Computer Sciences Commons^™

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 …

Predicting The Impact Of Iot Data Gathering On User’S Privacy Preferences, Ghassen Kilani

Theses and Dissertations

The proliferation of Internet of Things (IoT) devices has increased data sharing, profiling, and manipulation on various networks. The rapid growth of information disclosure has caused system users to lose motivation to enhance their data privacy. The repeated breaches on different networks worldwide have made people feel discouraged, as they perceive privacy schemes as futile. IoT systems introduce another dimension of privacy leakage due to their expendability nature and information collection features. The situation worsens when users have to manage multiple IoT devices, each following different security protocols, leading to poor decision-making and privacy leakage. This tremendous flow of unsecured …

A Machine Learning Approach To Forecasting Sep Intensity And Times Based On Cme And Other Solar Activities, Peter John Thomas

Theses and Dissertations

High intensity Solar Energetic Particle (SEP) events pose severe risks for astronauts and critical infrastructure. The ability to accurately forecast the peak intensity and times of these events would enable preparatory measures to mitigate much of this risk. Machine learning approaches have the potential to use characteristics of CMEs and other space weather phenomena to predict SEP intensities and times. However, the severe sparsity of SEP events in current datasets poses a problem to traditional machine learning techniques. In this work, we present a dataset of proton event intensities and times, as well as features for corresponding CMEs and space …

Image-Based Crack Detection By Extracting Depth Of The Crack Using Machine Learning, Nishat Tabassum

Theses and Dissertations

Concrete structures have been a major aspect of social infrastructure since the ancient Roman times, so they have been used for many centuries. Concrete is used for the durability and support it provides to buildings and bridges. Assessing the state of these structures is important in preserving the longevity of structures and the safety of the public. Detecting cracks in their early stage allows repairs to be made without the need to replace the whole structure, so it reduces the cost. Traditional methods are slowly falling behind as technology advances and an increase in demand for a practical method of …

On Incorporating The Stochasticity Of Quantum Machine Learning Into Classical Models, Joseph Lindsay

Theses and Dissertations

While many of the most exciting quantum computing algorithms are currently impossible to be implemented until fault-tolerant quantum error correction is achieved, noisy intermediate-scale quantum (NISQ) devices allow for smaller scale applications that leverage the paradigm for speed-ups to be researched and realized. A currently popular application for these devices is quantum machine learning (QML). Recent works over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically …

Image Restoration Under Adverse Illumination For Various Applications, Lan Fu

Theses and Dissertations

Many images are captured in sub-optimal environment, resulting in various kinds of degradations, such as noise, blur, and shadow. Adverse illumination is one of the most important factors resulting in image degradation with color and illumination distortion or even unidentified image content. Degradation caused by the adverse illumination makes the images suffer from worse visual quality, which might also lead to negative effects on high-level perception tasks, e.g., object detection.

Image restoration under adverse illumination is an effective way to remove such kind of degradations to obtain visual pleasing images. Existing state-of-the-art deep neural networks (DNNs) based image restoration …

Cross Domain Semantic Segmentation, Xinyi Wu

Theses and Dissertations

As a long-standing computer vision task, semantic segmentation is still extensively researched till now because of its importance to visual understanding and analysis. The goal of semantic segmentation is to classify each pixel of images based on the pre-defined classes. In the era of deep learning, convolutional neural networks largely improve the accuracy and efficiency of semantic segmentation. However, this success is achieved with two limitations: 1) a large-scale labeled dataset is required for training while the labeling process for this task is quite labor-intensive and tedious; 2) the trained deep networks can get promising results when testing on the …

Cnn-Based Semantic Segmentation With Shape Prior Knowledge, Yuhang Lu

Theses and Dissertations

Semantic segmentation that aims at grouping discrete pixels into connected regions is a fundamental step in many high-level computer vision tasks. In recent years, Convolutional Neural Networks (CNNs) have made breakthrough progresses in public semantic segmentation benchmarks. The ability of learning from large-scale labeled datasets empowers them to generalize to unseen images better than traditional nonlearning-based methods. Nevertheless, the heavy dependency on labeled data also limits their applications in tasks where high-quality ground truth segmentation masks are scarce or difficult to acquire. In this dissertation, we study the problem of alleviating the data dependency for CNN-based segmentation with a focus …

Knowledge-Infused Learning, Manas Gaur

Theses and Dissertations

In DARPA’s view of the three waves of AI, the first wave of AI, symbolic AI, focused on explicit knowledge. The second and current wave of AI is termed statistical AI. Deep learning techniques have been able to exploit large amounts of data and massive computational power to improve human levels of performance in narrowly defined tasks. Separately, knowledge graphs have emerged as a powerful tool to capture and exploit a variety of explicit knowledge to make algorithms better apprehend the content and enable the next generation of data processing, such as semantic search. After initial hesitancy about the scalability …

Identifying And Discovering Curve Pattern Designs From Fragments Of Pottery, Jun Zhou

Theses and Dissertations

The surface of many cultural heritage objects, such as pottery sherds found in the Southeastern Woodlands, were embellished with curve patterns. The original full designs of these patterns reflect rich historical and cultural information. However, in practice, most objects are fragmentary, making the complete underlying designs unknowable at the scale of the sherd fragment. The challenge to reconstruct and study complete designs is stymied because 1) most pottery sherds contain only a small portion of the underlying full design, 2) curve patterns detected on a sherd are usually incomplete and noisy, and 3) in the case of a stamping application, …

Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel

Theses and Dissertations

Infrastructure is a key component in the well-being of our society that leads to its growth, development, and productive operations. A well-built infrastructure allows the community to be more competitive and promotes economic advancement. In 2021, the ASCE (American Society of Civil Engineers) ranked the American infrastructure as substandard, with an overall grade of C-. The overall ranking suffers when key infrastructure categories are not maintained according to the needs of the population. Therefore, there is a need to consider alternative methods to improve our infrastructure and make it more sustainable to enhance the overall grade. One of the challenges …

Data As A Service Ecosystem For Data-Driven Research, Leonardo Vieira

Theses and Dissertations

For the last two decades, the cloud computing ecosystems has become a major defining force for America, because of their unique economic, social and national importance. These ecosystems have now taken their place alongside the nation’s other infrastructure such as the food/agricultural, energy healthcare, and roads/highways. Scientists, engineers, and researchers over the same period have experienced a tremendous growth in the need for resources to that support assorted research. Cloud computing enables these communities to undertake wide-ranging research efforts, while requiring no maintenance, management or significant invest of local resources. The genius of this project examines how create on-ramps for …

An Empirical Study On Sampling Approaches For 3d Image Classification Using Deep Learning, Nicholas Michelette

Theses and Dissertations

A 3D classification method requires more training data than a 2D image classification method to achieve good performance. These training data usually come in the form of multiple 2D images (e.g., slices in a CT scan) or point clouds (e.g., 3D CAD modeling) for volumetric object representation. The amount of data required to complete this higher dimension problem comes with the cost of requiring more processing time and space. This problem can be mitigated with data size reduction (i.e., sampling). In this thesis, we empirically study and compare the classification performance and deep learning training time of PointNet utilizing uniform …

A Digital Application For Assessment Of Neurocognitive Disabilities, Thomas H. Auriemma

Theses and Dissertations

Background: Neuropsychological assessment is designed to identify neurocognitive impairment and has traditionally relied on pen-and-paper tests. The behavior collected from these tests is usually expressed as a total summary score or a score that reflects a restricted number of features that assess errors. There is now interest in coupling traditional paper and pencil tests with digital assessment technology. In this context traditional metrics such as summary scores are still available. However, using digital technology, a host of time-based parameters can now be obtained. These time-based parameters include the total time to complete the task or total time to completion, as …

Scheduling For Space Tracking And Heterogeneous Sensor Environments, Gabriel H. Greve

Theses and Dissertations

This dissertation draws on the fields of heuristic and meta-heuristic algorithm development, resource allocation problems, and scheduling to address key Air Force problems. The world runs on many schedules. People depend upon them and expect these schedules to be accurate. A process is needed where schedules can be dynamically adjusted to allow tasks to be completed efficiently. For example, the Space Surveillance Network relies on a schedule to track objects in space. The schedule must use sensor resources to track as many high-priority satellites as possible to obtain orbit paths and to warn of collision paths. Any collisions that occurred …

Assessing The Effect Of Interactivity On Virtual Reality Second Language Learning, Christene Harris

Theses and Dissertations

Virtual Reality (VR) being used as a helpful tool in language education is widely supported by the current literature. It can provide a variety of stimulating scenarios that keep learner engagement high. The use of VR for language learning is a research area that has shown promise in recent years. This makes it necessary for further research to be conducted in the field to determine ways to maximize its potential. This thesis aims to determine if the level of interactivity present in a VR Language Learning Application is a factor that will impact a user's capability to successfully learn a …

Low Memory Continual Learning Classification Algorithms For Low Resource Hardware, Autumn Lilly Chadwick

Theses and Dissertations

Continual Learning (CL) is a machine learning approach which focuses on continuous learning of data rather than single dataset-based learning. In this thesis, this same focus is applied with respect to the field of machine learning for embedded devices which is still in the early stages of development. This focus is further used to develop various algorithms such as utilizing prior trained starting networks, weighted output schemes, and replay or reduced datasets for training while maintaining a consistent focus on low resource devices to maintain acceptable performance. The experimental results show an improvement in model training times as compared to …

Graph-Based Unsupervised Entity Resolution For Identifying Entity Profiles In Ambiguous Data, Islam Akef Ebeid

Theses and Dissertations

Entity resolution means finding duplicate records within the same table, across various tables, or in multiple databases. Traditional and rule-based approaches in entity resolution rely on handcrafting rules for matching records. On the other hand, machine learning and deep learning methods are data-intensive and require labeled training datasets. Thus the move toward automating entity resolution for data cleaning, curation, and integration has become the goal for many organizations. Accordingly, unsupervised entity resolution methods have proliferated, relying on an automated pipeline of preprocessing, blocking, feature extraction, matching, clustering, profiling, and canonicalization. Unsupervised entity resolution methods face many challenges due to the …

Incorporating Spatial Relationship Information In Signal-To-Text Processing, Jeremy Elon Davis

Theses and Dissertations

This dissertation outlines the development of a signal-to-text system that incorporates spatial relationship information to generate scene descriptions. Existing signal-to-text systems generate accurate descriptions in regards to information contained in an image. However, to date, no signalto- text system incorporates spatial relationship information. A survey of related work in the fields of object detection, signal-to-text, and spatial relationships in images is presented first. Three methodologies followed by evaluations were conducted in order to create the signal-to-text system: 1) generation of object localization results from a set of input images, 2) derivation of Level One Summaries from an input image, and …

Developing A Model Of Driver Performance, Situation Awareness, And Cognitive Load Considering Different Levels Of Partial Vehicle Autonomy, Jessie E. Cossitt

Theses and Dissertations

To fully utilize the abilities of current autonomous vehicles, it is necessary to understand the interactions between vehicles and their operators. Since the current state of the art of autonomous vehicles is partial autonomy that requires operators to perform parts of the driving task and be alert and ready to take over full control of the vehicle, it is necessary to know how operators' abilities are impacted by the amount of autonomy present in the system. Autonomous systems have known effects on performance, cognitive load, and situation awareness, but little is known about how these effects change in relation to …

A Novel Method For Sensitivity Analysis Of Time-Averaged Chaotic System Solutions, Christian A. Spencer-Coker

Theses and Dissertations

The direct and adjoint methods are to linearize the time-averaged solution of bounded dynamical systems about one or more design parameters. Hence, such methods are one way to obtain the gradient necessary in locally optimizing a dynamical system’s time-averaged behavior over those design parameters. However, when analyzing nonlinear systems whose solutions exhibit chaos, standard direct and adjoint sensitivity methods yield meaningless results due to time-local instability of the system. The present work proposes a new method of solving the direct and adjoint linear systems in time, then tests that method’s ability to solve instances of the Lorenz system that exhibit …

Pluto In Hand: Design And Implementation Of A Location-Based Mobile Augmented Reality Application For Viewing Open Data, Matthew O. Ward

Theses and Dissertations

Immersive mobile augmented reality (AR) technology has improved while geolocational data volume has grown. City governments can utilize this technology to share their geospatial data with the public, promoting smart city aims. This research describes the design and implementation of a novel open-source ARGIS application to view property tax lot information in New York City. This proof-of-technology demonstrates web-based AR can visualize location-based spatial data.

The Applications Of The Internet Of Things In The Medical Field, Cody Repass

Theses and Dissertations

The Internet of Things (IoT) paradigm promises to make “things” include a more generic set of entities such as smart devices, sensors, human beings, and any other IoT objects to be accessible at anytime and anywhere. IoT varies widely in its applications, and one of its most beneficial uses is in the medical field. However, the large attack surface and vulnerabilities of IoT systems needs to be secured and protected. Security is a requirement for IoT systems in the medical field where the Health Insurance Portability and Accountability Act (HIPAA) applies.

This work investigates various applications of IoT in healthcare …

Toward A Labeled Dataset Of Iot Malware Features, Stian Hagboe Olsen

Theses and Dissertations

IoT malware has accompanied the rapid growth of embedded devices over the last decade. The last few years have seen increased work on static and dynamic detection and classification techniques for IoT malware. However, this work requires a very diverse and fine-grained set of malware-specific characteristics. This paper takes a step toward constructing a large-scale, diverse, and open-source IoT malware dataset. To demonstrate the depth of the dataset, we propose an approach for recovering symbol tables and detecting the intent of stripped IoT malware binaries using function signature libraries and 14 defining Linux malware features with corresponding regular expressions. We …

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 …

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 …

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 …

Iot Security For Iotmon Attacks Based On Devices’ App Description, Raghad Jameel A. Alhazmi

Theses and Dissertations

There are concerns associated with ”inter-app” interactions, which occur when many independently developed home automation apps interact and affect one another, causing possibly dangerous unexpected app action. We extended a security tool named IoTMon, an IoT device management system capable of identifying all potential cross-app communication paths and analyzing their danger status. As part of our work, we keep an eye on the app description and safeguard IoTMon from being altered in any way that could obscure the real interaction related to another app action. We validate the IoTMon system’s integrity by applying the hash algorithm SHA512 with digital signature …

A Co-Evolutionary Approach To Test Case Generation For Safety-Critical Systems, Brad Thomas Costa

Theses and Dissertations

Safety-critical software development is a costly and time-consuming process that involves thousands of hours dedicated to test development. Tests must meet stringent developmental guidelines to verify the correct and complete implementation of their parent requirements. Further compounding any such effort is the tendency towards requirement churn or the frequent change to the software and other system requirements. This thesis presents a solution, PyTcGen, that alleviates these challenges by processing natural language requirements and programmatically generating the requisite test cases to ensure the software meets all of the conditions of that requirement. The solution uses template matching to marry requirements to …