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Articles 3811 - 3840 of 63010

Full-Text Articles in Computer Sciences

Cyber-Physical Security Through The Lens Of Ai-Enabled Systems, Zhiyuan Yu May 2025

Cyber-Physical Security Through The Lens Of Ai-Enabled Systems, Zhiyuan Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A unique aspect of these systems lies in their interactions with the physical world, by perceiving environments through heterogeneous modalities (perception), processing digital data with human-in-the-loop intelligence algorithms (computing), and autonomously actuating controls that affect physical processes (actuation). While this intricate fusion of cyber and physical components has unlocked unprecedented capabilities, it has also introduced new security challenges. However, traditional security measures often fall short in addressing these multifaceted threats. This …

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Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu May 2025

Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu

Engineering Faculty Articles and Research

Background: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for …

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Computational Modeling And Structural Generation Of Piano Music In The Classical Style, Yijing Feng May 2025

Computational Modeling And Structural Generation Of Piano Music In The Classical Style, Yijing Feng

Dartmouth College Ph.D Dissertations

Listening to fast-tempo piano sonatas of the Classical period (circa 1750-1820) has been shown to have therapeutic effects for neurological disorders such as epilepsy. The limited existing repertoire of music in this style motivates the creation of more long-form, coherent compositions with clearly defined structure. Despite the long history of computer-based music generation and recent progress in deep learning, particularly transformer-based models, generating structurally coherent long-form music remains a major challenge. This difficulty stems from the scarcity of reliable structural annotation datasets, the computational demands of modeling very long musical sequences, and the lack of effective structural encoding in both …

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Machine Learning - Driven Solar Forecasting In Dust-Prone Regions For Sustainable Energy Systems, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan May 2025

Machine Learning - Driven Solar Forecasting In Dust-Prone Regions For Sustainable Energy Systems, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan

All Works

This research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during such conditions. To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. The models were evaluated across multiple forecasti24 hoursons (1, 6, 12, and 24 hours), demonstrating that including dust-related variables significantly enhances prediction accuracy, particularly for short-term forecasts. Temporal and seasonal analyses revealed that dust events, …

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Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi May 2025

Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi

Open Educational Resources

This open-access machine learning course is a comprehensive 15-week curriculum developed and published on GitHub with full Google Colab compatibility. It combines theoretical concepts with hands-on Python coding, real-world datasets, and structured projects covering regression, classification, clustering, deep learning, transformers, and multimodal AI. The course is designed for students, educators, and researchers interested in applied machine learning, including biomedical applications. It includes explainable AI components and ethical discussions to align with modern AI standards. The course is maintained by BioMind AI Lab at CUNY.

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Robotic Rhythm: A Contemporary Look At Ai-Driven Editing Tools’ Efficacy In Understanding And Replicating Creative Rhythm Editing Techniques., Alexander Selby-Lara, Charles Howard May 2025

Robotic Rhythm: A Contemporary Look At Ai-Driven Editing Tools’ Efficacy In Understanding And Replicating Creative Rhythm Editing Techniques., Alexander Selby-Lara, Charles Howard

Honors Thesis

It is undeniable that artificial intelligence (AI) has made its way into the film world. From AI-generated imagery and sound design in two 2025 Oscar nominees — The Brutalist and Emilia Pérez (Pulver) — to weekly updates and monthly beta releases of existing and new generative image and video models, AI-driven filmmaking tools are here to stay. But what does this mean for the post-production workflow? Much like camera development, editing technology has come a long way from flatbed film editors to Adobe Premiere Pro v. 25.0. Yet, despite each technological leap from system to system, the delicate task of …

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Towards The Next Generation Of Storage Stack For Nand Flash Memory-Based Systems, Ziyang Jiao May 2025

Towards The Next Generation Of Storage Stack For Nand Flash Memory-Based Systems, Ziyang Jiao

Dissertations - ALL

The explosive growth of data has led to increased attention on NAND flash-based solid-state drives (SSDs), which offer high performance, low power consumption, and significant capacity per unit volume when compared to traditional hard-disk drives (HDDs). However, as NAND flash memory density continues to scale, modern SSDs suffer from what is known as fail-slow symptoms, and their performance degrades over time as they wear out. In this dissertation, we focus on understanding and addressing the performance, reliability, and sustainability challenges of modern flash-based storage systems by modeling key metrics, analyzing the design tradeoffs between these metrics, and optimizing existing storage …

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Automated Flaw Discovery In Decentralized Systems Via Semantic Fuzzing Tools, Yibo Wang May 2025

Automated Flaw Discovery In Decentralized Systems Via Semantic Fuzzing Tools, Yibo Wang

Dissertations - ALL

This dissertation studies the security challenges of blockchain transaction processing before consensus. While prior work has focused on consensus protocols and smart contract bugs, the pre-consensus infrastructure, such as the mempool and off-chain batching, remain underexplored. This dissertation aims to bridge that gap by systematically analyzing both transaction processing in the mempool and off-chain transaction batching for cost-optimization. This dissertation focuses on two core contributions. First, it presents MPFUZZ, an automated software fuzzing tool designed to uncover denial-of-service vulnerabilities in Ethereum mempools. This work is the first to formally define the mempool fuzzing problem and introduce bug oracles that detect …

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Exploring Higher-Order Networks, Hao Tian May 2025

Exploring Higher-Order Networks, Hao Tian

Dissertations - ALL

Networks are natural representations of interactions in the real world (social networks, bio-networks, road networks, and the like) and are utilized across various disciplines. By default, network interactions are pairwise; in recent years, the demand for model ing higher-order interactions has kept increasing. For example, in collaboration networks, we aim to distinguish between one publication coauthored by three or three publications coauthored by two in a triangle. In this work, we perform higher-order network analysis in the following two directions. First, we explore the influence of higher-order structures on dyadic (pairwise) graphs; second, we model higher-order interactions by ordered hy …

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Harnessing Llms To Detect Hate Speech, Weibin Cai May 2025

Harnessing Llms To Detect Hate Speech, Weibin Cai

Theses - ALL

Hate is a sentiment, while hate speech refers to the expression of hate in a form that targets and attacks specific groups, such as race, religion, or gender. With the rise of the internet and social media, hate speech has spread rapidly, gaining wide exposure and posing threats to individual well-being, the profits of major tech companies, and social stability. As a result, both industry and academia have turned their attention to the study of hate speech. One of the most active areas is hate speech detection, which involves training models to predict whether a given piece of content is …

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Survey Of Symmetric Encryption Techniques Implemented Over Gpu Platforms, Hawraa Moussa, Ahmed Fanfakh May 2025

Survey Of Symmetric Encryption Techniques Implemented Over Gpu Platforms, Hawraa Moussa, Ahmed Fanfakh

Journal of Intelligent Informatics, Networking, and Cybersecurity

Encryption is one of the most important techniques used to deliver protection solutions. Symmetric encryption uses a single key for encryption and the decryption process. Symmetric encryption implements block encryption, replacement, and switching. Consequently, it is problematic if the private keys obtained from the protocols are frequent in certain states or exhibit reduced unpredictability. Several researchers applied the cryptography method in a parallel fashion to minimize the time needed to complete encrypting and decrypting data procedures. Numerous viable solutions have been found to increase the levels of encryption algorithm performance made by the researchers to use parallelism to boost their …

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Integration Of Zero Trust Architecture And Machine Learning For Improving The Security Of Software Defined Networking: A Review, Manar H. Bashaa, Wesam S. Bhaya, Nabeel H. Kaghed Al-Aaraji May 2025

Integration Of Zero Trust Architecture And Machine Learning For Improving The Security Of Software Defined Networking: A Review, Manar H. Bashaa, Wesam S. Bhaya, Nabeel H. Kaghed Al-Aaraji

Journal of Intelligent Informatics, Networking, and Cybersecurity

Many add new networks, but management has a lot of work to do as well. Software Defined Networking (SDN) was conceived to address these challenges in a more structured way, SDN allows centralized management of the network and provision of software based traffic understanding making it relatively easier to manage large scale networks. The downside to SDN is its vulnerability to cyber attacks. The more centralized the structure the more efficient it is, however the more specific weaknesses it possesses such as DoS attacks for example. The ``perimeter'' approach to security is outdated with today's security technology. Zero Trust Architecture …

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Phishy Pages - The Design Of A User-Interactive Website For Phishing Attack Evaluation, Evan C. Gregory May 2025

Phishy Pages - The Design Of A User-Interactive Website For Phishing Attack Evaluation, Evan C. Gregory

Honors Theses

Phishing attacks are a widespread, malicious phenomenon. These attacks steal people’s personal information, causing them ruin and lining the pockets of criminals. What makes them so dangerous is that they come in a variety of forms, including emails, websites, phone calls, and social media can be vectors for attackers. Fortunately, these attacks can be stopped by informing potential victims of common signs to look out for. Training is one of the best methods people use to teach web-users how to protect themselves. To train them, however, users must be taken through many examples of phishing attacks to learn the characteristics …

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Evolving Enemy Behavior In Video Games, Hermie H. Adams Iii May 2025

Evolving Enemy Behavior In Video Games, Hermie H. Adams Iii

Honors Theses

The video game I developed for my senior project lacked complex and engaging enemy artificial intelligence. The standard implementations of AI systems such as finite state machines and behavior trees felt like side-steps rather than innovative solutions. Upon seeing the 'magic' of machine learning in perfecting games such as Snake, Super Mario, and Flappy Bird, I was inspired to seek my answer in the field of evolutionary computation. However, my challenge differed in that the problem space would be defined by dynamic player strategies, making it not well-defined or static. As such, my evaluations are based on enemies exhibiting emergent …

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Bridging Cattle Farming And Technology: The Development Of Moomanager, Matthew Hayes May 2025

Bridging Cattle Farming And Technology: The Development Of Moomanager, Matthew Hayes

Honors College Theses

Small-scale cattle producers face persistent challenges in adopting digital tools for herd management, often due to barriers such as limited digital literacy, software complexity, and poor alignment with practical workflows. Existing literature highlights the potential benefits of mobile applications in agricultural contexts, yet adoption rates remain low among smaller operations. This thesis investigates how a streamlined, mobile-first application can address these adoption barriers while supporting essential farm management tasks. The study details the design and development of MooManager, a mobile application built with React Native and Supabase and structured around core features such as cattle tracking, beef sales logging, and …

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Scaling Quantum Systems: Quantum Networks, Distributed Quantum Computing, And Security, Zebo Yang May 2025

Scaling Quantum Systems: Quantum Networks, Distributed Quantum Computing, And Security, Zebo Yang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Recent advancements in quantum computing have opened up new possibilities across various fields, offering significant potential to enhance computation, communication, cryptography, and applications in areas like sensing, medicine, and chemistry. However, the capabilities of individual quantum devices are still limited, and scaling quantum hardware monolithically presents substantial challenges. Interconnecting quantum systems offers a promising alternative by aggregating capabilities across a network, thereby enabling quantum advantages for larger and more practical problems. This interconnection is achieved through quantum networking, which links external quantum systems, and Distributed Quantum Computing (DQC), which connects quantum processors within a system, such as in quantum data …

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Towards Secure And Privacy-Preserving Machine Learning Systems, Han Liu May 2025

Towards Secure And Privacy-Preserving Machine Learning Systems, Han Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

In recent years, machine learning (ML) has advanced at an unprecedented pace, driving the widespread adoption of increasingly sophisticated models across a broad range of real-world applications, including healthcare, finance, autonomous systems, and critical infrastructure. While these models have delivered remarkable benefits and transformed numerous industries, they remain inherently vulnerable to a variety of security and privacy threats. Given their growing role in safety-critical domains, ensuring their security and privacy has become imperative. Systematically addressing these vulnerabilities requires comprehensive adversarial analyses to uncover weaknesses and inform the design of robust defenses. This dissertation systematically investigates ML vulnerabilities in adversarial settings …

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Trustworthy Autonomy Through Robust Control And Alignment, Junlin Wu May 2025

Trustworthy Autonomy Through Robust Control And Alignment, Junlin Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

As artificial intelligence systems are increasingly applied in safety critical domains such as robotics, autonomous driving, and decision making under uncertainty, ensuring their trustworthiness has become a central challenge. This dissertation addresses two major facets of trustworthy AI: reliable control through formal guarantees and alignment against adversarial manipulation. The first part of the dissertation focuses on provably stable, robust, and safe control for nonlinear systems using learning based methods. We introduce the first general framework for synthesizing neural Lyapunov controllers in discrete time systems. This method combines a sound verifier based on mixed integer linear programming with gradient based counterexample …

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Understanding And Mitigating Timing Issues In Autonomous Systems, Ao Li May 2025

Understanding And Mitigating Timing Issues In Autonomous Systems, Ao Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Autonomous systems, such as self-driving cars and drones, have become a part of our daily lives. Since these systems operate in and interact with the physical world, their correctness depends on both functional and temporal aspects. However, the increasing complexity of modern computing hardware and software often leads to unpredictable timing behavior in these systems, making temporal properties particularly challenging to ensure. This dissertation proposes novel approaches to specify and enforce temporal properties based on a comprehensive empirical study of real-world issues. The first half of this dissertation presents an empirical study that dissects the timing issues. The dissection begins …

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Optimized Student Grouping For Enhanced Classroom Performance, Kathryn E. Reardon May 2025

Optimized Student Grouping For Enhanced Classroom Performance, Kathryn E. Reardon

Honors Theses

Effective grouping methods enhance classroom collaboration and allow for a student-centered teaching approach; however, traditional grouping methods are time-consuming, subjective, and can create inconsistent group dynamics. This project addresses these challenges by employing a data-driven approach to optimize student groups based on academic performance, behavior, attendance, language barriers, and teacher preferences. The minimum viable product is a web application with an algorithm-driven system to group students and a database storage for group results. During the initiation phase, a problem was defined with a proposed solution. During the planning phase, potential design choices and grouping methods were researched and assessed. During …

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Effective And Efficient Graph Foundation Model, Lecheng Kong May 2025

Effective And Efficient Graph Foundation Model, Lecheng Kong

McKelvey School of Engineering Graduate Student Theses & Dissertations

Graph data has emerged as a central component in numerous real-world applications, spanning recommender systems, drug discovery, social networking, and traffic forecasting. While traditional and modern graph learning techniques—ranging from graph kernels to Graph Neural Networks (GNNs) and graph transformers—have achieved significant success, their task-specific nature and reliance on supervised learning limit their adaptability to new, unseen tasks. This rigidity becomes especially problematic in dynamic environments where retraining for every new task is costly and often infeasible. Inspired by the transformative impact of foundation models in natural language processing, this thesis explores the feasibility of developing a graph foundation model—a …

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System Security Foundations For Ai-Enabled Systems, Yuhao Wu May 2025

System Security Foundations For Ai-Enabled Systems, Yuhao Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

AI is being deployed broadly, from conventional computing systems like IoT systems to more advanced agentic systems. It is shifting from being a specialized component responsible for specific functions to becoming the core of agentic systems, where it drives autonomous decision-making and task execution. These AI-enabled systems bring tremendous benefits. For example, large language models can interpret user intent, select appropriate tools, and access data to complete tasks with minimal human guidance. However, they also introduce new security, privacy, and safety risks. These risks arise not only from the models themselves but also from the broader system design and integration. …

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Magic: The Gathering Deck Testing And Optimization, Ian B. Watson May 2025

Magic: The Gathering Deck Testing And Optimization, Ian B. Watson

Honors Theses

The goal of this project is to provide a tool for players of the trading card game Magic: the Gathering to determine whether or not a given deck is optimally built by outputting relevant information regarding its optimality. This is done through a simulator that plays a one-sided game, recording what cards are played, what turn they are played, and how much mana is left over at the end of every turn. The simulator was tested with both optimized and unoptimized decks to show how it can be used to diagnose both.

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Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez May 2025

Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez

Physical Therapy Faculty Articles and Research

Gait impairment post-stroke is highly heterogeneous. Prior studies classified heterogeneous gait patterns into subgroups using peak kinematics, kinetics, or spatiotemporal variables. A limitation of this approach is the need to select discrete features in the gait cycle. Using continuous gait cycle data, we accounted for differences in magnitude and timing of kinematics. Here, we propose a machine-learning pipeline combining supervised and unsupervised learning. We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. Then, we used unsupervised time-series k-means and Gaussian Mixture Models to identify gait clusters. We tested …

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Automation Of Javanese Shadow Puppets Using Machine Control, Kristian Rice, Yinson Tso, Mukhammadali Yuldoshev May 2025

Automation Of Javanese Shadow Puppets Using Machine Control, Kristian Rice, Yinson Tso, Mukhammadali Yuldoshev

Publications and Research

The virtualization of Javanese shadow puppetry (Wayang Kulit) offers a unique opportunity to preserve and revitalize traditional performance art through immersive digital platforms. This project explores the development of a virtual Wayang Kulit experience using real-time 3D engines like Unity/Unreal Engine while focusing on simulating the mechanics and aesthetics of shadow puppet performance. The puppets are designed using detailed 2D planes and rigged with skeletal systems to reflect the stylized motion of traditional puppetry. An aspect of this project is integrating an AI-driven control system that autonomously animates the puppets, learning from recorded puppeteer performances to replicate gesture, rhythm, and …

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Reverse Engineering Of Binary Programs Using Graph Attention Networks, Sai Nikhila Kanigiri May 2025

Reverse Engineering Of Binary Programs Using Graph Attention Networks, Sai Nikhila Kanigiri

Master's Theses

Understanding the functionality and behavior of binary code is essential for many software engineering tasks, including malware analysis, vulnerability detection, and program optimization. However, automating this process is challenging due to the complexity of machine code and the significant manual effort required from experienced software engineers. In this paper, we present BinGAT (Reverse Engineering of Binary Programs using Graph Attention Networks), a method for classifying binary programs into algorithmic categories using Graph Attention Neural Networks (GNNs) based on their Control-Flow Graphs (CFGs). Given a binary program, BinGAT extracts its CFG through static analysis and transforms the assembly instructions within each …

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Computational Design Of Potent Sirna For Braf Oncogene Silencing For Enhancing Cancer Therapy, Muhammad Hermawan Widyananda, Ricadonna Raissa May 2025

Computational Design Of Potent Sirna For Braf Oncogene Silencing For Enhancing Cancer Therapy, Muhammad Hermawan Widyananda, Ricadonna Raissa

Karbala International Journal of Modern Science

The discovery of oncogenic BRAF mutations has prompted the development of inhibitors, yet resistance remains widespread. A more effective strategy involves targeting BRAF mRNA with siRNA to overcome resistance to BRAF inhibitors. This study aims to design potent siRNA for BRAF oncogene silencing using a computational approach. The full coding sequence of BRAF was retrieved from the NCBI database and potential siRNAs were predicted using the Ui-Tei, Reynolds, and Amarzguioui rules. Identified siRNAs were further analyzed using various prediction systems and parameters, including their interaction with the hAgo2 protein. The results identified that seven siRNAs (siRNA 23, siRNA 24, siRNA …

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Algorithmically Optimal Outer Measures, J. H. Lutz, Neil Lutz May 2025

Algorithmically Optimal Outer Measures, J. H. Lutz, Neil Lutz

Computer Science Faculty Works

We investigate the relationship between algorithmic fractal dimensions and the classical local fractal dimensions of outer measures in Euclidean spaces. We introduce global and local optimality conditions for lower semicomputable outer measures. We prove that globally optimal outer measures exist. Our main theorem states that the classical local fractal dimensions of any locally optimal outer measure coincide exactly with the algorithmic fractal dimensions. Our proof uses an especially convenient locally optimal outer measure κ defined in terms of Kolmogorov complexity. We discuss implications for point-to-set principles.

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Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad May 2025

Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad

Student Scholar Symposium Abstracts and Posters

Developing an affordable and STEM learning-focused Braille display addresses a significant disparity in the market for Braille displays, where most fail to provide a cost-effective, accessible, and education-oriented solution. This research aims to bridge this gap through innovative hardware and software development, offering a comprehensive learning experience to elementary school children (K-6) who are blind/visually impaired. The hardware features a piezo-electric tactile display that displays up to six Braille characters at once or a shape in an 8x8 pin array configuration. The educational software includes a user-friendly website packed with engaging STEM activities specifically designed for blind/visually impaired children. The …

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Exploring Human-Centered Principles To Improve Lecture Slides, Joshua Harlev, Louanne Boyd May 2025

Exploring Human-Centered Principles To Improve Lecture Slides, Joshua Harlev, Louanne Boyd

Student Scholar Symposium Abstracts and Posters

As a university emphasizing student participation and attention, lectures are central to the educational experience at Chapman. However, slide-based lectures can widely vary in quality and approach. Considering various debunkings of learning styles over the years, it seems more difficult than ever to understand how to most effectively teach students. Effective use of computerized tools has thus become increasingly important as the pace of change continues to increase. Yet, research is available on the effectiveness of teaching methods that can provide guidelines for instructors. With increasingly connected and capable devices at our fingertips, it is imperative to both use the …

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