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Full-Text Articles in Computer Sciences
Grm-0267 Learned Heuristics For Efficient A* Search: Improving Pathfinding In Combinatorial Pathfinding Problems, Jamia Jackson
Grm-0267 Learned Heuristics For Efficient A* Search: Improving Pathfinding In Combinatorial Pathfinding Problems, Jamia Jackson
C-Day Computing Showcase
This work proposes a learned heuristic framework designed to improve planning efficiency in deterministic pathfinding tasks. Building on the classic 8-puzzle as an initial test domain, supervised models were trained to approximate heuristic values and guide node ordering during A* search. The proposed approach focuses on modifying and enhancing traditional heuristics such as Manhattan distance by incorporating learned corrections that reduce search depth and node expansions. Experimental results show that the learned heuristic consistently improves search efficiency over standard baselines. Although this evaluation began with the 8-puzzle, the framework establishes a foundation for scaling to significantly larger and more practical …
Grp-0200 Tadps: Thrashing-Aware Dynamic Priority Scheduler For Virtual Memory Systems, Nasim Ahmed
Grp-0200 Tadps: Thrashing-Aware Dynamic Priority Scheduler For Virtual Memory Systems, Nasim Ahmed
C-Day Computing Showcase
Modern operating systems use multiprogramming and virtual memory to run multiple programs efficiently. When memory demand is high, excessive page swapping causes thrashing, degrading performance. This work introduces a thrashing-aware dynamic priority scheduler (TADPS) that uses page fault frequency to guide CPU scheduling. Processes with high page faults get lower priority, allowing others to progress efficiently. Tests under different memory pressures show that this approach improves CPU use, throughput, and stability compared with baseline schedulers such as FCFS and RR. Specifically, TADPS maintained superior throughput and efficiency, and consistently lower average turnaround time than FCFS and RR.
Grp-0197 Evaluating The Ability Of Llms To Interpret, Optimize And Translate Llvm Ir, Reuven Mueller
Grp-0197 Evaluating The Ability Of Llms To Interpret, Optimize And Translate Llvm Ir, Reuven Mueller
C-Day Computing Showcase
This study investigates whether modern state-of-the-art Large Language Models (LLMs) can interpret, optimize, and translate low-level intermediate representations (IR) used in compilers and binary translation software. We evaluate LLM performance on LLVM IR across three tasks: 1) interpreting the underlying algorithmic behavior, 2) identifying missed optimization opportunities, and generating improved IR variants, 3) Translating IR between AArch64 and x86-64 targets. To ensure correctness, all LLM generated IR is checked using a validation pipeline that verifies its syntax, structural correctness, compiles it into machine code, and executes it on randomized test data. Early results show that LLMs can perform non-trivial IR …
Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha
Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha
C-Day Computing Showcase
Virtualization technologies form the backbone of modern cloud and serverless platforms, but the balance between performance efficiency and strong isolation remains a key research challenge. Using a comparative literature based methodology, benchmark data from empirical studies are synthesized and normalized across four metrics: startup latency, memory footprint, isolation strength, and scalability. The findings show that traditional VMs such as KVM and Xen provide robust, formally verified isolation but incur higher boot times and memory usage, whereas Micro VMs like Firecracker and Kata Containers achieve lower latency (often < 125 ms) and smaller memory footprints (5–30 MB) while preserving VM-level isolation. Overall, Micro VMs deliver near container responsiveness with VM-grade security, making them suited for serverless and edge environments. As a future direction, this study highlights the need for scalable, formally verified Micro VM architectures for next-generation cloud systems.
Grp-0165 Reinforcement Learning For Latency-Aware Priority Boosting In Linux Completely Fair Scheduler, Joseph Natter
Grp-0165 Reinforcement Learning For Latency-Aware Priority Boosting In Linux Completely Fair Scheduler, Joseph Natter
C-Day Computing Showcase
Tail latency remains a persistent challenge in Linux’s Completely Fair Scheduler (CFS), particularly when short, latency-sensitive jobs compete with longer ones. Traditional boosting heuristics improve tail latency but require manual tuning and generalize poorly across workloads. This project evaluates whether a reinforcement-learning (RL) controller can dynamically apply priority boosts more effectively than fixed heuristics. Using a discrete-event Python simulator modeled after CFS, this project compares baseline CFS, heuristic boosting, and PPO-based learned boosting under mixed workloads. Results show that while heuristics achieve the lowest absolute latency, RL achieves competitive tail-latency reduction with significantly better fairness and adaptability. A state-space study …
Grp-0161 Predicting The Linux Scheduler’S Next Move With Transformers, Amirmohammad Naddaf Shargh
Grp-0161 Predicting The Linux Scheduler’S Next Move With Transformers, Amirmohammad Naddaf Shargh
C-Day Computing Showcase
We study whether deep learning can help the Linux CFS, which makes fair scheduling decisions without using historical behavior and may preempt tasks that are near completion. We adopt a dataset and baseline LSTM from earlier work and introduce a Transformer model to explore predictive scheduling. We train both on real scheduling traces to learn the next selected task and timing trends, and evaluate them using task classification accuracy and direction accuracy. Our results show that the LSTM remains the stronger baseline and captures CFS patterns more effectively than our Transformer model in this comparison under identical conditions for fairness.
Grp-0160 Quantum Anonymous Notification Protocol, Nitin Jha, Prateek Paudel
Grp-0160 Quantum Anonymous Notification Protocol, Nitin Jha, Prateek Paudel
C-Day Computing Showcase
The scalability of current quantum networks is limited due to noisy quantum components and high implementation costs, thereby limiting the security advantages that quantum networks provide over their classical counterparts. Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security. To enable such integration, Quantum Anonymous Notification (QAN) is a method to anonymously inform a receiver of an incoming quantum communication. Therefore, several quantum primitives will serve as core tools, namely, quantum voting, quantum anonymous protocols, quantum secret sharing, etc. However, all current quantum protocols can be compromised in the …
Grp-21187 Adaptive Sched_Deadline On Linux For Robotic-Arm Control, Zhiguo Liu
Grp-21187 Adaptive Sched_Deadline On Linux For Robotic-Arm Control, Zhiguo Liu
C-Day Computing Showcase
Industrial robot arms often run 500–1000 Hz control loops on general-purpose Linux. Most cycles are on time, but rare long scheduling delays can cause visible end-effector jitter, force spikes, and unstable insertion behavior. Static tuning of priorities and budgets cannot fully remove these long-tail outliers without wasting CPU. This work asks whether a lightweight adaptive layer on top of SCHED_DEADLINE can reduce high-percentile latency while keeping good utilization.
Grp-21186 A Safe Vision-Guided Robotic Injection Control Framework Based On Reinforcement Learning, Zhiguo Liu
Grp-21186 A Safe Vision-Guided Robotic Injection Control Framework Based On Reinforcement Learning, Zhiguo Liu
C-Day Computing Showcase
This project presents a modular vision-guided robotic framework for safe deltoid intramuscular injection. An external YOLO-based detector localizes the deltoid region and outputs a 3D injection point in the robot base frame. In NVIDIA Isaac Sim, a simulated myCobot 280 arm receives this point, and a deep reinforcement learning policy generates a safe approach pose under kinematic and safety constraints, while a deterministic controller executes the final straight-line insertion and withdrawal. A safety supervisor monitors target validity, joint limits and distance to a simplified arm model, triggering immediate stop and retraction when unsafe conditions arise.
Grp-21156 Detecting Dealer Gamma Hedging Mechanics: How Llms Identify Market Structure Without Context, Christopher Regan
Grp-21156 Detecting Dealer Gamma Hedging Mechanics: How Llms Identify Market Structure Without Context, Christopher Regan
C-Day Computing Showcase
We introduce obfuscation testing, a novel methodology for validating whether large language models detect structural market patterns through causal reasoning rather than temporal association. Testing three dealer hedging constraint patterns (gamma positioning, stock pinning, 0DTE hedging) on 242 trading days (95.6% coverage) of S&P 500 options data, we find LLMs achieve 71.5% detection rate using unbiased prompts that provide only raw gamma exposure values without regime labels or temporal context. The WHO→WHOM→WHAT causal framework forces models to identify the economic actors (dealers), affected parties (directional traders), and structural mechanisms (forced hedging) underlying observed market dynamics. Critically, detection accuracy (91.2%) remains …
Grp-21155 Autotrader-Agentedge, Christopher Regan
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 …
Grp-20236 Zero-Day Host-Based Intrusion Detection Via Hybrid Deep Sequence Modeling Of Systemcalls , Syeda Umme Salma
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 …
Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan
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 …
Grp-20194 Can Mental Health Apps Really Help Caregivers? Usability Findings From Human-In-The-Loop Nlp And Sentiment-Aware Analytics, Syeda Umme Salma
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 …
Grp-20185 Energy-Aware Operating Systems For Edge Artificial Intelligence Inference, Nursat Jahan
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 …
Grp-1265 Optimizing Cpu Scheduling For Deep Learning And Llm Inference Using Onnx Runtime, Mohammod Akib Khan
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.
Grp-1231 Evaluating Generalization And Adaptation Of Learning-Based Schedulers For Directed Acyclic Graph Workloads, Rabia Rabia
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 …
Grp-1230 Environmental Protection: Development Of A Real-Time Multi-Stream Water Quality Monitoring System, Faruk Muritala
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 …
Grp-1203 Market-Driven Endurance Credits For Page Retention (Mecr) In Hybrid Dram–Nvm Systems, A E M Ridwan
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.
Grp-1190 Hybrid Virtualization Performance Modeling Using Monte Carlo Simulation, Amatul Akhi
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 …
Grp-1184 Edge-Llm Anomaly Detection On Raspberry Pi: Syscall Dataset Collection And Prototype Llm Explanation Layer, Shiva Shrestha, Shiva Shrestha
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.
Grp-1141 Decentralized Scheduling And Memory Management In A Simulated Multikernel Os Environment, Md Jahirul Islam
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.
Uc-1152 Sustainsync, Youssef El-Shaer, Zaid Khan
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.
Ur-0227 Compilation Of Binary Neurons To Obdds, Aidan Boyce
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 …
Ur-0199 Blood Flow Simulation From Coronary Computed Tomography Angiography Using Vnet, Pedro Pinto
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) …
Ur-1256 Realistic Paint-Like Color Mixing In Gimp, Calvin Crose, Cassidie Grogan, Dawson White, Joey Montalvo
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.
Ur-1202 Generative Ai & Cybersecurity, Emmanuel Okafor, David Nguyen, Alex Lu
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.
Ur-1195 Using Machine Learning To Diagnose Alzheimer's, Julia Johnson, Jordan Rainford
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 …
Ur-1167 Owlexchange, Laura Chaplin, Caitlin Johnson, Alexin D'Haiti, Jamaul Gordon, Leonardo Barranco
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, …
Ur-1163 Editing Films And Movies On Gimp, Ahmed Al Saad, William Grace, Brian Robinson
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.