LLMs) can detect such changes from commit-level diffs with explanations. We analyze 7,191 agent-generated and 1,402 human-authored pull requests from Python repositories using an AST-based approach to identify potential breaking changes. Our results show that AI agents introduce fewer breaking changes overall than humans (3.45% vs. 7.40%) in code generation tasks. However, agents show higher risk in maintenance tasks, where refactoring …

Grm-081-207 Leveraging Non-Parametric Longitudinal Rank Sum Tests (Lrst) For Robust Global Treatment Effect Estimation In Alzheimer’S Disease, Imaan Shahid

C-Day Computing Showcase

Parametric approaches, such as Mixed Models for Repeated Measures (MMRM), are standard in Alzheimer’s Disease (AD) clinical trials. However, these models often falter when data violates assumptions of normality or follows non-linear trajectories—common occurrences in AD due to floor/ceiling effects on cognitive scales and heterogeneous disease progression. This study evaluates Longitudinal Rank Sum Tests (LRST) as a non-parametric alternative to maintain statistical power and robustness.

Grm-094-176 Influence Of Speech Disfluencies And Prompt Optimization On Llm-Based Alzheimer's Detection, Muhammad Awais Arshad

C-Day Computing Showcase

This study evaluates how speech disfluencies and prompting strategies impact LLM-based Alzheimer’s Disease (AD) detection. We compared transcripts with preserved disfluencies (ADReSS) against clean transcripts (ADReSSo) using four state-of-the-art LLMs. Key Discovery: Complex prompts induce a "mirror-image" classification bias, where DeepSeek models severely over-classify AD, and GPT-5.2 over-classifies Cognitively Normal (CN) individuals. Optimization Fix: Applying DSPy MIPROv2 effectively mitigated bias in simpler prompts, while TextGrad successfully optimized complex, multi-step prompts.

Grm-095-230 A Multimodal Llm Framework For Automated Construction Blueprint Analysis With Real-Time Decision Support, Muhammad Awais Arshad

C-Day Computing Showcase

This study addresses the high hallucination rates of Vision-Language Models (LLMs) when analyzing complex, hybrid construction blueprints. We developed a dual-input pipeline that pairs high-resolution images with a four-layer JSON "Digital Twin" (vector text, raster OCR, geometry) to mathematically ground the LLM's visual interpretation. Key Engineering Achievement: We processed a massive 137-sheet civil engineering project with zero errors. By introducing a multi-tier JSON pruning strategy, we cut token usage by up to 70% and processed the entire batch from $13-$15 to just $0.93. Decision Support Extension: We integrated real-time traffic data (TomTom) and GDOT procedural policies to transform the pipeline …

Grm-132-159 Integrating Causal Inference With Graph Neural Networks For Alzheimer’S Disease Analysis, Pranay Kumar Peddi

C-Day Computing Showcase

Deep graph learning has advanced Alzheimer’s disease (AD) classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease-specific features. We present Causal-GCN, an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment to identify brain regions exerting stable causal influence on AD progression. Each subject’s MRI is represented as a structural connectome where nodes denote cortical and subcortical regions and edges encode anatomical connectivity. Confounders such as age, sex, and APOE4 genotype are summarized via principal components and included in the causal adjustment set. After training, interventions on individual regions are simulated by severing their …

Grm-159-214 Transportation Energy And Emission Modeling And Analysis Tool (Teemat), Laeticia Neno Aloyem

C-Day Computing Showcase

The Transportation Energy and Emission Modeling and Analysis Tool (TEEMAT) is a web-based decision-support framework for evaluating the environmental impacts of EV adoption across U.S cities. TEEMAT integrates a feedforward neural network trained on MOVES 4.0 for tract-level vehicle emissions (CO₂, NOₓ, PM₂.₅), a macroscopic traffic and activity-based model capturing congestion-driven emission spikes, and a Meta-Prophet model trained on NREL Cambium data for grid emissions (CO₂, CH₄, N₂O). Results show that while EV adoption reduces tailpipe emissions, rising travel demand and congestion-induced low speeds can significantly offset these gains underscoring that meaningful decarbonization requires coordinated transportation and energy grid strategies.

Grm-169-137 Predicting The Stock Market's Next Move: How Neural Network Architecture Shapes Forecasting Accuracy, Roderick Powell

C-Day Computing Showcase

Three feedforward neural network (FFNN) architectures — bottleneck, parallel multi-path, and residual parallel — were trained on ten years of daily S&P 500 (SPY ETF) price and volume data to predict next-day market direction (Up/Down). All three demonstrated predictive ability above random chance. Architectural choice directly determined class prediction bias: the bottleneck concentrated errors on Up days, the parallel architecture distributed them evenly, and residual connections inverted the bias toward Down days. Model 2 (parallel) achieved the highest test accuracy (58.2%) and the most balanced class predictions among the three configurations.

Grm-170-221 Mind Modeling For Neuroadaptive Vr Labs, Harish Chary Nagulapally

C-Day Computing Showcase

Students with motor impairments lack equal access to hands-on STEM laboratory experiences. This study investigates whether EEG signals and eye-gaze data contain distinguishable patterns linked to specific hand motor functions both physical and imagined. Identifying this relationship is the critical first step toward a BCI-driven VR system for accessible STEM education.

Grm-174-228 Mitigating Prompt-Induced Variability In Llm Outputs, Crystal Tubbs

C-Day Computing Showcase

Large language models in enterprise settings often produce structurally invalid outputs when users communicate informally, creating silent failure modes that pass unnoticed in downstream systems. This study investigates how prompt variation alone impacts schema compliance and output reliability. We evaluate three architectures across two tasks and four prompt styles, isolating the effect of interaction style on model behavior. Results show that baseline systems achieve 100% compliance under structured prompts but fail completely under ambiguous and casual inputs. A minimal reliability pipeline consisting of generation, self critique, and schema validation restores 100% compliance across all conditions at a predictable computational cost.

Grm-176-225 Memoryeil: An Enhanced Memory Layer Architecture For Heterogeneous Robots, Yukang Shen

C-Day Computing Showcase

Embodied agents still struggle to generalize across robot types, tasks, and environments because most policies remain tightly tied to robot-specific observations and action spaces. While recent VLA and planning methods improve task performance, they still lack a shared memory layer for storing and reusing experience across heterogeneous robotic systems. We propose MemoryEIL, a predicate-based memory layer that converts multimodal observations and execution traces into structured graph memories while preserving raw embeddings for fine-grained retrieval and disambiguation. MemoryEIL separates short-term task belief from long-term experience and plugs retrieved memories into either VLA policies or differentiable TAMP planners. Preliminary results show better …

Grp-01-196 Topological Drift Predicts Epidemic Instability, Charles Fanning

C-Day Computing Showcase

We study whether changes in contact-network topology predict transitions into high-risk epidemic periods across several classical empirical proximity network datasets. We use temporal graph learning with persistent homology-based topological signals and evaluate large-outbreak risk using SIR simulations to test whether topological drift serves as an early warning signal for epidemic instability.