Computer Sciences Commons^™

Multi-Agent Reinforcement Learning For Defensive Escort Teams, Hassam Sheikh

Electronic Theses and Dissertations, 2020-2023

Reinforcement learning has been applied to solve several real world challenging problems, from robotics to data center cooling. Similarly, adaption of reinforcement learning for multi-agent systems facilitated applications such as optimal multi-robot control and analysis of social-dilemmas. In this dissertation, we show that multi-agent reinforcement learning algorithms suffer from several stability issues such as multi-scenario learning, unstable training in dual-reward setting, overestimation bias and value function collapse, and provide solutions to each of these problems respectively. Several contributions of this dissertation have been formalized within the framework of a defensive escort team problems, a scenario where a team of learning …

3d Localization Of Defects In Facility Inspections, Nicholas Califano

Electronic Theses and Dissertations, 2020-2023

Wind tunnels are crucial facilities that support the aerospace industry. However, these facilities are large, complex, and pose unique maintenance and inspection requirements. Manual inspections to identify defects such as cracks, missing fasteners, leaks, and foreign objects are important but labor and schedule intensive. The goal of this thesis is to utilize small Unmanned Aircraft Systems with onboard cameras and computer vision-based analysis to automate the inspection of the interior and exterior of NASA's critical wind tunnel facilities. Missing fasteners are detected as the defect class, and existing fasteners are detected to provide potential future missing fastener sites for preventative …

Interactions Between Humans, Virtual Agent Characters And Virtual Avatars, Tamara Griffith

Electronic Theses and Dissertations, 2020-2023

Simulations allow people to experience events as if they were happening in the real world in a way that is safer and less expensive than live training. Despite improvements in realism in simulated environments, one area that still presents a challenge is interpersonal interactions. The subtleties of what makes an interaction rich are difficult to define. We may never fully understand the complexity of human interchanges, however there is value in building on existing research into how individuals react to virtual characters to inform future investments. Virtual characters can either be automated through computational processes, referred to as agents, or …

Design Of Ternary Operations Utilizing Flow-Based Computing, James Pyrich

Electronic Theses and Dissertations, 2020-2023

The development of algorithms and circuit designs that exploit devices that have the ability to persist multiple values will lead to alternative technologies to overcome the issues caused by the end of Dennard scaling and slowing of Moore's Law. Flow-based designs have been used to develop binary adders and multipliers. Data stored on non-volatile memristors are used to direct the flow of current through nanowires arranged in a crossbar. The algorithmic design of the flow-based crossbar is fast, compact, and efficient. In this paper, we seek to automate the discovery of flow-based designs of ternary circuits utilizing memristive crossbars.

Towards Scalable Network Traffic Measurement With Sketches, Rhongho Jang

Electronic Theses and Dissertations, 2020-2023

Driven by the ever-increasing data volume through the Internet, the per-port speed of network devices reached 400 Gbps, and high-end switches are capable of processing 25.6 Tbps of network traffic. To improve the efficiency and security of the network, network traffic measurement becomes more important than ever. For fast and accurate traffic measurement, managing an accurate working set of active flows (WSAF) at line rates is a key challenge. WSAF is usually located in high-speed but expensive memories, such as TCAM or SRAM, and thus their capacity is quite limited. To scale up the per-flow measurement, we pursue three thrusts. …

Open-Ended Search Through Minimal Criterion Coevolution, Jonathan Brant

Electronic Theses and Dissertations, 2020-2023

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field's biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, …

Computational Methods For Discovery And Analysis Of Rna Structural Motifs, Shahidul Islam

Electronic Theses and Dissertations, 2020-2023

Understanding the 3D structural properties of RNAs will play a critical role in identifying their functional characteristics and designing new RNAs for RNA-based therapeutics and nanotechnology. In an attempt to achieve a better insight into RNAs, biochemical experiments have been conducted to produce data with positional details of atoms in RNA structures. This data has created opportunities for applying computational analysis to solve various biological problems. In this dissertation, we have addressed annotation issues of base-pairing interactions in the low-resolution structure data and presented new methods to analyze RNA structural motifs. Annotating base-pairing interactions is one of the critical steps …

The Effects Of Gesture Presentation In Video Games, Jack Oakley

Electronic Theses and Dissertations, 2020-2023

As everyday and commonplace technology continues to move toward touch devices and virtual reality devices, more and more video games are using gestures as forms of gameplay. While there is much research focused on gestures as user interface navigation methods, we wanted to look into how gestures affect gameplay when used as a gameplay mechanic. In particular, we set out to determine how different ways of presenting gestures might affect the game's difficulty and flow. We designed two versions of a zombie game where the zombies are killed by drawing gestures. The first version of the game is a touchscreen-based …

Analyzing User Behavior In Collaborative Environments, Samaneh Saadat

Electronic Theses and Dissertations, 2020-2023

Discrete sequences are the building blocks for many real-world problems in domains including genomics, e-commerce, and social sciences. While there are machine learning methods to classify and cluster sequences, they fail to explain what makes groups of sequences distinguishable. Although in some cases having a black box model is sufficient, there is a need for increased explainability in research areas focused on human behaviors. For example, psychologists are less interested in having a model that predicts human behavior with high accuracy and more concerned with identifying differences between actions that lead to divergent human behavior. This dissertation presents techniques for …

Action Recognition In Still Images: Confluence Of Multilinear Methods And Deep Learning, Marjaneh Safaei

Electronic Theses and Dissertations, 2020-2023

Motion is a missing information in an image, however, it is a valuable cue for action recognition. Thus, lack of motion information in a single image makes action recognition for still images inherently a very challenging problem in computer vision. In this dissertation, we show that both spatial and temporal patterns provide crucial information for recognizing human actions. Therefore, action recognition depends not only on the spatially-salient pixels, but also on the temporal patterns of those pixels. To address the challenge caused by the absence of temporal information in a single image, we introduce five effective action classification methodologies along …

Algorithms For Inferring Multiple Microbial Networks, Sahar Tavakoli

Electronic Theses and Dissertations, 2020-2023

The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is a weighted graph that is constructed from a sample-taxa count matrix and can be used to model co-occurrences and/or interactions of the constituent members of a microbial community. The nodes in this graph represent microbial taxa and the edges represent pairwise associations amongst these taxa. A microbial network is …

Anticipating Widespread Augmented Reality: Insights From The 2018 Ar Visioning Workshop, Gregory F. Welch, Gerd Bruder, Peter Squire, Ryan Schubert

Faculty Scholarship and Creative Works

In August of 2018 a group of academic, government, and industry experts in the field of Augmented Reality gathered for four days to consider potential technological and societal issues and opportunities that could accompany a future where AR is pervasive in location and duration of use. This report is intended to summarize some of the most novel and potentially impactful insights and opportunities identified by the group.

Our target audience includes AR researchers, government leaders, and thought leaders in general. It is our intent to share some compelling technological and societal questions that we believe are unique to AR, and …

Leaning Robust Sequence Features Via Dynamic Temporal Pattern Discovery, Hao Hu

Electronic Theses and Dissertations

As a major type of data, time series possess invaluable latent knowledge for describing the real world and human society. In order to improve the ability of intelligent systems for understanding the world and people, it is critical to design sophisticated machine learning algorithms for extracting robust time series features from such latent knowledge. Motivated by the successful applications of deep learning in computer vision, more and more machine learning researchers put their attentions on the topic of applying deep learning techniques to time series data. However, directly employing current deep models in most time series domains could be problematic. …

Student Community Detection And Recommendation Of Customized Paths To Reinforce Academic Success, Yuan Shao

Electronic Theses and Dissertations

Educational Data Mining (EDM) is a research area that analyzes educational data and extracts interesting and unique information to address education issues. EDM implements computational methods to explore data for the purpose of studying questions related to educational achievements. A common task in an educational environment is the grouping of students and the identification of communities that have common features. Then, these communities of students may be studied by a course developer to build a personalized learning system, promote effective group learning, provide adaptive contents, etc. The objective of this thesis is to find an approach to detect student communities …

Mediated Physicality: Inducing Illusory Physicality Of Virtual Humans Via Their Interactions With Physical Objects, Myungho Lee

Electronic Theses and Dissertations

The term virtual human (VH) generally refers to a human-like entity comprised of computer graphics and/or physical body. In the associated research literature, a VH can be further classified as an avatar - a human-controlled VH, or an agent - a computer-controlled VH. Because of the resemblance with humans, people naturally distinguish them from non-human objects, and often treat them in ways similar to real humans. Sometimes people develop a sense of co-presence or social presence with the VH - a phenomenon that is often exploited for training simulations where the VH assumes the role of a human. Prior research …

Decision-Making For Vehicle Path Planning, Jun Xu

Electronic Theses and Dissertations

This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors. There are many different practical applications that map to this model. In this …

A Deep Learning Approach To Diagnosing Schizophrenia, Justin Barry

Electronic Theses and Dissertations

In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep learning techniques using three hidden layers in the model. The results obtained indicate that deep learning provides state-of-the-art accuracy in diagnosing schizophrenia. Based on these observations, there is a possibility that deep learning …

Realtime Editing In Virtual Reality For Room Scale Scans, Charles Greenwood

Electronic Theses and Dissertations

This work presents a system for the design and implementation of tools that support the editing of room-scale scans within a virtual reality environment, in real time. The moniker REVRRSS ("reverse") thus stands for Real-time Editing (in) Virtual Reality (of) Room Scale Scans. The tools were evaluated for usefulness based upon whether they meet the criterion of real time usability. Users evaluated the editing experience with traditional keyboard-video-mouse compared to a head mounted display and hand-held controllers for Virtual Reality. Results show that users prefer the VR approach. The quality of the finished product when using VR is comparable to …

Synergistic Visualization And Quantitative Analysis Of Volumetric Medical Images, Neslisah Torosdagli

Electronic Theses and Dissertations

The medical diagnosis process starts with an interview with the patient, and continues with the physical exam. In practice, the medical professional may require additional screenings to precisely diagnose. Medical imaging is one of the most frequently used non-invasive screening methods to acquire insight of human body. Medical imaging is not only essential for accurate diagnosis, but also it can enable early prevention. Medical data visualization refers to projecting the medical data into a human understandable format at mediums such as 2D or head-mounted displays without causing any interpretation which may lead to clinical intervention. In contrast to the medical …

Machine Learning From Casual Conversation, Awrad Mohammed Ali

Electronic Theses and Dissertations

Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base, based on the human's conversational text input. This system emulates how humans learn from each other through a dialog. LCC closes the gap in the current research …

Federal, State And Local Law Enforcement Agency Interoperability Capabilities And Cyber Vulnerabilities, Tyrone Trapnell

Electronic Theses and Dissertations

The National Data Exchange (N-DEx) System is the central informational hub located at the Federal Bureau of Investigation (FBI). Its purpose is to provide network subscriptions to all Federal, state and local level law enforcement agencies while increasing information collaboration across all domains. The National Data Exchange users must satisfy the Advanced Permission Requirements, confirming the terms of N-DEx information use, and the Verification Requirement (verifying the completeness, timeliness, accuracy, and relevancy of N-DEx information) through coordination with the record-owning agency (Management, 2018). A network infection model is proposed to simulate the spread impact of various cyber-attacks within Federal, state …

Efficient String Graph Construction Algorithm, S.M. Iqbal Morshed

Electronic Theses and Dissertations

In the field of genome assembly research where assemblers are dominated by de Bruijn graph-based approaches, string graph-based assembly approach is getting more attention because of its ability to losslessly retain information from sequence data. Despite the advantages provided by a string graph in repeat detection and in maintaining read coherence, the high computational cost for constructing a string graph hinders its usability for genome assembly. Even though different algorithms have been proposed over the last decade for string graph construction, efficiency is still a challenge due to the demand for processing a large amount of sequence data generated by …

Predicting Students' Academic Performance With Decision Tree And Neural Network, Junshuai Feng

Electronic Theses and Dissertations

Educational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural networks to predict student performance in the context of EDM. Firstly, it introduces EDM and some relative works that have been accomplished previously in this field along with their …

Describing Images By Semantic Modeling Using Attributes And Tags, Mahdi Mahmoudkalayeh

Electronic Theses and Dissertations

This dissertation addresses the problem of describing images using visual attributes and textual tags, a fundamental task that narrows down the semantic gap between the visual reasoning of humans and machines. Automatic image annotation assigns relevant textual tags to the images. In this dissertation, we propose a query-specific formulation based on Weighted Multi-view Non-negative Matrix Factorization to perform automatic image annotation. Our proposed technique seamlessly adapt to the changes in training data, naturally solves the problem of feature fusion and handles the challenge of the rare tags. Unlike tags, attributes are category-agnostic, hence their combination models an exponential number of …

Quality Diversity: Harnessing Evolution To Generate A Diversity Of High-Performing Solutions, Justin Pugh

Electronic Theses and Dissertations

Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type …

Analysis Literatures Of Machine Learning And Neural Networks For Real Time Scheduling, Phong Nguyenho, Mark Nguyen

Recent Advances in Real-Time Systems

Real time scheduling problems are present in every aspect of software development. An optimized real time scheduling scheme would determine the performance of an operating system. There are many different approaches that real time scheduling researchers developed to tackle scheduling problems in many computer systems that have great important roles in keeping our modern society running smoothly. Neural-network real time scheduling is one of those approaches that can solve many computer scheduling problems. As computing technology advanced, more and more real time scheduling problems arise that need new solutions to keep up with the demand of faster computer systems. In …

Framework For Modeling Attacker Capabilities With Deception, Sharif Hassan

Electronic Theses and Dissertations

In this research we built a custom experimental range using opensource emulated and custom pure honeypots designed to detect or capture attacker activity. The focus is to test the effectiveness of a deception in its ability to evade detection coupled with attacker skill levels. The range consists of three zones accessible via virtual private networking. The first zone houses varying configurations of opensource emulated honeypots, custom built pure honeypots, and real SSH servers. The second zone acts as a point of presence for attackers. The third zone is for administration and monitoring. Using the range, both a control and participant-based …

Transparency And Communication Patterns In Human-Robot Teaming, Shan Lakhmani

Electronic Theses and Dissertations

In anticipation of the complex, dynamic battlefields of the future, military operations are increasingly demanding robots with increased autonomous capabilities to support soldiers. Effective communication is necessary to establish a common ground on which human-robot teamwork can be established across the continuum of military operations. However, the types and format of communication for mixed-initiative collaboration is still not fully understood. This study explores two approaches to communication in human-robot interaction, transparency and communication pattern, and examines how manipulating these elements with a robot teammate affects its human counterpart in a collaborative exercise. Participants were coupled with a computer-simulated robot to …

Parameter Estimation Of Stochastic Models Against Probabilistic Temporal Logic Behavioral Specifications, Arfeen Khalid

Electronic Theses and Dissertations

The inherent behavioral variability exhibited by stochastic systems makes it a challenging task for human experts to manually analyze them. Computational modeling of such systems helps in investigating and predicting the behaviors of their underlying processes but at the same time introduces the presence of several unknown parameters. A key challenge faced in this scenario is to determine the values of these unknown parameters against known behavioral specifications. The solutions that have been presented so far estimate the parameters of a given model against a single specification whereas a correct model is expected to satisfy all the behavioral specifications when …

Approximate In-Memory Computing On Rerams, Salman Anwar Khokhar

Electronic Theses and Dissertations

Computing systems have seen tremendous growth over the past few decades in their capabilities, efficiency, and deployment use cases. This growth has been driven by progress in lithography techniques, improvement in synthesis tools, architectures and power management. However, there is a growing disparity between computing power and the demands on modern computing systems. The standard Von-Neuman architecture has separate data storage and data processing locations. Therefore, it suffers from a memory-processor communication bottleneck, which is commonly referred to as the 'memory wall'. The relatively slower progress in memory technology compared with processing units has continued to exacerbate the memory wall …