Physical Sciences and Mathematics Commons^™

Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau

Research Collection School Of Computing and Information Systems

With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works …

Equiangularity From Compatible Orthobiangularity, Tyler J. Myers

Theses and Dissertations

An equiangular tight frame (ETF) is an equal norm sequence of vectors in a Hilbert space whose coherence achieves equality in the Welch bound. Such sequences necessarily have minimal coherence and thus are, in some sense, as "spread out" in space as possible. ETFs have a variety of applications, such as compressed sensing and waveform design. The main problem in the study of ETFs is determining the pairs (D, N) for which an ETF with N vectors in a D-dimensional space exists. Real ETFs are moreover equivalent to a special subset of a well-studied class of graphs known as strongly …

What References Are Chatgpt, Gemini, Copilot, And Perplexity Providing For Consumer Health Questions?, Ivan Portillo, Scott Johnson, Catherine Johnson

Library Presentations, Posters, and Audiovisual Materials

No abstract provided.

Optical Character Recognition For Early Handwriting Legibility Assessment, Franceli L. Cibrian, Kayla Anderson, Yingying 'Yuki' Chen, Lauren Min, Lizbeth Escobedo

Engineering Faculty Articles and Research

Monitoring children’s handwriting, such as avoiding writing assignments, displaying uneven letter formation, or showing slow writing speed, can help identify developmental and academic issues early. Poor handwriting affects up to 34% of children, leading to academic and self-esteem challenges. Handwriting assessments, typically conducted by teachers, are often delayed due to workload and could be subjective and inconsistent. This paper explores the potential of Optical Character Recognition (OCR) technology to augment and ease handwriting assessments. Based on an evaluation of 10 OCR algorithms using 33 handwriting samples assessed by two experts, the research indicates that Pen to Print and Google are …

Clusters, Trends, And Choices: Feature Selection In Interactive Statistical Graphics, Dylan Le

Master's Theses

Exploratory data analysis (EDA) is a method for uncovering the structure and key characteristics of data, often through the use of statistical graphics. These visual tools can reveal patterns and trends, and their effectiveness can be enhanced through interactivity. By enabling users to filter data, zoom, and toggle visual features, interactive plots can accelerate and enrich the EDA process. This study extends a previous graphical study by incorporating an interactive framework. Using a statistical lineup protocol with two target patterns (a linear trend and a clustering trend) participants interacted with plots by toggling various aesthetic features, including cluster coloring, ellipses …

Advancing Fake News Detection With Graph Neural Network And Deep Learning, Haji Gul, Feras Al-Obeidat, Muhammad Wasim, Adnan Amin, Fernando Moreira

All Works

In the modern era of digital technology, the rapid distribution of news via social media platforms substantially contributes to the propagation of false information, presenting challenges in upholding the accuracy and reliability of information. This study presents an updated approach that utilizes graph neural networks (GNNs) alongside with advanced deep learning techniques to improve the identification of false information. In contrast to traditional approaches that primarily rely on analyzing text and assessing the credibility of sources, our methodology utilizes the structural information of news propagation networks. This allows for a detailed comprehension of the interconnections and patterns that are indicative …

Sla Evaluation And Composition In Reconfigurable Cloud-Based Services, Michael Iannelli

Dissertations, Theses, and Capstone Projects

Given the business model of offering data and computing services in a cloud setting, a major question arises: How do the services of one cloud provider compare to those of others? With the ubiquitous use of smartphones and tablets, the ability of a cloud provider to support QoS and client mobility becomes paramount. This research proposes a methodology for evaluating service-level agreements (SLAs) between cloud providers and their consumers, with a particular focus on dynamic SLA composition to adapt to changes in the application requirements and the external environment—such as traffic surges, security threats, or evolving business models.

In one …

A Realizability Approach To Constructing Higher Types Via Classifiers, Benjamin Carrick Logsdon

Dartmouth College Ph.D Dissertations

We construct an interpretation of higher types into Peano arithmetic, showing in particular that every model of PA is a model of higher types. This is a reversal of Gödel’s Dialectica construction. We also define the classifier degrees, a degree structure which subsumes the Turing degrees, the enumeration degrees, and the many-one degrees. The classifier degrees boast a rich structure and many well-behaved operations.

A Novel Fractional Order Model For Analyzing Counterterrorism Operations And Mitigating Extremism, Mutaz Mohammad, Isa Abdullahi Baba, Evren Hincal, Fathalla A. Rihan

All Works

This study examines the profound impact of terrorism on individuals and society by developing a fractional-order mathematical model to analyze and enhance counterterrorism efforts. The model accounts for the persistent and complex nature of extremist behavior, particularly emphasizing the importance of preventing violent extremism before it escalates into terrorism. Real-world data on terrorist activities in Nigeria – specifically from the Boko Haram insurgency – was used to calibrate and validate the model, ensuring its relevance and accuracy. The model reveals that the basic reproduction number (R0) plays a decisive role in determining the long-term success of counterterrorism strategies. Numerical simulations …

Circle Actions On Oriented 4-Manifolds, Donghoon Jang, Oleg R. Musin

School of Mathematical & Statistical Sciences Faculty Publications

In this present paper, we consider an action of the circle group on a compact oriented 4-manifold. We derive the Atiyah–Hirzebruch formula for the manifold, and associate a graph in terms of data on the fixed point set. We show in the case of isolated fixed points that if an abstract graph satisfies the Atiyah–Hirzebruch formula, then there exists a corresponding 4-dimensional oriented S1-manifold.

Probing Shielding Tensor Components Of Amino Acids Using Nuclear Magnetic Resonance, Shiva Agarwal

Dissertations

Chirality is fundamental to terrestrial life. While most amino acids exist as nonsuperimposable mirror images, amino acids in terrestrial life are homochiral, with the L-enantiomer being ubiquitous. The detection of an excess of L-amino acids in carbonaceous meteorites suggests that extraterrestrial processes may have contributed to this enantiomeric excess (ee). One proposed mechanism, the magnetochiral model, provides a potential explanation for this phenomenon in stellar environments characterized by strong magnetic and electric fields and the presence of relativistic leptons. According to this model, subtle differences in the electronic environments of chiral amino acids under such conditions …

Arkansas Corn And Grain Sorghum Research Studies 2024, Jason Kelley, Travis Faske

Arkansas Agricultural Experiment Station Research Series

The 2024 edition of the Arkansas Corn and Grain Sorghum Research Studies Series includes research results on topics pertaining to corn and grain sorghum production, including weed, disease, nematode, and insect management; economics; irrigation; agronomics; soil fertility; drone use; and research verification program results.

Our objective is to capture and broadly distribute the results of research projects funded by the Arkansas Corn and Grain Sorghum Board. The intended audience includes producers and their advisors, current investigators, and future researchers. The Series serves as a citable archive of research results.

The reports inform and guide our long-term recommendations, but should not …

State And Transition Models For Mulga Rangelands Of Western Australia, Alison O'Donnell, Anna E. Richards, Suzanne Prober, Peter-Jon A. Waddell, Sarah Luxton, Ian Watson, Brett Abbott, Philip Thomas, Joshua E. Foster

Natural resources published reports

This report details a collaborative project between the Western Australian Department of Primary Industries and Regional Development and CSIRO that focused on developing State and Transition Models (STMs) for mulga rangelands in Western Australia. The overarching aim of the project was to improve the common understanding of the characteristics and dynamics of mulga rangeland ecosystems and the expected impacts of management. Specifically, the project aimed to collate expert knowledge and monitoring information using a nationally consistent framework to develop quantitative and dynamic STMs. The geographic scope of the project covers the extensive mulga rangelands of Western Australia, particularly the Gascoyne …

Steiner Coset Partitions Of Groups, Fusun Akman, Papa Sissokho

Faculty Publications – Mathematics

A coset partition of a group G is a set partition of G into finitely many left cosets of one or more subgroups. A driving force in this research area is the Herzog–Schönheim Conjecture, which states that any nontrivial coset partition of a group contains at least two cosets with the same index. Although many families of groups have been shown to satisfy the conjecture, it remains open.

A Steiner coset partition of G, with respect to distinct subgroups H₁,...,H_r, is a coset partition of G that contains exactly one coset of each …

Streaming Instabilities In Class 0/I Disks, Shirin Gul Zaidi

Dissertations, Theses, and Capstone Projects

While it is clear that planets form in protoplanetary disks, fragmentation and the radial drift of pebbles inhibit the growth of grains to planetesimals. To overcome these barriers, effective localized dust clumping on a short enough time scale is required. One promising solution to this problem is the streaming instability. Streaming instability is only triggered with a large enough dust-to-gas ratio and grain size. Though it is unknown when and where those conditions are met in protoplanetary disks, recent studies have indicated that planetary cores likely require early formation to match detected exoplanet system masses, so that we need to …

Characterizing Electrospray Performance Of Hydroxylammonium Nitrate (Han) And 2-Hydroxyethylhydrazinium Nitrate (Hehn) Ionic Liquids By Mass Spectrometry And Dynamics Simulations, Wenjing Zhou

Dissertations, Theses, and Capstone Projects

Dual-mode propulsion combines chemical and electric propulsion methods into a single compact system by sharing hardware and propellant for both modes, offering complementary advantages tailored to specific mission needs. A promising propellant candidate for dual-mode propulsion is a binary mixture of hydroxylammonium nitrate (HAN) and 2-hydroxyethylhydrazinium nitrate (HEHN) ionic liquids (ILs). Each of the two ILs was developed as a greener alternative to the traditional, yet toxic, hydrazine monopropellant used in chemical propulsion. Recently, their potentials in electrospray propulsion have garnered attention. This thesis focused on the investigation of reaction dynamics and kinetics of HAN, HEHN and their binary mixture …

Computability Theoretic Aspects Of Profinite Groups And Models Of Presburger Arithmetic, Jason Block

Dissertations, Theses, and Capstone Projects

Profinite groups, which are exactly the Galois groups, are all either finite or uncountable. However, all second countable profinite groups can be presented as the set of paths through a countable tree. We use these tree presentations to find upper bounds on the complexity of the existential theories of profinite groups, as well as to prove sharpness for these bounds. These complexity results enable us to distinguish the class of profinite groups that are isomorphic to a direct product of finite groups, for which we find an upper bound on the complexity of the entire first order theory. Additionally, given …

High Moment And Pathwise Error Estimates For Fully Discrete Mixed Finite Element Approximations Of The Stochastic Stokes Equations With Multiplicative Noise, Liet Vo

School of Mathematical & Statistical Sciences Faculty Publications

This paper is concerned with high moment and pathwise error estimates for both velocity and pressure approximations of the Euler–Maruyama scheme for time discretization and its fully discrete mixed finite element discretization. Optimal rates of convergence are established for all pth moment errors for p ≥ 2 using a novel doubling of moments technique. The almost optimal rates of convergence are then obtained using Kolmogorov’s theorem based on the high moment error estimates. Unlike for the velocity error estimate, the high moment and pathwise error estimates for the pressure approximation are proved in a time-averaged norm. In addition, the …

Myceli-Yum: Elucidating Structure-Property Relationships For Polymer Degradation By Mycelial Digestion, Jordan Scott Ford

Master's Theses

Since the industrial entrance of polymer plastic materials, plastic has become ubiquitous in both everyday use and waste. Due to inefficiencies and knowledge gaps, current recycling methods are not able to account for the high scale of plastic waste, resulting in the bulk of this waste being landfilled, mishandled, and deposited in the environment. Mycelium, the microorganism responsible for fruiting mushroom bodies and mold growth, holds potential to reduce plastic waste and can potentially be utilized as a method of industrial recycling. Following a drug-design approach, the active site of mycelial enzymes responsible for natural biopolymer degradation have been assessed …

Final Bpsou Unreclaimed Sites: Ur-05 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

A Stacking Ensemble Model For Food Demand Forecasting: A Preventative Approach To Food Waste Reduction, Asmaa Seyam, Sujith Samuel Mathew, Bo Du, May El Barachi, Jun Shen

All Works

Building effective demand forecasting is crucial for better planning and ensuring sustainability within food supply chain systems. The food industry has received the least attention for building demand forecasting approaches, with a noticeable lack of utilizing ensemble stacking models. Additionally, while some models have achieved accurate predictions, they do not consider freshness variables and are not assessed for their impact on waste reduction. This paper develops a demand forecasting framework that is considered as a preventative approach to reduce food waste by enabling food retailers to better manage inventory and balance supply with demand. The paper first develops an ensemble …

A Bayesian Approach To Grappa Parallel Fmri Image Reconstruction Increases Snr And Power Of Task Detection, Chase J. Sakitis, Daniel B. Rowe

Mathematical and Statistical Science Faculty Research and Publications

In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Undersampling k-space reduces the acquisition time but results in aliased, or “folded,” images. GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpolation weights, which are estimated prescan and fixed over time, to fill in the missing …

Constraining Nuclear Data Uncertainty Requirements For The 19F(A, N)22Na Reaction For Non-Proliferation Applications, Tyler R. M. Smith

Theses and Dissertations

This thesis explores the requirements on nuclear data uncertainties needed for the use of the ¹⁹F(α, n)²²Na reaction for nuclear non-proliferation applications. An overview of how neutrons are produced from alpha decays in a UF₆ medium is discussed. Calculation demonstrate the role nuclear data uncertainties effect the neutron yield and energy spectra as a function of enrichment.

Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover

Theses and Dissertations

The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …

Simulations Of Differential Reflectivity Columns In Quasi-Linear Convective Systems, Devon Jacob Healey

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Much research over the past decade has revealed that dual-polarization radar is a powerful tool in furthering our understanding of severe storm dynamics and subsequently improving warning strategies for these hazardous events. However, there are currently very few studies examining a particularly prolific severe convective storm mode with dual-polarization radar: quasi-linear convective systems (QLCSs). These storms can occur in any season of the year throughout most of the contiguous United States and can have immense societal impacts. Warning for the hazards produced by QLCSs is currently a significant operational challenge, therefore, research is needed to see if applying dual-polarization radar …

The Weaving Of Machine Learning And Artificial Intelligence Into The Fabric Of Cybersecurity Curriculum: From Degree Plan To Capstone Projects, Mahmoud K. Quweider, Liyu Zhang, Jorge Castillo, Ala Qubbaj

Informatics and Engineering Systems Faculty Publications

As our newly designed degree in Cybersecurity enters its fourth year, students in the program are starting to take courses beyond the basic ones, including senior courses, technical electives, and capstone projects. While Cybersecurity is at the heart of our degree that addresses the national need for cybersecurity specialists, how we approach the education and pedagogy of cybersecurity in the era of Big Data and AI/ML (Artificial Intelligence/Machine Learning) is a question that we are addressing in real-time as techniques and measures and countermeasures of cybersecurity attacks keep evolving and taking advantages of the rapid advancements in computing, memory, storage, …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …