Computer Sciences Commons^™

Groundwater Modeling Of The Ogallala Aquifer: Use Of Machine Learning For Model Parameterization And Sustainability Assessment, Tewodros Aboret Tilahun

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

Addressing groundwater depletion problems in heterogeneous aquifer systems is a challenge. The heterogeneous Ogallala Aquifer, a critical source of groundwater in the central United States, has undergone decades of decline in water levels due to pumping. This project aims to build a robust groundwater model to evaluate optimal scenarios for sustainable use of the groundwater resource within a section of the Ogallala aquifer located in the Middle Republican Natural Resources District (MRNRD). This study follows a comprehensive approach involving parameterization, construction, and optimization. The model is parametrized using hydraulic conductivity and recharge values obtained from a random forest-based machine learning …

Applications Of Artificial Intelligence On Drought Impact Monitoring And Assessment, Beichen Zhang

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

Drought, a prevalent and consequential natural disaster, poses widespread, indirect challenges across environmental and societal dimensions. Despite considerable focus on monitoring meteorological and hydrological drought and studying their characteristics, there is a gap in assessing its multifaceted impacts, especially on societal sectors. The dissertation comprises three research essays utilizing artificial intelligence to quantitatively study multi-dimensional drought impacts. The first essay leveraged deep learning and natural language processing to predict multi-dimensional drought impacts from textual datasets, including social media, news media, and citizen scientist reports. The findings demonstrate superior performance over traditional methods and unveil the spatial and temporal heterogeneity of …

Optimizing Scalability For Formal Analysis With Evolutionary Algorithm, Jianghao Wang

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

Predominantly employed to tackle hardware validation challenges in the early years, formal methods have since expanded to software engineering, introducing a significant level of rigor and precision to software analysis. Its use of mathematical notations and logical reasoning allows for abstract modeling of programs, enabling researchers and engineers to perform a multitude of analysis tasks to verify system dependability and rigorously prove the correctness of system properties. Despite the availability of many automated analysis tools including those considered lightweight, the practical adoption of formal methods in software development has been limited due to scalability concerns, especially when applied to large …

Development Of Feature Extraction Models To Improve Image Analysis Applications In Cancer, Yu Shi

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

Cancer poses a significant global health challenge. With an estimated 20 million new cases diagnosed worldwide in 2022 and 9.7 million fatalities attributable to the disease, the economic burden of cancer is immense. It impacts healthcare systems and imposes substantial costs for its care on patients and their families. Despite advancements in early detection, prevention, and treatment that have reduced overall cancer mortality rates, the growing prevalence of cancer, particularly among younger individuals, remains a pressing issue.

Recent advancements in medical imaging technology have progressed significantly with the help of emerging computer vision and artificial intelligence (AI) technology. Despite these …

A Data-Driven Discovery System For Studying Extracellular Microrna Sorting And Rna-Protein Interactions, Sasan Azizian

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

Interactions between microRNAs (miRNAs) and RNA-binding proteins (RBPs) are pivotal in miRNA-mediated sorting, yet the molecular mechanisms underlying these interactions remain largely understudied. Few miRNA-binding proteins have been verified, typically requiring extensive laboratory work. This study introduces DeepMiRBP, a novel hybrid deep learning model designed to predict microRNA-binding proteins. The model integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with attention mechanisms, transfer learning, and cosine similarity to offer a robust computational approach for inferring miRNA-protein interactions.

DeepMiRBP is implemented through two distinct architectures. The first architecture employs a Y-shaped model that uses Bi-LSTM networks and transfer learning to extract contextual …

Long Term Ultrasonic Monitoring And Machine Learning Investigation Of Micro-Crack Damaged Concrete, Yalei Tang

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

The thermal modulation method is a recently developed nonlinear ultrasonic technique for evaluating material damage. This method utilizes thermal strain changes resulting from temperature variations to excite the nonlinear behavior of materials and modulate high-frequency ultrasonic waves within them. Its working principle suggests significant potential for application in large-scale concrete structures and in-situ monitoring of real structures. Despite numerous laboratory demonstrations of its effectiveness, several gaps remain before it can be applied to in-service large concrete structures.

This study investigates the potential of the thermal modulation technique for evaluating concrete structures in ambient conditions, addressing key uncertainties for practical implementation. …

Integration Of Matlab And Machine Learning To Accelerate Evaluation Of Biological Activity In Agricultural Soils And Promote Soil Health Improvement Goals, Andrew Stiven Ortiz Balsero

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Traditionally, assessments of soil biological activity have been confined to laboratory settings, creating a disconnect with practical in-field methods. To bridge this gap, cotton fabric degradation has been used to illustrate soil microbial activity under different management practices. While effective, these demonstrations are subjective and labor-intensive.

Researchers have explored using image processing software like ImageJ and Adobe Photoshop to streamline this process. Although these tools accurately quantified fabric degradation under varying soil conditions, the methods remained labor-intensive and complex. Consequently, these methods were still not ideal for on-farm use by agricultural practitioners.

To further address labor and complexity limitations, the …

Integrating Remote Sensing And Machine Learning To Determine Past, Current And Future Crop Water Use From The Nubian Sandstone Aquifer System, Moaz Ishag

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

The agriculture sector is a significant consumer of water, and sustainable water use begins with monitoring irrigated land. Delineating irrigated land supports decision-makers and promotes the sustainable use of this crucial resource. This study focuses on the Nubian Sandstone Aquifer System (NSAS), the largest aquifers in the world, which spans Egypt, Sudan, Libya, and Chad. The study aims to: 1) quantify the increase in irrigated hectares (both pivot and non-pivot) from 2000-2001 to 2023-2024; 2) identify major irrigated crop types and their water requirements; and 3) quantify groundwater crop water use from the NSAS using remote sensing via the Google …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Development Of A Rule-Based Monitoring System For Autonomous Heavy Equipment Safety, Amirpooya Shirazi

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Roadway construction work zones are constantly exposed to interactions among construction equipment, workers, and vehicles. Furthermore, ensuring safety in these areas is considered a challenging task due to the complexity of the environment. As shown in the rising trend of fatal accidents in roadway work zones, current OSHA regulations in construction safety are insufficient in effectively detecting unsafe situations and mitigating the risks. Furthermore, best practices, such as internal traffic control planning (ITCP), exhibit critical limitations requiring continuous monitoring of active work zones as well as adjustments to the site coordination plans due to the dynamic nature of work zone …

My Ai Companion: An Examination Of The Removal Of Erotic Role Play From Replika Through User Discussion On Reddit, Chelsee M. Allen

Department of Sociology: Dissertations, Theses, and Student Research

The development of artificial intelligence (AI) software has expanded rapidly in recent years, and thus has emerged the importance of exploring human relationships with AI chatbots. Replika, an app which uses AI to mimic human conversation, removed a function called Erotic Role Play (ERP) that allowed for sexual conversation with users’ customizable chatbots in February of 2023. This exploratory qualitative study examines the aftermath of ERP’s removal through an analysis of user interactions on Reddit. Five overarching themes emerged through the analysis of top posts to a Replika-specific subreddit, encompassing topics around mental health, stigma, coping, sex work and gendered …

Cellmarkerpipe: Cell Marker Identification And Evaluation Pipeline In Single Cell Transcriptomes, Yinglu Jia, Pengchong Ma, Qiuming Yao

School of Computing: Faculty Publications

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https:// github. com/ yao- labor atory/ cellM arker Pipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker’s overall reliable performance …

Nonlinear Classifiers For Wet-Neuromorphic Computing Using Gene Regulatory Neural Network, Adrian Ratwatte, Samitha Somathilaka, Sasitharan Balasubramaniam, Assaf A. Gilad

School of Computing: Faculty Publications

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the …

Building A Data Pipeline And Machine Learning Model For Insurance Data, Connor Weyers

Honors Program: Senior Projects (Public)

Insurance telematics is an emerging and exciting field. It combines the advancements in GPS tracking, computational analytics, data processing, and machine learning into a useful tool to help insurance companies make the best product for their consumers. This is why National Indemnity looked to implement a telematics portion to their business processes of underwriting insurance policies and sponsored a School of Computing Senior Design project. In this report, we will first review existing solutions that been used to solve problems and subproblems similar to that we are given in this project. We then propose designs for the data pipeline and …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

School of Computing: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

A Little Loud And A Little Alone: A Phenomenology Of Leadership Identity Construction Among Women In Higher Education Technology, Amy Barry

Department of Teaching, Learning, and Teacher Education: Theses and Other Student Research

This qualitative study is an exploration of how women in higher education information technology (IT) positions navigate constructing their leadership identities. This includes the messy, personal, internal identity work that occurs prior to claiming their leadership identities on the public stage, followed by an examination of what the experience of attempting to claim and negotiate a leadership identity is like in the social context of their organizations. This educational and sociological study employs an Interpretative Phenomenological Analysis approach with a series of three interviews per participant that allowed the researcher to deeply explore the personal identity experiences of participants. Findings …

Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi

School of Computing: Dissertations, Theses, and Student Research

The focus of this PhD thesis is on various distance and domination properties in graphs. In particular, we prove strong results about the interactions between asteroidal sets and dominating targets. Our results add to or extend a plethora of results on these properties within the literature. We define the class of strict dominating pair graphs and show structural and algorithmic properties of this class. Notably, we prove that such graphs have diameter 3, 4, or contain an asteroidal quadruple. Then, we design an algorithm to to efficiently recognize chordal hereditary dominating pair graphs. We provide new results that describe the …

Vr Circuit Simulation With Advanced Visualization For Enhancing Comprehension In Electrical Engineering, Elliott Wolbach

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

As technology advances, the field of electrical and computer engineering continuously demands innovative tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. Through a comprehensive literature review, it was discovered that there was a gap in the current research on using VR technology to effectively visualize and comprehend non-observable electrical characteristics of electronic circuits. This thesis explores the integration of Virtual Reality (VR) technology and real-time electronic circuit simulation with enhanced visualization of non-observable concepts such as voltage distribution and current flow within these circuits. The primary objective is to develop an immersive educational platform that makes …

Aggregate Games: Computations And Applications, Jared Soundy

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

Existing computational game theory studies consider compact representations of games that capture agent interaction in real-world environments and examine computation aspects of computing equilibrium concepts to analyze or predict agent behavior.

One of the most well-studied representations that capture many commonly studied real-world environments is aggregate games. Aggregate games, first systematically studied by Nobel laureate Reinhard Selten, have various applications in modeling the decision-making interdependence of agents, where each agent’s utility function depends on their own actions and the aggregations or summarizations of the actions of all agents. These applications include Cournot oligopoly competition, public good contribution, and voting, where …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

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

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Sliding Markov Decision Processes For Dynamic Task Planning On Uncrewed Aerial Vehicles, Trent Wiens

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Mission and flight planning problems for uncrewed aircraft systems (UASs) are typically large and complex in space and computational requirements. With enough time and computing resources, some of these problems may be solvable offline and then executed during flight. In dynamic or uncertain environments, however, the mission may require online adaptation and replanning. In this work, we will discuss methods of creating MDPs for online applications, and a method of using a sliding resolution and receding horizon approach to build and solve Markov Decision Processes (MDPs) in practical planing applications for UASs. In this strategy, called a Sliding Markov Decision …

The Kruger Collection Reimagined: A Case Study In 3d Scanning And Interactive Exhibit Design, Annissa Davis

Department of Anthropology: Theses and Student Research

This thesis examines the use of 3D modeling in museum exhibition to create exploratory exhibits that facilitate unique relationships between the visitors and the collection beyond what is provided by the collection’s in person counterparts. Typical use of 3D modeling in museums is currently often representative rather than exploratory. By employing a Digital Humanities lens to approach the development of a digital exhibition utilizing 3D technology and interactive elements created in a video game engine (Unity), this thesis project evaluates these potential new relationships. Using the Eloise Kruger Collection of Miniatures as a case study, the following text details the …

Scalable Relational Analysis Via Relational Bound Propagation, Clay Stevens, Hamid Bagheri

School of Computing: Faculty Publications

Bounded formal analysis techniques (such as bounded model checking) are incredibly powerful tools for today’s software engineers. However, such techniques often suffer from scalability challenges when applied to large-scale, real-world systems. It can be very difficult to ensure the bounds are set properly, which can have a profound impact on the performance and scalability of any bounded formal analysis. In this paper, we propose a novel approach—relational bound propagation—which leverages the semantics of the underlying relational logic formula encoded by the specification to automatically tighten the bounds for any relational specification. Our approach applies two sets of semantic rules to …

Data-Driven Evidence-Based Syntactic Sugar Design, David Obrien, Robert Dyer, Tien N. Nguyen, Hridesh Rajan

School of Computing: Faculty Publications

Programming languages are essential tools for developers, and their evolution plays a crucial role in supporting the activities of developers. One instance of programming language evolution is the introduction of syntactic sugars, which are additional syntax elements that provide alternative, more readable code constructs. However, the process of designing and evolving a programming language has traditionally been guided by anecdotal experiences and intuition. Recent advances in tools and methodologies for mining open-source repositories have enabled developers to make datadriven software engineering decisions. In light of this, this paper proposes an approach for motivating data-driven programming evolution by applying frequent subgraph …

Measuring Jury Perception Of Explainable Machine Learning And Demonstrative Evidence, Rachel Edie Sparks Rogers

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

Subjective pattern comparison has been subject to increased scrutiny by the courts and by the general public, resulting in an increased interest in pattern comparison algorithms that provide quantitative assessments of similarity for use by forensic scientists. While these algorithms would mark an improvement over current subjective comparison methods, individuals without a statistical background may struggle with the statistical concepts and language necessary for describing algorithmic methods. If algorithms are to be used, examiners must be able to testify about their use in a way that is accessible to the jury. In a series of studies, we conduct an assessment …

Micrornas In Pancreatic Cancer: Advances In Biomarker Discovery And Therapeutic Implications, Roland Madadjim, Thuy An, Juan Cui

School of Computing: Faculty Publications

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a …

Icolc Statement On Ai In Licensing, International Coalition Of Library Consortia

Copyright, Fair Use, Scholarly Communication, etc.

The International Coalition of Library Consortia (ICOLC) statement on artifical intelligence in licensing.

Weakly Supervised Attention-Based Recognition Under Spectral, Turbulence, And Resource Variations, Kshitij Naresh Nikhal

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

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability.

This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search …

Securing Synchrophasors Using Data Provenance In The Quantum Era, Kashif Javed, Mansoor Ali Khan, Mukhtar Ullah, Muhammad Naveed Aman, Biplab Sikdar

School of Computing: Faculty Publications

Trust in the fidelity of synchrophasor measurements is crucial for the correct operation of modern power grids. While most of the existing research on data provenance focuses on the Internet of Things, there is a significant need for effective malicious data detection in power systems. Current methods either fail to detect malicious data modifications or require certain Phasor Measurement Units (PMUs) to be physically secured. To solve these issues, this paper presents a new protocol to establish data provenance in synchrophasor networks. The proposed protocol is based on Physically Unclonable Functions (PUFs) and harnesses the principles of quantum unreality and …

Intermittent-Aware Design Exploration Of Systolic Array Using Various Non-Volatile Memory: A Comparative Study, Nedasadat Taheri, Sepehr Tabrizchi, Arman Roohi

School of Computing: Faculty Publications

This paper conducts a comprehensive study on intermittent computing within IoT environments, emphasizing the interplay between different dataflows—row, weight, and output—and a variety of non-volatile memory technologies. We then delve into the architectural optimization of these systems using a spatial architecture, namely IDEA, with their processing elements efficiently arranged in a rhythmic pattern, providing enhanced performance in the presence of power failures. This exploration aims to highlight the diverse advantages and potential applications of each combination, offering a comparative perspective. In our findings, using IDEA for the row stationary dataflow with AlexNet on the CIFAR10 dataset, we observe a power …