Artificial Intelligence and Robotics Commons^™

Can We Discover Physical Models Using Machine Learning? A Case Study Of Galaxy Sizes, Festa Buçinca-Çupallari, Ariyeh Maller, Viviana Acquaviva, Austen Gabrielpillai, Rachel S. Somerville

Publications and Research

We explore the ability of machine learning methods to discover underlying equations of physics by searching for the equations governing galaxy size in a semianalytic model. This case study allows us to evaluate the process as we know the ground truth. We find that we fail to find an equation to predict galaxy size on the entire data set, but are successful when we separate out disk galaxies where we expect the physics driving galaxy size to be different than in bulge-dominated systems. We are also able to find an equation for bulge size, but not without adding an additional …

In The Shadow Of Prompts: Adversarial Attacks And Model Cloning In Large Language Models, Kanchon Gharami

Doctoral Dissertations and Master's Theses

Large-language models (LLMs) already power mission critical tasks such as command-and-control chat, satellite ground-station automation, military analytics, and cyber-defense. Since most of these services are offered through application programming interfaces (APIs) that still expose full or top-k logits and lack mature safeguards, they present a serious, often overlooked attack surface. Earlier work has shown how to rebuild the output projection layer or distill surface behavior, but no attack has produced a deployable clone within a tight query budget. In this thesis, we address this problem by presenting a practical pipeline for cloning LLMs under constrained settings. The approach first estimates …

The Impact Of Artificial Intelligence As An Intervening Variable Between The Digital Government Strategy And Competency Development "An Applied Study At Sharjah Police Sciences Academy ", Elsayed Kamal Risha, Abd Al-Rahman Al-Naqbi

Journal of Police and Legal Sciences

The study aimed to determine the impact of the digital government strategy on competencies development, through artificial intelligence as an intervening variable, and to achieve the objectives, the study relied on the quantitative approach and the questionnaire was used as the main tool for collecting data. The study community represented officers, non-commissioned officers and individuals at the Sharjah Academy for Police Sciences, and the study sample amounted to 30 affiliates, i.e. the total number of employees in the Competency Development Department at the Academy. The study reached a set of results, the most prominent of which are:

- The existence …

Legislative And Security Confrontation Of Crimes Artificial Intelligence In The State Of Kuwait (An Analytical Study), Rashid Mohammed Al Marri

Journal of Police and Legal Sciences

The study aimed to demonstrate the mechanisms of legislative and security confrontation of artificial intelligence crimes. The use of technologies associated with artificial intelligence may go beyond the imposed limits, whether by exploiting it through its program developers & specialists to commit crimes in the cyber field, or it may be with the growing capabilities of artificial intelligence to make decisions in the field. Many behaviors occur automatically. Our research also aims to study the criminal responsibility for these crimes, & determine it in order to hold the real perpetrator accountable in accordance with the legal rules in force to …

Benford's Law In Basic Rnn And Long Short-Term Memory And Their Associations, Farshad Ghassemi Toosi

Department of Computer Science Publications

Benford's Law describes the distribution of numerical patterns, specifically focusing on the frequency of the leading digit in a set of natural numbers. It divides these numbers into nine groups based on their first digit, with the largest category comprising numbers beginning with 1, followed by those starting with 2, and so on. Each neuron within a neural network (NN) is associated with a numerical value called a weight, which is updated according to specific functions. This research examines the Degree of Benford's Law Existence (DBLE) across two language model methodologies: (1) recurrent neural networks (RNNs) and (2) long short-term …

Bridging Classical Rhetoric And Ai: A Systematic Framework For Developing Authorial Voice Through Large Language Models, Daniel Plate

Theses

This project addresses critical gaps in AI-assisted writing by developing the first systematic framework that integrates classical rhetorical principles with modern large language model capabilities for authorial voice development. The primary focus is on creating reliable methods for stylistic control through strategic AI collaboration rather than ad hoc prompting approaches. The project develops a comprehensive coding system for analyzing prose style, creates ten distinct authorial personas, and establishes a dual curation methodology that structures both stylistic analysis and content preparation. Implementation through the AI Writing Guide website provides practical tools including prompt templates, annotated examples, and instructional materials that demonstrate …

Graph Traverse Reference Network For Sign Language Corpus Retrieval In The Wild, Kun Hu, Fengxiang He, Adam Schembri, Zhiyong Wang

Research outputs 2022 to 2026

Sign languages are the primary languages of the deaf community as well as hearing individuals who are unable to speak, which engage the visual-manual modality to convey meanings. In recent years, there has been an explosive growth of sign language videos available from video streaming and social media service platforms. Given the size of these corpora, sign language users often face significant challenges in effectively acquiring the information they need. Therefore, we propose a novel deep learning architecture, namely Graph Traverse Reference Network (GTRN), allowing visual signing queries to retrieve relevant sign language videos (documents) from a large corpus. GTRN …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Kabe Aberle, Nadia Kako, Kateri Mcrae, Brooke Agulnek, Sky Palmon, Yasmine Ramirez, Francisca Aguirre Beltran, Ashley Juarez, Bridget Kim, Tessa Appel, Sterling Kerr, Spencer Ingley, Gabe Meyer, Robin Tinghitella, Dale Broder, Lily Baeza, Chloe Beers, Julia Coakley, Whitney Kelsey, Sydney Gainforth, Gabi Wing, Audrey Martin, Aaliyah Amore Berry, Brooke Watley, Kiruthika Venkatesan, Rachel Bienstock, Annabella Brotherston, Madison Bryant, Mia Burgener, Emma P. Lieb, Rachel A. Johnson, Jennifer L. Hoffman, Kania Campbell, Kiena Campbell, Courtney Cassidy, Sage Krzyzkowski, Maddox Jones, Skylar Abookire, Luke Hawkins, Sunnah Yoon, Andrea Chu, Yan Qin, Nyah Cubbison, Brian Gearity, Daniel Mcintosh, Mariely Cruz, Edward Garrido, Grady Dionne, Nicole Doris, Lyndsie Salvagio, Ann-Charlotte Granholm-Bentley, Anna Dymov, Hannah Eckert, Gabrielle Welsh, Erica Larson, Charlie Ernst, Anna Zhou, Sarah Watamura, Larissa Fedorovich-Klein, Georgie Fields, Kimberly A. Guevara, Aven Mccall, Ben Peltier, Feruz Yahia, Patrick Flores, Jadyn Floyd, Sophia Forcier, J. Von R. Monteza, Peter Sokol-Hessner, Gwendolyn Geiger, Scott Nichols, Camryn Gunter, Kendal Hengst, Charlie Bednarz, Issy Garside, Addison Baker, Rachel Mina, Brooke Hermanson, Amanda Klingler, William Highfill, Sydney Jaques, Kerstin Lewey, Allison Grossery, Daniel Linseman, Ethan Lim, Jagger Livengood, Owen Mantelli, Gabby Pappas, Abby Mcdonald, Madeleine Dierking, Eve Miller, Emma Loeber, Anna Marlow, Michael Kerwin, Ella Mathews, Hillary Hamann, Khadija Mohamed, Vivian Nguyen, Gabri Notov, Ifunayachi Ogbonna-Ukuku, Sunil Kumar, Charles Baysah, Sarah Olson, Don Sullivan, Anna Paradiso, Jay Parrish, Mira Pronobis, Alisha Pravasi, Kerstin Haring, Diego Ramirez, Christopher Reardon, Juliana Ramirez, Casey Doherty, Ella Kestner, Teagan Weindel, Cate Billings, Pablo Torre-Walter, Lucy Rand, Samantha Reynolds, Mark Siemens, Khadeeja Rashid, Laine Satterlee, Piper Heilbronner, Lily Pound, Ben Whitehurst, Anna Respet, Lizzie Lesoing, Sydney Hertel, Aya Saad-Masri, Brooke Ballenger, Max Proske, Hannah Rosenberg, Ellia Nakahara, Sophia Espinoza, Ivan Woolhouse, Simon Ruland, Gorkem Er, Timothy Sweeny, Melaku Saketa, Michela Schenk, Maren Lynch, Madi Hamm, Grace Schroeder, Michelle Rozenman, Rana Seif, Jackson Hall, Marisela Simental, Daniel Paredes, Aaron Mena, Preston Spaan, Evelyn Stovin, David Andrew Swartz, Anh Tran, Daniel Pittman, Luke Farchione, Emily Boyer, Ukari Verner, Lacey Conrad, Jonathan Velotta, James Weiner, Jagger Gossett, Noah Sherry, Sam Proud, Ben Block, Avi Narayana, Zoey Weiss, Alyssa Wilson, Gabrielle Walsh, David Zonana, Keely Wright, Kena Riveria, Lillybelle Deer, Jena Doom, Elysia Davis, Isabelle Yaremenko, Caitlyn Young

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

Emergent Collective Reproduction Via Evolving Neuronal Flocks, Nam H. Le, Michael Levin, Richard A. Watson, Josh Bongard, Christopher L. Buckley

Northeast Journal of Complex Systems (NEJCS)

This study advances the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, VitaNova, which integrates self-organization and natural selection to simulate the emergence of complex, reproductive groups. By dynamically modeling individual agents within an environment shaped by predators and spatial constraints, VitaNova reveals mechanisms by which simple agents evolve into cohesive units exhibiting collective reproduction. The findings highlight the synergy between self-organized behaviors and adaptive evolutionary strategies as fundamental drivers of ETIs. This approach deepens our understanding of higher-order biological individuality and offers a new empirical pathway for investigating ETIs, extending current theoretical frameworks.

Pure And Strong Nash Equilibrium Computation In Compactly Representable Aggregate Games, Jared Soundy, Mohammad T. Irfan, Hau Chan

Research & Publications

Aggregate games model interdependent decision making when an agent’s utility depends on their own choice and the aggregation of everyone's choices. We define a compactly representable subclass of aggregate games we call additive aggregate games, which encompasses popular games like congestion games, anonymous games, Schelling games, etc. We study computational questions on pure Nash equilibrium (PNE) and pure strong Nash equilibrium (SNE). We show that PNE existence is NP-complete for very simple cases of additive aggregate games. We devise an efficient algorithmic scheme for deciding the existence of a PNE and computing one (if it exists) for bounded aggregate space. …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Foodlmm: A Versatile Food Assistant Using Large Multi-Modal Model, Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple …

Prompttutor: Effects Of An Llm-Based Chatbot On Learning Outcomes And Motivation In Flipped Classrooms, Yuhao Zhang, Eng Lieh Ouh, Chong Jee Adam Ho, Siaw Ling Lo, Kar Way Tan, Feng Lin

Research Collection School Of Computing and Information Systems

This study explores the integration of a Large Language Model (LLM) based chatbot, PromptTutor, into flipped classrooms (FC) for undergraduate Computer Science (CS) education. PromptTutor is designed to provide personalized, immediate feedback to support student learning in FC by incorporating reflective learning and scaffolding strategies. The traditional FC typically lacks this immediate feedback during the pre-class learning phase, risking decreased student motivation according to existing literature. This study examines if students improve in learning outcomes and motivation after using PromptTutor. Through a controlled crossover experiment with 50 students, the study demonstrates statistically significant improvements in students' quiz performance and motivation …

Unveiling Knowledge Boundary Of Large Language Models For Trustworthy Information Access, Yang Deng, Moxin Li, Liang Pang, Wenxuan Zhang, Wai Lam

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as powerful tools for generating content and facilitating information seeking across diverse domains. While their integration into conversational systems opens new avenues for interactive information-seeking experiences, their effectiveness is constrained by their knowledge boundaries—the limits of what they know and their ability to provide reliable, truthful, and contextually appropriate information. Understanding these boundaries is essential for maximizing the utility of LLMs for real-time information seeking while ensuring their reliability and trustworthiness. In this tutorial, we will explore the taxonomy of knowledge boundary in LLMs, addressing their handling of uncertainty, response calibration, and mitigation of …

Aircraft Bird Strike Risk Prediction Using Machine Learning And Analytic Hierarchy Process, Jason Anthony Powell

Doctoral Dissertations and Master's Theses

To address the limitations of Next Generation Radar-based bird strike forecasting, this study modeled 12 spatiotemporal weather features from the National Oceanic and Atmospheric Administration alongside bird strike risk using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), XGBoost regression tree, and Bayesian network algorithms. Five years of bird strike data from four geographically diverse airfields served as the target risk variable, categorized as low, moderate, or severe based on Department of the Air Force risk models. The ensemble model, which combines the LSTM-RNN and XGBoost regression algorithms, yielded the most accurate forecasts, achieving 80% to 93% accuracy across all airfields, …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

From Palimpsest To Prompt: Rewriting Shakespeare, Creative Authorship, And The Generative Logics Of Large Language Models In Contemporary Theatre, Michael Harding, James Hutson

Faculty Scholarship

This article examines the convergence of creative authorship, adaptation, and generative artificial intelligence within contemporary theatre, taking Michael Harding‘s Awake, Young King as a central case study. Through the rewriting of Shakespearean drama, Harding‘s creative process demonstrates how theatrical meaning emerges through ongoing negotiation among playwright, performer, and audience, with scripts historically subject to revision, improvisation, and reinterpretation. Concerns regarding copyright, intellectual property, and the role of AI in the performing arts are reframed as extensions of enduring debates over originality and authorship, rather than novel threats. Tracing the evolution from The Rise of James VI to Awake, Young King, …

From Sparse Feedback To Sequential Decision-Making: Learning Safety Constraints With Weak Supervision, Siow Meng Low

Dissertations and Theses Collection (Open Access)

Real-world decision-making often involves safety constraints that are implicit, non-Markovian, or difficult to specify directly. Standard reinforcement learning (RL) approaches typically assume access to fully specified cost functions and constraint budgets—assumptions that limit their applicability in domains where such structure must instead be inferred from data. This dissertation develops a sequence of methods for learning safety-relevant structure from weak supervision, such as sparse binary feedback on trajectory segments, and using these signals to guide planning and policy optimization.

The first part of the dissertation introduces a sample-efficient method for planning in continuous Markov Decision Processes (MDPs) using deep reactive policies. …

Machine Learning For Digital Biomarker-Based Detection Of Cognitive Decline, Seng Khoon The

Dissertations and Theses Collection (Open Access)

Dementia is a neurodegenerative disease with a prevalence rate expected to triple by 2050, posing a significant challenge for health services. To impede the increasing prevalence, medical professionals and scientists are actively investigating technology to detect cognitive decline at a reversible stage known as Mild Cognitive Impairment (MCI). Digital biomarker technology is an emerging pragmatic approach to permit objective, ecologically valid, and long-term continuous measurement of cognitive health status, rendering it as one of the promising technologies for early MCI detection. Despite its potential, it is nontrivial to encode, extract and combine predictive information from these digital biomarker technologies; advanced …

Query Understanding In Llm-Based Conversational Information Seeking, Yifei Yuan, Zahra Abbasiantaeb, Mohammad Aliannejadi, Yang Deng

Research Collection School Of Computing and Information Systems

Query understanding in CIS involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. LLM enhance this process by interpreting nuanced language and adapting dynamically, improving the relevance and precision of search results in real-time. In this tutorial, we explore advanced techniques to enhance query understanding in LLM-based CIS systems. We delve into LLM-driven methods for developing robust evaluation metrics to assess query understanding quality in multi-turn interactions, strategies for building more interactive systems, and applications like proactive query management and query reformulation. We also discuss key challenges in integrating …