Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

2025

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 1681 - 1710 of 3497

Full-Text Articles in Computer Sciences

Memory-Augmented Llm Agent For Predicting Locomotion Modes In Construction Activities, Ehsan Ahmadi May 2025

Memory-Augmented Llm Agent For Predicting Locomotion Modes In Construction Activities, Ehsan Ahmadi

LSU Doctoral Dissertations

The construction industry faces significant challenges, including labor shortages, high physical demands, and safety risks, necessitating advanced assistive technologies like exoskeletons to enhance worker efficiency and reduce injuries. However, effective exoskeleton control in dynamic construction environments requires accurate locomotion prediction, a task complicated by the diversity of activities and reliance on supervised learning methods that struggle to generalize. This study investigates a multimodal approach to locomotion prediction, leveraging speech commands and visual data from smart glasses to enable adaptive and safe human-exoskeleton interaction. The research unfolds in two stages: the first develops a framework to evaluate the zero-shot capability and …

Go to article

System Administration Practices And Experimentation, Nicholas Z. Young May 2025

System Administration Practices And Experimentation, Nicholas Z. Young

Honors Program Theses and Projects

This undergraduate departmental honors capstone project experiments with and demonstrates System Administration practices that are used in enterprise environments. The skills and practices of System Administrators are crucial to maintain large-scale IT infrastructure. This project aimed to gain a deeper, practical understanding of the role of a System Administrator in an emulated environment. Through hands-on experimentation, this project addressed the responsibilities of a System Administrator, such as controlling user access, adding hardware, automating tasks, monitoring systems, overseeing and developing a backup strategy, maintaining local documentation, and security practices. This project demonstrated some of the complexities that lie in each of …

Go to article

Computational Thinking, Informal Learning, And Makerspace, Redar Ismail May 2025

Computational Thinking, Informal Learning, And Makerspace, Redar Ismail

College of Computing and Digital Media Dissertations

The continuous advancement of technology has made it a crucial tool across various disciplines. As adaptation to this rapidly progressing field occurred, teaching and learning problem-solving skills are more essential than ever for empowering individuals to succeed across diverse fields. Studies have shown that engaging K-12 students in activities encouraging science, technology, engineering, mathematics (STEM), and computational thinking (CT) are critical for teaching them how to deal with complex problems (Rode, Barkhuus, & Ioannou, 2024; Shu & Huang, 2021). Makerspaces and making activities became popular among researchers and educators due to their potential to advance learning, enhance problem-solving skills, and …

Go to article

Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher May 2025

Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). …

Go to article

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam May 2025

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam

Engineering Faculty Articles and Research

Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …

Go to article

Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi May 2025

Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi

SKMC Student Presentations and Publications

Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. …

Go to article

Efficient Eeg Epilepsy Classification And Feature Selections Based On Hellinger Distance, Muhammed Sadiq May 2025

Efficient Eeg Epilepsy Classification And Feature Selections Based On Hellinger Distance, Muhammed Sadiq

Theses and Dissertations

Accurate and efficient detection of epileptic seizures from EEG signals remains a critical challenge due to high-dimensional data, class imbalance, and the limitations of standard classifiers. This thesis introduces two novel models to address these challenges. The first model presents a new classifier based on the Hellinger Distance, specifically designed to enhance discriminative capability and robustness against imbalanced datasets. By integrating the Hellinger Distance Classifier with Particle Swarm Optimization (PSO) for feature selection, this model significantly improves classification performance while reducing computational complexity. Experimental evaluations on the Bonn dataset demonstrate an accuracy of 96.25%, an F1-score of 97.74%, a recall …

Go to article

Applications Of The Mathieu Groups And Information Theory In Dna Encoding Functions, Juan C. Nava Jr May 2025

Applications Of The Mathieu Groups And Information Theory In Dna Encoding Functions, Juan C. Nava Jr

Theses and Dissertations

A foundational idea in mathematics lies in breaking down existing components into their bare fundamentals. As evidenced by prime numbers and composites, we learn this idea at an early age. Categorizing these broken-down components into their simplest form allows mathematicians to construct proofs from emergent patterns. John Conway’s Atlas of Finite Groups in the 1990s was particularly concerned with the categorization of structures known as groups. There are certain axioms a group must adhere to, which amount to the retention of symmetry; ultimately a group helps us to better understand symmetric actions performed on a set with a binary operation. …

Go to article

Gnns For Network Classification In Single Cell Rna Sequencing Data, Reid C. Sewell May 2025

Gnns For Network Classification In Single Cell Rna Sequencing Data, Reid C. Sewell

Capstone Projects

A common technique when investigating a disease is to profile gene expression, as this gives unique insights into the functions of a cell. Gene expression data gathered from single cell RNA sequencing can be encoded into a gene co-expression network, which is a graph of potential relationships between different genes. One method for interpreting data encoded as a graph is to use a graph neural network, or GNN. This project designs and implements a GNN architecture to accomplish classification tasks on graph data. Then, given a dataset of gene co-expression networks made from multiple single cell RNA sequencing studies, the …

Go to article

Deep Learning Classification Of Drainage Crossings Based On High-Resolution Dem-Derived Geomorphological Information, Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang May 2025

Deep Learning Classification Of Drainage Crossings Based On High-Resolution Dem-Derived Geomorphological Information, Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-resolution digital elevation models (HRDEMs) from LiDAR and InSAR technologies have significantly improved the accuracies of mapping hydrographic features such as river boundaries, streamlines, and waterbodies over large areas. However, drainage crossings that facilitate the passage of drainage flows beneath roads are not often represented in HRDEMs, resulting in erratic or distorted hydrographic features. At present, drainage crossing datasets are largely missing or available with variable quality. While previous studies have investigated basic convolutional neural network (CNN) models for drainage crossing characterization, it remains unclear if advanced deep learning models will improve the accuracy of drainage crossing classification. Although HRDEM-derived …

Go to article

Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan May 2025

Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan

Computer Science Faculty Research & Creative Works

As Artificial Intelligence (AI) systems increasingly underpin critical applications, from autonomous vehicles to biometric authentication, their vulnerability to transferable attacks presents a growing concern. These attacks, designed to generalize across instances, domains, models, tasks, modalities, or even hardware platforms, pose severe risks to security, privacy, and system integrity. This survey delivers the first comprehensive review of transferable attacks across seven major categories, including evasion, backdoor, data poisoning, model stealing, model inversion, membership inference, and side-channel attacks. We introduce a unified six-dimensional taxonomy: cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-hardware, which systematically captures the diverse transfer pathways of adversarial strategies. Through …

Go to article

The Evolution And Impact Of Blog Analysis Tools: A Study Of Blogtracker's Comprehensive Approach To Digital Discourse Analysis, Oyindamola Koleoso May 2025

The Evolution And Impact Of Blog Analysis Tools: A Study Of Blogtracker's Comprehensive Approach To Digital Discourse Analysis, Oyindamola Koleoso

Theses and Dissertations

This study presents BlogTracker, a comprehensive web-based platform designed to address the growing complexities of analyzing the modern blogosphere. We detail BlogTracker's evolution from earlier blog analysis tools, highlighting its innovative integration of features including real-time data collection, advanced content analysis, sentiment analysis, influence tracking, and narrative analysis. At the core of our contribution is a robust content extraction system that achieves 91.33% accuracy across diverse blog formats, providing a reliable foundation for all analytical functions. This extraction system effectively distinguishes between primary content and peripheral elements, ensuring high-quality inputs for downstream analysis regardless of source blog structure. The platform's …

Go to article

Hierarchical Log Bayesian Neural Network For Enhanced Aorta Segmentation, Delin An, Pan Du, Pengfei Gu, Jian-Xun Wang, Chaoli Wang May 2025

Hierarchical Log Bayesian Neural Network For Enhanced Aorta Segmentation, Delin An, Pan Du, Pengfei Gu, Jian-Xun Wang, Chaoli Wang

Computer Science Faculty Publications

Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segmentation, they remain challenging due to the intricate multiscale structure and the complexity of the surrounding tissues. This paper presents a novel approach for enhancing aorta segmentation using a Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model. Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the former provides an initial aorta segmentation, and the latter enhances blood vessel detection across varying scales by learning suitable LoG kernels, enabling self-adaptive handling …

Go to article

Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri May 2025

Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri

McKelvey School of Engineering Graduate Student Theses & Dissertations

Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be …

Go to article

Modeling Cross-Platform Narrative Templates: A Temporal Knowledge Graph Approach, Ridwan Amure May 2025

Modeling Cross-Platform Narrative Templates: A Temporal Knowledge Graph Approach, Ridwan Amure

Theses and Dissertations

Over the past decade, social media platforms have rapidly evolved in scale, functionality, and user engagement, encouraging individuals to maintain active presences across multiple networks. This complex, interconnected ecosystem has also enabled information actors to exploit cross-platform dynamics to amplify the reach of their content and strategically target diverse audiences. Recognizing the persistence and adaptability of such actors, this research emphasizes the need for robust models that can effectively capture and analyze cross-platform narrative diffusion. To this end, we propose a framework that utilizes temporal knowledge graphs to model the evolution and relationships among narratives across platforms. We extract temporal …

Go to article

Should Physicians Take The Rap? Normative Analysis Of Clinician Perspectives On Responsible Use Of 'Black Box' Ai Tools, Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby May 2025

Should Physicians Take The Rap? Normative Analysis Of Clinician Perspectives On Responsible Use Of 'Black Box' Ai Tools, Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

Background: Increasing interest in deploying artificial intelligence tools in clinical contexts has raised several ethical questions of both normative and empirical interest. One such question in the literature is whether "responsibility gaps" (r-gaps) are created when clinicians utilize or rely on such tools for providing care, and if so, what to do about them. These gaps are particularly likely to arise when using opaque, "black box" AI tools. Compared to normative and legal analysis of AI-generated responsibility gaps in health care, little is known, empirically, about health care providers views on this issue. The present study examines clinician perspectives on …

Go to article

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 …

Go to article

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 …

Go to article

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, …

Go to article

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 …

Go to article

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.

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

First Prev Next Last