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Articles 2431 - 2460 of 63010

Full-Text Articles in Computer Sciences

Grp-21155 Autotrader-Agentedge, Christopher Regan Nov 2025

Grp-21155 Autotrader-Agentedge, Christopher Regan

C-Day Computing Showcase

This work presents AutoTrader-AgentEdge, a human-in-loop trading system that positions AI agents as collaborative partners rather than autonomous replacements. We demonstrate that multi-indicator consensus voting combined with human approval achieves superior risk-adjusted returns while maintaining interpretability and control. Core Contribution: A production-ready VoterAgent implementing democratic voting between MACD momentum and RSI extremes, generating transparent trading signals for human evaluation. Unlike black-box automation, our interactive CLI augments trader expertise through interpretable consensus logic. The human retains final decision authority at all critical junctures. Validated Performance: Empirical validation demonstrates multi-indicator voting superiority over single-indicator automation: Sharpe ratio 0.856 vs 0.841, max drawdown …

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Grp-20236 Zero-Day Host-Based Intrusion Detection Via Hybrid Deep Sequence Modeling Of Systemcalls​ ​, Syeda Umme Salma Nov 2025

Grp-20236 Zero-Day Host-Based Intrusion Detection Via Hybrid Deep Sequence Modeling Of Systemcalls​ ​, Syeda Umme Salma

C-Day Computing Showcase

Protecting endpoints has become increasingly challenging, as adversaries have been effective in bypassing defenses. Traditional signature-based Host-based IDS performs quite well at recognizing known patterns but often struggles with previously unseen activity. This study incorporates deep neural sequence modeling with classic OS telemetry to flag novel behavior from Linux system-cell traces. This study design is paired with a sequence encoder over syscall streams, incorporating lightweight statistical signals that are derived from process activity. We believe that our hybrid neural network approach will outperform conventional baselines and boost recall on unknown attacks while maintaining low false-positive rates, providing a practical and …

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Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan Nov 2025

Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) is one of the leading causes of global health concern, but current risk assessments are limited to episodic clinical visits. Most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of …

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Grp-20194 Can Mental Health Apps Really Help Caregivers? Usability Findings From Human-In-The-Loop Nlp And Sentiment-Aware Analytics​, Syeda Umme Salma Nov 2025

Grp-20194 Can Mental Health Apps Really Help Caregivers? Usability Findings From Human-In-The-Loop Nlp And Sentiment-Aware Analytics​, Syeda Umme Salma

C-Day Computing Showcase

Caregivers face distinctive emotional and logistical burdens, yet many mental-health apps overlook their needs and show usability issues. We introduce an automated pipeline that analyzes 317K app-store reviews from 9 apps, mapping them to Nielsen’s usability components and heuristics, together with sentiment. To assess reliability, we run a human–AI agreement study (N=50) where a domain expert (A2) and a non- expert (A1) label reviews. For heuristics, the pipeline achieves 66% exact agreement and moderate κ=0.579 with the expert, outperforming human–human agreement; components remain harder, revealing a need to refine the codebook (e.g., learnability vs satisfaction). Complementary clustering and sentiment analyses …

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Grp-20185 Energy-Aware Operating Systems For Edge Artificial Intelligence Inference, Nursat Jahan Nov 2025

Grp-20185 Energy-Aware Operating Systems For Edge Artificial Intelligence Inference, Nursat Jahan

C-Day Computing Showcase

Edge Artificial Intelligence (AI) refers to running AI inference directly on local devices such as wearables, sensors, and mobile systems rather than relying on cloud computing. The growth of Edge AI has created strong demand for efficient inference on resource-limited devices. Edge AI devices must perform real-time inference while operating under strict battery constraints. Although significant model optimizations exist for managing power-intensive inference models, operating system (OS) level support is limited. Existing OS schedulers often neglect energy limits in edge devices as they prioritize fairness or throughput. In this research we proposed an OS level framework to bridge this gap …

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Grp-1265 Optimizing Cpu Scheduling For Deep Learning And Llm Inference Using Onnx Runtime, Mohammod Akib Khan Nov 2025

Grp-1265 Optimizing Cpu Scheduling For Deep Learning And Llm Inference Using Onnx Runtime, Mohammod Akib Khan

C-Day Computing Showcase

Modern applications rely on AI models that must perform real-time predictions on resource-constrained edge devices like laptops. The default OS scheduler often increases context switching, which slows down deep learning and LLM inference. Since these models depend heavily on parallel processing, efficient CPU scheduling becomes essential. In this project, we analyzed how core pinning and thread-level parallelism improve inference performance on a Windows system. Using multiple micro-batch sizes. We compare latency, throughput, and per-sample inference time. The goal is to show how simple OS-level optimization can significantly improve real-time performance for both deep learning models and LLM.

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Grp-1231 Evaluating Generalization And Adaptation Of Learning-Based Schedulers For Directed Acyclic Graph Workloads, Rabia Rabia Nov 2025

Grp-1231 Evaluating Generalization And Adaptation Of Learning-Based Schedulers For Directed Acyclic Graph Workloads, Rabia Rabia

C-Day Computing Showcase

Learning-based schedulers such as Decima can optimize directed acyclic graph (DAG) workloads, yet their robustness under changing workload conditions is not well understood. This project evaluates how a Decima-trained policy transfers across different workload scenarios using an automated training and testing pipeline. Results show that the scheduler generalizes well to a workload with the same job scale, achieving a 1.9% improvement in average job completion time. Performance remains stable under a larger workload, but a shift in arrival pattern leads to an 83.7% increase in completion time and reduced fairness. These findings highlight both the potential and the limitations of …

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Grp-1230 Environmental Protection: Development Of A Real-Time Multi-Stream Water Quality Monitoring System, Faruk Muritala Nov 2025

Grp-1230 Environmental Protection: Development Of A Real-Time Multi-Stream Water Quality Monitoring System, Faruk Muritala

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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Grp-1203 Market-Driven Endurance Credits For Page Retention (Mecr) In Hybrid Dram–Nvm Systems, A E M Ridwan Nov 2025

Grp-1203 Market-Driven Endurance Credits For Page Retention (Mecr) In Hybrid Dram–Nvm Systems, A E M Ridwan

C-Day Computing Showcase

This work proposes MECR, a lightweight market-driven page-retention model for hybrid DRAM–NVM memory systems. MECR integrates a contextual bandit learner with endurance-aware credit bidding to optimize hit rate, fairness, and NVM lifetime. A deep verification layer ensures safe eviction decisions under wear-sensitive workloads. Experimental evaluation using synthetic memory traces shows stable accuracy, improved fairness, and adaptive DRAM allocation.

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Grp-1190 Hybrid Virtualization Performance Modeling Using Monte Carlo Simulation, Amatul Akhi Nov 2025

Grp-1190 Hybrid Virtualization Performance Modeling Using Monte Carlo Simulation, Amatul Akhi

C-Day Computing Showcase

This project presents a performance analysis of virtualization, containerization, and hybrid container-in-VM architectures in modern operating systems. Virtual machines provide strong isolation and system stability but incur higher CPU, memory, and startup costs. Containers offer lightweight and fast execution but rely on weaker isolation. To evaluate a balanced alternative, performance data was extracted from recent research and modeled using a Monte Carlo simulation with 1,000 randomized workloads. A unified Hybrid Efficiency Score (HES) was introduced to compare systems consistently, weighting efficiency at 70% and isolation at 30%. Simulation results demonstrate that hybrid systems achieve the highest efficiency–isolation balance, with an …

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Grp-1184 Edge-Llm Anomaly Detection On Raspberry Pi: Syscall Dataset Collection And Prototype Llm Explanation Layer, Shiva Shrestha, Shiva Shrestha Nov 2025

Grp-1184 Edge-Llm Anomaly Detection On Raspberry Pi: Syscall Dataset Collection And Prototype Llm Explanation Layer, Shiva Shrestha, Shiva Shrestha

C-Day Computing Showcase

This research work offers a light-weight, end-to-end, syscall-level anomaly detection approach for the Raspberry Pi platform. The proposal involves the collection of around 2000 NORMAL and 200 ANOMALY syscall observation groups using the Linux Auditd safe synthetic generators. The work also utilizes a prototype LLM Explanation Layer, allowing the provision of human-friendly explanations pertaining to identified anomalies leveraging small LLM models like the Gemma-3 1B, Phi-3 Mini, or other sub 1B LLMs employing the Ollama platform. The LLM inference layer in this research work has partial implementations, as the fine-tuning of the model remains to be done.

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Grp-1141 Decentralized Scheduling And Memory Management In A Simulated Multikernel Os Environment, Md Jahirul Islam Nov 2025

Grp-1141 Decentralized Scheduling And Memory Management In A Simulated Multikernel Os Environment, Md Jahirul Islam

C-Day Computing Showcase

Multikernel operating systems treat each processor core as an independent computing node rather than shared memory. This experiments presents the design and implementation of a multikernel OS simulator that models both decentralized and global scheduling architectures across multiple simulated cores. Each core executes tasks using either Round Robin (RR) or Shortest Job First (SJF) scheduling. The simulator incorporates memory management, task tracking, visualization, generating performance metrics including turnaround time, waiting time, CPU utilization, and heatmap. Experimental results demonstrate that scheduler architecture significantly influences system performance. Decentralized scheduling favors RR where in global scheduling SJF performs better through balanced workload distribution.

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Uc-1152 Sustainsync, Youssef El-Shaer, Zaid Khan Nov 2025

Uc-1152 Sustainsync, Youssef El-Shaer, Zaid Khan

C-Day Computing Showcase

Sustain Sync investigates how organizations can standardize sustainability tracking and how AI can convert that data into actionable insights. The platform normalizes utility data into a consistent schema aligned with industry frameworks. It applies machine learning forecasting and a retrieval-augmented co-benefit engine to relate sustainability goals across domains such as CO₂, water, and biodiversity. Through a simple dashboard, Sustain Sync demonstrates an end-to-end data-driven approach for goal tracking, trend analysis, and AI-driven sustainability recommendations for an organization.

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Ur-0227 Compilation Of Binary Neurons To Obdds, Aidan Boyce Nov 2025

Ur-0227 Compilation Of Binary Neurons To Obdds, Aidan Boyce

C-Day Computing Showcase

A neuron with binary inputs & outputs corresponds to a Boolean function. To explain and verify the behavior a neuron (and by extension, a neural network), we can explain and verify its Boolean function. There has been recent interest in representing the Boolean function of such a neuron as an Ordered Binary Decision Diagram (OBDD), which facilitates such analyses. We propose an algorithm for compiling a binary neuron into an OBDD using a compiler that decomposes a Boolean function into a decision graph. We augment this compiler so that it outputs an OBDD instead. Our augmented compiler produces intermediate OBDDs …

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Ur-0199 Blood Flow Simulation From Coronary Computed Tomography Angiography Using Vnet, Pedro Pinto Nov 2025

Ur-0199 Blood Flow Simulation From Coronary Computed Tomography Angiography Using Vnet, Pedro Pinto

C-Day Computing Showcase

Coronary artery disease (CAD) is one of the leading causes of death worldwide, making the assessment of blood flow and pressure distribution within coronary vessels essential for diagnosis and treatment planning. Fractional Flow Reserve (FFR) is a key measure used to determine the severity of arterial blockages, but traditional methods, such as invasive measurements or computational fluid dynamics (CFD) simulations, are not only time-consuming and costly but also invasive. This project explores the use of deep learning to predict blood pressure distribution in coronary arteries using a 3D convolutional neural network. The dataset consists of Coronary Computed Tomography Angiography (CCTA) …

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Ur-1256 Realistic Paint-Like Color Mixing In Gimp, Calvin Crose, Cassidie Grogan, Dawson White, Joey Montalvo Nov 2025

Ur-1256 Realistic Paint-Like Color Mixing In Gimp, Calvin Crose, Cassidie Grogan, Dawson White, Joey Montalvo

C-Day Computing Showcase

Gimp updated its support for the MyPaint brushes, from version 1.0 to version 2.0. However, despite the added support, the application still uses legacy blending from version 1.0 of MyPaint brushes and lacks the new tools for spectral blending offered in version 2.0. A request has been made to implement the new blending so users can produce art using this feature that mirrors real-world blending.

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Ur-1202 Generative Ai & Cybersecurity​, Emmanuel Okafor, David Nguyen, Alex Lu Nov 2025

Ur-1202 Generative Ai & Cybersecurity​, Emmanuel Okafor, David Nguyen, Alex Lu

C-Day Computing Showcase

Generative AI both strengthens and threatens cybersecurity. This project develops six reproducible, Google-Colab modules that demonstrate real attack paths — direct and indirect prompt injection, deepfake phishing, phishing-URL classification, insecure code/hallucination risks, and a malware reconstruction (Honor) challenge — and evaluates practical defenses including prompt wrapping, input sanitization, output redaction, and LLM-based guard chains. Using open-weights models and transparent data handling, we provide runnable notebooks, a public Google Site, and an IEEE-style paper to support education and defense-in-depth design.

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Ur-1195 Using Machine Learning To Diagnose Alzheimer's, Julia Johnson, Jordan Rainford Nov 2025

Ur-1195 Using Machine Learning To Diagnose Alzheimer's, Julia Johnson, Jordan Rainford

C-Day Computing Showcase

Early detection of Alzheimer’s disease (AD) remains a significant clinical challenge, as the changes associated with cognitive decline are often subtle and difficult to identify through visual assessment alone. This study investigates modern machine learning methodologies to improve the prediction of cognitive impairment using volumetric MRI–derived region-of-interest (ROI) features. We constructed three binary classifiers [NC vs. AD, MCI vs. AD, and NC vs. MCI] and evaluated various algorithms, including logistic regression, random forests, neural networks, and support vector machines (SVMs). Using measurements generated from eight anatomical brain templates, our models learned patterns indicative of normal cognition, mild cognitive impairment, and …

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Ur-1167 Owlexchange, Laura Chaplin, Caitlin Johnson, Alexin D'Haiti, Jamaul Gordon, Leonardo Barranco Nov 2025

Ur-1167 Owlexchange, Laura Chaplin, Caitlin Johnson, Alexin D'Haiti, Jamaul Gordon, Leonardo Barranco

C-Day Computing Showcase

OwlExchange is a secure, role-based campus marketplace designed to help Kennesaw State University students buy, sell, exchange, and donate items in a trusted digital environment. The platform replaces unorganized and unsafe exchanges occurring across group chats and social media by providing a centralized system with authenticated user accounts, item listings, and transparent communication. Built with a Flask (Python) MVC architecture, Auth0 for secure identity management, and MySQL for persistent storage, OwlExchange supports modular dashboards for buyers, sellers, and administrators, enabling item management, interest requests, and platform oversight. By promoting reuse and donation of textbooks, furniture, electronics, and other student items, …

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Ur-1163 Editing Films And Movies On Gimp, Ahmed Al Saad, William Grace, Brian Robinson Nov 2025

Ur-1163 Editing Films And Movies On Gimp, Ahmed Al Saad, William Grace, Brian Robinson

C-Day Computing Showcase

This project is to develop an import feature for Digital Picture Exchange (.DPX) files with GIMP. The DPX format is popularly used in the film and visual effects industry. DPX stores uncompressed high bit depth images. Although the project is still in progress, a working prototype is provided.

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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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