Computer Sciences Commons^™

Scalable Graph Indexing Using Gpus For Approximate Nearest Neighbor Search, Zhonggen Li, Xiangyu Ke, Yifan Zhu, Bocheng Yu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficiently, while graph-based indexes have gained prominence due to their high accuracy and efficiency. However, the indexing overhead of graph-based indexes remains substantial. With exponential growth in data volume and increasing demands for dynamic index adjustments, this overhead continues to escalate, posing a critical challenge.In this paper, we introduce Tagore, a fasT library accelerated by GPUs for graph indexing, which has powerful capabilities of constructing refinement-based graph indexes such as NSG and Vamana. We first introduce …

Navigating Ai-Nature Frictions: Autonomous Vehicle Testing And Nature-Based Constraints, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

In cities, the application of Artificial Intelligence (AI) is being directed towards transforming different aspects of urban life. These applications take material form in urban spaces, with autonomous vehicles (AVs) providing a prominent example. AI systems rely on large volumes of data on their surroundings to refine the algorithms and enhance the accuracy of prediction for operational efficiency and safety. However, such algorithmic learning and execution can present challenges when dealing with the unpredictable, complex, and dynamic aspects of urban spaces. Nature is a paradigmatic example of such unpredictability, because natural phenomena usually defy consistent patterns and precise data-based modelling. …

Attachment To Artificial Intelligence: Development Of The Ai Attachment Scale, Construct Validation, And The Psychological Mechanisms Of Human-Ai Attachment, K Tennakoon Appuhamillage Sandeeshwara Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

Artificial intelligence (AI) systems are increasingly integrated into daily life, not only as tools but also as social partners that people may turn to for interaction and support. This raises important questions about whether, how, and why individuals form attachment-like bonds with AI, and the psychological implications of such attachments. Across five studies involving 1259 unique participants from Singapore and the U.S., the current work developed and validated the 15-item AI Attachment Scale and investigated the dispositional and motivational factors associated with attachment to AI, as well as its emotional and social outcomes. The AI Attachment Scale displayed strong psychometric …

Investigating The Efficiency Of Ingan P-N-P-N Homojunction Solar Cells, Moath Alhejji, Mohammad Alavijeh, Jacob Kupernik, Mirsaeid Sarollahi, Abbas Jammali, Seyed Taghavi, Reem Alhelais, Md Hel Uddin Maruf, Morgan Ware

Electrical Engineering and Computer Science Faculty Publications and Presentations

This research investigates the development of a novel p-n-p-n homostructure solar cell, through semiconductor simulations using the Nextnano software. InGaN was used as a model system in order to achieve a bandgap with optimized efficiency for a p-n homojunction solar cell. By increasing the uniform doping concentration from 1.5*10(16) cm(-3) to 1.5*10(17) cm(-3), the open circuit voltage (V-oc) increased while the short-circuit current density (J(sc)) decreased, as expected in simple p-n junctions. The p-n-p-n structure achieved a peak efficiency of 32.91% at a doping level of 6.5*10(16) cm(-3), a similar to 7% improvement over a conventional p-n junction's 25.31% efficiency …

Qualitative Stereo Vision Using Distributed Extended Waltz Filtering, Ben Mathew

Theses and Dissertations

Stereo vision is a fundamental problem in computer vision, aimed at reconstructing three-dimensional scene structure from two or more two-dimensional images. Traditional stereo algorithms rely on quantitative disparity estimation, often constrained by calibration precision, lighting variations, and surface texture. In contrast, our proposed Qualitative Stereo Vision seeks to understand depth relationships and spatial configurations from multiple planar views through symbolic reasoning and constraint satisfaction, offering a more flexible and cognitively plausible approach to scene interpretation.

This dissertation presents a novel framework called Distributed Extended Waltz Filtering, designed to provide qualitative stereo vision, particularly in the presence of occlusions—a persistent challenge …

Ai-Based Mapping Of Offshore Wind Energy Around The Korean Peninsula Using Sentinel-1 Sar And Numerical Weather Prediction Data, Jason Sung-Uk Joh, Son V. Nghiem, Menas Kafatos, Jay Liu, Jinsoo Kim, Seung Hee Kim

Institute for ECHO Articles and Research

Offshore wind farm projects are being promoted in the seas surrounding the Korean Peninsula to secure renewable energy. To support site selection, offshore wind resource maps were generated using deep neural networks trained on Sentinel-1 SAR imagery, numerical weather prediction data, offshore wind observations, sea surface temperature, and bathymetry. The deep neural network (DNN) framework consisted of six sub-models targeting eastward and northward wind components across three regions—the Yellow Sea, Korea Strait, and East Sea—to account for spatial heterogeneity. The proposed models outperformed existing approaches, achieving mean absolute errors (MAE) ranging from 1.31 to 1.69 m/s and correlation coefficients (CC) …

From Digital Divide To Equity-Enhancing Diffusion: Generative Ai And Writing Quality, Rebecca Tukachinsky Forster, Kerk Kee, Gabriel Miao Li

Communication Faculty Articles and Research

This study investigates whether generative AI can narrow the gap between stronger and developing writers and explores the mechanisms underlying these effects. In a within-subject experiment, students wrote two essays, with and without AI assistance. Computer-aided analysis of the writing quality confirmed that while all students benefited from AI, that less skillful writers gained more. There was also no evidence of skillful writers using AI in more sophisticated and beneficial ways. The study contributes to theorizing the digital divide and offers insights into maximizing the benefits of AI tools. Theoretically, we situate generative-AI use within Diffusion of Innovations, treating ChatGPT …

Benchmarking Dna Foundation Models For Genomic And Genetic Tasks, Haonan Feng, Lang Wu, Bingxin Zhao, Chad Huff, Jianjun Zhang, Jia Wu, Lifeng Lin, Peng Wei, Chong Wu

School of Medicine Faculty Publications

The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a comprehensive, unbiased benchmark of five models (DNABERT-2, Nucleotide Transformer V2, HyenaDNA, Caduceus-Ph, and GROVER) across diverse genomic and genetic tasks including sequence classification, gene expression prediction, variant effect quantification, and topologically associating domain (TAD) region recognition, using zero-shot embeddings. Our analysis reveals that mean token embedding consistently and significantly improves sequence classification performance, outperforming other pooling strategies. Model performance varies among tasks and datasets; while general purpose DNA foundation models showed competitive performance in pathogenic variant identification, they were less …

Evaluating Large Language Models For Automated Cv Ranking: A Hybrid Embedding Approach For Enhanced Recruitment, Sarah Mohamed Alhindaassi

Thesis/ Dissertation Defenses

Increasing numbers of applications have revealed limitations in legacy keyword-filtering-based Applicant Tracking Systems (ATS), which commonly overlook candidate potential and ignore contextual or transferable skills. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) offer an exhilarating alternative, supporting context-sensitive and human-crafted reasoning in candidate evaluation. This thesis systematically evaluates four classes of approaches, lexical models, embedding-based methods, Large Language Models (LLMs), and hybrid ensembles, for automation of Curriculum Vitae (CV) to Job Description (JD) matching without exploiting prior annotations or annotations at match time. Using a combination of publicly available datasets and real-world sample data covering three …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Performance Analysis Of Ris-Empowered Ofdm-Im Communications Under Weibull Fading And Joint Tx/Rx I/Q Imbalance, Büşra Ceni̇kli̇oğlu, İbrahi̇m Develi̇, Ayşe Eli̇f Canbi̇len

Turkish Journal of Electrical Engineering and Computer Sciences

A modernist technique, reconfigurable intelligent surface (RIS) provides outstanding signal reflection and amplification, making it highly valuable for upcoming communication systems. Besides, a major contributor is index modulation (IM), attaining superior spectral and energy efficiency, and achieving hardware sufficiency. The primary and novel contribution of this work is the derivation of a highly accurate, closed-form approximate expression for the average bit error rate (ABER) of an orthogonal frequency division multiplexing (OFDM)-IM system operating in the complex and challenging environment characterized by joint transmitter/receiver (Tx/Rx) in-phase and quadrature phase imbalance (IQI) and Weibull fading. This essential analytical achievement is facilitated by …

Fraud Detection And Explanation In Medical Claims Using Gnn Architectures, Reem Muhammad, Dina Tbaishat, Amril Nazir, Seif Yacoub, Mustafa Abdulrazek, Mohamed Ahmed Abo El-Enen, Ahmed T. Sahlol

All Works

This paper addresses the critical challenge of fraud detection in medical insurance claims-a pervasive issue causing significant financial losses in healthcare-using Graph Neural Networks (GNNs). Given the intricate nature of healthcare data, traditional fraud detection methods do not inherently capture the complex relationships and patterns among different entities. We explore the potential of GNNs to effectively identify fraudulent claims by modeling the interactions among various entities-such as patients, healthcare providers, diagnoses, and services-as a heterogeneous graph. We employ two state-of-the-art heterogeneous GNN architectures, HINormer (Heterogeneous Information Network Transformer) and HybridGNN, along with a modified homogeneous GNN, RE-GraphSAGE (GraphSAGE Graph Sample …

Exploring The Link Between Emotional States And Coding Task Quality: A Pilot Study, Aquib Reshad, Valentina Nino, Maria Valero, Adriane Randolph, Yang Shi

Faculty Articles

Emotions play a crucial role in shaping cognitive performance, yet their influence on programing remains understudied. This pilot study investigates the relationship between emotional states and coding task quality. Ten participants completed a programing task while their brain activity was recorded using electroencephalography (EEG), with frontal alpha asymmetry (FAI) applied as a neural marker of emotional valence. Emotional self-reports were collected using the Scale of Positive and Negative Experience (SPANE), and coding quality was evaluated through a structured rubric. Preliminary findings indicate a potential association between FAI and coding performance, whereas self-reported affect showed weaker or inconsistent patterns. Given the …

A Feature-Free Deep Learning Approach For Midair Hand Gesture Recognition From Surface Electromyogram (Semg) Data, Yasir Altaf, Abdul Wahid, Mudasir Manzoor Kirmani

Turkish Journal of Electrical Engineering and Computer Sciences

Midair hand gesture recognition plays a crucial role in applications such as sign language recognition and human-computer interaction, particularly for supporting individuals with partial or complete hearing loss. However, recognizing gestures in midair remains challenging due to the rapid and complex nature of hand movements. To address this, noninvasive techniques like surface electromyography (sEMG)—which captures muscle activity through sensors placed on the skin—have gained attention. sEMG provides rich time-series data that reflect both spatial and temporal muscle dynamics. In this study, we propose a deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify …

Integrated Log Spectrogram Convolutional Neural Network (Ils-Cnn) For Robust Spoken Digit Recognition, Awais Ahmed

Turkish Journal of Electrical Engineering and Computer Sciences

Spoken digit recognition (SDR), a type of supervised automatic speech recognition, is essential for various human-machine interaction applications, including banking operations, dialing systems, price extraction, and airline reservation systems. However, designing an effective SDR system presents several challenges, such as developing labeled audio data, selecting appropriate feature extraction methods, and creating high-performance models. To overcome these challenges, a novel approach for robust spoken digit recognition using an integrated log spectrogram convolutional neural network (ILS-CNN) has been proposed. The proposed work presents an efficient SDR method by taking advantage of a log spectrogram layer directly within the neural network to enhance …

Railway Track Condition Monitoring Based On Sensor Data And Artificial Neural Networks, Ivan Kots, Alina Isaeva, Mark Denisenko, Alexander Sinyukin, Andrey Kovalev

Turkish Journal of Electrical Engineering and Computer Sciences

Monitoring the condition of engineering objects is one of the urgent tasks of industry, construction, and transport infrastructure. This article describes a system for condition monitoring and diagnostics of rail tracks in real time. Compared with other similar studies, the proposed system has the advantages of compactness, usability, scalability and versatility of application. The proposed monitoring system is based on an Nvidia Jetson Nano embedded computing board and also includes inertial sensor modules, a microphone, a geolocation module, communication modules, an SSD storage device, and a battery. The prototype of the diagnostic module is a portable device that can be …

Modeling And Simulation Of Dynamic Energy Management Systems For Smart Buildings, Onur Özel, Ali̇ Rifat Boynueğri̇, Hayri̇ Yi̇ği̇t, Burak Tekgün

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both …

Grey Wolf Optimization Of Pi Controller For Power Management In Wind Farms: A Novel Approach, Anis Feddaoui, Lotfi Farah, Abdelouahab Benretem, Mohammed Abdeldjalil Djehaf

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel power management strategy for wind farms using a grey wolf optimization (GWO)-based PI controller. The method aims to enhance active and reactive power control in systems employing dou bly fed induction generators. Three control strategies are evaluated—namely, a classical frequency-domain PI controller, an Artificial Neural Network (ANN)-based controller, and the proposed GWO-based PI controller—the last of which represents the main contribution. The classical PI and ANN controllers are included strictly for comparative bench marking. MATLAB simulations demonstrate that the GWO-beased PI controller offers superior dynamic performance, particularly in settling time and overshoot reduction. A power …

Fpga-Based Takagi-Sugeno Fuzzy Controller For Quadrotor Uav Stabilization And Trajectory Tracking, Hocine Khati, Mohamed Amine Nehmar, Arezki Fekik, Mohand Achour Touat, Hand Talem, Rabah Mellah

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the implementation of a fuzzy logic–based control system on a field-programmable gate array (FPGA) for a quadrotor autonomous aerial vehicle (UAV). The objective is to design and integrate six Takagi–Sugeno fuzzy controllers to regulate roll, pitch, and yaw angles, along with longitudinal, latitudinal, and altitude movements, thereby stabilizing the UAV and enabling it to follow a desired trajectory. Due to the computational complexity of the six controllers, achieving the desired performance requires considerable processing time, which can adversely affect the quadrotor’s mission. Owing to their high processing power and operating frequency, FPGAs enable the control algorithm to …

Gc-0258 Safecircle:​ Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd​, Awan-Ur- Rahman, Soarov Borty, Shakib Quddus

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) is an irreversible and degenerative neurological condition that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

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

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

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

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

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

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

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

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

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

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 …