Computer Sciences Commons^™

Educational Opportunities Of Participatory Gis For Accessibility On A College Campus, Shiya Cao, Heather Rosenfeld, Sarah Susnea

Statistical and Data Sciences: Faculty Publications

The educational benefits of Participatory GIS (PGIS) in geographic higher education have received limited direct attention, often because of the complexities of integrating PGIS into university curricula. While a few exceptions found important educational benefits of PGIS, extant studies focused primarily on the educational benefits for students who worked in the research teams, instead of participants who contributed their local knowledge and perspectives to mapping. Our research aims to understand the educational benefits of PGIS for participants in a campus accessibility mapping project using the modes of experiential learning, positionality, and service learning. Through this, we also provide strategies for …

Introduction: Symposium ‒ Ai Disrupting Law, Edward Lee

Chicago-Kent Law Review

No abstract provided.

Autonomous Uav Swarm Formation Utilizing Gradient-Driven Contour Mapping For Radiation Source Localization, Edgar Amalyan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents a drone swarm for radiation mapping to aid source localization. The Department of Energy advocates employing UAVs for this task, but existing approaches remain inefficient and impractical in real-world scenarios. Three custom drones are built and flight-tested. A control algorithm to follow a contour, a constant-intensity path, is designed using a gradient fit. By knowing the source’s direction, the drone swarm can fly in the optimal trajectory at every step, leaving nothing to assumption. A program is created that implements formation flight and autonomous navigation. It is tested via a software-in-the-loop simulation utilizing radiation sources and detectors …

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

Advanced Robotic Multimodal Imaging With Real-Time Motion Compensation For Dynamic Structural Health Monitoring, George Papaioannou, Christos Mitrogiannis, Mark Schweitzer, Maria Pappa, Pegah Khosravi, Apostolos Karantanas, Chris Ruberg, Shawn Owens, Nikolaos Michailidis

Publications and Research

Modern composite materials promise superior performance and load-bearing capabilities, yet evaluating their structural integrity remains challenging. Current testing methods, such as visual, thermographic, ultrasonic, optical, electromagnetic, terahertz, shearography, X-ray, and neutron imaging, are hampered by long scan durations, limited field of view, suboptimal accuracy, and high costs, particularly when applied to large structures.

This paper addresses these issues by introducing a novel robotic multimodal imaging system that overcomes the limitations of traditional methods. This system dynamically captures both static and dynamic properties of materials using advanced motion compensation techniques. By integrating multiple radiographic modalities into a coordinated robotic platform, it …

Cybersecurity: Digital Stewardship In A Violent World, Rocky K. C. Chang

University Faculty Publications and Creative Works

This paper defines cybersecurity based on the principle of biblical stewardship. The focus of this principle is God, not the technologies, who is the creator and owner of cyberspace and everything in it. Humankind is called by God to steward them by protecting the digital property of our neighbors in cyberspace from malicious attacks. In the context of cybersecurity, a neighbor can be any individual cyber user, practitioner, educator, organization, tech company, or even hacker. However, as argued from the perspectives of human sin and human finitude, the defending stewards can never win over evil in this spiritual battle. Instead, …

Note For Quadripartitioned Neutrosophic Offset, Pentapartitioned Neutrosophic Offset, And Heptapartitioned Neutrosophic Offset, Takaaki Fujita

Neutrosophic Systems with Applications

Neutrosophic sets assign each element three independent membership values—truth, indeterminacy, and falsity—and their partitioned extensions introduce additional components subject to sum-bounded constraints. Notable examples include quadripartitioned, pentapartitioned, and heptapartitioned neutrosophic sets. The O set concept further extends this framework by allowing membership values outside the standard [0, 1] interval, including negative values and values exceeding one. In this paper, we introduce and analyze the Quadripartitioned Neutrosophic O set, Pentapartitioned Neutrosophic O set, and Heptapartitioned Neutrosophic O set. These extensions are intended to enhance the expressiveness of neutrosophic theory and to stimulate further research in neutrosophic and fuzzy uncertainty.

Integrated Algorithm And Hardware Design For Hybrid Neuromorphic Systems, James Seekings, Mahsa Ardakani, Peyton Chandarana, Arshia Eslami, Mohammadreza Mohammadi, Ramtin Zand

Faculty Publications

This paper investigates the combined potential of neuromorphic and edge computing to develop a flexible machine learning (ML) system designed for processing data from dynamic vision sensors. We build and train hybrid models that integrate spiking neural networks (SNNs) and artificial neural networks (ANNs) using the PyTorch and Lava frameworks. We explore the effects of quantization on ANN models to assess its impact on both accuracy and energy efficiency. Additionally, we address the challenges of deploying hybrid models on hardware by implementing individual components on specific edge platforms. We also propose an accumulator circuit to bridge the spiking and non-spiking …

Digital Trading Platform Selection Under Neutrosophic Numbers And Multi-Criteria Decision-Making Methodology: An Illustrative Example, Eman Sayed, Karam M. Sallam, Ibrahim Alrashdi

Neutrosophic Systems with Applications

Uncertainty, vagueness, and incomplete information are pervasive in real-world decision-making scenarios, particularly in multi-criteria decision-making (MCDM) contexts involving expert judgments. To address these challenges, this study introduces a comprehensive decision-making framework that integrates Triangular Neutrosophic Numbers (TNNs) with the Weighted Aggregated Sum Product Assessment (WASPAS) method. The framework begins by capturing expert evaluations using TNNs, which effectively represent the degrees of truth, indeterminacy, and falsity inherent in subjective assessments. These evaluations are then transformed into crisp values using a novel score function that preserves the embedded uncertainty. The resulting decision matrices are aggregated into a unified structure to ensure consistency …

Quantified Possibility Neutrosophic Soft Set Based Decision Support System For Enhanced Accuracy In Sustainable Supplier Selection Decision Analysis, Neha Andaleeb Khalid, Muhammad Saeed

Neutrosophic Systems with Applications

To deal with the concepts of vulnerability, ambiguity, and indeterminacy that are typical in complex decision analysis contexts, neutrosophic set-like structures are frequently used. Although neutrosophic sets are particularly good at handling indeterminate circumstances, indeterminacy plays a part in making the decision making process imprecise and ambiguous. This paper develops a more advanced technique to improve the accuracy of decision analysis problems: the Quantified Possibility Neutrosophic Soft Set Decision Support System (Q^t PNSSDSS). In order to reduce the element of indeterminacy present in conventional neutrosophic sets, the proposed DSS is based on the Quantified Neutrosophic Set, which employs a …

Some Properties Of Neutrosophic Cubic Hypersoft Sets, Lubna Nayab, Muhammad Gulistan, Fawad Hussain

Neutrosophic Systems with Applications

This study addresses the limitations of NCS in managing uncertainties associated with multiple attributes and their further bifurcation. To address this challenge, we propose a generalization of the neutrosophic cubic soft set, introducing the concept of "neutrosophic cubic hyper soft set." Within this framework, we define internal and external neutrosophic cubic hypersoft sets, along with operations such as P-intersection, P-union, P-restricted union, P-extended intersection, P-OR operator, P-AND operator, R-intersection, R-union, R-restricted union, R-extended intersection, R-OR operator, R-AND operator, complement, and relative complement of neutrosophic cubic hyper soft sets. The study explores and presents relevant results, demonstrating the enhanced capability of …

Application Of Multi-Criteria Decision Analysis For Quantifying Responsibility In Ml-Based Intrusion Detection System, Mona Mohamed, Zekra Sakr

Neutrosophic Systems with Applications

With the swift growth in Internet of Things (IoT), certifying secure and trustworthy networks has turned to be a critical challenge, particularly as IoT devices are increasingly vulnerable to sophisticated cyberattacks. As a remedy, intelligent intrusion detection systems (IDS) evolved as promising solutions in recent years, but deciding on the appropriate model remains difficult because of competing performance and trustworthiness criteria. To this end, this paper explores a novel application of an ML-augmented decision-making framework to enhance security-related decision-making in IoT environments. The framework systematically evaluates and ranks ML-based IDS systems according to different evaluation criteria with distinct trade-offs, including …

Land8fire: A Complete Study On Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, And Extensive Benchmarking, Anh Tran, Minh Tran, Esteban Marti, Jackson Cothren, Chase Rainwater, Sandra Eksioglu, Ngan Le

Electrical Engineering and Computer Science Faculty Publications and Presentations

Early and accurate wildfire detection is critical for minimizing environmental damage and ensuring a timely response. However, existing satellite-based wildfire datasets suffer from limitations such as coarse ground truth, poor spectral coverage, and class imbalance, which hinder progress in developing robust segmentation models. In this paper, we introduce Land8Fire, a new large-scale wildfire segmentation dataset composed of over 20,000 multispectral image patches derived from Landsat 8 and manually annotated for high-quality fire masks. Building on the ActiveFire dataset, Land8Fire improves ground truth reliability and offers predefined splits for consistent benchmarking. We evaluate a range of state-of-the-art convolutional and transformer-based models, …

Jazz Scale Patterns With Abjad And Lilypond, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Background

Jazz method books often advise students to “learn it in all keys,” yet most present examples in only one or two keys (if that, as they start from C, the easiest key, and usually stop there). For many "classically-trained" players—especially those who check fingerings, enharmonics, and voice-leading by reading—the absence of complete, notated materials is a barrier. While most scales can be internalized as Whole (W) / Half (H) step patterns, important exceptions (e.g., harmonic and melodic minor, octatonic, whole tone, and blues) are aided by having notated patterns in front of us.

Aims

To generate clear, consistent notation-first …

Unboxing The Black Box: Graph-Based Interpretability In Transformer Models, Ramak Nassiri

Theses and Dissertations

This thesis introduces a novel interpretability framework for transformer encoder models by hypothesizing that their internal embedding updates define a state transition system. We propose that transformers implicitly learn token-to-token influence dynamics, which can be analyzed using graph-theoretic methods. To validate this, we construct transition graphs from embedding changes and rank token importance using the PageRank algorithm. We develop two transformer models from scratch: a self-supervised model that incorporates serotype tokens to learn contextualized embeddings, and a supervised model initialized with these embeddings. Cosine similarity analysis of their token influence patterns reveals strong structural alignment, with values exceeding 93%. This …

Autonomous Generation Of Ids Rules From Threat Intelligence, Azim Bazarov

Theses and Dissertations

Signature-based Intrusion Detection Systems (IDS) detect malicious activities by matching network or host activity against predefined rules. These rules are derived from Cyber Threat Intelligence (CTI), which includes attack signatures and behavioral patterns obtained through automated tools and manual threat analysis, such as sandboxing. The CTI is then transformed into actionable rules for the IDS engine, enabling real-time detection and prevention of threats. The constant evolution of cyber threats necessitates frequent rule updates, which delay deployment time and weaken overall security readiness. Recent advancements in autonomous agentic systems powered by Large Language Models (LLMs) offer the potential for automatic IDS …

Topnet R1: A Multi-Stage Ai Framework For Topic Discovery In Scientific Abstracts, Md Elias Hossain

Theses and Dissertations

Scientific abstracts are rich sources of knowledge, yet extracting meaningful topics remains challenging due to limitations in existing topic modeling techniques. Traditional methods often struggle with interpretability, scalability, and contextual understanding. To overcome these issues, we introduce TopNet R1, a multi-stage ensemble framework that integrates traditional topic models with contextual embeddings from Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 4 (GPT-4) large language models (LLMs). Top- Net R1 operates in three phases: (1) topic generation using Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Latent Semantic Analysis (LSA), and Hierarchical Dirichlet Process (HDP); (2) LLM-assisted pattern recognition …

Augmenting Healthcare Communication: Context-Aware Ai Frameworks For Clinical Decision Support And Automated Summarization, Subash Neupane

Theses and Dissertations

Healthcare communication is plagued by fragmented medical knowledge, patient misunderstanding of care plans, and clinician burnout from documentation burdens. These inefficiencies cost the U.S. healthcare system over $300 billion annually due to preventable nonadherence and administrative waste [47, 19]. While Artificial Intelligence (AI) technology like Large Language Models (LLMs) offer potential solutions, existing systems fail to deliver personalized, context-aware guidance or automate documentation without sacrificing accuracy. This dissertation addresses these gaps through three novel context-aware AI frameworks such as MedInsight, ClinicSum, and ClinicDuo. MedInsight leverages a multi-source context augmentation approach to synthesize patient centric medical responses by integrating Electronic Health …

Extreme Artifacts Removal With Curriculum Learning, Sushant Gautam

Theses and Dissertations

Restoring severely blurred images remains a significant challenge in computer vision, impacting applications in autonomous driving, medical imaging, and photography. This thesis introduces a novel training strategy based on curriculum learning to improve the robustness of deep learning models for extreme image deblurring. Unlike conventional approaches that train on only low to moderate blur levels, this method progressively increases the difficulty by introducing images with higher blur severity over time, allowing the model to adapt incrementally. Additionally, perceptual loss and hinge loss were integrated during training to enhance fine detail restoration and improve training stability. Various curriculum learning strategies were …

Estimating Reliability Of Electric Vehicle Charging Ecosystem Using Principle Of Maximum Entropy, Himanshu Tripathi

Theses and Dissertations

This thesis addresses the challenge of estimating electric vehicle (EV) charging system reliability against unpredictable threats like cyberattacks and extreme weather, where traditional methods fail. We utilize the Principle of Maximum Entropy (PME), a statistical tool that provides unbiased risk estimates using limited information. Applied to the EV charging ecosystem, our case study shows how PME models stress factors to predict failures and optimize maintenance. This approach extends beyond EVs to other complex systems with scarce data, such as smart grids or healthcare devices. By linking uncertainty directly to reliability, PME offers a universal method to improve decision-making under unpredictable …

Bring Back The Blue-Book Exam: In An Age Of Ai, We Need To Return To Handwritten Assignments., Katie Day Good

University Faculty Publications and Creative Works

When ChatGPT was released three years ago, its ability to mimic human writing unsettled me. I’m a professor of communication; what did it mean that my students now had access to a machine that could communicate for them? My initial unease led to a half-joke with my colleagues. Universities could survive this threat, I ventured, but only if we reverted back to 19th-century teaching methods like Socratic dialogue, oral defenses, and lengthy essay exams. This once-laughable scenario is now a serious consideration for many faculty, including me.

Like many professors, I’ve recently abandoned take-home essays in favor of blue-book exams. …

Dynamic Mutational Profiling Of Binding Interactions And Allosteric Networks In Conformational Ensembles Of The Sars-Cov-2 Spike Protein Complexes With Classes Of Antibodies Targeting Cryptic Binding Sites: Confluence Of Binding And Allostery Determines Molecular Mechanisms And Hotspots Of Immune Escape, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The ongoing evolution of SARS-CoV-2 variants has underscored the need to understand not only the structural basis of antibody recognition but also the dynamic and allosteric mechanisms that could underlie complexity of broad and escape-resistant neutralization. In this study, we employed a multi-scale approach integrating structural analysis, hierarchical molecular simulations, mutational scanning and network-based allosteric modeling to dissect how Class 4 antibodies (represented by S2X35, 25F9, and SA55) and Class 5 antibodies (represented by S2H97, WRAIR-2063 and WRAIR-2134) can modulate conformational behavior, binding energetics, allosteric interactions and immune escape patterns of the SARS-CoV-2 spike protein. Using hierarchical simulations of the …

Antifungal Peptides From Casein Milk Of Etawa Crossbreed (Capra Hircus) As Biopreservation Agent For Bread And Molecular Docking Studies, Dian Riana Ningsih, Winarto Haryadi, Rachma Wikandari, Tri Joko Raharjo

Karbala International Journal of Modern Science

Bread without peptide fraction treatment started to grow mold on the fourth day with a total of 24x10⁷ fungi colonies/ml. Meanwhile, the addition of peptide fractions GMC5, GMC6, GMC7, and GMC8 effectively preserved bread for up to 4 days, indicated by no mold growth. The best treatment was the GMC6 peptide fraction on day 8 which grew the least amount of fungus at 7x10⁷ spores/ml. Peptide interaction with the CaATPase receptor Aspergillus sp. was carried out using HADDOCK 2.4. The 3D CaATPase structure prediction resulted in a model with good structural quality. This was supported by residues in …

A Deep Learning Framework With Explainable Ai For Atmospheric Blocking Detection And Interpretation, Devansh Khandelwal

Discovery Undergraduate Interdisciplinary Research Internship

Atmospheric blocking is a large-scale quasi-stationary phenomenon in mid-latitude circulation, characterized by persistent high-pressure systems that disrupt the typical west-to-east flow of the jet stream. These systems can cause extreme weather events—such as heatwaves, cold spells, or droughts—that persist for days or even weeks. This study proposes a deep learning framework to predict and interpret the occurrence of atmospheric blocking by integrating geophysical precursors such as geopotential height (Z500), stream function (SF200), and potential vorticity. These features, which are dynamically linked to blocking onset and persistence, serve as inputs to a Convolutional Neural Network model trained on the CESM Large …

Development And Validation Of Venous Thromboembolism-Bidirectional Encoder Representations From Transformers (Vte-Bert) Natural Language Processing Model, Omid Jafari, Shengling Ma, Barbara D Lam, Jun Y Jiang, Emily Zhou, Mrinal Ranjan, Justine Ryu, Raka Bandyo, Arash Maghsoudi, Bo Peng, Christopher I Amos, Abiodun Oluyomi, Nathanael R Fillmore, Jennifer La, Ang Li

Faculty, Staff and Students Publications

Background: Accurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) tool externally validated in representative patients is lacking.

Objectives: To train and validate an efficient NLP model to detect incident VTE event.

Methods: We designed a novel NLP platform, NLPMed, to assist thrombosis researchers with data preprocessing, phenotype annotation, language model finetuning, and NLP application. Using clinical notes, discharge summaries, and radiology reports from patients with cancer at 2 healthcare institutions, we finetuned Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) to develop VTE-BERT. The new model was trained to detect acute …

Crop Yield Prediction At Multiple Spatial Scales With Statistical Machine Learning, Vaibhav Charan, Pratishtha Poudel

Discovery Undergraduate Interdisciplinary Research Internship

Understanding and accurately predicting crop yield is becoming increasingly important today in the face of global food security challenges, and thus, the availability of standardized data and scalable models is the need of the hour. To support this, researchers have developed CY-Bench (Crop Yield Benchmark), a comprehensive dataset that helps forecast maize and wheat yields on a global scale. This research project primarily involved working with the CY-Bench dataset aiming to improve crop yield prediction through machine learning. Initially, papers explaining the CY-Bench dataset and other papers for agriculture modeling were studied and analyzed in detail. The research then progressed …

Imputation Via Domain Adaptation: Rethinking Variable Subset Forecasting From Knowledge Transfer, Runchang Liang, Qi Hao, Yue Gao, Kunpeng Liu, Lu Jiang, Pengyang Wang, Minghao Yin

Computer Science Faculty Publications and Presentations

Multivariate time series forecasting in practical deployment faces a critical challenge termed Variable Subset Forecasting (VSF), where certain variables accessible during training are entirely missing during inference. This creates a stark discrepancy between the training (source domain with full variables) and inference (target domain with partial variables) environments, disrupting cross-variable dependencies and fragmenting global temporal patterns. Existing imputation methods, limited to transferring local knowledge (e.g., temporal neighbors or pairwise correlations), fail to capture essential global dynamics, leading to severe performance degradation under distribution shifts. To address these challenges, we redefine VSF as a cross-domain knowledge transfer problem and propose VIDA, …

Systematic Review On The Technology’S Role In Supporting Lung Cancer Patients In The Treatment Journey, Safa Elkefi, Polly Wu, Roa Sabra, Steven Feiner, Lanyi Chen, Guy Hembroff, Alicia K. Matthews

Michigan Tech Publications

This systematic review examines the role of technology-based interventions in supporting lung cancer patients during their treatment. It identifies (1) the different technologies utilized, (2) their functions and benefits, and (3) the barriers encountered by patients. The authors searched six databases for literature examining the use of technology to support treatment among lung cancer patients. Twenty-three papers were included. We mapped each technology, telehealth platforms, online portals, and mobile apps, to specific treatment phases (pre-treatment, active treatment, post-treatment) and symptom domains (symptom management (N = 17), emotional distress (N = 12), and patient–provider communication (N = 7)). Our results demonstrate …

Exploitation For All: The Factors That Enable Pig Butchering Schemes To Weaponize Cybersecurity's Weakest Link, Melaney Freeman

Boise State Graduate Student Projects

This paper discusses pig butchering schemes and how they leverage human weaknesses through advanced social engineering and manipulation techniques that are enabled through the use of human trafficking, forced labor, and government corruption in Southeast Asia. Details regarding scammer exploitation and the formation of a victim-offender identity is presented, followed by the explanation of factors that allow organized crime groups to exist. Essential tactics used by scammers are examined to understand how they weaponize people's vulnerabilities for the duration of the scam to build a relationship with the victim and earn their trust in order to financially exhaust them through …

Ev Charging Management In A Real-Time Optimization Framework Considering Operational Constraints, Hilmi Cihan Güldorum, Ayşe Kübra Erenoğlu, İbrahim Şengör, Barry P. Hayes, Ozan Erdinç

Department of Computer Science Publications

The electrification of transportation plays a central role in the decarbonization of energy systems. Although electric vehicles (EVs) are expected to reduce energy related emissions, the increasing demand imposed by large scale EV adoption presents serious challenges for distribution systems (DSs), which were not originally designed to accommodate such loads. This study proposes a mixed integer quadratically constrained programming (MIQCP) framework to optimize the operation of an EV parking lot (EVPL) under DS constraints. The model compares three widely adopted objective functions: minimization of active power loss, charging cost, and uncontrolled charging impact, which is represented by minimizing the total …