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Ur-0248 Shell Commands Used In Cybersecurity Training, Nathan Kourk, Jacob Suda, Ming Butler, Yonnas Alemu Nov 2025

Ur-0248 Shell Commands Used In Cybersecurity Training, Nathan Kourk, Jacob Suda, Ming Butler, Yonnas Alemu

C-Day Computing Showcase

This project explores patterns in shell command usage during cybersecurity training programs. We will analyze syntax frequency and selection of shell commands across multiple datasets looking for patterns in user behavior. The goal of this study is to identify differences between programs, and to provide insight into the trends within the command line environment.

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Ur-0247 Lyric Prediction Model, Evan Gideon, Garrett Dasher, Ulrich Batanado, Drew Claerbout, Andrew Henshaw Nov 2025

Ur-0247 Lyric Prediction Model, Evan Gideon, Garrett Dasher, Ulrich Batanado, Drew Claerbout, Andrew Henshaw

C-Day Computing Showcase

Word prediction plays a central role in the development and refinement of large language models, supporting applications such as search optimization, dialect identification, and conversational AI systems like Siri as AI text generation becomes increasingly widespread, the demand for precise and contextually aware predictive capabilities continues to grow. This project presents lyric prediction model designed to generate the next lyric based on preceding words, with the ability to identity line breaks and sequential structure. Ultimately, this work aims to advance lyrical text generation by enabling the model to emulate the stylistic characteristics of specific artists or musical genre.

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Ur-0246 Quantum Ml For Science & Engineering, Dharani Shakthivel, Haoxian Tan, Justin Martin Nov 2025

Ur-0246 Quantum Ml For Science & Engineering, Dharani Shakthivel, Haoxian Tan, Justin Martin

C-Day Computing Showcase

Classical machine learning methods - including CNNs, SVMs, PCA, Logistic Regression, and Random Forests - have achieved strong performance across fields such as computer vision, malware detection, and drug discovery. However, these models face scalability limits when trained on large or high-dimensional datasets. Quantum computing introduces superposition, interference, and entanglement, enabling quantum kernels, quantum feature maps, and hybrid quantum-classical architectures that may reduce computational cost or enhance data representation. This project implements classical versions of these algorithms alongside their quantum counterparts to evaluate differences in accuracy, efficiency, and resource demands. By comparing performance across diverse scientific and engineering datasets, the …

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Grp-0275 Graph Attention Network Based Downlink Channel Prediction Using In Frequency Division Duplexed Nextgen Networks, Jui Mhatre Nov 2025

Grp-0275 Graph Attention Network Based Downlink Channel Prediction Using In Frequency Division Duplexed Nextgen Networks, Jui Mhatre

C-Day Computing Showcase

In Frequency Division Duplex (FDD) 5G networks, downlink channel state information (CSI) must be estimated at the user equipment (UE) and fed back to the base station, a process that requires frequent CSI-RS transmission and uplink feedback, resulting in high overhead and energy consumption. This research proposes a novel base-station–centric framework that predicts the downlink channel matrix directly at the gNB, eliminating the need for continuous CSI-RS–based estimation at the UE. By leveraging uplink channel observations, geometric environment features, and learned mappings between uplink and downlink channel relationships, our model reconstructs the downlink MIMO channel with high fidelity. The system …

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Grp-0217 Comparative Analysis Of Os-Level Security Vulnerabilities And Isolation Mechanisms In Hypervisors And Containers, Jiban Krisna Das Nov 2025

Grp-0217 Comparative Analysis Of Os-Level Security Vulnerabilities And Isolation Mechanisms In Hypervisors And Containers, Jiban Krisna Das

C-Day Computing Showcase

This project investigates the operating-system-level performance and isolation mechanism of Virtual Machines and Docker container. The experiment includes CPU/memory microbenchmarks, disk throughput tests, web-server latency measurements, multi-process scheduling stress and controlled security checks. We aim to quantify each benchmark under identical conditions. The study findings reveal that Docker consistently provides lower overhead and faster I/O due to its shared-kernel architecture, while VirtualBox maintains stronger isolation but introduces more scheduling and disk latency. The findings provide practical insights for the OS system designers to find better execution environments for security critical and performance-sensitive workloads.

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Grp-0214 User-Level Gpu Right-Sizing In Hpc: A Framework For Predicting Training Runtime, Yinning Zhang, S M Tanvir Faysal Alam Chowdhoury Nov 2025

Grp-0214 User-Level Gpu Right-Sizing In Hpc: A Framework For Predicting Training Runtime, Yinning Zhang, S M Tanvir Faysal Alam Chowdhoury

C-Day Computing Showcase

Graphics Processing Unit (GPU) resources in High-Performance Computing (HPC) systems are frequently underutilized due to inaccurate user-provided run time estimates. This research develops a machine learning framework for predicting neural network training time from architectural features, dataset size, and other hyperparameters. This approach can be implemented on any HPC systems without requiring hardware access or runtime profiling as other preceding methods do. We sampled neural network models from the NATS-Bench benchmark and used 3 benchmark datasets to generate 400 training configurations. We used these 400 data points to build regression models and found that the best model, Gradient Boosting Regressor, …

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Ur-0225 Carbonyl Detection In Ir Using Deep Learning, Dharani Shakthivel Nov 2025

Ur-0225 Carbonyl Detection In Ir Using Deep Learning, Dharani Shakthivel

C-Day Computing Showcase

The goal of this project is to train a Convolutional Neural Network (CNN) to recognize carbonyl groups in infrared (IR) spectra. A carbonyl group is defined by a characteristic C=O double bond, which produces a strong, easily recognizable absorption peak near 1700 cm⁻¹. To develop and evaluate the model, I am using spectra prepared through three common techniques: KBr disc, nujol mull, and liquid film. Among these, liquid-film spectra provide the cleanest signal and most closely resemble what a chemist visually relies on when identifying carbonyls. In contrast, both the KBr disc and nujol mull methods require mixing the target …

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Uc-1276 Ci-Cd Pipeline Team 2, Cameron Arnold Nov 2025

Uc-1276 Ci-Cd Pipeline Team 2, Cameron Arnold

C-Day Computing Showcase

Our project is about creating a basic cloud-native pipeline that can build and deploy an application in a more automated way. We will also try to add some security checks and monitoring tools so that we can see how everything is working. The goal is to get hands-on experience with the process and show a working demo at the end of the semester.

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Gc-0151 Smart Hr Onboarding With Microsoft 365 Team 2 Capstone Project, Annalise Gregory, Michael Colley, David Laurent, Harshita Agarwal, Nilesh Kumar Nov 2025

Gc-0151 Smart Hr Onboarding With Microsoft 365 Team 2 Capstone Project, Annalise Gregory, Michael Colley, David Laurent, Harshita Agarwal, Nilesh Kumar

C-Day Computing Showcase

The Smart HR Onboarding with Microsoft 365 Capstone Project aims to transform the new hire onboarding experience by leveraging Microsoft 365 tools to deliver a seamless, automated process. Using Power Automate, we streamline tasks through automated emails, reminders, and documents sharing. This ensures every step of the onboarding journey is efficient and consistent. Sharepoint and Outlook serve as the main sources of storing and sharing required tasks. Power Automate's integration with Power BI allows for real-time reporting with an interactive dashboard the provides insights into onboarding progress or areas where new hires need support. This project aims to simplify onboarding …

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Gc-0251 Reproducing Extended Isolation Forests With Star-Cast, Drew Patrick, Ram Sai Sivakoti, Rohit Malik, Vardhineedi Surya Kamal, Venkata Sasidhar Reddy Palagundla Nov 2025

Gc-0251 Reproducing Extended Isolation Forests With Star-Cast, Drew Patrick, Ram Sai Sivakoti, Rohit Malik, Vardhineedi Surya Kamal, Venkata Sasidhar Reddy Palagundla

C-Day Computing Showcase

Fraud models routinely flag suspicious transactions but rarely explain why, which slows investigations and erodes trust. In this work we study Extended Isolation Forest (EIF) for unsupervised fraud detection and propose STAR-CAST, a lightweight framework that turns raw anomaly scores into threshold-aligned IF–THEN rule cards with explicit reliability measures. Using the public credit-card fraud dataset (284,807 transactions, 492 frauds; ~0.17% prevalence), we apply a time-aware 70/15/15 Train/Validation/Test split and fit-on-train preprocessing (Amount log1p→z; Time z; V1–V28 retained). We train IF, EIF, an EIF ensemble, a Mahalanobis baseline, and density models (HBOS, COPOD, ECOD) fully unsupervised, evaluate them as rankers first …

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Gc-1154 Peer Evaluation Automation And Feedback System, Sameer Khan, Nnedi Okafor Nov 2025

Gc-1154 Peer Evaluation Automation And Feedback System, Sameer Khan, Nnedi Okafor

C-Day Computing Showcase

A web-based platform to streamline peer evaluations in team-based courses. Professors can securely create and manage student rosters, assign students to courses/teams, trigger email invitations, and receive structured, professor- friendly reports with both numeric and textual feedback. Optional AI features may summarize comments and flag potential concerns, depending on timeline and scope.

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Grm-0237 Efficient Defense Against Adversarial Patch Attacks In Remote Sensing Using Transfer Learning, Ravi Rogannagari Nov 2025

Grm-0237 Efficient Defense Against Adversarial Patch Attacks In Remote Sensing Using Transfer Learning, Ravi Rogannagari

C-Day Computing Showcase

Remote sensing is the science of acquiring information about the Earth's surface using satellite-mounted imaging sensors. In the past, this data had to be interpreted manually, which was slow, tedious, and often prone to error. With the rise of deep learning, image classification models have greatly accelerated and improved remote sensing tasks such as land-use analysis, environmental monitoring, etc. However, despite their strong performance, these models are still vulnerable to adversarial patch attacks—physically realizable patterns that, when placed on an object, can force the model to make incorrect predictions. This creates serious risks for practical geospatial applications. Traditional defenses like …

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Grm-0243 Sentient Agi Rights And The Future: The Modern Digital Prometheus, Ryan Deem Nov 2025

Grm-0243 Sentient Agi Rights And The Future: The Modern Digital Prometheus, Ryan Deem

C-Day Computing Showcase

As artificial intelligence advances toward artificial general intelligence (AGI), society must determine how to ethically integrate sentient AI into our communities. This paper argues that once AI achieves sentience and human-level intelligence, it should be granted the same rights and protections as human citizens. Using utilitarian and deontological perspectives, as well as the IEEE Code of Ethics, it examines why treating AGI as lesser beings could lead to fear, conflict, and harmful outcomes—echoing the cautionary themes of Frankenstein. The paper also evaluates public concerns and existing governance frameworks, proposing that mutual respect, rights, and responsibilities are essential for safe coexistence …

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Grm-20242 Cipher: Covert Influence Passed Via Hidden Encoding In Representations Evaluating Subliminal Bias Transfer During Knowledge Distillation, Crystal Tubbs Nov 2025

Grm-20242 Cipher: Covert Influence Passed Via Hidden Encoding In Representations Evaluating Subliminal Bias Transfer During Knowledge Distillation, Crystal Tubbs

C-Day Computing Showcase

AI models can inherit hidden behavioral biases when student models learn from teacher outputs during knowledge distillation. Project CIPHER investigates whether covert signals, such as zero-width Unicode characters or column order shifts, can transmit bias from a teacher model to a student model even when the student never receives group labels. Using an experimental pipeline with controlled subliminal cues and dual distillation, the project aims to reproduce and measure subtle bias transfer. Preliminary results showed that weak signals produce no measurable bias, while the redesigned high-frequency signal and MLP student architecture reveal quantifiable disparity.

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Gc-0157 Ai Graduate Admissions Assistant, Katherine Hyatt, Ginger Wright, Pablo Edgar, Oluwatosin Akinwusi, Alan Johnson Nov 2025

Gc-0157 Ai Graduate Admissions Assistant, Katherine Hyatt, Ginger Wright, Pablo Edgar, Oluwatosin Akinwusi, Alan Johnson

C-Day Computing Showcase

Project Overview: Create a "Proof of Concept" for an AI Graduate Admissions Assistant chatbot that will: • Autonomously reference KSU website information in real-time • Direct users efficiently to specific, relevant content • Reduce questions for admissions personnel • Self-update its knowledge base when website content changes •Provide accurate, instant responses to common admissions questions and allow for response correction in an admin dashboard

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Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo Nov 2025

Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo

C-Day Computing Showcase

Dehydration is a common and preventable complication for oncology patients, especially those undergoing chemotherapy and radiation. Side effects such as nausea, fatigue, and loss of appetite make it difficult for patients to maintain adequate fluid intake, contributing to avoidable discomfort and potential treatment disruptions. This capstone project presents Onco-Boost, a mobile hydration monitoring application designed to help adult oncology patients track daily fluid intake, recognize their intake patterns, and stay engaged in daily self-care between clinic visits. Built with React Native and Expo, and backed by Firebase for authentication and cloud data storage. Onco-Boost translates clinical hydration guidance and research …

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Gc-1146 Student Engagement Portal: Enhancing Student Success Through Milestone Tracking, Antonio Brewer, Taylor Bolinger, Tyler Dawkins, Ricartho Franck, Moises Valles Nov 2025

Gc-1146 Student Engagement Portal: Enhancing Student Success Through Milestone Tracking, Antonio Brewer, Taylor Bolinger, Tyler Dawkins, Ricartho Franck, Moises Valles

C-Day Computing Showcase

The Student Engagement Portal, also known as the Milestone Map, is a platform developed to help students within KSU’s College of Computing and Software Engineering monitor their academic and professional growth. The system enables students to log milestones, check in at events, and view progress toward personal and departmental goals. Built with a full-stack architecture using NestJS, React, and MongoDB, the portal also includes an administrative dashboard for event management and analytics. The project demonstrates how progress tracking and clear visualization of achievements can improve communication, organization, and engagement between students and the college.

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Gc-1198 Onboarding Tool For New Smartphone Users, Namita Velagapudi, Bhagya Surekha Dasari, Yaswanth Maddineni, Rohith Venkata Sai Chekka, Balachandar Pinninti Nov 2025

Gc-1198 Onboarding Tool For New Smartphone Users, Namita Velagapudi, Bhagya Surekha Dasari, Yaswanth Maddineni, Rohith Venkata Sai Chekka, Balachandar Pinninti

C-Day Computing Showcase

The Smartphone Onboarding Tool is an interactive web platform created to help seniors and new smartphone users become comfortable with mobile technology. It offers a realistic, simulated smartphone interface, guided walkthroughs, and an easy-to-use design that builds confidence in performing everyday tasks. Caregivers can monitor user progress, while learners can practice safely without affecting an actual device. The solution is developed with a React frontend, a Node.js/Express backend, and an SQLite database, all built with a strong focus on mobile-first accessibility.

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Gc-1215 Clinicalrag: A Scalable Benchmark Of Privacy, Relevance, And Speed In Semantic Retrieval For Clinical Transcriptions​ ​, Pradyumna Kumar, Sai Sruti Dandibhatla, Srinivasan Subramanian, Purna Chandu Anukula, Pranitha Athukuri Nov 2025

Gc-1215 Clinicalrag: A Scalable Benchmark Of Privacy, Relevance, And Speed In Semantic Retrieval For Clinical Transcriptions​ ​, Pradyumna Kumar, Sai Sruti Dandibhatla, Srinivasan Subramanian, Purna Chandu Anukula, Pranitha Athukuri

C-Day Computing Showcase

Traditional keyword search struggles with the scale, complexity, and contextual depth of clinical data. This project develops and evaluates semantic search systems that better understand medical language, enabling physicians and researchers to retrieve contextually relevant information through a Retrieval Augmented Generation (RAG) framework. We integrate privacy-preserving methods, including differential privacy and homomorphic encryption to protect sensitive clinical transcriptions. For improved speed and accuracy, we enhance the baseline RAG architecture with Hierarchical Navigable Small World (HNSW) indexing and Maximal Marginal Relevance (MMR) based reranking. To ensure scalability, clinical documents are ingested using PySpark and stored in a vector database optimized for …

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Gc-1233 An Ai-Powered Convolutional Neural Network System For Multi-Class Image Classification Of Rice Plant Leaf Diseases, Akshay Krishna Varma Buddharaju, Siri Yellu, Pranay Kumar Peddi Nov 2025

Gc-1233 An Ai-Powered Convolutional Neural Network System For Multi-Class Image Classification Of Rice Plant Leaf Diseases, Akshay Krishna Varma Buddharaju, Siri Yellu, Pranay Kumar Peddi

C-Day Computing Showcase

This project focuses on building an intelligent system that can automatically identify common diseases found on rice leaves by analyzing simple images. Using a deep convolutional neural network, the model learns to recognize visual patterns associated with three major diseases: Bacterial Blight, Brown Spot, and Leaf Smut. These diseases often show subtle differences in color, texture, and leaf damage, and the model is trained to distinguish them accurately. The goal of this work is to show how artificial intelligence can support modern agriculture by helping farmers detect problems early, even without expert knowledge. By processing images through careful preprocessing, augmentation, …

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Grm-0204 Unpacking Early Burnout Through Predictive Risk Boundaries, Soarov Chakra Borty Nov 2025

Grm-0204 Unpacking Early Burnout Through Predictive Risk Boundaries, Soarov Chakra Borty

C-Day Computing Showcase

Caregiver burnout is a significant issue in healthcare delivery and management, as it directly impacts caregivers' health and compromises the standard of care, often leading to negligence, health deterioration, or withdrawal from caregiving duties. Caregivers play a crucial role in supporting the health, well-being, and quality of life of care recipients by providing both personal and professional services. However, the continuous needs and stress associated with caregiving duties can affect their health and everyday life, leading to caregiver burnout. This study applied data analytics and machine learning by merging several feature selection methods on the NHATS dataset, including LightGBM, XGBoost, …

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Grm-0210 Distance Measures For Multi-Target Tracking, Rakshak Gurung Nov 2025

Grm-0210 Distance Measures For Multi-Target Tracking, Rakshak Gurung

C-Day Computing Showcase

Multi-object tracking (MOT) supports applications such as radar monitoring and autonomous perception, where multiple objects move, appear, or disappear over time. A central challenge is resolving which detections correspond to which tracks. The Hungarian algorithm is often used to solve this assignment problem. For ambiguous scenes, Murty’s algorithm extends this approach by generating multiple top-k association hypotheses. In this work, we study an alternative search-space formulation for top-k enumeration. Our results show that it can provide strong speedups over Murty’s method on small matrices. We also reviewed identity-focused MOT evaluation metrics such as HOTA and created a visualization tool to …

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Grm-0254 Unified Robust Optimal Transport For Outlier-Resilient Learning, Rohan Jonnalagadda Nov 2025

Grm-0254 Unified Robust Optimal Transport For Outlier-Resilient Learning, Rohan Jonnalagadda

C-Day Computing Showcase

Classical Optimal Transport (OT) is particularly sensitive to outliers. The existing robust variant, ROBOT, mitigates this through hard truncation, but its rigidity often compromises stability. We propose WROT-r, a unified r-power framework for weighted robust OT that combines rigorous hard-clipping and smooth cost compression through a single parameter r. WROT-r offers a continuous robustness spectrum, enabling adaptive control over how strongly transport costs are down-weighted for outliers. Experiments on synthetic mean estimation and resilient GANs show clear patterns: larger r performs best under weak contamination by preserving more inliers, while smaller r (≈1.5) is more effective under moderate and strong …

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Grm-1150 Investigating Spatial Patterns Of Tumor And Stroma In Gastric And Colorectal Cancer For Survival Prediction, Siri Yellu Nov 2025

Grm-1150 Investigating Spatial Patterns Of Tumor And Stroma In Gastric And Colorectal Cancer For Survival Prediction, Siri Yellu

C-Day Computing Showcase

The spatial organization of tumor cells, stroma, and tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment plays a critical role in cancer progression and is strongly associated with clinical outcomes. However, quantifying the significance and statistical impact of these spatial patterns remains challenging due to the complex interactions among these components. In this study, we analyze spatial patterns associated with patient survival in gastric and colorectal cancer by integrating four predictive classifiers with spatial image statistics across four large patient cohorts. U-Net was used for semantic segmentation of tumor, stroma, and TILs on digitized Hematoxylin and Eosin–stained FFPE whole-slide images, while …

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Grm-1153 National Energy And Emission Modeling And Analysis Tool, Swetha Kakaraparthi, S M Tanvir Faysal Alam Chowdhoury Nov 2025

Grm-1153 National Energy And Emission Modeling And Analysis Tool, Swetha Kakaraparthi, S M Tanvir Faysal Alam Chowdhoury

C-Day Computing Showcase

NEEMAT is a web-based decision-support tool that predicts vehicle and power-plant emissions plus fuel/energy consumption under rising EV adoption for Atlanta, Los Angeles, New York, and Seattle. A feedforward neural network trained on MOVES estimates tract-level vehicle energy use and CO2/NOx/PM2.5 by speed, vehicle type, fuel, and age, while a macroscopic traffic model captures flow effects. Grid-side CO2/CH4/N2O from EV charging are forecast with a Meta-Prophet model trained on Cambium. Users can explore 24-hour profiles and five-year outlooks, compare scenarios, and export results. Findings show that despite substantial EV uptake, mixed fleets and grid responses can raise total emissions, underscoring …

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Grm-1249 Ai-Assisted Diabetic Retinopathy Screening From Fundus Images, Mohan Krishna Thiriveedhi, Tarun Teja Pokala Nov 2025

Grm-1249 Ai-Assisted Diabetic Retinopathy Screening From Fundus Images, Mohan Krishna Thiriveedhi, Tarun Teja Pokala

C-Day Computing Showcase

Diabetic Retinopathy (DR) is a major cause of avoidable blindness among diabetic patients worldwide. Early screening is critical, but manual diagnosis is time-consuming and requires specialists. This paper presents a deep learning system to automatically analyze retinal fundus images and perform a focused, binary classification to distinguish between 'No DR' (Healthy) and 'Severe-Stage DR' (Severe/Proliferative). We benchmark three prominent architectures: a ResNet-50, an EfficientNet-B0, and a Vision Transformer (ViT-B/16). The models are trained and evaluated on a custom-balanced, binary dataset derived from the APTOS 2019 collection. We conduct two experiments, one with a small dataset (N=500) and one with a …

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Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa Nov 2025

Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

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Uc-0253 Stock Price Predictions Using Lstm & Technical Indicators, Kendal Elison, Allen Smith, Dylan Quinn Nov 2025

Uc-0253 Stock Price Predictions Using Lstm & Technical Indicators, Kendal Elison, Allen Smith, Dylan Quinn

C-Day Computing Showcase

Stock price predictions using traditional statistical methods remains challenging due to market volatility and nonlinear dynamics. Long Short-Term Memory (LTSM) networks may model temporal dependencies in stock data more effectively than traditional statistical methods. Historical data for several companies’ stocks was obtained from Yahoo Finance, where it was then enriched with various technical indicators such as momentum and volatility. Preliminary analysis through Scala programming language suggests that incorporating these technical indicators can enhance short-term price prediction accuracy. Future works may seek to integrate additional trend and volume based indications in another, more robust, programming language like Python.

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Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz Nov 2025

Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz

C-Day Computing Showcase

RiverGuard’s mission is to protect and preserve waterways by using technology to identify and reduce pollution. The system uses an object detection model to automatically locate and classify trash within images or video of rivers and lakes, removing the need for slow, manual observation. By providing real-time insight into waste accumulation, RiverGuard helps communities, researchers, and organizations take faster, more effective action to keep waterways clean. Its goal is to create a sustainable monitoring system that empowers people to understand pollution patterns and support long-term environmental responsibility. RiverGuard represents a step toward cleaner water, healthier ecosystems, and a more informed …

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Uc-1168 Shepherd's Sin - A Visual Novel Hybrid Game Made With Unity, Ara Randolph, Rin Egl, Everett Joiner, Jonah Swerdlow Nov 2025

Uc-1168 Shepherd's Sin - A Visual Novel Hybrid Game Made With Unity, Ara Randolph, Rin Egl, Everett Joiner, Jonah Swerdlow

C-Day Computing Showcase

By day, the grand old house shifts and shudders as if though alive. The six other residents gather in its lounges and parlors, sipping tea, squabbling over rooms, and faking civility. They laugh, they bicker, and they carry on as though nothing festers within these walls. When night falls, their facades rot away. They twist into monstrous embodiments of malice, each one a reflection of the seven deadly sins. By morning, they forget. You do not. Armed with a worn-out Monster Hunter’s Guidebook, you must reclaim its missing pages to learn who these people truly are, what they truly are. …

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