Physical Sciences and Mathematics Commons^™

General Game Playing As A Bandit-Arms Problem: A Multiagent Monte-Carlo Solution Exploiting Nash Equilibria, Brandon Mathewe Banda

Honors Papers

This project approaches general game playing in a unique way by combining popular methods of stochastic tree searching with a Multiagent system and a unique algorithm that I call the Wise Explorer algorithm. The goal of the system is to explore the worst possible branches of the game first to rule them out, followed by an in-depth search on the most promising branches. The system constantly refers to the data it collects during its extensive search, and it outputs a strategic move for any given state of a game. In essence, if you’re ever in a bind during a game …

Reliability Analysis For Systems With Outsourced Components, Zhengwei Hu

Doctoral Dissertations

"The current business model for many industrial firms is to function as system integrators, depending on numerous outsourced components from outside component suppliers. This practice has resulted in tremendous cost savings; it makes system reliability analysis, however, more challenging due to the limited component information available to system designers. The component information is often proprietary to component suppliers. Motivated by the need of system reliability prediction with outsourced components, this work aims to explore feasible ways to accurately predict the system reliability during the system design stage. Four methods are proposed. The first method reconstructs component reliability functions using limited …

Applications Of Machine Learning In Nuclear Imaging And Radiation Detection, Shaikat Mahmood Galib

Doctoral Dissertations

"The main focus of this work is to use machine learning and data mining techniques to address some challenging problems that arise from nuclear data. Specifically, two problem areas are discussed: nuclear imaging and radiation detection. The techniques to approach these problems are primarily based on a variant of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN), which is one of the most popular forms of 'deep learning' technique.

The first problem is about interpreting and analyzing 3D medical radiation images automatically. A method is developed to identify and quantify deformable image registration (DIR) errors from lung CT scans …

Improving Anomaly Detection In Bgp Time-Series Data By New Guide Features And Moderated Feature Selection Algorithm, Mahmoud Hashem, Ahmed Bashandy, Samir Shaheen

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet infrastructure relies on the Border Gateway Protocol (BGP) to provide essential routing information where abnormal routing behavior impairs global Internet connectivity and stability. Hence, employing anomaly detection algorithms is important for improving the performance of BGP routing protocol. In this paper, we propose two algorithms; the first is the guide feature generator (GFG), which generates guide features from traditional features in BGP time-series data using moving regression in combination with smoothed moving average. The second is a modified random forest feature selection algorithm which is employed to automatically select the most dominant features (ASMDF). Our mechanism shows that …

Towards Wearable Blood Pressure Measurement Systems From Biosignals: A Review, Ümi̇t Şentürk, Kemal Polat, İbrahi̇m Yücedağ

Turkish Journal of Electrical Engineering and Computer Sciences

Blood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, is the cause of nearly 13 % of mortality all over the world. Blood pressure is not only measured in the medical environment, but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systems with low error rates have been developed besides the new technologies and algorithms. Blood pressure measurements are differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurement methods. Although IBP measurement provides the most accurate …

A Hybrid Feature-Selection Approach For Finding The Digital Evidence Of Web Application Attacks, Mohammed Babiker, Eni̇s Karaarslan, Yaşar Hoşcan

Turkish Journal of Electrical Engineering and Computer Sciences

The most critical challenge of web attack forensic investigations is the sheer amount of data and level of complexity. Machine learning technology might be an efficient solution for web attack analysis and investigation. Consequently, machine learning applications have been applied in various areas of information security and digital forensics, and have improved over time. Moreover, feature selection is a crucial step in machine learning; in fact, selecting an optimal feature subset could enhance the accuracy and performance of the predictive model. To date, there has not been an adequate approach to select optimal features for the evidence of web attack. …

Energy Saving Scheduling In A Fog-Based Iot Application By Bayesian Task Classification Approach, Gholamreza Heydari, Dadmehr Rahbari, Mohsen Nickray

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet of things increases information volume in computer networks and the concept of fog will help us to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem. This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain the task requirements like the processing ones. After classification, virtual machines will be created in accordance with the predicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling (FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on …

A Statistical Analysis And Machine Learning Of Genomic Data, Jongyun Jung

All Graduate Theses, Dissertations, and Other Capstone Projects

Machine learning enables a computer to learn a relationship between two assumingly related types of information. One type of information could thus be used to predict any lack of informaion in the other using the learned relationship. During the last decades, it has become cheaper to collect biological information, which has resulted in increasingly large amounts of data. Biological information such as DNA is currently analyzed by a variety of tools. Although machine learning has already been used in various projects, a flexible tool for analyzing generic biological challenges has not yet been made. The recent advancements in the DNA …

Attractive Or Aggressive? A Face Recognition And Machine Learning Approach For Estimating Returns To Visual Appearance, Guodong Guo, Brad R. Humphreys, Mohammad I. Nouyed, Yang Zhou

Economics Faculty Working Papers Series

A growing literature documents the presence of appearance premia in labor markets. We analyze appearance premia in a high-profile, high-pay setting: head football coaches at bigtime college sports programs. These employees face job tasks involving repeated interpersonal interaction on multiple fronts and also act as the “face” of their program. We estimate the attractiveness of each employee using a neural network approach, a pre-trained Convolutional Neural Network fine tuned for this application. This approach can eliminate biases induced by volunteer evaluators and limited numbers of photos. We also use this approach to estimate the perceived aggressiveness of each employee based …

Global Shipping Container Monitoring Using Machine Learning With Multi-Sensor Hubs And Catadioptric Imaging, Victor Esteban Trujillo

Dissertations, Theses, and Masters Projects

We describe a framework for global shipping container monitoring using machine learning with multi-sensor hubs and infrared catadioptric imaging. A wireless mesh radio satellite tag architecture provides connectivity anywhere in the world which is a significant improvement to legacy methods. We discuss the design and testing of a low-cost long-wave infrared catadioptric imaging device and multi-sensor hub combination as an intelligent edge computing system that, when equipped with physics-based machine learning algorithms, can interpret the scene inside a shipping container to make efficient use of expensive communications bandwidth. The histogram of oriented gradients and T-channel (HOG+) feature as introduced for …

Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …

Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, Geobia, And Naip Orthophotography: Findings And Recommendations, Aaron E. Maxwell, Michael P. Strager, Timothy A. Warner, Christopher A. Ramezan, Alice N. Morgan, Cameron E. Pauley

Faculty & Staff Scholarship

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of …

Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan

LSU Master's Theses

The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 …

Learning About Large Scale Image Search: Lessons From Global Scale Hotel Recognition To Fight Sex Trafficking, Abby Stylianou

McKelvey School of Engineering Theses & Dissertations

Hotel recognition is a sub-domain of scene recognition that involves determining what hotel is seen in a photograph taken in a hotel. The hotel recognition task is a challenging computer vision task due to the properties of hotel rooms, including low visual similarity between rooms in the same hotel and high visual similarity between rooms in different hotels, particularly those from the same chain. Building accurate approaches for hotel recognition is important to investigations of human trafficking. Images of human trafficking victims are often shared by traffickers among criminal networks and posted in online advertisements. These images are often taken …

Enabling Auditing And Intrusion Detection Of Proprietary Controller Area Networks, Brent C. Stone

Theses and Dissertations

The goal of this dissertation is to provide automated methods for security researchers to overcome ‘security through obscurity’ used by manufacturers of proprietary Industrial Control Systems (ICS). `White hat' security analysts waste significant time reverse engineering these systems' opaque network configurations instead of performing meaningful security auditing tasks. Automating the process of documenting proprietary protocol configurations is intended to improve independent security auditing of ICS networks. The major contributions of this dissertation are a novel approach for unsupervised lexical analysis of binary network data flows and analysis of the time series data extracted as a result. We demonstrate the utility …

Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss

Computer Science and Computer Engineering Undergraduate Honors Theses

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing …

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, …

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.

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

Computer Science and Engineering Dissertations

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 …

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 …

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 …

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 …

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 …

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

Computer Science and Engineering Theses

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 …

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

Computer Science and Engineering Theses

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 …

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 …

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 …

A Model-Based Ai-Driven Test Generation System, Dionny Santiago

FIU Electronic Theses and Dissertations

Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. …

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 …