Digital Commons Network^™

Bikebot: A Study In Reinforcement Learning, April L. Kutz, Kristen A. Brennan, Jeremy Dixon, Nathan Bogdonas

Honors Capstones

In the United States, there is a lack of young adults interested in studying Science, Technology, Engineering, and Math (STEM). One way to help spark interest in STEM is a classroom aid that demonstrates how STEM can be fun and how it is used to push the limits of modern technology. BikeBot was designed for just this purpose. BikeBot is a robotic bicycle that utilizes an algorithm called Q-learning. Q-Learning is a type of reinforcement machine learning program, a basic form of artificial intelligence, that will allow BikeBot to balance without any explicit controls. The algorithm was tested and proven …

Collective Personalized Change Classification With Multiobjective Search, Xin Xia, David Lo, Xinyu Wang, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Many change classification techniques have been proposed to identify defect-prone changes. These techniques consider all developers' historical change data to build a global prediction model. In practice, since developers have their own coding preferences and behavioral patterns, which causes different defect patterns, a separate change classification model for each developer can help to improve performance. Jiang, Tan, and Kim refer to this problem as personalized change classification, and they propose PCC+ to solve this problem. A software project has a number of developers; for a developer, building a prediction model not only based on his/her change data, but also on …

Analog Spiking Neuromorphic Circuits And Systems For Brain- And Nanotechnology-Inspired Cognitive Computing, Xinyu Wu

Boise State University Theses and Dissertations

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves …

Using Machine Learning To Predict Student Achievement On The State Of Texas Assessment Of Academic Readiness Examination In Charter Schools, Christopher D. Gonzalez

Theses and Dissertations

The purpose of this study was to research and develop a way to use machine learning algorithms (MLAs) to predict student achievement on the State of Texas Assessment of Academic Readiness (STAAR), specifically in the charter school setting. Charter schools have the disadvantage of a constant influx in students, so providing historical student data in order to analyze trends proves difficult. This study expands on previous research done on students in secondary and post-secondary school and determining features that indicate success in these settings. The data used is from the district of IDEA Public Schools who focuses on providing education …

Graph-Based Data Modeling And Analysis For Data Fusion In Remote Sensing, Lei Fan

Theses

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a …

Adaptive Classification Framework For Control Myoelectric Prostheses During Muscle Fatigue, Hamdi Allbunashee Allbunashee

Theses and Dissertations

A myoelectric-controlled prosthesis holds the promise of renewed quality of life for the amputee population. When it comes to prosthesis control, muscle fatigue presents a significant challenge for all task discrimination algorithms due to changed muscle activation patterns and significant shifts in the spectral contents of the surface electromyogram (sEMG) signals. The effect of muscle fatigue on the classification accuracy of two supervised machine-learning algorithms, i.e. the linear discriminant analysis (LDA), and the muscle synergy based task discrimination (MSD) was investigated. Due to muscle fatigue, the classification accuracies declined from (>90%) to 41% and 59% for the LDA and …

An Exploration Of Style Transfer Using Deep Neural Networks, Cameron Y. Smith

Computer Science ETDs

Convolutional Neural Networks and Graphics Processing Units have been at the core of a paradigm shift in computer vision research that some researchers have called `'the algorithmic perception revolution." This thesis presents the implementation and analysis of several techniques for performing artistic style transfer using a Convolutional Neural Network architecture trained for large-scale image recognition tasks. We present an implementation of an existing algorithm for artistic style transfer in images and video. The neural algorithm separates and recombines the style and content of arbitrary images. Additionally, we present an extension of the algorithm to perform weighted artistic style transfer.

Approaches For Capturing Time-Varying Functional Network Connectivity With Application To Normative Development And Mental Illness, Barnaly Rashid

Electrical and Computer Engineering ETDs

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are …

Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm

Computer Science ETDs

Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …

Spiteful, One-Off, And Kind: Predicting Customer Feedback Behavior On Twitter, Agus Sulistya, Abhishek Sharma, David Lo

Research Collection School Of Computing and Information Systems

Social media provides a convenient way for customers to express their feedback to companies. Identifying different types of customers based on their feedback behavior can help companies to maintain their customers. In this paper, we use a machine learning approach to predict a customer’s feedback behavior based on her first feedback tweet. First, we identify a few categories of customers based on their feedback frequency and the sentiment of the feedback. We identify three main categories: spiteful, one-off, and kind. Next, we build a model to predict the category of a customer given her first feedback. We use profile and …

Online Cross-Validation-Based Ensemble Learning, David Benkeser, Samuel D. Lendle, Cheng Ju, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to …

Comparing Machine Learning And Logistic Regression Methods For Predicting Hypertension Using A Combination Of Gene Expression And Next-Generation Sequencing Data, Elizabeth Held, Joshua Cape, Nathan L. Tintle

Faculty Work Comprehensive List

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically …

Data Analytics For Fault Localization In Complex Networks, Maggie X. Cheng, Wei Biao Wu

Computer Science Faculty Research & Creative Works

We consider the problem of identifying the source of failure in a network after receiving alarms or having observed symptoms. To locate the root cause accurately and timely in a large communication system is challenging because a single fault can often result in a large number of alarms, and multiple faults can occur concurrently. In this paper, we present a new fault localization method using a machine-learning approach. We propose to use logistic regression to study the correlation among network events based on end-to-end measurements. Then based on the regression model, we develop fault hypothesis that best explains the observed …

Multiple Imputation Of Missing Data In Structural Equation Models With Mediators And Moderators Using Gradient Boosted Machine Learning, Robert J. Milletich Ii

Psychology Theses & Dissertations

Mediation and moderated mediation models are two commonly used models for indirect effects analysis. In practice, missing data is a pervasive problem in structural equation modeling with psychological data. Multiple imputation (MI) is one method used to estimate model parameters in the presence of missing data, while accounting for uncertainty due to the missing data. Unfortunately, commonly used MI methods are not equipped to handle categorical variables or nonlinear variables such as interactions. In this study, we introduce a general MI framework that uses the Bayesian bootstrap (BB) method to generate posterior inferences for indirect effects and gradient boosted machine …

A Clustering Approach To Industrial Network Intrusion Detection, Leary Tomlin Jr., Marsella R. Farnam, Shengyi Pan

Information Security Research and Education (INSuRE) Conference

Industrial control system (ICS) networks and supervisory control and data acquisition (SCADA) system networks are less likely to be within a strict closed network environment, which increases the likelihood of cyber-attacks. Over the last decade, intrusion detection has become an additional security measure for ICS and SCADA system networks to help prevent and minimize loss that may be sustained from cyber-attacks. ICS and SCADA network communication is typically repetitive and deterministic, which allows normal activity to be more easily modeled on the behavior of system specific events. Given this deterministic behavior, an unsupervised anomaly-based intrusion detection system may provide increased …

A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, Sadat U. Chowdhury

Dissertations, Theses, and Capstone Projects

In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS).

This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one …

A Novel Machine Learning Classifier Based On A Qualia Modeling Agent (Qma), Sandra L. Vaughan

Theses and Dissertations

This dissertation addresses a problem found in supervised machine learning (ML) classification, that the target variable, i.e., the variable a classifier predicts, has to be identified before training begins and cannot change during training and testing. This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories: Stanovich's tripartite framework, which proposes learning results from interactions between conscious and unconscious processes; and, the Integrated Information Theory (IIT) of Consciousness, which proposes that the fundamental structural elements of consciousness are qualia. By modeling the informational relationships of qualia, the QMA allows …

Creating And Automatically Grading Annotated Questions, Alicia Crowder Wood

Theses and Dissertations

We have created a question type that allows teachers to easily create questions, helps provide an intuitive user experience for students to take questions, and reduces the time it currently takes teachers to grade and provide feedback to students. This question type, or an "annotated" question, will allow teachers to test students' knowledge in a particular subject area by having students "annotate" or mark text and video sources to answer questions. Through user testing we determined that overall the interface and the implemented system decrease the time it would take a teacher to grade annotated quiz questions. However, there are …

Soft Confidence-Weighted Learning, Jialei Wang, Peilin Zhao, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Online learning plays an important role in many big datamining problems because of its high efficiency and scalability. In theliterature, many online learning algorithms using gradient information havebeen applied to solve online classification problems. Recently, more effectivesecond-order algorithms have been proposed, where the correlation between thefeatures is utilized to improve the learning efficiency. Among them,Confidence-Weighted (CW) learning algorithms are very effective, which assumethat the classification model is drawn from a Gaussian distribution, whichenables the model to be effectively updated with the second-order informationof the data stream. Despite being studied actively, these CW algorithms cannothandle nonseparable datasets and noisy datasets very …

Real Time Activity Recognition Of Treadmill Usage Via Machine Learning, Nathan Blank, Matt Buckner, Christian Owen, Anna Scott

Rose-Hulman Undergraduate Research Publications

Our objective is to provide real-time classification of treadmill usage patterns based on accelerometer and magnetometer measurements. We collected data from treadmills in the Rose-Hulman Student Recreation Center (SRC) using Shimmer3 sensor units. We identified useful data features and classifiers for predicting treadmill usage patterns. We also prototyped a proof of concept wireless, real-time classification system.

Prediction And Optimal Scheduling Of Advertisements In Linear Television, Mark J. Panaggio, Pak-Wing Fok, Ghan S. Bhatt, Simon Burhoe, Michael Capps, Christina J. Edholm, Fadoua El Moustaid, Tegan Emerson, Star-Lena Estock, Nathan Gold, Ryan Halabi, Madelyn Houser, Peter R. Kramer, Hsuan-Wei Lee, Qingxia Li, Weiqiang Li, Dan Lu, Yuzhou Qian, Louis F. Rossi, Deborah Shutt, Vicky Chuqiao Yang, Yingxiang Zhou

Mathematical Sciences Faculty Research

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of "impressions" in the target demographic. However, because the number of impressions for a given program is not known a priori and because …

Methodology To Predict Daily Groundwater Levels By The Implementation Of Machine Learning And Crop Models, Sandra Milena Guzman Gutierrez

Theses and Dissertations

The continuous decline of groundwater levels caused by variations in climatic conditions and crop water demands is an increased concern for the agricultural community. It is necessary to understand the factors that control these changes in groundwater levels so that we can better address declines and develop improved conservation practices that will lead to a more sustainable use of water. In this study, two machine learning techniques namely support vector regression (SVR) and the nonlinear autoregressive with exogenous inputs (NARX) neural network were implemented to predict daily groundwater levels in a well located in the Mississippi Delta Region (MDR). Results …

Classifying Pattern Formation In Materials Via Machine Learning, Lukasz Burzawa, Shuo Liu, Erica W. Carlson

The Summer Undergraduate Research Fellowship (SURF) Symposium

Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated materials often reveal complex pattern formation that occurs on multiple length scales. We have shown in two disparate correlated materials that the pattern formation is driven by proximity to a disorder-driven critical point. We developed new analysis concepts and techniques that relate the observed pattern formation to critical exponents by analyzing the geometry and statistics of clusters observed in these experiments and converting that information into critical exponents. Machine learning algorithms can be helpful correlating data from scanning probe experiments to theoretical models …

Personalization And Data Relation Exploration Using Predictive Analytics For The Production And Distributed Analysis System (Panda), Mikhail Titov

Computer Science and Engineering Dissertations - Archive

Efficient data distribution among computing centers is one of the biggest challenges in large-scale scientific distributed computing systems. Such data distribution issues include: i) the rational utilization of storage and computing resources, ii) the minimization of the completion time for data processing (which requires a reduction in redundant data transfers, and intelligent allocation of processing tasks), and iii) user experience enhancement, i.e., availability and fast access to the desired data, and discovery of new relevant data. In the literature and in practice, there have been significant new approaches to the improvement of workflow management to address the above described issues, …

A Study Of Security Issues Of Mobile Apps In The Android Platform Using Machine Learning Approaches, Lei Cen

Open Access Dissertations

Mobile app poses both traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile apps, a running app in mobile devices may be only part of the software, and the server side behavior is usually not covered by analysis. Therefore, direct analysis on the app itself may be incomplete and additional sources of information are needed. In this dissertation, we …

Computational Labeling, Partitioning, And Balancing Of Molecular Networks, Biaobin Jiang

Open Access Dissertations

Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional …

Data Driven Low-Bandwidth Intelligent Control Of A Jet Engine Combustor, Nathan L. Toner

Open Access Dissertations

This thesis introduces a low-bandwidth control architecture for navigating the input space of an un-modeled combustor system between desired operating conditions while avoiding regions of instability and blow-out. An experimental procedure is discussed for identifying regions of instability and gathering sufficient data to build a data-driven model of the system's operating modes. Regions of instability and blow-out are identified experimentally and a data-driven operating point classifier is designed. This classifier acts as a map of the operating space of the combustor, indicating regions in which the flame is in a "good" or "bad" operating mode. A data-driven predictor is also …

Learning From Data: Plant Breeding Applications Of Machine Learning, Alencar Xavier

Open Access Dissertations

Increasingly, new sources of data are being incorporated into plant breeding pipelines. Enormous amounts of data from field phenomics and genotyping technologies places data mining and analysis into a completely different level that is challenging from practical and theoretical standpoints. Intelligent decision-making relies on our capability of extracting from data useful information that may help us to achieve our goals more efficiently. Many plant breeders, agronomists and geneticists perform analyses without knowing relevant underlying assumptions, strengths or pitfalls of the employed methods. The study endeavors to assess statistical learning properties and plant breeding applications of supervised and unsupervised machine learning …

Lidar-Assisted Extraction Of Old Growth Baldcypress Stands Along The Black River Of North Carolina, Weston Pierce Murch

Graduate Theses and Dissertations

The remnants of ancient baldcypress forests continue to grow across the Southeastern United States. These long lived trees are invaluable for biodiversity along riverine ecosystems, provide habitat to a myriad of animal species, and augment the proxy climate record for North America. While extensive logging of the areas along the Black River in North Carolina has mostly decimated ancient forests of many species including the baldcypress, conservation efforts from The Nature Conservancy and other partners are under way. In order to more efficiently find and study these enduring stands of baldcypress, some of which are estimated to be more than …

User (K-Means) For Clustering In Data Mining With Application, Qutaiba N. Nayef Al-Qazaz, Muhyiddin Khalaf Ayyub

Journal of Economics and Administrative Sciences

The great scientific progress has led to widespread Information as information accumulates in large databases is important in trying to revise and compile this vast amount of data and, where its purpose to extract hidden information or classified data under their relations with each other in order to take advantage of them for technical purposes. And work with data mining (DM) is appropriate in this area because of the importance of research in the (K-Means) algorithm for clustering data in fact applied with effect can be observed in variables by changing the sample size (n) and the number of clusters …