Computer Sciences Commons^™

Deep Video Understanding With Model Efficiency And Sparse Active Labeling, Aayush Jung Bahadur Rana

Electronic Theses and Dissertations, 2020-2023

Videos capture the inherently sequential nature of the real world, making automatic video understanding an essential need for automatic understanding of the real world. Due to major advancements in camera, communication, and storage hardware, videos have become a widely used data format for crucial applications such as home automation, security, analysis, robotics, and autonomous driving. Existing methods for video understanding require heavy computation and large training data for good performance, this limits how quick the videos can be processed and how much data can be labeled for training. Real-world video understanding requires analyzing dense scenes and sequential information, which increases …

Human Recognition Theory And Facial Recognition Technology: A Topic Modeling Approach To Understanding The Ethical Implication Of A Developing Algorithmic Technologies Landscape On How We View Ourselves And Are Viewed By Others, Hajer Albalawi

Electronic Theses and Dissertations, 2020-2023

The emergence of algorithmic-driven technology has significantly impacted human life in the current century. Algorithms, as versatile constructs, hold different meanings across various disciplines, including computer science, mathematics, social science, and human-artificial intelligence studies. This study defines algorithms from an ethical perspective as the foundation of an information society and focuses on their implications in the context of human recognition. Facial recognition technology, driven by algorithms, has gained widespread use, raising important ethical questions regarding privacy, bias, and accuracy. This dissertation aims to explore the impact of algorithms on machine perception of human individuals and how humans perceive one another …

Studying Memes During Covid Lockdown As A Lens Through Which To Understand Video-Mediated Communication Interactions, Tatyana Claytor

Electronic Theses and Dissertations, 2020-2023

The purpose of this study is to analyze image macros about video-mediated communication (VMC) created during the time frame of 2020-2021 when people all over the world started using Zoom and VMC for work and school. It is a unique opportunity to study how users' interactions with themselves and with others were affected at a time when a lot of people started using the technology at the same time. Because the focus is on interactions, I narrowed it down to three topics to analyze the memes: presence, self, and space and place to analyze the memes. I chose memes relating …

Efficient Convolutional Neural Networks For Image Classification And Regression, Muhammad Tayyab

Electronic Theses and Dissertations, 2020-2023

Neural networks have been a topic of research since 1970s and the Convolutional Neural Networks (CNNs) were first shown to work well for hand written digits recognition in 1998. These early networks were however still shallow and contained only a few layers. Moreover these networks were mostly trained on a small amount of data in contrast to the modern CNNs which contain hundreds of convolution layers and are trained on millions of images. However, this recent shift in machine learning comes at a cost. Modern neural networks have extremely large number of parameters and require huge amount of computations for …

A Study On Robustness And Semantic Understanding Of Visual Models, Madeline Chantry

Electronic Theses and Dissertations, 2020-2023

Vision models have improved in popularity and performance on many tasks since the emergence of large-scale datasets, improved access to computational resources, and new model architectures like the transformer. However, it is still not well understood if these models can be deployed in the real world. Because these models are "blackbox" architectures, we do not fully understand what these models are truly learning. An understanding of what models learn "underneath the hood" would result in better improvements for real-world scenarios. Motivated by this, we benchmark these impressive visual models using newly proposed datasets and tasks on their robustness and their …

Towards Efficient And Effective Representation Learning For Image And Video Understanding, Taojiannan Yang

Electronic Theses and Dissertations, 2020-2023

Deep learning has achieved tremendous success on various computer vision tasks. However, deep learning methods and models are usually computationally expensive, making it hard to train and deploy, especially on resource-constrained devices. In this dissertation, we explore how to improve the efficiency and effectiveness of deep learning methods from various perspectives. We first propose a new learning method to learn computationally adaptive representations. Traditional neural networks are static. However, our method trains adaptive neural networks that can adjust their computational cost during runtime, avoiding the need to train and deploy multiple networks for dynamic resource budgets. Next, we extend our …

A Neural-Network-Based Landscape Search Engine: Lse Wisconsin, Matthew Haffner, Matthew Dewitte, Papia F. Rozario, Gustavo A. Ovando-Montejo

Environment and Society Faculty Publications

The task of image retrieval is common in the world of data science and deep learning, but it has received less attention in the field of remote sensing. The authors seek to fill this gap in research through the presentation of a web-based landscape search engine for the US state of Wisconsin. The application allows users to select a location on the map and to find similar locations based on terrain and vegetation characteristics. It utilizes three neural network models—VGG16, ResNet-50, and NasNet—on digital elevation model data, and uses the NDVI mean and standard deviation for comparing vegetation data. The …

Ocr Post-Processing Using Large Language Models, Mahdi Hajiali

UNLV Theses, Dissertations, Professional Papers, and Capstones

Optical Character Recognition (OCR) technology transforms textual visuals into an electronically readable, non-graphical format of the text. This allows the editing and other text manipulation of the content by language technology software such as machine translation, text comprehension, query-answering systems, and search engines. While Optical Character Recognition (OCR) systems continually progress towards greater precision, several complications persist when dealing with low-resolution source images or those with multicolored backgrounds. Consequently, the text derived from OCR necessitates additional refinement to optimize accuracy, beneficial for various subsequent applications. It is recognized that the character accuracy of OCR-generated text may influence certain natural language …

A Multi-Layer Information Dissemination Model And Interference Optimization Strategy For Communication Networks In Disaster Areas, Yuexia Zhang, Yang Hong, Mohsen Guizani, Sheng Wu, Peiying Zhang, Ruiqi Liu

Machine Learning Faculty Publications

The communication network in disaster areas (CNDA) can disseminate the key disaster information in time and provide basic information support for decision-making and rescuing. Therefore, it is of great significance to study the information dissemination mechanism of CNDA. However, a CNDA is vulnerable to interference, which affects information dissemination and rescuing. To solve this problem, this paper established a multi-layer information dissemination model of CNDA (MMND) which models the CNDA from the perspective of degree distribution of nodes. The information dissemination process and equilibrium state in CNDA is analyzed by an improved dynamic dissemination method. Then, the effects of the …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Efficient And Secure Digital Signature Algorithm (Dsa), Nissa Mehibel, M'Hamed Hamadouche

Emirates Journal for Engineering Research

The digital signature is used to ensure the integrity of messages as well as the authentication and non-repudiation of users. Today it has a very important role in information security. Digital signature is used in various fields such as e-commerce and e-voting, health, internet of things (IOT). Many digital signature schemes have been proposed, depending on the computational cost and security level. In this paper, we analyzed a recently proposed digital signature scheme based on the discrete logarithm problem (DLP). Our analysis shows that the scheme is not secure against the repeated random number attack to determine the secret keys …

Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen

Dartmouth College Ph.D Dissertations

Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between …

The Quantum Mechanical Background Of Quantum Computing, Isaac Hanna

The Kabod

Quantum mechanics arose out of the question "Is light a particle or a wave?" and has laid forth a model of reality in which particles are modeled by wave functions. The particle is in a superposition of states and can be entangled with other particles to create more complex systems. Observation of the system collapses the wave function to a single point. By using quantum gates, we can manipulate these particles to create algorithms to solve computational problems. Quantum computing does not collapse the complexity hierarchy by providing an across the board exponential speedup but can provide such a speedup …

Ranking And Analysis The Strategies Of Crowd Management To Reduce The Risks Of Crushes And Stampedes In Crowded Environments And Ensure The Safety Of Passengers, Waleed Tawfiq Al-Nami

Neutrosophic Systems with Applications

Public gatherings, transit hubs, stadiums, and crowded retail malls are just a few examples of places where crowd management has become an urgent issue in recent years. Effective crowd management strategies have been required due to the increasing population, urbanization, and frequency of large-scale meetings. These strategies are used in dynamic, sometimes chaotic, circumstances to protect people and facilitate their free movement. The purpose of this study is to analyze and rank various strategies for crowd management to reduce the risks of crushes and stampedes, improve security, and facilitate smoother traffic flow. This study used the single-valued neutrosophic set to …

Ranking And Analysis The Strategies Of Crowd Management To Reduce The Risks Of Crushes And Stampedes In Crowded Environments And Ensure The Safety Of Passengers, Waleed Tawfiq Al-Nami

Neutrosophic Systems with Applications

Public gatherings, transit hubs, stadiums, and crowded retail malls are just a few examples of places where crowd management has become an urgent issue in recent years. Effective crowd management strategies have been required due to the increasing population, urbanization, and frequency of large-scale meetings. These strategies are used in dynamic, sometimes chaotic, circumstances to protect people and facilitate their free movement. The purpose of this study is to analyze and rank various strategies for crowd management to reduce the risks of crushes and stampedes, improve security, and facilitate smoother traffic flow. This study used the single-valued neutrosophic set to …

Static Malware Family Clustering Via Structural And Functional Characteristics, David George, Andre Mauldin, Josh Mitchell, Sufiyan Mohammed, Robert Slater

SMU Data Science Review

Static and dynamic analyses are the two primary approaches to analyzing malicious applications. The primary distinction between the two is that the application is analyzed without execution in static analysis, whereas the dynamic approach executes the malware and records the behavior exhibited during execution. Although each approach has advantages and disadvantages, dynamic analysis has been more widely accepted and utilized by the research community whereas static analysis has not seen the same attention. This study aims to apply advancements in static analysis techniques to demonstrate the identification of fine-grained functionality, and show, through clustering, how malicious applications may be grouped …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …

Ide-Based Learning Analytics For Assessing Introductory Programming Skill, Phyllis J. Beck

Theses and Dissertations

Providing a sufficient level of personalized feedback on students' current level of strategic knowledge within the context of the natural programming environment through IDE-based learning analytics would transform learning outcomes for introductory programming students. However, providing sufficient insight into the programming process was previously inaccessible due to the need for more complex and scalable data collection methods and metrics with a wider variety for understanding programming metacognition and the full programming process.

This research developed a custom-built web-based IDE and event compression system to investigate two of the five components of a five-dimensional model of cognition for programming skill estimation …

Signings Of Graphs And Sign-Symmetric Signed Graphs, Ahmad Asiri

Theses and Dissertations

In this dissertation, we investigate various aspects of signed graphs, with a particular focus on signings and sign-symmetric signed graphs. We begin by examining the complete graph on six vertices with one edge deleted ($K_6$\textbackslash e) and explore the different ways of signing this graph up to switching isomorphism. We determine the frustration index (number) of these signings and investigate the existence of sign-symmetric signed graphs. We then extend our study to the $K_6$\textbackslash 2e graph and the McGee graph with exactly two negative edges. We investigate the distinct ways of signing these graphs up to switching isomorphism and demonstrate …

Visual And Spatial Audio Mismatching In Virtual Environments, Zachary Lawrence Garris

Theses and Dissertations

This paper explores how vision affects spatial audio perception in virtual reality. We created four virtual environments with different reverb and room sizes, and recorded binaural clicks in each one. We conducted two experiments: one where participants judged the audio-visual match, and another where they pointed to the click direction. We found that vision influences spatial audio perception and that congruent audio-visual cues improve accuracy. We suggest some implications for virtual reality design and evaluation.

Expanding One-Dimensional Game Theory-Based Group Decision Models: Extension To N-Dimension And Integration Of Distributed Position Function, Mirhossein Mousavi Karimi

Theses and Dissertations

This dissertation aims to expand the current one-dimensional game theory based model to a multidimensional model for multi-actor predictive analytics and generalize the concept of position to address problems where actors’ positions are distributed over a position spectrum. The one-dimensional models are used for the problems where actors are interacting in a single issue space only. This is less than an ideal assumption since, in most cases, players’ strategies may depend on the dynamics of multiple issues when dealing with other players. In this research, the one-dimensional model is expanded to N-Dimensional model by considering different positions, and separate salience …

Libra: Harvesting Idle Resources Safely And Timely In Serverless Clusters, Hanfei Yu, Christian Fontenot, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park

Computer Science Faculty Research & Creative Works

Serverless computing has been favored by users and infrastructure providers from various industries, including online services and scientific computing. Users enjoy its auto-scaling and ease-of-management, and providers own more control to optimize their service. However, existing serverless platforms still require users to pre-define resource allocations for their functions, leading to frequent misconfiguration by inexperienced users in practice. Besides, functions' varying input data further escalate the gap between their dynamic resource demands and static allocations, leaving functions either over-provisioned or under-provisioned. This paper presents Libra, a safe and timely resource harvesting framework for multi-node serverless clusters. Libra makes precise harvesting decisions …

An Investigation Into The Application Of The Meijering Filter For Document Recapture Detection, John Magee, Stephen Sheridan Phd, Christina Thorpe Phd

Conference papers

The proliferation of mobile devices allows financial institutions to offer remote customer services, such as remote account opening. Manipulation of identity documents using image processing software is a low-cost, high-risk threat to modern financial systems, opening these institutions to fraud through crimes related to identity theft. In this paper we describe our exploratory research into the application of biomedical image algorithms to the domain of document recapture detection. We perform a statistical analysis to compare different types of recaptured documents and train a support vector machine classifier on the raw histogram data generated using the Meijering filter. The results show …

Green Infrastructure Intervention To Improve Waste And Water System In Urban Areas, Muthiah Hakim Hadini, Farah Bulqis Muzakar, Nurlina Yustiningrum, Nicole Hall, Peicong Li, Freya Ward, Ova Candra Dewi, Coriesta Dian Sulistiani, Andrew Flynn

Smart City

The increase in waste generation and water use in urban areas may lead to local flooding that is dangerous for health if it is not appropriately managed by waste and water systems in urban infrastructure. Sponge City is a concept that utilizes Green Infrastructures (GIs) to manage waste and water systems while still maintaining open and public spaces function. This study aims to review the waste and water infrastructure systems in Depok, Indonesia which are compared with Gainesville in the United States, America and Cardiff, United Kingdom, to give recommendations for preventing flooding in urban areas. Analysis is done by …

Representing Quantum Spins In Different Coordinate Systems For Modelling Rigid Body Orientation, Nadjet Zioui, Aicha Mahmoudi, Mohamed Tadjine

Karbala International Journal of Modern Science

Various methods for representing the spatial orientation and rotation of objects are presented and compared with the quantum bit state representation. By contrasting spherical, Euler angle, quaternion, and quantum spin coordinate systems, this work highlights important concepts regarding the rotation axis of the X gate. Several ambiguities and incomplete definitions associated with the qubit state representation are discussed, such as the spin around the qubit itself and the explanation of the considered rotation angles and signs. A mathematical analysis of the physical meaning of each eigenstate is provided along with a new comprehensive and meaningful YPR-based 3D representation of a …

Quercetin As An Anticancer Candidate For Glioblastoma Multiforme By Targeting Akt1, Mmp9, Abcb1, And Vegfa: An In Silico Study, Muhammad Hermawan Widyananda, Setyaki Kevin Pratama, Arif Nur Muhammad Ansori, Yulanda Antonius, Viol Dhea Kharisma, Ahmad Affan Ali Murtadlo, Vikash Jakhmola, Maksim Rebezov, Mars Khayrullin, Marina Derkho, Emdad Ullah, Raden Joko Kuncoroningrat Susilo, Suhailah Hayaza, Alexander Patera Nugraha, Annise Proboningrat, Amaq Fadholly, Mada Triandala Sibero, Rahadian Zainul

Karbala International Journal of Modern Science

Quercetin, a natural compound present in various fruits and vegetables, shows promise as a potential inhibitor for glioblastoma multiforme (GBM) development. This study aims to examine the anti-GBM potential of Quercetin. The protein target of Quercetin is identified and analyzed using databases such as NCBI, SEA, CTD, and STRING. Protein-protein interaction (PPI) and functional annotation are carried out based on the obtained target proteins. Molecular docking and dynamics simulations are employed using AutoDock Vina and WebGro tools to analyze the interaction between Quercetin and its target proteins. The prediction of protein targets reveals that Quercetin directly targets four proteins associated …

Improving The Burt’S Sensitivity Using Noise Calibration Unit Via Crab Nebula Observations, Uday E. Jallod, Lana T. Ali, Hareth S. Mahdi, Kamal M. Abood

Karbala International Journal of Modern Science

Radio observations from astronomical sources like supernovae became one the most important sources of information about the physical properties of those objects. However, such radio observations are affected by various types of noise such as those from sky, background, receiver, and the system itself. Therefore, it is essential to eliminate or reduce these undesired noise from the signals in order to ensure accurate measurements and analysis of radio observations. One of the most commonly used methods for reducing the noise is to use a noise calibrator. In this study, the 3-m Baghdad University Radio Telescope (BURT) has been used to …

New Statistical Methodology For Capacitor Data Analysis Via Lcr Meter, Usama Afzal, Muhammad Aslam

Neutrosophic Systems with Applications

This research work introduces a novel methodology to establish the relationship between capacitance and resistance when dealing with imprecise data obtained from LCR meters. The proposed relationship is based on the principles of neutrosophic statistics, enabling the utilization of interval data of resistance or capacitance without losing the indeterminacy of the intervals. By employing this relationship, we can accurately determine capacitance values from interval data of resistance, thereby generating more flexible and informative graphs. Additionally, we have applied the neutrosophic analysis method to the interval data of resistance to further enhance our findings. The comparative analysis demonstrates the superiority of …

New Types Of Soft Sets” Hypersoft Set, Indetermsoft Set, Indetermhypersoft Set, And Treesoft Set”: An Improved Version, Florentin Smarandache

Neutrosophic Systems with Applications

This is an improved paper of [10], where we recall the definitions together with practical applications of the Soft Set and its extensions to HyperSoft Set, IndetermSoft Set, IndetermHyperSoft Set, and TreeSoft Set.

New Statistical Methodology For Capacitor Data Analysis Via Lcr Meter, Usama Afzal, Muhammad Aslam

Neutrosophic Systems with Applications

This research work introduces a novel methodology to establish the relationship between capacitance and resistance when dealing with imprecise data obtained from LCR meters. The proposed relationship is based on the principles of neutrosophic statistics, enabling the utilization of interval data of resistance or capacitance without losing the indeterminacy of the intervals. By employing this relationship, we can accurately determine capacitance values from interval data of resistance, thereby generating more flexible and informative graphs. Additionally, we have applied the neutrosophic analysis method to the interval data of resistance to further enhance our findings. The comparative analysis demonstrates the superiority of …