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Articles 391 - 420 of 2733
Full-Text Articles in Computer Sciences
Identifying Properties Of Blockchain Making It Relevant To Iot Applications, Ashwaq Alaklabi
Identifying Properties Of Blockchain Making It Relevant To Iot Applications, Ashwaq Alaklabi
Theses and Dissertations
Blockchain is a distributed ledger system that enables machine-to-machine interactions combined with the Internet of Things (IoT). It employs a series of transactions stored in a database, confirmed by numerous sources, and written into a shared ledger distributed across all nodes. The marriage of IoT with blockchain has several potential benefits, including the ability for a smart device to act autonomously without the need for a centralized authority. It can also monitor how devices communicate with one another. A blockchain network can track and sell almost anything of value, lowering risk and costs for everyone involved. Numerous applications from diverse …
Classroom Audio Classification Using Deep Learning Frameworks, Afsana Rahman Mou
Classroom Audio Classification Using Deep Learning Frameworks, Afsana Rahman Mou
Theses and Dissertations
For both teachers and students studying science, technology, engineering, and mathematics (STEM), active learning is more likely to be productive because learners engage in a variety of classroom activities. As instructors are trying different pedagogies in classroom, it is also important to check the effectiveness of those methods. The aim of our work is to identify the classroom activities with more accuracy which will help to measure the student involvement in the class. Using automatic audio classification, we can help to improve active learning strategies in the classroom, and it will be cost effective too. Various deep learning techniques, such …
Exploring Misinformation Campaigns And How To Defend Against Them, Michelle S. Kuralt
Exploring Misinformation Campaigns And How To Defend Against Them, Michelle S. Kuralt
Theses and Dissertations
Misinformation campaigns can have very real and lasting effects. Misinformation has been known to impact elections, create vaccine hesitancy, and increase polarization within societies. This paper reviewed existing literature regarding misinformation research, collected information from research participants to discern factors which may contribute to an increased susceptibility to misinformation, and examined Rule Based Training and a training program which utilized both rules and mindfulness to ascertain if these training programs might be effective in reducing participant susceptibility to misinformation.
Utilizing Federated Learning And Meta Learning For Few-Shot Learning On Edge Devices, Kousalya Soumya Lahari Voleti
Utilizing Federated Learning And Meta Learning For Few-Shot Learning On Edge Devices, Kousalya Soumya Lahari Voleti
Theses and Dissertations
The efficient and effective handling of few-shot learning tasks on mobile devices is challenging due to the small training set issue and the physical limitations in power and computational resources on these devices. In this thesis, we propose a solution that combines federated learning and meta-learning to handle independent few-shot learning tasks on multiple devices (or clients) and the server. In particular, we utilize the Prototypical Networks to perform meta-learning on all devices to learn multiple independent few-shot learning models and to combine the models in a centralized data distributed architecture using federated learning which can be reused by the …
Cnn-Based Dendrite Core Detection From Microscopic Images Of Directionally Solidified Ni-Base Alloys, Xiaoguang Li
Cnn-Based Dendrite Core Detection From Microscopic Images Of Directionally Solidified Ni-Base Alloys, Xiaoguang Li
Theses and Dissertations
Dendrite core is the center point of the dendrite. The information of dendrite core is very helpful for material scientists to analyze the properties of materials. Therefore, detecting the dendrite core is a very important task in the material science field. Meanwhile, because of some special properties of the dendrites, this task is also very challenging. Different from the typical detection problems in the computer vision field, detecting the dendrite core aims to detect a single point location instead of the bounding-box. As a result, the existing regressing bounding-box based detection methods can not work well on this task because …
Human Activity Recognition (Har) Using Wearable Sensors And Machine Learning, Chrisogonas Odero Odhiambo
Human Activity Recognition (Har) Using Wearable Sensors And Machine Learning, Chrisogonas Odero Odhiambo
Theses and Dissertations
Humans engage in a wide range of simple and complex activities. Human Activity Recognition (HAR) is typically a classification problem in computer vision and pattern recognition, to recognize various human activities. Recent technological advancements, the miniaturization of electronic devices, and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments, alongside smart wearable sensors, have opened the door to numerous opportunities for adding value and personalized services to citizens. Vision-based and sensory-based HAR find diverse applications in healthcare, surveillance, sports, event analysis, Human-Computer …
Applications Of Machine Learning For Improved Patient Selection And Therapy Recommendations, Brendan Elochukwu Odigwe
Applications Of Machine Learning For Improved Patient Selection And Therapy Recommendations, Brendan Elochukwu Odigwe
Theses and Dissertations
The public health domain continues to battle with illness and the growing need for continuous advancement in our approach to clinical care. Individuals experiencing certain conditions undergo tried and tested therapies and medications, practices that have become the mainstay and standard of care in clinical medicine. As with all therapies and medications, they don't always work the same way and do not work for everyone. Some Treatment regimens, like Hydroxyurea medication, which is commonly administered to Sickle cell anemia patients, come with some adverse side effects due to the chemotherapeutic nature of the drug. This would be particularly disappointing if …
Empirical Studies On Automated Software Testing Practices, Alireza Salahirad
Empirical Studies On Automated Software Testing Practices, Alireza Salahirad
Theses and Dissertations
Software testing is notoriously difficult and expensive, and improper testing carries economic, legal, and even environmental or medical risks. Research in software testing is critical to enabling the development of the robust software that our society relies upon. This dissertation aims to lower the cost of software testing without decreasing the quality by focusing on the use of automation. The dissertation consists of three empirical studies on aspects of software testing. Specifically, these three projects focus on (1) mapping the connections between research topics and the evolution of research topics in the field of software testing, (2) an assessment of …
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
Theses and Dissertations
Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …
Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon
Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon
Theses and Dissertations
The threat of eavesdropping, and the challenge of recognizing and correcting for corrupted or suppressed information in communication systems is a consistent challenge. Effectively managing protection mechanisms requires an ability to accurately gauge the likelihood or severity of a threat, and adapt the security features available in a system to mitigate the threat. This research focuses on the design and development of a security-focused communication protocol at the session-layer based on a re-prioritized communication architecture model and associated metrics. From a probabilistic model that considers data leakage and data corruption as surrogates for breaches of confidentiality and integrity, a set …
Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo
Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo
Theses and Dissertations
Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a …
Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero
Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero
Theses and Dissertations
Quantum computers are beset by errors from a variety of sources. Although quantum error correction and detection codes have been developed since the 1990s, these codes require mid-circuit measurements in order to operate. In order to avoid these measurements we have developed a new error detection code that only requires state collapses at the end of the circuit, which we call no ancilla error detection (NAED). We investigate some of the mathematics behind NAED such as which codes can detect which errors. We then run NAED on three separate types of circuits: Greenberger–Horne–Zeilinger circuits, phase dependent circuits, and a quantum …
Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho
Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho
Theses and Dissertations
We develop new machine learning and statistical methods that are tailored for Air and Space applications through the incorporation of subject matter expertise. In particular, we focus on three separate research thrusts that each represents a different type of subject matter knowledge, modeling approach, and application. In our first thrust, we incorporate knowledge of natural phenomena to design a neural network algorithm for localizing point defects in transmission electron microscopy (TEM) images of crystalline materials. In our second research thrust, we use Bayesian feature selection and regression to analyze the relationship between fighter pilot attributes and flight mishap rates. We …
Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone
Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone
Theses and Dissertations
This research is intended to determine which styles of layout algorithm are well suited to Command and Control (C2) network graphs to replace current manual layout methods. Manual methods are time intensive and an automated layout algorithm should decrease the time spent creating network graphs. Simulations on realistic synthetically generated graphs provide information to help infer which algorithms perform better than others on this problem. Data is generated using statistics drawn from multiple real world C2 network graphs. The three algorithms tested against this data are the Spectral algorithm, the Dot algorithm, and the Fruchterman-Reingold algorithm. The results include a …
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chalé
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chalé
Theses and Dissertations
Cyberspace is the digital communications network that supports the internet of battlefield things (IoBT), the model by which defense-centric sensors, computers, actuators and humans are digitally connected. A secure IoBT infrastructure facilitates real time implementation of the observe, orient, decide, act (OODA) loop across distributed subsystems. Successful hacking efforts by cyber criminals and strategic adversaries suggest that cyber systems such as the IoBT are not secure. Three lines of effort demonstrate a path towards a more robust IoBT. First, a baseline data set of enterprise cyber network traffic was collected and modelled with generative methods allowing the generation of realistic, …
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Theses and Dissertations
Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Theses and Dissertations
Analysis of cyber-physical systems (CPS) has emerged as a critical domain for providing US Air Force and Space Force leadership decision advantage in air, space, and cyberspace. Legacy methods have been outpaced by evolving battlespaces and global peer-level challengers. Automation provides one way to decrease the time that analysis currently takes. This thesis presents an event detection automation system (EDAS) which utilizes deep learning models, distance metrics, and static thresholding to detect events. The EDAS automation is evaluated with case study of CPS domain experts in two parts. Part 1 uses the current methods for CPS analysis with a qualitative …
Effective Immersive Analytics For Everyday Use, Benjamin D. Weidner
Effective Immersive Analytics For Everyday Use, Benjamin D. Weidner
Theses and Dissertations
Data visualization is an important field of work that takes in uncountable amounts of indexes to create an easy-to-read interpretation of what was previously unreadable. Immersive analytics is the new field that brings 3D data visualization to virtual reality, immersing users directly into the data. Focusing on bringing humans and computers closer together through natural function can benefit the world of data science. In order to accurately utilize this field to benefit this world, principles must be laid out and observed to see which techniques and methods are best fit for an everyday immersive analytics platform. Our findings show that, …
X-Ray Vision At Action Space Distances: Depth Perception In Context, Nate Phillips
X-Ray Vision At Action Space Distances: Depth Perception In Context, Nate Phillips
Theses and Dissertations
Accurate and usable x-ray vision has long been a goal in augmented reality (AR) research and development. X-ray vision, or the ability to comprehend location and object information when such is viewed through an opaque barrier, would be imminently useful in a variety of contexts, including industrial, disaster reconnaissance, and tactical applications. In order for x-ray vision to be a useful tool for many of these applications, it would need to extend operators’ perceptual awareness of the task or environment. The effectiveness with which x-ray vision can do this is of significant research interest and is a determinant of …
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn
Theses and Dissertations
With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …
Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt
Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt
Theses and Dissertations
Irregular applications, such as unstructured mesh operations, do not easily map onto the typical GPU programming paradigms endorsed by GPU manufacturers, which mostly focus on maximizing concurrency for latency hiding. In this work, we show how alternative techniques focused on latency amortization can be used to control overall latency while requiring less concurrency. We used a custom-built microbenchmarking framework to test several GPU kernels and show how the GPU behaves under relevant workloads. We demonstrate that coalescing is not required for efficacious performance; an uncoalesced access pattern can achieve high bandwidth - even over 80% of the theoretical global memory …
A Tool-Supported Metamodel For Program Bugfix Analysis In Empirical Software Engineering, Manal Zneit
A Tool-Supported Metamodel For Program Bugfix Analysis In Empirical Software Engineering, Manal Zneit
Theses and Dissertations
This thesis describes a software modeling approach aimed at addressing empirical studies in software engineering. We build a metamodel that provides an overview of the taxonomy of program bugfixes in deep learning programs. For modeling purposes, we present a prototype tool that is an implementation of the model-driven techniques presented.
Abm Simulation Model Of A Pandemic For Optimizing Vaccination Strategy, Gibeom Park
Abm Simulation Model Of A Pandemic For Optimizing Vaccination Strategy, Gibeom Park
Theses and Dissertations
This study presents a process-oriented hybrid model for individuals' immune responses and interactions involving vaccination to describe the trend of contagious disease and estimate the future societal cost. The model considers "recovery" as a non-absorbing state and incorporates various infection stage states including two symptomatic states. To model contagiousness to be consistent with the current pandemic and include that the spread of a disease depends on the mobility of people, we developed an Agent-Based Simulator that fitted to the particular model used in this study and can test various what-if scenarios. We improved the simulator considerably by appying data structures …
Evaluation Of Automation Techniques For Data Quality Assessment For Party And Product Master Data, Mahmood Mohammed
Evaluation Of Automation Techniques For Data Quality Assessment For Party And Product Master Data, Mahmood Mohammed
Theses and Dissertations
In an era, where data is being used by organizations in achieving their business goals and driving their business decisions, it is key to ensure the quality of data. For an organization, the most important data assets include master data assets such as product master data and customer master data, supplier master data, employee master data which can be generalized as party master data. There has been significant growth and variety in data in recent years because of which the traditional rules-based approach and dependency on data experts for assessing data quality is no longer working. This dissertation evaluates and …
Formal Verification Applications For The Treekem Continuous Group Key Agreement Protocol, Alexander J. Washburn
Formal Verification Applications For The Treekem Continuous Group Key Agreement Protocol, Alexander J. Washburn
Theses and Dissertations
The features of Secure Group Messaging, the security guarantees of Message Layer Security, and the TreeKEM protocol designed to satisfy these guarantees and features are explored. A motivation and methodology for verification via explicit model checking is presented. Subsequently, a translation of the TreeKEM protocol into a Promela reference model is described, examining the nuances explicit model checking brings. Finally the results of the formal verification methods are discussed.
A Machine Learning Approach To Forecasting Sep Intensity And Times Based On Cme And Other Solar Activities, Peter John Thomas
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 …
Advancing Human-Agent Teamwork, Vijayanth Tummala
Advancing Human-Agent Teamwork, Vijayanth Tummala
Theses and Dissertations
Progress in the use of Artificial Intelligence (AI) has been made in many different fields. We are now reaching a point in AI development typical AI implementations are not enough: where we would need humans and AI systems to actively collaborate with each other, basing their actions on the actions and capabilities of each other. This collaboration could be in the form of agents assisting humans processing and analyzing information, assisting humans with smaller physical tasks or working with humans as an equal team member – having the same goals and performing the same tasks – to accomplish a goal. …
Asynchronous Messaging In A P2p System: Defending Against A Storage Exhaustion Attack On Kademlia Dht, Maxim Biro
Asynchronous Messaging In A P2p System: Defending Against A Storage Exhaustion Attack On Kademlia Dht, Maxim Biro
Theses and Dissertations
An instant messaging service designed using a peer to peer distributed network architecture has many appealing properties it gets for free: high scalability, cheap operational cost and no reliance on a third party to provide the service. However, the nature of the distributed network architecture makes implementing some of the instant messaging features rather challenging, asynchronous messaging being one of them. The asynchronous messaging requires that peers store arbitrary data on behalf of other peers for prolonged periods of time, often measured in days, which, if not kept in check, can be easily abused by malicious actors by spamming the …
Process For Designing And Implementing Provably Verifiable Voting Systems, Kholud Alghamdi
Process For Designing And Implementing Provably Verifiable Voting Systems, Kholud Alghamdi
Theses and Dissertations
This research aims to explore processes for designing verifiable voting systems in which certain properties can be proven, exemplified with systems applicable to election processes in Saudi Arabia. The electronic government model has become a substantial channel for governments to connect to businesses and citizens, to develop services, and provide general information. E-voting and in particular online voting is one of the important tools that can be used in political and administrative places where information and communications technology devices and tools are utilized to simplify people’s lives and facilitate the election process and decision making. Election processes let a population …
Learning Depth From Images, Zhenyao Wu
Learning Depth From Images, Zhenyao Wu
Theses and Dissertations
Estimating depth from images has become a very popular task in computer vision which aims to restore the 3D scene from 2D images and identify important geometric knowledge of the scene. Its performance has been significantly improved by convolutional neural networks in recent years, which surpass the traditional methods by a large margin. However, the natural scenes are usually complicated, and hard to build the correspondence between pixels across frames, such as the region containing moving objects, illumination changes, occlusions, and reflections. This research explores rich and comprehensive spatial correspondence across images and designs three new network architectures for depth …