Digital Commons Network^™

Learning Scene Semantics For 3d Scene Retrieval, Natalie Gleason

Honors Theses

This project presents a comprehensive exploration into semantics-driven 3D scene retrieval, aiming to bridge the gap between 2D sketches/images and 3D models. Through four distinct research objectives, this project endeavors to construct a foundational infrastructure, develop methodologies for quantifying semantic similarity, and advance a semantics-based retrieval framework for 2D scene sketch-based and image-based 3D scene retrieval. Leveraging WordNet as a foundational semantic ontology library, the research proposes the construction of an extensive hierarchical scene semantic tree, enriching 2D/3D scenes with encoded semantic information. The methodologies for semantic similarity computation utilize this semantic tree to bridge the semantic disparity between 2D …

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 …

Automated Cinematographer For Vr Viewing Experiences, Zihan Wu

Dartmouth College Master’s Theses

As the virtual reality (VR) industry continues to evolve, the question of how to effectively capture VR experiences for an audience remains a challenge. The predominant method of showcasing VR applications through first-person recordings lacks cinematic interest, failing to capture other viewpoints and the essence of the moment. Meanwhile, manually setting up cameras and editing videos requires technical expertise on behalf of the user. In this paper, we propose the use of machine learning (ML) to automatically select the most compelling predefined viewpoint in a VR environment, at any given moment. Our models, trained on actor motion and voice volume, …

White Cell Support Application For Expo Ops Tactical Wargame System, Zackery Joseph Milder

Theses

The purpose of this project was to create a support application for a tabletop wargame currently used for training and scenario simulation by the United States Marine Corps. The EXPO OPS Companion is meant to enhance the capabilities of the White Cell/table director, the unbiased third party responsible for running adjudication for the EXPO OPS Tactical Wargames System. EXPO OPS TWS is “…a table top wargame covering contemporary and future conflict at the platoon, company and battalion level. It is a wargame toolkit that enables wargaming scenarios in the 2020 to 2030 timeframe. The design centers on plans and decisions …

The Impact Of Ai On Ux: Challenges And Opportunities, Susan Stephanie Wells

Theses

Integrating artificial intelligence (AI) in user experience (UX) design is reshaping the field of UX, offering new opportunities and challenges for designers. This thesis project explores the multifaceted relationship between AI and UX design, focusing on the challenges, opportunities, and skills demanded of UX designers in the age of AI. Through a review of academic research and real-world experiences, this project studies the impact of AI on web design processes, UX testing, and data analysis. Key findings highlight the transformative potential of AI in enhancing user experiences, from suggesting website structures to facilitating UX testing and data analysis.

Comparative analysis …

Enhancing Monthly Streamflow Prediction Using Meteorological Factors And Machine Learning Models In The Upper Colorado River Basin, Saichand Thota, Ayman Nassar, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh

Computer Science Student Research

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using …

Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher

Research Collection School Of Computing and Information Systems

Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the attention of the inference system should “allocate its attention”. Data from these locales (e.g., parts of the input video frames containing objects of interest) are packed together into a smaller canvas frame which is processed by the downstream machine learning algorithm. Compared with processing the entire input data frame, this practice saves resources while maintaining inference quality. Previous work was limited to a simplified solution where the focus locales are quantized …

Non-Vacuous Generalization Bounds For Adversarial Risk In Stochastic Neural Networks, Mustafa Waleed, Liznerski Philipp, Antoine Ledent, Wagner Dennis, Wang Puyu, Kloft Marius

Research Collection School Of Computing and Information Systems

Adversarial examples are manipulated samples used to deceive machine learning models, posing a serious threat in safety-critical applications. Existing safety certificates for machine learning models are limited to individual input examples, failing to capture generalization to unseen data. To address this limitation, we propose novel generalization bounds based on the PAC-Bayesian and randomized smoothing frameworks, providing certificates that predict the model’s performance and robustness on unseen test samples based solely on the training data. We present an effective procedure to train and compute the first non-vacuous generalization bounds for neural networks in adversarial settings. Experimental results on the widely recognized …

Enabling Roll-Up And Drill-Down Operations In News Exploration With Knowledge Graphs For Due Diligence And Risk Management, Sha Wang, Yuchen Li, Hanhua Xiao, Zhifeng Bao, Yanfei Dong

Research Collection School Of Computing and Information Systems

Efficient news exploration is crucial in real-world applications, particularly within the financial sector, where numerous control and risk assessment tasks rely on the analysis of public news reports. The current processes in this domain predominantly rely on manual efforts, often involving keyword-based searches and the compilation of extensive keyword lists. In this paper, we introduce NCEXPLORER, a framework designed with OLAP-like operations to enhance the news exploration experience. NCEXPLORER empowers users to use roll-up operations for a broader content overview and drill-down operations for detailed insights. These operations are achieved through integration with external knowledge graphs (KGs), encompassing both fact-based …

Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia

Research Collection School Of Computing and Information Systems

Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap …

Lecture-Style Tutorial: Towards Graph Foundation Models, Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun, Philip Yu

Research Collection School Of Computing and Information Systems

Emerging as fundamental building blocks for diverse artificial intelligence applications, foundation models have achieved notable success across natural language processing and many other domains. Concurrently, graph machine learning has gradually evolved from shallow methods to deep models to leverage the abundant graph-structured data that constitute an important pillar in the data ecosystem for artificial intelligence. Naturally, the emergence and homogenization capabilities of foundation models have piqued the interest of graph machine learning researchers. This has sparked discussions about developing a next-generation graph learning paradigm, one that is pre-trained on broad graph data and can be adapted to a wide range …

Exploring Binding Pockets In The Conformational States Of The Sars-Cov-2 Spike Trimers For The Screening Of Allosteric Inhibitors Using Molecular Simulations And Ensemble-Based Ligand Docking, Grace Gupta, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and …

Magdm Model Using Single-Valued Neutrosophic Credibility Matrix Energy And Its Decision-Making Application, Jun Ye, Rui Yong, Wanlu Du

Neutrosophic Systems with Applications

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in …

Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi

LSU New Orleans Theses and Dissertations

This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …

Experiment Development And Validation Of A Granular Jamming Robotic Gripper, Jacob R. Dowd

UNLV Theses, Dissertations, Professional Papers, and Capstones

A granular jamming gripper (GJG) is widely known as a Universal Gripper because of the wide range of objects that it can grasp and the simplicity of control, design, and manufacturing. Despite multitude of research improving the GJG, here, we focus on the base version of the GJG and attempt to glean the range of objects that it may reliably grasp. Despite the limited range of objects, which were a sphere, rectangular prism, and cylinder, we gleaned geometric properties as it relates to successful and unsuccessful grasping. This was based on the two types of testing: push and pull testing …

Triangulation Guided High Clearance Collision-Free Paths, Sandeep Maharjan

UNLV Theses, Dissertations, Professional Papers, and Capstones

Algorithms dealing with the construction of high clearance collision-free paths in the presence of polygonal obstacles is an important problem in robotics and transportation engineering. Method of extracting collision-free paths guided by triangulation of free space is examined. Two algorithms for improving the standard triangulation guided algorithms are presented. The time complexities of the presented algorithms are analysed. Finally, further applications of the proposed techniques are discussed.

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Honors Theses

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …

Ethical Imperatives And Challenges: Review Of The Use Of Machine Learning For Predictive Analytics In Higher Education, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The escalating integration of machine learning (ML) in higher education necessitates a critical examination of its ethical implications. This article conducts a comprehensive review of the application of ML for predictive analytics within higher education institutions (HEIs), emphasizing the technology's potential to enhance student outcomes and operational efficiency. The study identifies significant ethical concerns, such as data privacy, informed consent, transparency, and accountability, that arise from the use of ML. Through a detailed analysis of current practices, this review underscores the need for HEIs to develop robust ethical frameworks and technological infrastructures to navigate these challenges effectively. The findings reveal …

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 …

Choreographing The Rhythms Of Observation: Dynamics For Ranged Observer Bipartite-Unipartite Spatiotemporal (Robust) Networks, Edward A. Holmberg Iv

LSU New Orleans Theses and Dissertations

Existing network analysis methods struggle to optimize observer placements in dynamic environments with limited visibility. This dissertation introduces the novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework, offering a significant advancement in modeling, analyzing, and optimizing observer networks within complex spatiotemporal domains. ROBUST leverages a unique bipartite-unipartite approach, distinguishing between observer and observable entities while incorporating spatial constraints and temporal dynamics.

This research extends spatiotemporal network theory by introducing novel graph-based measures, including myopic degree, spatial closeness centrality, and edge length proportion. These measures, coupled with advanced clustering techniques like Proximal Recurrence, provide insights into network structure, resilience, and the effectiveness …

Cybervictimization And Depression: A Cultural Standpoint, Paige Heagy

University Honors College

The author’s aim was to investigate the relationship between cybervictimization and depression, as well as using peer attachment and culture as moderators by giving questionnaires to 1347 participants (age range = 11-15 years) from India and the United States. Through four questionnaires, adolescents reported their levels of endorsement in either individualism or collectivism culture, levels of cybervictimization, levels of depression/depressive symptoms, and their levels of peer attachment. Adolescents reported that there is a significantly positive correlation between cybervictimization and depression. Differences were found according to culture and peer attachment, as well. Cybervictimization has begun to erupt worldwide as internet usage …

The Effects Of Using Machine Translators On The Performance Of Second Language Learners, Kasey Myer

University Honors College

With the rise of technology has also come the development of various online language translators and artificial intelligence that are often utilized by individuals learning a second language. However, there is a wide range of quality between the different machine translation tools, and many people tend to be under the impression that it is inferior to the quality of interpretations provided by human translators. This paper considers the positives and negatives of machine translation as a tool for second language learning. Variations between the input and output languages on a grammatical and cultural level are analyzed. Machine translation is compared …

From Bits To Beds: Design And Implementation Of A Hotel Booking System A Coding Project, Alexa Gilman

University Honors College

This thesis presents a comprehensive hotel booking system designed and implemented to showcase the transition "From Bits to Beds". There are two main motivations behind this project: firstly, to provide users with a user-friendly and efficient platform for booking accommodations. Secondly, it serves as a great learning opportunity, providing hands-on practice with coding, collaboration with teammates, and exposure to real-world challenges. The system utilizes technologies such as JavaScript, Express.js, and HTML/CSS to offer features like browsing hotels, filtering options, user authentication, and booking management. The implementation of this project demonstrates a successful integration of backend and frontend components, ensuring reliability …

Guardians Of The Data: Government Use Of Ai And Iot In The Digital Age, Jannat Saeed

Honors Theses

The exponential growth of technology, epitomized by Moore's Law – “the observation that the number of transistors on an integrated circuit will double every two years”– has propelled the swift evolution of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. This phenomenon has revolutionized various facets of daily life, from smart home devices to autonomous vehicles, reshaping how individuals interact with the world around them. However, as governments worldwide increasingly harness these innovations to monitor and collect personal data, profound privacy concerns have arisen among the general populace. Despite the ubiquity of AI and IoT in modern society, formal …

Privacy Protection In Mobile Photography With Face Cloaking, Rithyka Heng

Computer Science and Computer Engineering Undergraduate Honors Theses

In a world of increasing connectivity, privacy is becoming ever-more difficult to maintain. People have little control over the capture of their image while in public and have even less control over the online sharing or posting of their image. This leaves many people vulnerable to being tracked or profiled via their image’s presence in other people’s photos. This thesis implements and evaluates an approach to privacy protection that involves the photographers protecting the privacy of bystanders. Because most photographs are now being taken by smartphones, a mobile application is decidedly the technology that would best achieve widespread adoption and …

Exploring Decentralized Computing Using Solid And Ipfs For Social Media Applications, Pranav Balasubramanian Natarajan

Computer Science and Computer Engineering Undergraduate Honors Theses

As traditional centralized social media platforms face growing concerns over data privacy, censorship, and lack of user control, there has been an increasing interest in decentralized alternatives. This thesis explores the design and implementation of a decentralized social media application by integrating two key technologies: Solid and the InterPlanetary File System (IPFS). Solid, led by Sir Tim Berners-Lee, enables users to store and manage their personal data in decentralized "Pods," giving them ownership over their digital identities. IPFS, a peer-to-peer hypermedia protocol, facilitates decentralized file storage and sharing, ensuring content availability and resilience against censorship. By leveraging these technologies, the …

An In-Network Approach For Pmu Missing Data Recovery With Data Plane Programmability, Jack Norris

Computer Science and Computer Engineering Undergraduate Honors Theses

Phasor measurement unit (PMU) systems often experience unavoidable missing and erroneous measurements, which undermine power system observability and operational effectiveness. Traditional solutions for recovering missing PMU data employ a centralized approach at the control center, resulting in lengthy recovery times due to data transmission and aggregation. In this work, we leverage P4-based programmable networks to expedite missing data recovery. Our approach utilizes the data plane programmability offered by P4 to present an in-network solution for PMU data recovery. We establish a data-plane pipeline on P4 switches, featuring a customized PMU protocol parser, a missing data detection module, and an auto-regressive …

The Quantitative Analysis And Visualization Of Nfl Passing Routes, Sandeep Chitturi

Computer Science and Computer Engineering Undergraduate Honors Theses

The strategic planning of offensive passing plays in the NFL incorporates numerous variables, including defensive coverages, player positioning, historical data, etc. This project develops an application using an analytical framework and an interactive model to simulate and visualize an NFL offense's passing strategy under varying conditions. Using R-programming and data management, the model dynamically represents potential passing routes in response to different defensive schemes. The system architecture integrates data from historical NFL league years to generate quantified route scores through designed mathematical equations. This allows for the prediction of potential passing routes for offensive skill players in response to the …

Automatic Extraction Of Vulnerability Information For Security Operators Using Gpt Models, Waeland Elder

Computer Science and Computer Engineering Undergraduate Honors Theses

Artificial intelligence has progressed rapidly in recent years, greatly revolutionizing the world and helping to automate increasingly complex tasks. However, there are still some disciplines where the problem of automation has not yet been thoroughly tackled, such as in software vulnerability management. Vulnerability management is a critical component of cybersecurity for an organization. Until recently, learning about a vulnerability required a security operator to manually search and read through online information and security advisories to find needed information. Doing so is a significantly time-consuming task, as these advisories tend to be quite lengthy and packed with information about various technologies. …

Dancetag: Using Sensors To Improve Feedback Given To Dance Students, Yanelly Mego

Student Scholar Symposium Abstracts and Posters

The structure of dance classrooms has remained unchanged for several years. Very little, if any, technology has been incorporated to improve the quality of teaching. This has motivated our research project, whose goal is to capture dance movements with wearable sensors, to develop DANCETAG (Data Analytics and Notation with Captured Event Tagging). This is a platform that allows the gathering of data captured by Sony’s Mocopi sensors and annotating them with the dancer's movements. The Mocopi sensors make up a motion capture system. It is comprised of six small, round sensors that can be attached to velcro straps and clips. …