Digital Commons Network^™

Advancing Context-Aware Detection Of Socially Harmful Discourse Using Transformer-Based Models, Santosh Chapagain

All Graduate Theses and Dissertations, Fall 2023 to Present

Social media platforms are a central part of modern communication, shaping how people share ideas, build communities, and discuss social issues. While these spaces can support connection and self expression, they also enable the spread of harmful language such as hate speech. At the same time, social media is an important place where members of marginalized communities, including sexual and gender minorities, express stress, discrimination, and emotional challenges in ways that are often indirect and context dependent.

This research examines whether modern artificial intelligence systems can better identify harmful language and expressions of minority stress in online posts. The study …

Visualizing Probabilistic Model Checking: An Interactive Framework For Exploring Ctmc Models, Ishara Mawelle Kankanamge

All Graduate Theses and Dissertations, Fall 2023 to Present

Probabilistic model checking is a critical method for analyzing systems characterized by uncertainty, such as communication protocols, randomized algorithms, and biochemical networks. While formal verification tools provide precise numerical data about these systems, interpreting these results is often limited by large state space, high-dimensional state space and time dependent evolution. Current tools typically output raw numerical data, offering limited support for intuitively understanding the time-dependent behavior of a model. This research presents an interactive visualization framework designed to bridge the gap between complex numerical analysis and human intuition. The framework integrates coordinated visual interfaces, including lower-dimensional state-space projections and synchronized …

How Novices Write Code: Discovering Best Practices, Matt Rau

All Graduate Theses and Dissertations, Fall 2023 to Present

Learning to program is a difficult endeavor, leading to chronically high failure rates in introductory programming courses. One thing that makes teaching programming difficult is that we don’t fully understand what problem solving habits and writing strategies separate successful programmers from struggling ones. Knowing how to teach these habits to a new programmer is an equally difficult challenge,

Studying the way people write code has proved difficult. Until recently, there was very little relevant publicly available data, and no good ways to analyze student programming behavior at a large scale. In this thesis, I address both issues. I publish a …

Student Programming Behavior With And Without Phone Notification Suppression, Gavin T. Eddington

All Graduate Theses and Dissertations, Fall 2023 to Present

Many students work on programming assignments while receiving notifications from their phones, such as text messages or social media alerts. These notifications can interrupt focus and make it harder to stay engaged with a task. This study examines whether silencing phone notifications helps students stay more focused while programming. 

We collected data from students in an introductory computer science course while they worked on programming assignments. Students completed some assignments with notifications silenced and others without. We measured their activity using software that records typing behavior and identifies when students take long pauses, which can indicate interruptions or loss of …

Large Language Models For Introductory Computer Science Education: Content Generation, Intelligent Tutoring, And Learner Modeling, Muhammad Fawad Akbar Khan

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation studies how artificial intelligence, especially large language models such as GPT, can help students learn introductory computer programming when the models are used inside a carefully designed learning system. Instead of focusing on AI as a standalone tool, the dissertation follows a connected story: generating learning resources, building a tutoring platform, running a user study, and then analyzing how students behave while they program.

The work first uses prompt engineering to create a large collection of 11,700 Python exercises aligned with introductory computer science topics. Students and instructors then evaluate these exercises to check whether they are clear, …

St-Fmformer: An Autoregressive Generation Framework For Scientific Ensemble Data Predictions, Md Robiul Islam

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding how physical systems change over time is important in areas such as weather prediction, fluid dynamics, and environmental science. However, accurately predicting future behavior is difficult because these systems are complex and constantly evolving. 

This research develops a deep learning approach to predict how such systems evolve over time. The model learns patterns from past observations and uses them to generate future states step by step. This provides a faster alternative to traditional simulation methods while maintaining strong predictive performance. 

The proposed method focuses on improving the consistency of predictions over time and is designed to work across different …

Empowering Sustainable Household Waste Management Through Rubbin: App-Based Transactions Using Google Maps Api And Qr Code Recognition, H.A. Danang Rimbawa, Muhammad Abditya Arghanie, Muhammad Rey Renoult, Dea Dwi Ananda

Smart City

The Digitization of environmentally friendly technology must be applied to advance smart cities. Waste management carried out conventionally often causes irregularities in the classification data collection, difficulties in accessing information related to garbage collection schedules, and a lack of transaction history information, which causes a decrease in the quality of waste collection. The development of functional application features is urgently needed as a container for proper waste management. The Rubbin App has been created, which implements Google Maps API for route optimization and waste mapping, QR code recognition using Enhanced Adaptive Median Filter, and private chat for clients and collectors …

Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach

Honors Theses

Alzheimer's disease (AD) is a growing global health concern, with millions of people affected worldwide and cases expected to rise significantly in the coming decades. Early detection is critical for patient treatment and care, and recent advances in natural language processing (NLP) have shown promise in identifying linguistic markers associated with AD. However, most existing work has focused on English, leaving speakers of other languages with limited access to such tools. This study investigates how effective AD detection models trained on English data are at transferring to Greek, a low-resource language with limited dementia-related speech data available. We propose a …

It's Not Nde Without U And X: Preparing For Change With Inclusive Research, Sanjeet Mann, Heather L. Cribbs

Library Faculty Publications & Presentations

The upcoming Next Discovery Experience (NDE) introduces major changes to how users search, interpret information, and navigate Primo. Preparing for NDE is an opportunity to center the diverse students and faculty who rely on discovery systems every day, ensuring that their lived experiences, accessibility needs, and research practices guide interface design, configuration, and communication. This session presents a consortial approach to NDE readiness that positions students and community members as partners in the development process. We describe strategies for creating ethical and rigorous UX research workflows that include IRB approval, purposeful recruitment, accessible study design, and clear documentation on the …

Ai-Driven Multispectral Drone Monitoring For Afforestation In Arid Environments, Hesham Morgan, Ali Elgendy, Brandon Tran, Tamer Ismail, Mohamed M. Moursy, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Mubarak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Monitoring large-scale afforestation projects in arid and semi-arid environments requires accurate, high-resolution, and repeatable methods to assess tree survival and growth. In this study, we integrated unmanned aerial vehicle (UAV) multispectral imaging with an advanced object detection framework to evaluate vegetation establishment in the Shuayb Al-Budai afforestation site, part of the Imam Turki bin Abdullah Royal Natural Reserve, Kingdom of Saudi Arabia (KSA). Multispectral datasets were acquired using a MicaSense Altum-PT sensor and processed through a masked Region-based Convolutional Neural Network (RCNN) with two backbone architectures: ResNet-101 and VGG19-BN. The Mask R-CNN–ResNet-101 model achieved superior performance, with an overall accuracy …

Ai Literacy: An Annotated Oer Bibliography, Houy Yvonne

UNLV Best Teaching Practices Expo

"Scalable, discipline-agnostic AI literacy instruction can be implemented incrementally without requiring full course redesign in higher education, supporting both technical understanding and critical engagement with the social and ethical dimensions of AI: The curated list of open educational resources (OER) on this poster enable a flexible, modular approach to teaching foundational AI literacy. The annotated list includes self-paced, hands-on projects with complementary educator-guided activities and discussion to support conceptual understanding of machine learning, training data, and algorithmic bias, drawing on OER such as Code.org’s AI curriculum, MIT RAISE’s Day of AI, and the multi-lingual Elements of AI course. Many resources …

A Comparative Study Of Traditional Training And Xr-Based Simulation In Healthcare Professional Education, Fazal Qudus Khan, Gohar Khan, Ibrar Ahmad, Owais Khan, Suleman Shah

All Works

Immersive Virtual Reality or VR/VX stands poised to revolutionize healthcare education through interactive learning modules, overcoming current deficiencies in existing methodologies for instruction. This research compared the effectiveness of VR/VX-based training to traditional CT scanner operator training using a within-subjects design, involving 30 subjects, and concluded the effectiveness of using VR/VX in enhancing knowledge retention, task accomplishment, and engagement, and proved it by showing significant enhancement in immediate knowledge acquisition scores (Δ = 8.87, t(29) = 6.71, p < .0001), relative to delayed knowledge retention scores (Δ = 11.03, t(29) = 6.85, p < .0001), procedural achievement scores (Δ = 5.40, t(29) = 4.45, p = .0001), reduced overall task completion time using VR/VX for increased speed of execution (t(29) = 10.74, p < .0001), as well as reduced task errors for lower error rates using VR/VX in comparison to existing methodologies, as testified by the results, t(29) = 8.14, p < .0001, respectively, while showing no significant difference in usability, although assessed superior in terms of engagement and relative usefulness by the involved subjects.

Enhanced Parametric Approach For Solving Interval-Valued Trapezoidal Neutrosophic Linear Fractional Programming Problems, Hamiden Abd El- Wahed Khalifa H.A.Khalifa, Moodi Abdulrahman Abdullah Al-Rajeh, Sultan S. Alodhaibi

Neutrosophic Systems with Applications

Neutrosophic sets (NSs) generalize the classical versions, by providing a flexible framework capable of representing incomplete, inconsistent, and unclassified data that frequently arises in practical decision frameworks. In this study, a linear fractional programming (LFP) problem with uncertain parameters is investigated. All coefficients in the objective function (OF) as well as the left- and right-hand sides of the constraints are represented using fully trapezoidal neutrosophic numbers (NNs). By employing a suitable score function, the proposed neutrosophic LFP model is transformed into an equivalent scalar LFP problem. Subsequently, a parametric solution procedure is established to regulate the neutrosophic optimum solution. This …

Modeling Agentic Artificial Intelligence Uncertainty In Agriculture Based 6generation: A Hybrid Q-Rung Orthopair Fuzzy Mcdm Methodology, Zekra Sakr, Mona Mohamed

Neutrosophic Systems with Applications

In era of advanced intelligent revolutions, the collaboration between intelligent technologies became imperative. For instance, integrating 6G communications with agentic artificial intelligence considered a catalyst to shift agriculture sector into optimized and intelligence sector. This integration resulted in transitioning the sector from static automation to autonomous, agent-based ecosystems. Accordingly, the efficiency roles for artificial intelligence agents (AIAs), deploying and selecting optimal AIA is important. Yet, selection process is still difficult because agricultural criteria are multifaceted and there are inherent environmental uncertainties. To address these challenges and bolster the selection process, this paper suggests a hybrid multi-criteria decision-making (MCDM) that bolstered …

Gauss Elimination Method For Solving The System Of Neutrosophic Linear Equations, Elsayed Badr, Shokry Nada, Saeed Ali, Aya Rabie

Neutrosophic Systems with Applications

This paper proposes a unified computational framework for solving linear systems under trapezoidal Neutrosophic uncertainty. The system is formulated as A( I )x = b( I ), where both the coefficient matrix and the right-hand side vector incorporate an indeterminacy parameter I, expressed as A( I ) = A₀ + IA₁ and b( I ) = b₀ + Ib₁. A decomposition strategy is developed to separate the model into deterministic and indeterminacy components, yielding a solution of the form x( I ) = x₀ + Ix₁. The deterministic component is obtained via …

Uncertainty Handling In Stock Market Prediction: A Fuzzy Markov Chain Approach, Alpa Singh Rajput, Arpan Singh Rajput

Neutrosophic Systems with Applications

Predicting the stock market is never easy because it is influenced by many uncertain and constantly changing factors such as economic conditions, investor behaviour, and global events. Traditional models like the Crisp Markov Chain (CMC) try to predict market movements by using fixed probabilities for different states like bullish, bearish, or stagnant. However, real markets do not behave in such a strict way—they often move gradually between states, which these models fail to capture. To overcome this limitation, this study introduces a Fuzzy Markov Chain (FMC) model, where fuzzy logic is used to handle uncertainty and allow smoother transitions between …

An Improved Logarithmic Ratio-Product Type Estimator For Mean Modeling And Estimation Under Neutrosophic Uncertainty, Anchal Yadav, Mukesh Kumar

Neutrosophic Systems with Applications

In classical statistics, population mean estimation generally assumes precise and determinate data along with known auxiliary information. However, in real-world situations where observations are imprecise or expressed in interval form, such as temperature variations or financial market data, classical approaches become less effective. To address this limitation, neutrosophic statistics provide a more flexible framework for handling uncertainty and indeterminacy. This study proposes a neutrosophic logarithmic ratio-product type estimator for estimating the finite population mean using auxiliary information. The bias and mean squared error (MSE) of the proposed estimator are derived using a first-order approximation. Furthermore, performance evaluation is carried out …

Automated, Modular, Agentless Adversarial Emulation In Cloud Environments For Higher Education And Student Training, Doc Harley

Senior Honors Theses

Currently, the leading technologies in the market of adversarial emulation are MITRE Caldera, Atomic Red Team by IBM, and multiple proprietary products that come with support packages for different vendors like AttackIQ, Cymulate, SafeBreach, and many more. While it is clear that much work has been done in the broad category of adversarial emulation, when it comes to open source solutions, there are no agentless options with built in automation and modularity that have good support for cloud environments. Agentless adversarial emulation provides a unique advantage in that it can be both simpler and a better representation of the true …

A Deep Learning-Based Approach For Bot Detection In Trending Hashtags On X, Mehboob Hussain, Muhammad Rizwan Rashid Rana, Muhammad Imran, Muhammad Shoaib, Muhammad Hasaan Mujtaba

Makara Journal of Technology

The widespread presence of bots on social media platforms, such as X (formerly Twitter), poses a significant threat to the integrity of online information by facilitating the dissemination of misinformation and manipulating public discourse. This study proposes a robust deep learning-based framework, DeepBot, to detect bot participation in trending hashtags and discussions on X. The approach uses a dataset sourced from Kaggle, comprising user profile metadata, including follower count, tweet frequency, account verification status, and engagement metrics. The data were subjected to comprehensive preprocessing, including noise removal, part-of-speech (POS) tagging, and word embedding using the pre-trained GloVe model. RoBERTa is …

Building Human-Aware Ai: Learning From And Assisting Human Decision-Makers, Saumik Narayanan

McKelvey School of Engineering Graduate Student Theses & Dissertations

As artificial intelligence systems become more capable, they are increasingly used not only as standalone problem-solvers, but as systems that learn from and interact with humans. In these settings, success depends not only on the strength of the model in isolation, but also on how well it fits the humans it is trained on or deployed alongside. This dissertation argues that human heterogeneity is a central ingredient in the design of effective human-aware machine learning systems. Rather than treating differences between people as noise to be averaged away, I show that variation in human expertise and preferences can provide useful …

Smart Kitchen: Towards Real-Time Ai Systems For Cognitive Support In Daily Activities, Ruiqi Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The rapid growth of the aging population and the rising prevalence of Subjective Cognitive Decline (SCD) highlight the need for continuous, unobtrusive assessment of functional cognition during everyday activities. Vision-based smart home systems offer a promising pathway for monitoring behavior and supporting independent living. However, enabling real-time cognitive assistance remains challenging. It requires not only accurately interpreting complex human behaviors to detect cognitive errors, but also supporting real-time deployment on resource-constrained edge devices and under dynamic wireless network conditions. This dissertation presents Smart Kitchen, an AI-driven system for real-time cognitive error detection through the monitoring of daily cooking activities. Under …

Revisiting Visualization Literacy: What Standardized Assessments Reveal And Conceal Across Instruments, Cultures, And Ai Systems, Saugat Pandey

McKelvey School of Engineering Graduate Student Theses & Dissertations

Data visualizations are now a primary medium for public communication, scientific reasoning, and decision-making. By transforming complex data into accessible graphical forms, visualizations are widely assumed to make information comprehensible to broad audiences. Yet the ability to accurately read, interpret, and critically evaluate a visualization, what researchers call visualization literacy, is neither uniform nor universal. It is a learned, multidimensional skill shaped by prior exposure, educational opportunity, and context. While the field has developed standardized instruments to measure visualization literacy, these tools were built under narrow assumptions: participants are typically paid, English-speaking, and recruited from Western online panels. When any …

Benefits Of Traffic Reprofiling For Delay Sensitive Networking, Jiaming Qiu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Deterministic networking systems, such as Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet), require strict end-to-end delay guarantees while efficiently utilizing limited network resources. Conventional approaches typically focus on fixed traffic profiles, which can lead to suboptimal resource utilization in scheduling or admission control problems. This dissertation investigates traffic reprofiling—the proactive reshaping of traffic arrival patterns—as a complementary mechanism for improving both resource efficiency and delay performance under strict service guarantees. The dissertation consists of three parts. The first part studies bandwidth minimization under hard delay constraints for Service Curve Earliest Deadline First (SCED) schedulers. We show that traffic reprofiling can …

Artificial Intelligence, Synthetic Media, And The Criminal Justice System, Jeffrey J. Jacobs

Doctoral Dissertations and Projects

Artificial intelligence (AI) technologies continue to evolve at an exponential pace, and their utilization of synthetic media in criminal activity presents both unprecedented opportunities and significant challenges for criminal justice systems. This dissertation explores the intersection of AI-driven synthetic media crime and the U.S. criminal justice system, identifying how such technologies are being utilized in criminal activity and evaluating the system’s capacity to adapt. This study uses a longitudinal, mixed-methods research design; it also collects and analyzes data from law enforcement agencies, AI developers, and cybersecurity experts to forecast future crime trends and assess the efficacy of current justice system …

Research On The Accessibility Elements Of Rural Public Digital Cultural Services Based On Aism-Fmicmac, Lihui Peng, Banruo Gu, Chuang Hong, Xisheng Hu

Journal of Scientific Information Research

[Purpose/significance] A structural analysis of the stratified dependencies among accessibility determinants in rural public digital cultural ecosystems enables identification of pivotal developmental nodes, thereby establishing theoretical foundations for optimizing service delivery mechanisms and achieving spatial equilibrium in digital cultural provision. [Method/process] This study applies grounded theory to conduct a comprehensive coding analysis of policy documents related to China's rural public digital cultural services. Through this process, five dimensions and fifteen sub-components of accessibility are identified. An index system for accessibility factors is then developed using the AISM approach. The identified elements are classified into three hierarchical levels. To further validate …

Drug Risk Knowledge Discovery For Western Medicines Based On Knowledge Graph Link Prediction, Jianxiang Wei, Ma Hengyuan Ma, Yuehong Sun, Wenwen Du, Letian Hu

Journal of Scientific Information Research

[Purpose/significance] The risk information contained in drug instructions is usually incomplete, and some new adverse reactions can only be discovered in actual clinical use. This paper proposes an information organization and knowledge discovery method for pharmacovigilance, in order to timely and accurately identify missing risk knowledge in drug instructions. [Method/process] Drug instructions of 8 152 Western medicines are collected as the research data; On the basis of ontology construction, data annotation, and model training, the UIE model is used to jointly extract entity and relationship triplets from the research data; A new knowledge graph link prediction method CompGCN-RotatE, is proposed, …

Research On Temporal Knowledge Graph Completion Method For Emergent Events Based On Bigru And Graph Contrastive Learning, Peng Wu, Zhenyu Lu, Xuechen Zhang

Journal of Scientific Information Research

[Purpose/significance] During emergencies, social media short texts contain critical information but are heavily interfered with by noise. Traditional static knowledge graph completion techniques struggle to effectively address their dynamic evolution and data sparsity issues, making it imperative to introduce temporal modeling methods. [Method/process] This study proposes a dynamic completion framework that combines the temporal feature capture capability of Bidirectional Gated Recurrent Units (BiGRU) with the noise-resistant representation learning advantages of Graph Contrastive Learning (GCL). At the completion level, the ConBiTE method is introduced, which captures temporal dependencies through self-attention mechanisms and BiGRU, while leveraging GCL to enhance the completion of …

Assessing The Robustness Of Combinatorial Multi-Armed Bandit For Geofencing With Reconfigurable Intelligent Surfaces., Luciano De Souza, André Gomes

STEM Student Research Symposium Posters

Reconfigurable Intelligent Surfaces (RIS) can influence beam selection in next-generation wireless systems by shaping the channel conditions observed during beam sweeping. Prior work assumes deterministic beam sweeping, enabling Combinatorial Multi-Armed Bandits (CMAB) to learn beam-specific policies for geofencing. In this work, we evaluate the robustness of this approach under randomized beam sweeping, where the mapping between bandits and beam directions is disrupted episode by episode. Under these conditions, the learning algorithm no longer converges to a structured solution, instead behaving closely to a random policy, with higher variability and lower mean reward. These results indicate that the CMAB relies on …

Detection, Mapping, And Spraying Of Carolina Redroots In Cranberry Bogs Using Ai And Autonomous Drones, Duwon Ham, Bishal Neupane, Thien Ba Nguyen, Thanh Nguyen, Hieu D. Nguyen, Thierry Besancon

STEM Student Research Symposium Posters

Use artificial intelligent and autonomous drones to automatically detect Carolina Redroots in cranberry bogs, create density maps of the weed, and perform spot spraying.

On The Need For Intelligent Link Management In Wifi 7 Mlo, Logan Lux, André Gomes

STEM Student Research Symposium Posters

Wi-Fi 7 EMLSR allows devices to use multiple wireless links to improve throughput and reduce latency, but its real-world performance depends heavily on network conditions. This project investigates the need for intelligent link management in Wi-Fi 7 EMLSR by building a real testbed to measure performance.