Computer Sciences Commons^™

Bucket-Based Priority Queues For A* And Related Bounded-Suboptimal And Anytime Search Algorithms: Theoretical And Practical Advancements, Garrett Michael Fereday

Theses and Dissertations

For shortest-path problems with a small number of integer transition costs, it is well-known that the performance of the classic A* algorithm can be improved by using bucketing to reduce priority queue overhead—in particular, by using a bucket queue data structure for the priority queue, instead of a binary heap. This dissertation describes several theoretical and practical extensions of this approach. First, the traditional two-level bucket queue data structure is modified in simple ways to improve the worst-case complexity of its operations, which leads to the first demonstration that the priority queue operations of a two-level bucket queue for A* …

Establishing A Baseline For Detecting Lotl Attacks In Windows Operating Systems, Ashlyn Martin Phillips

Theses and Dissertations

There has been an increasing realization of the rise in living off the land (LOTL) attacks where adversaries misuse legitimate system tools, particularly with state-sponsored actors targeting critical infrastructure in the United States. These attacks are difficult to detect because they allow attackers to remain present in a system without the user’s knowledge for an extended period. This thesis establishes an initial baseline specifically for Windows operating systems to measure normal system activity, focusing on CPU usage, memory utilization, and process activity. It particularly examines the use of PowerShell alongside other applications. The findings from this baseline are used to …

Application Of Pu Learning In Detection Of Ddos Attacks, Gagana Sathya Narayana Prasad

Theses and Dissertations

The gcore radar 2024 says, the number of DDoS attacks has been increased by 46% in 12 months. Supervised and unsupervised techniques struggle detecting DDoS attacks due to the scarcity of labeled attack samples and an overwhelming presence of benign traffic. In contrast PU- Learning offers a promising solutions by dividing the data into positive and unlabeled data. This study explores the effectiveness of PU-learning in detecting DDoS attacks by comparing it with unsupervised methods. This method employs PU Bagging, Two Step method and auto-encoder based models to extract meaningful patters from network traffic data, utilizing CICDDoS2017 dataset for evaluation. …

From Data To Action: An Adaptable Crosstabs Template For Participatory Survey Data Analysis, Natalia Pinzon, Vikram Koundinya, William O'R Dowling, Ryan Galt

Journal of Extension

We present a practical and accessible template for quantitative survey data analysis designed for non-academic researchers in order to facilitate engagement from community collaborators. The template, created in Google Sheets, is mainly for computing cross-tabulations, but it also displays frequency distributions and p-values for determining statistical significance. The template allows collaborators to record their observations and questions, promoting an efficient yet interactive review process and fostering a democratic analysis environment. Based on our own experience using this template for a data party, we highlight its effectiveness in promoting collaborative data interpretation, decision-making, and the actionable use of survey findings.

The Confluence, Volume 4, Issue 1, Full Issue

The Confluence

No abstract provided.

Cybersecurity And Global Threats: A Comparative Analysis Of Estonia And Russia’S Policies, María Paula Morales Palacios

The Confluence

As digital technology continues to reshape the foundations of modern life, the question of how states respond to cyber threats has become increasingly urgent. This paper examines how political systems shape national cybersecurity policies by comparing Estonia and Russia, two countries facing similar external threats but governed by vastly different structures. Estonia’s democratic framework emphasizes transparency, citizen participation, and international cooperation, while Russia’s semi-authoritarian model centers on state sovereignty, centralized control, and strategic offensive capabilities. Drawing on key historical events, including the 2007 cyberattacks on Estonia and the 2016 attacks on Russian banks, the paper explores how each state’s political …

Anonymous Cyber Threat Intelligence Sharing On Blockchain, Chol Hyun Park

UNLV Theses, Dissertations, Professional Papers, and Capstones

In cybersecurity, sharing of cyber threat intelligence (CTI) plays a pivotal role in our collective defense against emerging threats. However, the current paradigm of CTI sharing is one that participants are reluctant to share due to serious concerns about privacy and the potential exposure of sensitive information.We provide a comprehensive look at the potential of blockchain technology in cybersecurity, highlighting its advantages in creating an immutable, transparent, and decentralized ledger for CTI sharing. We also explore the mechanism of decentralized identity (DID) and explain how ZKP can be used to verify the authenticity of shared data without compromising the anonymity …

Exploring The Facets Of Responsible Ai: Interpretability, Biases, And Morality Of Large Language Models, Sean Xie

Dartmouth College Ph.D Dissertations

This thesis investigates critical aspects of responsible artificial intelligence (AI) — specifically model interpretability, bias detection and mitigation, and moral alignment in large language models (LLMs) — due to their pivotal role in the deployment of transparent, fair, and ethical AI systems. By addressing these dimensions of responsible AI, we hope to foster the increased trust and understanding necessary for wider AI adoption.

We begin by surveying the existing landscape of interpretability metrics and critically assess the effectiveness of interpretability methods designed to generate reliable explanations. Building upon this evaluation, we introduce novel model architectures and frameworks explicitly developed to …

Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine reading comprehension is a critical step in development of applications that require the semantic understanding of human speech-to-text driven work. Many devices such as smart home appliances like the Amazon Echo Dot, Google Home, or smart assistants like Apple Siri or Microsoft Cortana are examples of these applications. The comprehension task involves a deeper understanding and recognition of named entities such as person names, locations, medicals codes, quantities, abbreviations, and acronyms in speech or text data. In this dissertation, we explore and extend the different approaches and techniques in modern research that tackles the problem of recognition and definition …

An Edge Computing Device Optimized And Transfer Learning Enhanced Deep Learning Model For Detecting Wildfire Flame And Smoke, Giovanny Vazquez

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of autonomous unmanned aerial vehicles (UAVs) with edge computing technology and deep learning (DL)-based object detection offers a groundbreaking solution for real-time wildfire detection, enabling rapid data processing directly on devices and minimizing response delays in critical scenarios. However, although showing early promise, performance is often constrained by limited training data and edge computing devices that lack graphics processing unit (GPU) acceleration. This thesis seeks to address these limitations in two stages.First, this work explores the transformative potential of Transfer Learning (TL) to enhance wildfire object detection model accuracy while also investigating TL’s impact, for DL-based object detection …

Elevating Education: Leveling Up Individual Learning Plans, Maximum Mgrdich-Ararat Sirabian

UNLV Theses, Dissertations, Professional Papers, and Capstones

This three-article dissertation investigated the effectiveness, implementation quality, and automation of Individual Learning Plans (ILPs) in promoting college and career readiness. Article 1 analyzed High School Longitudinal Study of 2009 data and found that ILPs did not significantly guide course alignment. Article 2 examined ILP implementation across Nevada high schools, revealing inconsistent quality, limited standardization, and few culturally responsive practices. These findings informed the creation of a new high-quality ILP framework. Article 3 employed a convergent parallel mixed methods design to assess an automated ILP prototype based on this framework. Participants in the automated group reported significantly higher scores in …

White Light Specular Reflection Data Augmentation For Deep Learning Polyp Detection, Jose Angel Nuñez, Fabian Vazquez Jr., Diego Adame, Xiaoyan Fu, Pengfei Gu, Bin Fu

Computer Science Faculty Publications

Colorectal cancer is one of the deadliest cancers today, but it can be prevented through early detection of malignant polyps in the colon, primarily via colonoscopies. While this method has saved many lives, human error remains a significant challenge, as missing a polyp could have fatal consequences for the patient. Deep learning (DL) polyp detectors offer a promising solution. However, existing DL polyp detectors often mistake white light reflections from the endoscope for polyps, which can lead to false this http URL address this challenge, in this paper, we propose a novel data augmentation approach that artificially adds more white …

Predicting Battery Levels Of Sensor Nodes Using Reinforcement Learning In Harsh Underground Mining Environments, Manish Anand Yadav, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining is a hazardous environment, with frequent accidents leading to significant loss of life each year. To enhance safety, sensor nodes monitor key environmental factors such as temperature, toxic gases, and miners' locations, as well as transmit critical messages. Miners interact with these sensors, which track their movements, enabling their location to be determined even without GPS signals. Therefore, predicting the battery life of these sensors is essential for: (i) rerouting miners during emergencies, (ii) ensuring timely maintenance, and most importantly (iii) identifying sensors that need energy harvesting to maintain vital communication within the mine. In this work, we …

Algorithms To Estimate Contours: Two Applications Of Analytical Tools In Differential Geometry And Topology, Mohammad Abirul Islam

Computer Science ETDs

We develop distributed robotics algorithms with analytical tools needed to define and analyze angle turned and distance traversed by robots executing geometric algorithms. We then use these analytical tools to obtain information, via sensor measurements, about an a priori unknown surface. Our contributions are threefold. First, we develop the Sketch Algorithm, which estimates the boundary of any unknown contour and is asymptotically optimal in terms of distance traversed and angle turned. Second, we present experimental field work that validates the Sketch Algorithm. Finally, we propose an approach to find multiple sources of a surface with potential applications to approximate that …

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 …

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 …

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 …

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). …

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 …

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. …

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 …

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. …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …