Physical Sciences and Mathematics Commons^™

Private Ethereum Blockchain Implementation And Its Security Features For Smart Home Iot, Hasibul Grande Alam

Theses and Dissertations

The security and privacy of IoT devices have become primary concerns as smart home networks are connected to the internet. Ethereum blockchain can be a solution to mitigate or prevent attacks – sniffing attacks, malware attacks, Eavesdropping, and Distributed Denial of Services (DDoS) attacks. Deploying Ethereum in resource constraint IoT devices is challenging due to resultant energy consumption, computational overhead, and delay. We adopted smart home as a case study to examine our methodology as a model for general IoT applications. This thesis work presents the implementation of private Ethereum blockchain that is optimized and installable on smart home IoT. …

Enhancing Time Series Hashing Performance Via Deep Orthogonal Hashing, Mahmudul Hasan Robin

Theses and Dissertations

Deep hashing has been widely used for efficient retrieval and classification of high-dimensional data like images and text. However, its application to time series data is still challenging due to the data’s temporal nature. To tackle this issue, a new deep hashing method has been proposed that generates efficient hash codes and enhances the time series hashing performance using a ResNet model with Orthohash (Cosine Similarity Loss). The proposed method uses one loss architecture while using ResNet model for efficient hashing. It uses the Character Trajectories dataset to extract discriminative features from the time series data. These features are then …

Robust And Uncertainty-Aware Image Classification Using Bayesian Vision Transformer Model, Fazlur Rahman Bin Karim

Theses and Dissertations

Transformer Neural Networks have emerged as the predominant architecture for addressing a wide range of Natural Language Processing (NLP) applications such as machine translation, speech recognition, sentiment analysis, text anomaly detection, etc. This noteworthy achievement of Transformer Neural Networks in the NLP field has sparked a growing interest in integrating and utilizing Transformer models in computer vision tasks. The Vision Transformer (ViT) model efficiently captures long-range dependencies by employing a self-attention mechanism to transform different image data into meaningful, significant representations. Recently, the Vision Transformer (ViT) has exhibited incredible performance in solving image classification problems by utilizing ViT models, thereby …

Intellibeehive, Christian Ivan Narcia-Macias

Theses and Dissertations

Utilizing computer vision and the latest technological advancements, in this study, we developed a honey bee monitoring system that aims to enhance our understanding of Colony Collapse Disorder, honey bee behavior, population decline, and overall hive health. The system is positioned at the hive entrance providing real-time data, enabling beekeepers to closely monitor the hive's activity and health through an account-based website. Using machine learning, our monitoring system can accurately track honey bees, monitor pollen-gathering activity, and detect Varroa mites, all without causing any disruption to the honey bees. Moreover, we have ensured that the development of this monitoring system …

Simulating Motion Success With Muscle Deficiency In A Musculoskeletal Model Using Reinforcement Learning, Daniel Castillo

Theses and Dissertations

Humans possess an extraordinary ability to execute complex movements, captivating the attention of researchers who strive to develop methods for simulating these actions within a physics-based environment. Motion Capture data stands out as a crucial tool among the proven approaches to tackle this challenge. In this research, we explore the effects of decreased muscle force on the body's capacity to perform various tasks, ranging from simple walking to executing complex jumping jacks. Through a systematic reduction of the allowed force applied to individual muscles or muscle groups, we aim to identify the threshold at which the body's muscles tolerate the …

Invading The Integrity Of Deep Learning (Dl) Models Using Lsb Perturbation & Pixel Manipulation, Ashraful Tauhid

Theses and Dissertations

The use of deep learning (DL) models for solving classification and recognition-related problems are expanding at an exponential rate. However, these models are computationally expensive both in terms of time and resources. This imposes an entry barrier for low-profile businesses and scientific research projects with limited resources. Therefore, many organizations prefer to use fully outsourced trained models, cloud computing services, pre-trained models are available for download and transfer learning. This ubiquitous adoption of DL has unlocked numerous opportunities but has also brought forth potential threats to its prospects. Among the security threats, backdoor attacks and adversarial attacks have emerged as …

Fedbiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid Cnns-Svm In Federated Learning, S M Sarwar

Theses and Dissertations

In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem …

Using Deep Learning For Encrypted Traffic Analysis Of Amazon Echo, Surendra Pathak

Theses and Dissertations

The adoption of the Amazon Echo family of devices in modern homes has become very widespread at the current time, with hundreds of millions of devices sold. Moreover, the global smart speaker market size is growing vigorously and is projected to continue to bigger. Smart speakers allow users hands-free interaction by allowing voice control, promoting human-computer interaction to greater avenues. Though smart speaker can be useful assistant, it has some serious security concerns that need to be studied. In this study, an analysis of the security and privacy concerns of smart speakers is presented along with a passive attack, namely …

Analysis Of Post-Translational Modifications (Ptm) Crosstalk, Amit Das

Theses and Dissertations

Mass spectrometry-based proteomics is a powerful tool for identifying post-translational modifications (PTMs) across the proteome. O-GlcNAcylation and phosphorylation are two PTMs that play crucial roles in regulating cellular processes, including cardiac contractile function. Dysregulation of these PTMs has been implicated in the development and progression of diabetic cardiomyopathy. In this study, we aimed to investigate the interplay between O-GlcNAcylation and phosphorylation in healthy and type 2 diabetic hearts, with a specific focus on the functional relationships between these PTMs and their potential therapeutic implications.

Utilizing mass spectrometry data, we identified and quantified specific PTMs on myofilament proteins, uncovering 1354 O-GlcNAcylated …

Self-Supervised Representation Learning For Motion Time Series: A Case Study In Activity Recognition, Luis Carlos Garza Perez

Theses and Dissertations

In this thesis we will learn about what contrastive learning and time series are and understand the differences between supervised and self-supervised frameworks in machine learning. In addition, we will describe how the newest and most efficient self-supervised learning framework for visual representations to this date works, called SimCLR, which was originally developed to obtain useful vector representations from static images. We will also explain what TS2Vec is, and how a combination of both approaches can be applied to the concept of a time series, and still be able to extract a vector representation of the subject described by the …

The Effect Of Cybersecurity Training On Government Employee’S Knowledge Of Cybersecurity Issues And Practices, Juan Jaime Saldana Ii

Theses and Dissertations

There is an ever-pressing need for cybersecurity awareness and implementation of learning strategies in the workplace to mitigate the increased threat posed by cyber-attacks and exacerbated by an untrained workforce. The lack of cybersecurity knowledge amongst government employees has increased to critical levels due to the amount of sensitive information their agencies are responsible for. The digital compromise of a government entity often leads to a compromise of constituent data along with the disruption of public services (Axelrod, 2019; Yazdanpanahi, 2021). The need for awareness is further complicated by agencies looking to cater to a digital culture looking for a …

Results And Simulation Of Active Self-Assembly, Robert M. Alaniz

Theses and Dissertations

Self-assembly is the process by which simple elements in a system organize themselves into more complex structures based on a set of rules that govern their interactions. With many ways to create self-assembling systems, new models of abstraction have also arisen to handle specific mechanisms and procedures. We explore several open problems in the seeded model of Active Self-Assembly, Chemical Reaction Networks, and Surface Chemical Reaction Networks, proving new results while developing a robust simulation environment, AutoTile, to help build and test these results.

Problems In Algorithmic Self-Assembly And A Genetic Approach To Patterns, Andrew Rodriguez

Theses and Dissertations

As it becomes increasingly harder to make transistors smaller, replacements for traditional silicon computers become sought after. To study the computing power of these potential computers, various theoretical models have been proposed, such as the abstract Tile Assembly Model (aTAM) and chemical reaction networks (CRNs). This thesis compiles research in various models such as the aTAM, Tile Automata, and CRNs. This work shows an investigation of covert computation in the aTAM and an evolutionary algorithm to approximate solutions to the pattern self-assembly tile set synthesis (PATS) problem. Next, optimal state complexity for building squares in Tile Automata is shown along …

Atomlbs: An Atom Based Convolutional Neural Network For Druggable Ligand Binding Site Prediction, Md Ashraful Islam

Theses and Dissertations

Despite advances in drug research and development, there are few and ineffective treatments for a variety of diseases. Virtual screening can drastically reduce costs and accelerate the drug discovery process. Binding site identification is one of the initial and most important steps in structure-based virtual screening. Identifying and defining protein cavities that are likely to bind to a small compound is the objective of this task. In this research, we propose four different convolutional neural networks for predicting ligand-binding sites in proteins. A parallel optimized data pipeline is created to enable faster training of these neural network models on minimal …

Encoding Color Sequences In Active Tile Self-Assembly, Sonya Cirlos

Theses and Dissertations

Constructing patterns is a well-studied problem in both theoretical and experimental self-assembly with much of the work focused on multi-staged assembly. In this paper, we study building 1D patterns in a model of active self assembly: Tile Automata. This is a generalization of the 2-handed assembly model that borrows the concept of state changes from Cellular Automata. In this work we further develop the model by partitioning states as colors and show lower and upper bounds for building patterned assemblies based on an input pattern. Our first two sections utilize recent results to build binary strings along …

Verification In Generalizations Of The 2-Handed Assembly Model, David Caballero

Theses and Dissertations

Algorithmic Self Assembly is a well studied field in theoretical computer science motivated by the analogous real world phenomenon of DNA self assembly, as well as the emergence of nanoscale technology. Abstract mathematical models of self assembly such as the Two Handed Assembly model (2HAM) allow us to formally study the computational capabilities of self assembly. The 2HAM is one of the most thoroughly studied models of self assembly, and thus in this paper we study generalizations of this model. The Staged Tile Assembly model captures the behavior of being able to separate assembly processes and …

Hardware Isolation Approach To Securely Use Untrusted Gpus In Cloud Environments For Machine Learning, Lucas D. Hall

Theses and Dissertations

Machine Learning (ML) is now a primary method for getting useful information out of the immense volumes of data being generated and stored in society today. Useful data is a commodity for training ML models and those that need data for training are often not the owners of the data leading to a desire to use cloud-based services. Deep learning algorithms are best suited to run on a graphical processing unit (GPU) which presents a specific problem since the GPU is not a secure or trusted piece of hardware in the cloud computing environment.

In this paper, we will analyze …

Computational Complexity In Tile Self-Assembly, Timothy Gomez

Theses and Dissertations

One of the most fundamental and well-studied problems in Tile Self-Assembly is the Unique Assembly Verification (UAV) problem. This algorithmic problem asks whether a given tile system uniquely assembles a specific assembly. The complexity of this problem in the 2-Handed Assembly Model (2HAM) at a constant temperature is a long-standing open problem since the model was introduced. Previously, only membership in the class coNP was known and that the problem is in P if the temperature is one (τ = 1). The problem is known to be hard for many generalizations of the model, such as allowing one …

Engaging Students During Research Through The Use Of Games, Francisco Gonzalez

Theses and Dissertations

Engaging students during a research seminar/meeting can be a difficult challenge, and as as student myself, I can attest to how difficult actively listening to a presentation can be. As such, upon researching more ways to have an audience engaged, one of the most promising concepts is the use of games. Games, in any form, can be very engaging to a person, and even more so if there is active engagement and participation within an audience group. With this concept in mind, I decided to take it upon myself to create a game based around a theoretical computer …

Effects Of Saltatory Rewards And Generalized Advantage Estimation On Reference-Based Deep Reinforcement Learning Of Humanlike Motions, Md Rysul Kabir

Theses and Dissertations

In the application of learning physics-based character skills, deep reinforcement learning (DRL) can lead to slow convergence and local optimum solutions during the training process of a reinforcement learning (RL) agent. With the presence of an environment with reward saltation, we can easily plan to magnify those saltatory rewards with the perspective of sample usage to increase the experience pool of an agent during this training process. In our work, we have proposed two modified algorithms. The first one is the addition of a parameter based reward optimization process to magnify the saltatory rewards and thus increasing an agent’s utilization …

Temporal Convolutional Neural Network For Intrusion Detection, Luis Javier Romo Jr.

Theses and Dissertations

Intrusion detection is an important endeavor for large organizations who are constantly targeted by malicious actors. The nature of network traffic data creates many challenges for researchers that want to create an accurate and efficient system for detecting attacks on networks. Many machine learning algorithms have been developed to take on this task. In this paper, we will review some of these techniques, some data sets used to test these techniques, and an experiment where we developed an intrusion detection system that uses a convolution neural network that can perform sequence modeling. This convolutional neural network outperformed a long-shorted term …

Semantic Adversarial Attack On Support Vector Machine, Yessica Rodriguez

Theses and Dissertations

Despite the breakthroughs in machine learning, most classifiers are not robust against adversarial attacks. They can be easily fooled by adversarial examples. These examples can be created in a variety of ways. In this thesis, the ideas of detecting edges or critical pixels in an image are investigated that could be used for fooling classifiers. Identifying those critical pixels in an image can lead the way to fix the vulnerabilities and thus making it robust against cyber-attacks. For testing, a Support Vector Machine (SVM) is used to see the success of the adversarial examples generated.

Neural Network Development In An Artificial Intelligence Gomoku Program, David Garcia

Theses and Dissertations

The game of Gomoku, also called Five in a Row, is an abstract strategy board game. The Gomoku program is constructed upon an algebraic monomial theory to aid values for each possible move and estimate chances for the artificial intelligence program to accomplish a winning path for each move and rounds. With the utilization of the monomial theory, winning configurations are successfully converted into monomials of variables which are represented on board positions. In the artificial intelligence program, an arduous task is how to perform the present configuration of the Gomoku game along with the past moves of the two …

Reinforcement Learning Environment For Orbital Station-Keeping, Armando Herrera Iii

Theses and Dissertations

In this thesis, a Reinforcement Learning Environment for orbital station-keeping is created and tested against one of the most used Reinforcement Learning algorithm called Proximal Policy Optimization (PPO). This thesis also explores the foundations of Reinforcement Learning, from the taxonomy to a description of PPO, and shows a thorough explanation of the physics required to make the RL environment. Optuna optimizes PPO's hyper-parameters for the created environment via distributed computing. This thesis then shows and analysis the results from training a PPO agent six times.

A Targeted Adversarial Attack On Support Vector Machine Using The Boundary Line, Yessenia Rodriguez

Theses and Dissertations

In this thesis, a targeted adversarial attack is explored on a Support Vector Machine (SVM). SVM is defined by creating a separating boundary between two classes. Using a target class, any input can be modified to cross the “boundary line,” making the model predict the target class. To limit the modification, a percentage of an image of the target class is used to get several random sections. Using these sections, the input will be moved in small steps closer to the boundary point. The section that took the least number of steps to cause the model to predict the target …

Artificial Intelligence In A Main Warehouse In Panasonic: Los Indios, Texas, Edison Antonio Trejo Hernandez

Theses and Dissertations

The Panasonic Company warehouse is located in Los Indios Texas. The warehouse presents the limitation of the great distances between its headquarters and the Main Warehouse for supplying the branches and main customers, which requires a considerable amount of time to maintain effective communication in the inventory area. In addition, during an online review, it can be confirmed that the website is disabled, contradicting its corporate policy.

The structure of the thesis proposal is arranged in four chapters from the Introduction, Statement of the Problem and Purposes; Previous Studies and Definition of the literature; the Research Methodology and the resources …

Diffusion Of Falsehoods On Social Media, Kelvin Kizito King

Theses and Dissertations

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher …

Creativity And Engagement In Ideas Crowdsourcing: A Situation Awareness Perspective, James Gitau Wairimu

Theses and Dissertations

This dissertation investigates the influence of performance feedback in user motivation and creativity development in idea crowdsourcing engagement. Creativity occurs when users of idea crowdsourcing communities engage in direct and indirect interactions that expose them to a pool of knowledge that enhances their cognitive development leading to the contribution of novel ideas for innovation in organizations. Additionally, participant motivation to engage in ideas crowdsourcing is increased through rewards and conditions that make the ideation process more inclusive and enjoyable. An idea network design is developed by applying social network analysis principles. The idea network design consists of mechanisms for motivating …

Analysis Of The Functional Relationship Of Protein Kinase Families Using Phospho-Proteomics Data, David A. Parra Peña

Theses and Dissertations

As cancer research advances, Mass-spectrometry based proteomics is becoming a widely used technique for proteome characterization. Phosphoproteomics is a specific type of proteomics that characterizes proteins with the reversible post-translational modification of phosphorylation PTM), which has allowed the identifications of thousands of phosphorylation sites. These phosphorylation sites, also known as substrates, are known to interact with a protein type named kinases. Studies have shown that abnormal phosphorylation activity is related to cancer diseases. Moreover, these kinases are divided into families, based on the similarity of their catalytic domain, as this part of their amino acid sequence determines a large part …

Algorithmic Assembly Of Nanoscale Structures, Austin Luchsinger

Theses and Dissertations

The development of nanotechnology has become one of the most significant endeavors of our time. A natural objective of this field is discovering how to engineer nanoscale structures. Limitations of current top-down techniques inspire investigation into bottom-up approaches to reach this objective. A fundamental precondition for a bottom-up approach is the ability to control the behavior of nanoscale particles. Many abstract representations have been developed to model systems of particles and to research methods for controlling their behavior. This thesis develops theories on two such approaches for building complex structures: the self-assembly of simple particles, and the use of simple …