Digital Commons Network^™

Mtu-Llm: Llm-Based Multi-Robot Task Allocation And Path Planning For Heterogeneous Robots In Search And Rescue Operations, Kaushik Kannan, Jungyun Bae

Michigan Tech Publications

Urban Search and Rescue operations after natural disasters involve locating and assisting victims in hazardous environments, which is challenging. Classical Multi-Robot Task Allocation (MRTA) and path planning approaches have been used to deploy heterogeneous robot teams in unsafe areas. However, existing methods often lack focus on workload balance and requirement fulfillment and struggle to generalize across different scenarios. To address these challenges, we propose Multi-robot Task allocation Utilizing LLMs (MTU-LLM), a framework designed to reduce the development time for task allocation and path planning approaches, enabling faster robot deployment. The framework uses an LLM-based “prompt engineering” approach that generates task …

Aiding Depth Perception In Initial Drone Training: Evidence From Camera-Assisted Distance Estimation, John Murray, Steven Richardson, Keith Joiner, Graham Wild

Research outputs 2022 to 2026

Remotely Piloted Aircraft (RPA) pilots frequently experience difficulties with depth perception, particularly when estimating distances between the drone and environmental obstacles. This study evaluates whether the use of onboard camera imagery can improve exocentric distance estimation accuracy among ab initio drone pilots operating under visual line-of-sight (VLOS) conditions. Two groups of undergraduate students performed distance estimation tasks at 20 and 50 m. One group used direct observation only to estimate the exocentric distance between the drone and an obstacle. The second group, as well as direct observation, had access to a live video feed from the drone’s onboard camera via …

Real-World Continuous Smartwatch-Based User Authentication, N. Al-Naffakh, N. Clarke, F. Li, P. Haskell-Dowland

Research outputs 2022 to 2026

User authentication is often regarded as the "gatekeeper"of cyber security. It has, however, long suffered from significant usability issues that have resulted in research focussing upon frictionless and transparent biometric approaches. Activity-based user authentication - a technique that authenticates a user by what they are physically doing at a specific point in time has attracted significant attention, particularly due to the increasing popularity of smartwatches. This research aims to overcome limitations in prior work by exploring the viability of the approach in real-world conditions. The study presents two principal experiments, one focused upon a constrained environment to provide a control …

Quantum Analysis Of Protein–Ligand Binding By Integrating Structural Resolution, Sequence Homology, And Ligand Properties, Don Roosan, Samira Samrose, Rubayat Khan, Saif Nirzhor, Brian Provencher

Computer and Data Science Faculty Publications

Predicting protein–ligand binding affinity is a fundamental challenge in computational biology and drug discovery, complicated by diverse factors including protein sequence variability, ligand chemical diversity, and structural resolution. Here, we present an integrative study that combines classical machine learning and quantum-enhanced modeling to investigate how crystal structure resolution, sequence similarity, and ligand properties jointly influence binding affinity. Using a curated “refined” dataset from PDBbind and an expanded general dataset, we first conduct correlation and regression analyses to quantify the relationships among binding affinity, ligand descriptors (e.g., molecular weight, logP), and protein structural metrics (resolution, R-factor). We observe moderate positive correlations …

Fuzzy-Ahp Based Decision Support System For The Selection Of Optimal Maintenance Strategy For Meter Gauge Railway Infrastructure: A Review, Hamisi J. Maulid

Tanzania Journal of Engineering and Technology (TJET)

There are many uncertainties and complexities associated with maintaining Meter Gauge Railway (MGR) infrastructure, which calls for a methodical approach to decision-making. The development and application of a fuzzy-AHP-based decision support system (DSS) to select the optimal maintenance strategy for the MGR are presented in this study. The review covers research from 2013 to 2023 and focusses on the use of Multi-Criteria Decision Making (MCDM) and Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) techniques in railway infrastructure maintenance. To manage the inherent uncertainties and subjective judgements involved in maintenance decision-making, the Fuzzy-AHP methodology combines fuzzy logic with the Analytic Hierarchy Process (AHP). …

Towards Future Sustainable Infrastructure: The Role Of Technical Audit In Tanzania’S Public Works, George C. Haule

Tanzania Journal of Engineering and Technology (TJET)

This study aimed to investigate the vital role and impact of technical audits in promoting sustainable infrastructure development in Tanzania. The role and effects of technical audits in long-term infrastructure development were studied using a mixed-methods approach with both quantitative and qualitative parts. Data were collected through analysis of technical audit documentation, a semi-structured questionnaire, and stakeholder interviews. The study revealed the various dimensions of infrastructure investment projects, including initiation and planning, design, procurement of contractors and consultants, contract management, environment, health, and safety. The technical audit findings reported weaknesses or non-performance issues in infrastructure planning at the national level …

The Future Of Foreign Language Learning In The Age Of Artificial Intelligence: A Critical Analysis Of Trends, Challenges, And Opportunities, Ahmad Aljanadbah, Rashid Hamad Al Marri, Hamad Mubarak Almarri

All Works

The integration of Artificial Intelligence (AI) in foreign language education is transforming how languages are taught and learned. This study investigates the current trends, advantages, and challenges of AI applications in language acquisition. Employing a qualitative content analysis method, the research analyzes scholarly publications and practical innovations in AI-based tools, such as adaptive learning systems, intelligent chatbots, and automated writing feedback. The findings highlight AI's potential to enhance personalized instruction, boost learner engagement, and expand access to authentic language materials. Nonetheless, the study also reveals notable limitations, including concerns about data privacy, diminished human interaction, and ethical risks related to …

Beyond The Hype: The Fundamental Challenges Of Machine Learning-Based Android Malware Detection In Cybersecurity, Guojun Liu

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) algorithms have achieved remarkable success across various domains, including cybersecurity. Inspired by these advancements, the academic security community has explored numerous ML-based approaches for Android malware detection. While ML holds significant promise in this domain, its practical deployment faces substantial challenges, including data collection, feature selection, app representation across different models, performance instability across datasets, and inherent limitations of learning-based malware detection. These challenges can lead to overly optimistic detection results and weaken the reliability of malware detection frameworks.

Android malware detection has been extensively studied using both traditional ML and deep learning (DL) approaches. Although many …

Enhancing Security And Healthcare Through Continuous Monitoring On Wearable Devices, Sicong Chen

Dissertations - ALL

Wearable and mobile devices are becoming deeply embedded in daily life, supporting a growing range of tasks - from communication and entertainment to health monitoring and productivity. As people increasingly rely on these devices, it is essential to ensure both the protection of the sensitive data they store and the ability to derive meaningful insights that enhance users’ well-being. This dissertation investigates how wearable and mobile devices can be leveraged to provide robust and user-friendly solutions through continuous monitoring across two critical domains: security and mental health. The foundation of this dissertation is the development of a security infrastructure, as …

Online And Offline Learning For Embodied Ai In Autonomous Systems, Kun Wu

Dissertations - ALL

Embodied Artificial Intelligence (AI), which integrates physical embodiment with intelligent decision-making, is increasingly critical in advancing autonomous systems across diverse domains such as autonomous driving and robotic manipulation. This dissertation presents a comprehensive exploration of online and offline learning approaches for Embodied AI in autonomous systems, addressing both algorithmic innovations and dataset construction to overcome fundamental challenges in perception, decision-making, and control. Through five interconnected studies, we systematically advance the state of the art in deep reinforcement learning (DRL) and imitation learning for embodied control. First, we introduce CADRE, a cascade online DRL framework for vision-based autonomous urban driving that …

Grades Are Bugs, Jordan Freitas

Computer Science Faculty Works

This paper argues that grades are bugs in our educational system, undermining desired behaviors and outcomes. Grades were introduced into higher education for purposes directly at odds with the goals of inclusive pedagogy today, as well as the neuroscience of human motivation and learning. Students enter computer science programs from increasingly varied backgrounds and experiences, and face a rapidly evolving landscape of prospective career paths while higher education costs in the United States are ever increasing. Computer science educators have a responsibility to adapt and carefully re-examine typical approaches to all aspects of the learning environments we build and curricula …

Refinement Or Disruption: Patterns Of Critical Inquiry In Computing Ethics Assignments, Vincent Barfield, Charles Wallace

Michigan Tech Publications

Ethical inquiry in computing has an evolving nature, where repeated critical questioning may lead to a series of reframings of the problem. Since many computing students have experience with agile project-based courses, an iterative approach can provide a familiar entry into ethics. Agile development, however, is typically oriented around satisfaction of customer goals, while ethical inquiry requires constant questioning that may cast those goals into doubt. Does the similarity between critical ethical inquiry and iterative development provide a helpful model or breed a false familiarity? To address this question, we examine student work in a computing ethics course, involving iterative …

Efficient Methods And Algorithms For Analyzing Stochastic Systems, Mohammad Ahmadi

USF Tampa Graduate Theses and Dissertations

This dissertation addresses the challenges of stochastic analysis of safety-critical systems with biological components, where unexpected behavior can lead to catastrophic events. Two fundamental challenges hinder the analysis of such systems: their typically large or infinite state spaces, and the extreme rarity of error states of interest. While Monte Carlo simulation can analyze biochemical systems without storing the state space, accurately estimating rare event probabilities becomes computationally prohibitive. Conversely, probabilistic model checking excels at analyzing extremely low probability events but becomes impractical for systems with large or infinite state spaces due to memory constraints.This work proposes two main contributions to …

Exploring The Translation Lookaside Buffer (Tlb) For Low-Level Task Differentiation And Classification, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert, Jose A. Gutierrez Del Arroyo, Tor J. Langehaug, Scott R. Graham

Faculty Publications

The primary focus of modern Central Processing Unit (CPU) technologies is performance improvement, with security often considered a secondary concern. As a result, vulnerabilities within the system are overlooked. While significant research, both offensive and defensive, has been conducted on CPU caches, relatively little attention has been given to the Translation Lookaside Buffer (TLB) due to its perceived lack of data granularity. Prior studies have typically combined multiple Hardware Performance Counters (HPCs) or relied on timing analysis to extract meaningful insights. In contrast, this study introduces a novel methodology that leverages only TLB related HPCs for multi-task classification, without incorporating …

Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott

Northeast Journal of Complex Systems (NEJCS)

Trust plays a crucial role in human-computer interaction, particularly in scenarios involving artificial intelligence (AI) systems. This study explores the feasibility of using functional near-infrared spectroscopy (fNIRS) data to classify trust levels in human-AI interaction scenarios. A total of 18 participants completed an image classification task with an AI team member while their hemodynamic responses were recorded using fNIRS. Preprocessing of fNIRS data involved motion artifact removal, filtering, and normalization. Exploratory analysis identified significant associations between hemodynamic responses in the prefrontal cortex and trust levels. An across-subject binary trust classification model was developed using machine learning techniques, achieving an F1 …

A Modern Approach To Classifying Medieval Latin Scripts, Robert L. Lane Jr, Rafia Mirza, Robert Slater

SMU Data Science Review

Paleography, the study of historical handwriting, is essential for preserving societal understanding of cultural, social, and legal frameworks from the past. Medieval manuscripts, often exhibiting refined craftsmanship, present unique challenges to modern readers due to differences in handwriting conventions and the absence of standardized punctuation and spaces. These texts hold valuable insights into the evolution of written communication, literacy, and language development. However, interpreting them requires specialized knowledge and technological solutions. Convolutional Neural Networks (CNNs) can be leveraged to classify scripts, an important step in Historical Document analysis. These models extract and analyze hierarchical features from images, addressing inconsistencies in …

Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler

SMU Data Science Review

Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …

Auroral Hemispheric Power Asymmetry During Geomagnetic Storms: A Multi-Database Investigation Using Ae And Supermag Indices, Melvin J. Reyes Lozada

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Melvin J. Reyes Lozada on June 20, 2025.

Incremental Planar Nearest Neighbor Queries With Optimal Query Time, John Iacono, Yakov Nekrich

Michigan Tech Publications

In this paper we show that two-dimensional nearest neighbor queries can be answered in optimal O(log n) time while supporting insertions in O(log1+ϵ n) time. No previous data structure was known that supports O(log n)-time queries and polylog-time insertions. In order to achieve logarithmic queries our data structure uses a new technique related to fractional cascading that leverages the inherent geometry of this problem. Our method can be also used in other semi-dynamic scenarios.

Evaluating The Evaluation Matrices: Integrating Spatial Assessment In Geospatial Ai Model Training And Evaluation, Fangzheng Lyu

I-GUIDE Forum

This paper examines the limitations of current evaluation metrics in GeoAI. Through two case studies on deep learning models—a building detection classification problem and a remote sensing image fusion regression problem—this paper demonstrates how traditional statistical evaluation matrices alone can be misleading in geospatial problems. The findings indicate that traditional metrics (e.g., RMSE, MAE) used in current GeoAI models can have difficulty capturing the spatial dimensions inherent to geospatial problems. This paper suggests that the model evaluation process in GeoAI should move beyond traditional evaluation matrices by integrating spatial thinking throughout the modeling pipeline—not only incorporating spatial accuracy in model …

Expanding Access To Cybergis-Compute Through Support For Heterogeneous Workflows, Alexander C. Michels, Ian Zhang, Anand Padmanabhan, John Speaks, Rebecca Vandewalle, Shaowen Wang

I-GUIDE Forum

CyberGIS-Compute is a geospatial middleware tool designed to lower technical barriers to High-Performance Computing (HPC) resources. It provides end-users with a Graphical User Interface (GUI) for submitting models to HPC and allows model developers to contribute their workflows by adding a manifest to their repositories. However, the simplification of the user interface and streamlining of model contribution have unintentionally limited the scope of models that could be run on CyberGIS-Compute. In this paper, we discuss recent developments to the CyberGIS-Compute project that are aimed at supporting a wider variety of workflows including performance enhancements, supporting additional configuration options for jobs, …

Diversifying Cybersecurity: Evaluation Of An Internet Of Things (Iot)-Based Cybersecurity Training Course Designed To Bridge The Diversity Gap, Maureen Namukasa, Bhoomin B. Chauhan, Carlie Swords, Curtice Gough, Weronika Dymanus, Catherine Diresta, John Vitali, Vivek Sharma, T J. Oconnor, Meredith Carroll

Aeronautics Faculty Publications

This study aimed to evaluate the effectiveness of an eight-module Cybersecurity course at increasing the learning outcomes of middle and high school students with little to no experience, including underrepresented minorities (URMs) in Cybersecurity. Twice we administered and evaluated the Cybersecurity course, which included hands-on IoT-based activities, utilizing collaborative learning, scaffolding, and representation-based learning strategies. Using a quasi-experimental, within-subjects, repeated measures design, each participant experienced a pretest, the course, and a post-test to evaluate the impact on learners’ self-efficacy, interest, and knowledge. The results revealed that (1) at pre-test, female (p = .001) and in one course administration minority …

Texture Classification Through Deep Residual Networks And Feature Interpretability, Ankit Kumar

Master’s Dissertations

Texture classification plays a critical role in various real-world and industrial applications such as material recognition in manufacturing, medical image diagnostics, surface defect detection, and agricultural monitoring. The ability to distinguish textures reliably enables automation and enhances the precision of intelligent systems. Traditional methods like Local Binary Patterns (LBP), Gabor filters, and wavelet-based descriptors have been used extensively for texture analysis. While these techniques are effective under controlled conditions, they suffer from limited robustness to changes in illumination, scale, and viewpoint. Moreover, handcrafted features often fail to capture the intricate texture structures present in real-world surfaces. The KTH-TIPS2a dataset introduces …

Topic Shift Detection And Triggering In Natural Dialogue Systems: A Lightweight Approach, Rohith Perumandla

College of Computing and Digital Media Dissertations

This research address a key challenge in dialogue system: enabling the proactive, human-like shifting using lightweight approaching using MobileBERT (~25M) model was proposed and fine-tuned for topic shift detection, augmented with liguistic featuers for for topic trigger detection. Despite its smaller size (~25M parameters), the MobileBERT-based system achieved competitive results (F1 = 74.16%,) compared to the much larger XLNet model (~110M parameters, F1 = 79.95%), while offering greater efficiency. The topic trigger module, combining MobileBERT with linguistic features, further demonstrated effective performance (F1 = 71.61%).

Implications Of Neural Compression To Scientific Images, João Phillipe Cardenuto, Joshua Krinsky, Lucas Nogueira, Aparna Bharati, Daniel Moreira

Computer Science: Faculty Publications and Other Works

While neural compression has the potential to revolutionize image compression, recent studies have emphasized its ability to introduce subtle artifacts that could alter the image content. Concerned about the impact of such modifications on scientific images, this work explores the potential effects of neural compression on these images, focusing on two critical aspects: semantic understanding and forensic integrity. We use scientific image datasets to assess the performance of neural compression techniques on Visual Question Answering (VQA) and copy-move forgery detection tasks. Our findings indicate that the subtle changes introduced by neural ] compression do not significantly degrade the performance of …

Hierarchy Viz: A Visual Analytics Framework For Visualizing Hierarchical Data Using Machine Learning, Vinay Kumar Uppalapati

Theses and Dissertations

Automated visualization systems aim to generate visualizations directly from raw data with minimal user inputs. However, while existing systems focus on data visualizations mainly using line charts and scatter plots to explore the data patterns, they struggle with hierarchical data representation where data relationship is essential. Hierarchical visualization, crucial for understanding multi-level relationships, typically requires users to manually define hierarchies and have expertise in visualization tools to create meaningful representations. This makes the process complex, time-consuming, and reliant on domain knowledge. To address this, we propose HierarchyViz, an automated system that detects multiple hierarchies in raw datasets and generates intuitive …

Exploring Character-Level Attacks On Neural Ranking Models, Surjyanee Halder

Master’s Dissertations

Neural ranking models (NRMs) have achieved state-of-the-art performance in information retrieval, yet they remain highly susceptible to subtle adversarial inputs such as character-level typos. This project explores the robustness of such systems by introducing a reinforcement learning (RL)-based query perturbation framework. RL agents—PPO, DQN, and A2C—were trained to minimally modify user queries (e.g., through character deletions or swaps) with the goal of significantly altering the resulting document rankings, as measured by Kendall’s Tau. Experiments were conducted on the TREC DL 2019 and 2020 benchmarks using two different neural rankers: Mini LM and a fine-tuned Character BERT model. The perturbation attacks …

Revitalization Of Endangered Languages With Ai, Ivory Yang

Dartmouth College Master’s Theses

The preservation and revitalization of endangered languages, particularly those with minimal digital presence, presents significant challenges for computational linguistics. This thesis addresses these challenges by proposing novel methods for language identification and data generation, focusing on underrepresented Indigenous languages, specifically Nüshu, Native American and Native Alaskan languages.

In the first study, a COLING 2025 paper, we present NüshuRescue, an AI-driven framework designed to facilitate the preservation of Nüshu, an endangered script used exclusively by Yao women in China. Using minimal seed data, we demonstrate how GPT-4-Turbo can generate new translations, expanding a publicly available Nüshu-Chinese corpus, achieving 48.69% accuracy in …

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Deep Neural Networks For Particle Identification In Simulated Proton-Proton Collisions At Lhc And Rhic, Omar Mazhar Khalaf

Theses and Dissertations

Particle identification is an essential part of experimental high-energy physics, which allows the study of the most fundamental constituents of matter. This thesis explores the use of deep neural networks for identifying particles in simulated proton-proton collisions at the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC). The deep neural networks were trained on LHC datasets which have various momentum ranges including regions of high transverse momentum above 3 GeV/c. The key findings of thesis include achieving an accuracy of 99.99%, 98.3%, and 90.14% for 3-5 pt, 5-7 pt and above 7 pt regions respectively for the …