Digital Commons Network^™

Dwrelu : Double Weighted Rectifier Linear Unit An Activation Function With Trainable Scaling Parameter, Bhaskar Chandra Trivedi

Computer Science and Engineering Theses - Archive

Deep Neural Network have become very popular for computer vision application in recent years. At the same time, it remains important to understand the different implementation choices that need to be made when designing a neural network and to thoroughly investigate existing and novel alternatives for those choices. One of those choices is the activation function. The ReLU activation function is a widely used activation function. It discards all the values below zero and keeps the ones greater than zero. Variations such as Leaky ReLU and Parametric ReLU do not discard values, so that gradiants are nonzero for the entire …

Health Monitoring Of Atlas Data Center Clusters And Failure Analysis, Meenakshi Balasubramanian

Computer Science and Engineering Theses - Archive

Monitoring the health of data center clusters is an integral part of any industrial facility. ATLAS is one of the High Energy Physics experiments at the Large Hadron Collider (LHC) at CERN. ATLAS DDM (Distributed Data Management) is a system that manages data transfer, staging, deletions and experimental data on the LHC grid. Currently, the DDM system relies on Rucio software, with Cloud based object storage and No-SQL solutions. It is a cumbersome process in the current system, to fetch and analyze the transfer, staging and deletion metrics of a specific site for any regional center. In this thesis, a …

Large-Scale Deep Learning With Application In Medical Imaging And Bio-Informatics, Zheng Xu

Computer Science and Engineering Dissertations - Archive

With the recent advancement of the deep learning technology in the artificial intelligence area, nowadays people's lives have been drastically changed. However, the success of deep learning technology mostly relies on large-scale high-quality data-sets. The complexity of deeper model and larger scale datasets have brought us significant challenges. Inspired by this trend, in this dissertation, we focus on developing efficient and effective large-scale deep learning techniques in solving real-world problems, like cell detection in hyper-resolution medical image or drug screening from millions of compound candidates. With respect to the hyper-resolution medical imaging cell detection problem, the challenges are mainly the …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

Towards Scalable Characterization Of Noisy, Intermediate-Scale Quantum Information Processors, Travis Luke Scholten

Physics & Astronomy ETDs

In recent years, quantum information processors (QIPs) have grown from one or two qubits to tens of qubits. As a result, characterizing QIPs – measuring how well they work, and how they fail – has become much more challenging. The obstacles to characterizing today’s QIPs will grow even more difficult as QIPs grow from tens of qubits to hundreds, and enter what has been called the “noisy, intermediate-scale quantum” (NISQ) era. This thesis develops methods based on advanced statistics and machine learning algorithms to address the difficulties of “quantum character- ization, validation, and verification” (QCVV) of NISQ processors. In the …

Generating Exploration Mission-3 Trajectories To A 9:2 Nrho Using Machine Learning, Esteban Guzman

Master's Theses

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can be an abstract task and relies on …

Efficacy Of Deep Learning In Support Of Smart Services, Basheer Mohammed Basheer Qolomany

Dissertations

The massive amount of streaming data generated and captured by smart service appliances, sensors and devices needs to be analyzed by algorithms, transformed into information, and minted to extract knowledge to facilitate timely actions and better decision making. This can lead to new products and services that can dramatically transform our lives. Machine learning and data analytics will undoubtedly play a critical role in enabling the delivery of smart services. Within the machine-learning domain, Deep Learning (DL) is emerging as a superior new approach that is much more effective than any rule or formula used by traditional machine learning. Furthermore, …

Classification Of Arbitrary Motion Into A Canonical Basis, Michael Moger

Master's Theses and Capstones

The Empatica E4 wristwatch utilizes four sensors to capture medical data from its user - an accelerometer, a plethysmograph, an electro-dermal activity sensor, and an infrared thermophile. Utilizing these sensors, the device can provide detection-based feedback for patients suffering from various ailments. However, each sensor is coupled with the other readings, so any raw data will have a degree of noise accompanying the actual signal. After detailing a conceptual and programming knowledge of various industry-standard data processing techniques, we follow the appropriate steps to take in order to clean up a noisy E4 data signal, starting with supervised basis signals …

Flow Adaptive Video Object Segmentation, Fanqing Lin

Theses and Dissertations

We tackle the task of semi-supervised video object segmentation, i.e, pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. Recently introduced online adaptation of convolutional neural networks for video object segmentation (OnAVOS) has achieved good results by pretraining the network, fine-tuning on the first frame and training the network at test time using its approximate prediction as newly obtained ground truth. We propose Flow Adaptive Video Object Segmentation (FAVOS) that refines the generated adaptive ground truth for online updates and utilizes temporal consistency between video frames with the help …

Amplifying The Prediction Of Team Performance Through Swarm Intelligence And Machine Learning, Erick Michael Harris

Master's Theses

Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to …

Improving Ultra-Wideband Localization By Detecting Radio Misclassification, Cory A. Mayer

Master's Theses

The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.

Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of …

Toward Real-Time Flip Fluid Simulation Through Machine Learning Approximations, Javid Kennon Pack

Theses and Dissertations

Fluids in computer generated imagery can add an impressive amount of realism to a scene, but are particularly time-consuming to simulate. In an attempt to run fluid simulations in real-time, recent efforts have attempted to simulate fluids by using machine learning techniques to approximate the movement of fluids. We explore utilizing machine learning to simulate fluids while also integrating the Fluid-Implicit-Particle (FLIP) simulation method into machine learning fluid simulation approaches.

Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu

Master's Theses

Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …

Making A Good Thing Better: Enhancing Password/Pin-Based User Authentication With Smartwatch, Bing Chang, Yingjiu Li, Qiongxiao Wang, Wen-Tao Zhu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Wearing smartwatches becomes increasingly popular in people’s lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this …

Beam-Target Helicity Asymmetry E In K0Λ And K0Σ0 Photoproduction On The Neutron, D. H. Ho, R. A. Schumacher, A. D’Angelo, A. Deur, J. Fleming, C. Hanretty, T. Kageya, F. J. Klein, E. Klempt, M. M. Lowry, H. Lu, V. A. Nikonov, P. Peng, A. M. Sandorfi, A. V. Sarantsev, I. I. Strakovsky, N. K. Walford, X. Wei, R. L. Workman, K. P. Adhikari, S. Adhikari, D. Adikaram, Z. Akbar, J. Ball, L. Barion, M. Bashkanov, C. D. Bass, M. Battaglieri, I. Bedlinskiy, A. S. Biselli, Wesley P. Gohn

Physics and Astronomy Faculty Publications

We report the first measurements of the E beam-target helicity asymmetry for the γ^→ n^→ → K⁰Λ and K⁰Σ⁰ channels in the energy range 1.70 ≤ W ≤ 2.34 GeV. The CLAS system at Jefferson Lab uses a circularly polarized photon beam and a target consisting of longitudinally polarized solid molecular hydrogen deuteride with low background contamination for the measurements. The multivariate analysis method boosted decision trees is used to isolate the reactions of interest. Comparisons with predictions from the KaonMAID, SAID, and Bonn-Gatchina models are presented. These results will help separate the …

Using Machine Learning To Accurately Predict Ambient Soundscapes From Limited Data Sets, Katrina Lynn Pedersen

Theses and Dissertations

The ability to accurately characterize the soundscape, or combination of sounds, of diverse geographic areas has many practical implications. Interested parties include the United States military and the National Park Service, but applications also exist in areas such as public health, ecology, community and social justice noise analyses, and real estate. I use an ensemble of machine learning models to predict ambient sound levels throughout the contiguous United States. Our data set consists of 607 training sites, where various acoustic metrics, such as overall daytime L50 levels and one-third octave frequency band levels, have been obtained. I have data for …

Using Chronicling America’S Images To Explore Digitized Historic Newspapers & Imagine Alternative Futures, Elizabeth Lorang, Leen-Kiat Soh

University of Nebraska-Lincoln Libraries: Presentations

This presentation situates the work of the Aida team broadly as well as hinges this work on some very specific challenges for digital libraries. In doing so demonstrate the many types of questions and domains to be explored in digitized newspapers.

Hr500k – A Reference Training Corpus Of Croatian., Nikola Ljubešić, Željko Agić, Filip Klubicka, Vuk Batanović, Tomaž Erjavec

Conference papers

In this paper we present hr500k, a Croatian reference training corpus of 500 thousand tokens, segmented at document, sentence and word level, and annotated for morphosyntax, lemmas, dependency syntax, named entities, and semantic roles. We present each annotation layer via basic label statistics and describe the final encoding of the resource in CoNLL and TEI formats. We also give a description of the rather turbulent history of the resource and give insights into the topic and genre distribution in the corpus. Finally, we discuss further enrichments of the corpus with additional layers, which are already underway.

Approaching Arabic Automatic Speech Recognition With Usage, James Longstaff, Deryle Lonsdale

Journal of Undergraduate Research

The purpose of this project is to improve Arabic automatic speech recognition (ASR) by distinguishing between different dialects with the use of machine learning. Machine learning is the teaching of computers to recognize and distinguish between categories by themselves. Machine learning works off of statistical and mathematical algorithms implemented with computer programming.

Using Aviris And Machine Learning To Map And Discriminate Bull Kelp And Giant Kelp Along The Pacific Coast Of The United States, Tanner Thompson, Dr. Ryan Jensen

Journal of Undergraduate Research

Kelp forests provide food and shelter for many organisms, and they are an important part of coastal ecosystems throughout the world. Along the Pacific coast of the United States, kelp forests are made up of two species of kelp: bull kelp (Nereocystis Leutkana) and giant kelp (Macrocystis Pyrifera). While similar, these two species are physiologically and structurally different.

Toward Audio Beehive Monitoring: Deep Learning Vs. Standard Machine Learning In Classifying Beehive Audio Samples, Vladmir Kulyukin, Sarbajit Mukherjee, Prakhar Amlathe

Computer Science Faculty and Staff Publications

Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing …

Music For Ai Reports: Dual Prospects In Music Production, Achim Koh

Dissertations, Theses, and Capstone Projects

Recent developments in artificial intelligence (AI) technology have led to industrial attempts at applying AI to music making, namely AI music. In the context of the history of music technology, AI music raises the prospect of a new phase that extends digital technology’s role as central mode of music production. The computer has become an essential metamedium in contemporary cultural production, leading in the field of music to the digitization of tools and content and the digitalization of social institutions and relationships. This technological change had the dual effect of decentralizing music production while reinforcing capitalist logic in it. The …

Predicting National Basketball Association Success: A Machine Learning Approach, Adarsh Kannan, Brian Kolovich, Brandon Lawrence, Sohail Rafiqi

SMU Data Science Review

In this paper, we present a machine learning based approach to projecting the success of National Basketball Association (NBA) draft prospects. With the proliferation of data, analytics have increasingly be- come a critical component in the assessment of professional and collegiate basketball players. We leverage player biometric data, college statistics, draft selection order, and positional breakdown as modelling features in our prediction algorithms. We found that a player's draft pick and their college statistics are the best predictors of their longevity in the National Basketball Association.

Supervised Sparse Learning With Applications In Bioinformatics, Kin Ming Puk

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

In machine learning and mathematical optimization, sparse learning is the use of mathematical norms such as L1-norm, group norm and L21-norm in order to seek a trade-off between the goodness-of-fit measure and sparsity of the result. Sparsity of result leads to a parsimonious learning model - in other words, only few features from the data matrix are required to build the learning model and for further interpretation. The motivations of employing sparse learning in bioinformatics are two-fold: firstly, a parsimonious learning model enhances the explanatory power; and secondly, a parsimonious model generally allows better prediction and generalizes better to new …

Inverse Analysis Of Heat Generating Body For Safety Applications, Sandeep Patil

Mechanical and Aerospace Engineering Dissertations - Archive

Inverse thermal analysis and its applications have been applied to numerous fields of science and engineering. Historically during the 1950's and early 1960's, space programs played a significant role in the advancement of solution techniques for Inverse Heat transfer Problems(IHTP). It was applied to measure the surface temperature of thermal shield of a space vehicle during its re-entry into atmosphere. Inverse analysis was also used in the estimation of thermo-physical properties of the shield at high operating temperatures. Besides thermal application, inverse technique was also used in other engineering applications such as estimation of alloy specification, design of a shape …

Evaluating The Efficacy Of Convolution Neural Networks In Age At Deat Estimation Using 3d Scans Of The Pubic Symphyseal Face, Melissa A. Brown

Masters Theses

The research presented assesses the utility of machine learning approaches, specifically convolutional neural networks (CNNs), to the estimation of age at death in adult decedents by analysis of the pubic symphyseal face of the os coxa rendered as a 3D image. Age at death estimation is an important duty of forensic anthropologists working in medico-legal contexts, as well as bioarcheological researchers. The purpose of this study is to evaluate the accuracy of a CNN relative to the performance of human observers using traditional methods of age estimation. To accomplish this, a CNN created for this project and expert anthropologists were …

Prediction Of 1p/19q Codeletion Status In Diffuse Glioma Patients Using Preoperative Multiparametric Magnetic Resonance Imaging, Donnie Kim

Dissertations and Theses (Open Access)

A complete codeletion of chromosome 1p/19q is strongly correlated with better overall survival of diffuse glioma patients, hence determining the codeletion status early in the course of a patient’s disease would be valuable in that patient’s care. The current practice requires a surgical biopsy in order to assess the codeletion status, which exposes patients to risks and is limited in its accuracy by sampling variations. To overcome such limitations, we utilized four conventional magnetic resonance imaging sequences to predict the 1p/19q status. We extracted three sets of image-derived features, namely texture-based, topology-based, and convolutional neural network (CNN)-based, and analyzed each …

Use Of Artificial Intelligence, Machine Learning, And Autonomous Technologies In The Mining Industry, Z. Hyder, Keng Siau, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Mining is an important industrial and economic sector that plays a major role in the economic development of a country and provides many employment opportunities. Implementation of Artificial Intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with the first application to autonomous trucks. The autonomous technologies provide many economic benefits to the mining industry through cost reduction, productivity improvement, reduction in exposure of workers to hazardous conditions, continuous production, and improved safety. However, implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This paper discusses the current status …

Novel Detection And Analysis Using Deep Variational Autoencoders, Tucker B. Graydon

Theses

This paper presents a Novel Identification System which uses generative modeling techniques and Gaussian Mixture Models (GMMs) to identify the main process variables involved in a novel event from multivariate data. Features are generated and subsequently dimensionally reduced by using a Variational Autoencoder (VAE) supplemented by a denoising criterion and a β disentangling method. The GMM parameters are learned using the Expectation Maximization(EM) algorithm on features collected from only normal operating conditions. A one-class classification is achieved by thresholding the likelihoods by a statistically derived value. The Novel Identification method is verified as a detection method on existing Radio Frequency …

Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi

SMU Data Science Review

In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …