Physical Sciences and Mathematics Commons^™

Vascular Tree Structure: Fast Curvature Regularization And Validation, Egor Chesakov

Electronic Thesis and Dissertation Repository

This work addresses the challenging problem of accurate vessel structure analysis in high resolution 3D biomedical images. Typical segmentation methods fail on recent micro-CT data sets resolving near-capillary vessels due to limitations of standard first-order regularization models. While regularization is needed to address noise and partial volume issues in the data, we argue that extraction of thin tubular structures requires higher-order curvature-based regularization. There are no standard segmentation methods regularizing surface curvature in 3D that could be applied to large 3D volumes. However, we observe that standard measures for vessels structure are more concerned with topology, bifurcation angles, and other …

Pattern Discovery In Dna Using Stochastic Automata, Shweta Shweta

Master's Projects

We consider the problem of identifying similarities between different species of DNA. To do this we infer a stochastic finite automata from a given training data and compare it with a test data. The training and test data consist of DNA sequence of different species. Our method first identifies sentences in DNA. To identify sentences we read DNA sequence one character at a time, 3 characters form a codon and codons form proteins (also known as amino acid chains).Each amino acid in proteins belongs to a group. In total we have 5 groups’ polar, non-polar, acidic, basic and stop codons. …

Email Similarity Matching And Automatic Reply Generation Using Statistical Topic Modeling And Machine Learning, Zachery L. Schiller

Electronic Theses and Dissertations

Responding to email is a time-consuming task that is a requirement for most professions. Many people find themselves answering the same questions over and over, repeatedly replying with answers they have written previously either in whole or in part. In this thesis, the Automatic Mail Reply (AMR) system is implemented to help with repeated email response creation. The system uses past email interactions and, through unsupervised statistical learning, attempts to recover relevant information to give to the user to assist in writing their reply.

Three statistical learning models, term frequency-inverse document frequency (tf-idf), Latent Semantic Analysis (LSA), and Latent Dirichlet …

Applying Bayesian Machine Learning Methods To Theoretical Surface Science, Shane Carr

McKelvey School of Engineering Theses & Dissertations

Machine learning is a rapidly evolving field in computer science with increasingly many applications to other domains. In this thesis, I present a Bayesian machine learning approach to solving a problem in theoretical surface science: calculating the preferred active site on a catalyst surface for a given adsorbate molecule. I formulate the problem as a low-dimensional objective function. I show how the objective function can be approximated into a certain confidence interval using just one iteration of the self-consistent field (SCF) loop in density functional theory (DFT). I then use Bayesian optimization to perform a global search for the solution. …

Neuron Clustering For Mitigating Catastrophic Forgetting In Supervised And Reinforcement Learning, Benjamin Frederick Goodrich

Doctoral Dissertations

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space.

Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. …

Battle Bot Ai – Patriot Bot, James Johnston

Computer Engineering

An entry in the the 'Battle Block AI' competition hosted by 'The AI Games'.

Evaluating The Intrinsic Similarity Between Neural Networks, Stephen Charles Ashmore

Graduate Theses and Dissertations

We present Forward Bipartite Alignment (FBA), a method that aligns the topological structures of two neural networks. Neural networks are considered to be a black box, because neural networks contain complex model surface determined by their weights that combine attributes non-linearly. Two networks that make similar predictions on training data may still generalize differently. FBA enables a diversity of applications, including visualization and canonicalization of neural networks, ensembles, and cross-over between unrelated neural networks in evolutionary optimization. We describe the FBA algorithm, and describe implementations for three applications: genetic algorithms, visualization, and ensembles. We demonstrate FBA's usefulness by comparing a …

Multiple Instance Fuzzy Inference., Amine Ben Khalifa

Electronic Theses and Dissertations

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Fuzzy Inference Systems (MI-FIS). Fuzzy inference is a powerful modeling framework that can handle computing with knowledge uncertainty and measurement imprecision effectively. Fuzzy Inference performs a non-linear mapping from an input space to an output space by deriving conclusions from a set of fuzzy if-then rules and known facts. Rules can be identified from expert knowledge, or learned from data. In multiple instance problems, the training data …

Automated Multi-Modal Search And Rescue Using Boosted Histogram Of Oriented Gradients, Matthew A. Lienemann

Master's Theses

Unmanned Aerial Vehicles (UAVs) provides a platform for many automated tasks and with an ever increasing advances in computing, these tasks can be more complex. The use of UAVs is expanded in this thesis with the goal of Search and Rescue (SAR), where a UAV can assist fast responders to search for a lost person and relay possible search areas back to SAR teams. To identify a person from an aerial perspective, low-level Histogram of Oriented Gradients (HOG) feature descriptors are used over a segmented region, provided from thermal data, to increase classification speed. This thesis also introduces a dataset …

Informed Search For Learning Causal Structure, Brian J. Taylor

Doctoral Dissertations

Over the past twenty-five years, a large number of algorithms have been developed to learn the structure of causal graphical models. Many of these algorithms learn causal structures by analyzing the implications of observed conditional independence among variables that describe characteristics of the domain being analyzed. They do so by applying inference rules, data analysis operations such as the conditional independence tests, each of which can eliminate large parts of the space of possible causal structures. Results show that the sequence of inference rules used by PC, a widely applied algorithm for constraint-based learning of causal models, is effective but …

Exploiting Social Media Sources For Search, Fusion And Evaluation, Chia-Jung Lee

Doctoral Dissertations

The web contains heterogeneous information that is generated with different characteristics and is presented via different media. Social media, as one of the largest content carriers, has generated information from millions of users worldwide, creating material rapidly in all types of forms such as comments, images, tags, videos and ratings, etc. In social applications, the formation of online communities contributes to conversations of substantially broader aspects, as well as unfiltered opinions about subjects that are rarely covered in public media. Information accrued on social platforms, therefore, presents a unique opportunity to augment web sources such as Wikipedia or news pages, …

Safe Reinforcement Learning, Philip S. Thomas

Doctoral Dissertations

This dissertation proposes and presents solutions to two new problems that fall within the broad scope of reinforcement learning (RL) research. The first problem, high confidence off-policy evaluation (HCOPE), requires an algorithm to use historical data from one or more behavior policies to compute a high confidence lower bound on the performance of an evaluation policy. This allows us to, for the first time, provide the user of any RL algorithm with confidence that a newly proposed policy (which has never actually been used) will perform well. The second problem is to construct what we call a safe reinforcement learning …

General Program Synthesis From Examples Using Genetic Programming With Parent Selection Based On Random Lexicographic Orderings Of Test Cases, Thomas Helmuth

Doctoral Dissertations

Software developers routinely create tests before writing code, to ensure that their programs fulfill their requirements. Instead of having human programmers write the code to meet these tests, automatic program synthesis systems can create programs to meet specifications without human intervention, only requiring examples of desired behavior. In the long-term, we envision using genetic programming to synthesize large pieces of software. This dissertation takes steps toward this goal by investigating the ability of genetic programming to solve introductory computer science programming problems. We present a suite of 29 benchmark problems intended to test general program synthesis systems, which we systematically …

Exploiting Concepts In Videos For Video Event Detection, Ethem Can

Doctoral Dissertations

Video event detection is the task of searching videos for events of interest to a user where an event is a complex activity which is localized in time and space. The video event detection problem has gained more importance as the amount of online video is increasing by more than 300 hours every minute on Youtube alone. In this thesis, we tackle three major video event detection problems: video event detection with exemplars (VED-ex), where a large number of example videos are associated with queries; video event detection with few exemplars (VED-ex_few), in which only a small number of example …

Using Eye And Head Movements As A Control Mechanism For Tele-Operating A Ground-Based Robot And Its Payload, Kathryn C. Hicks

Computational Modeling & Simulation Engineering Theses & Dissertations

To date, eye and head tracking has been used to indicate users' attention patterns while performing a task or as an aid for disabled persons, to allow hands-free interaction with a computer. The increasing accuracy and the reduced cost of eye- and head-tracking equipment make utilizing this technology feasible for explicit control tasks, especially in cases where there is confluence between the visual task and control.

The goal of this research was to investigate the use of eye-tracking as a more natural interface for the control of a camera-equipped, remotely operated robot in tasks that require the operator to simultaneously …

Adaptive Automation Design And Implementation, Jason M. Bindewald

Theses and Dissertations

Automations allow us to reduce the need for humans in certain environments, such as auto-pilot features on unmanned aerial vehicles. However, some situations still require human intervention. Adaptive automation is a research field that enables computer systems to adjust the amount of automation by taking over tasks from or giving tasks back to the user. This research develops processes and insights for adaptive automation designers to take theoretical adaptive automation ideas and develop them into real-world adaptive automation system. These allow developers to design better automation systems that recognize the limits of computers systems, enabling better designs for systems in …

Clustering-Based Personalization, Seyed Nima Mirbakhsh

Electronic Thesis and Dissertation Repository

Recommendation systems have been the most emerging technology in the last decade as one of the key parts in e-commerce ecosystem. Businesses offer a wide variety of items and contents through different channels such as Internet, Smart TVs, Digital Screens, etc. The number of these items sometimes goes over millions for some businesses. Therefore, users can have trouble finding the products that they are looking for. Recommendation systems address this problem by providing powerful methods which enable users to filter through large information and product space based on their preferences. Moreover, users have different preferences. Thus, businesses can employ recommendation …

What It Is To Be Conscious: Exploring The Plasibility Of Consciousness In Deep Learning Computers, Peter Davis

Honors Theses

As artificial intelligence and robotics progress further and faster every day, designing and building a conscious computer appears to be on the horizon. Recent technological advances have allowed engineers and computer scientists to create robots and computer programs that were previously impossible. The development of these highly sophisticated robots and AI programs has thus prompted the age-old question: can a computer be conscious? The answer relies on addressing two key sub-problems. The first is the nature of consciousness: what constitutes a system as conscious, or what properties does consciousness have? Secondly, does the physical make-up of the robot or computer …

Sudden Cardiac Arrest Prediction Through Heart Rate Variability Analysis, Luke Joseph Plewa

Master's Theses

The increase in popularity for wearable technologies (see: Apple Watch and Microsoft Band) has opened the door for an Internet of Things solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of hospital sudden cardiac arrests (9.5% on 360,000 cases in the USA in 2013). It has been proven that heart rate derived features can give an early indicator of sudden cardiac arrest, and that providing an early warning has the potential to save many lives. Many of these new wearable devices are capable of providing this warning through their heart rate …

Calculating Staircase Slope From A Single Image, Nicholas Joseph Clarke

Master's Theses

Realistic modeling of a 3D environment has grown in popularity due to the increasing realm of practical applications. Whether for practical navigation purposes, entertainment value, or architectural standardization, the ability to determine the dimensions of a room is becoming more and more important. One of the trickier, but critical, features within any multistory environment is the staircase. Staircases are difficult to model because of their uneven surface and various depth aspects. Coupling this need is a variety of ways to reach this goal. Unfortunately, many such methods rely upon specialized sensory equipment, multiple calibrated cameras, or other such impractical setups. …

Optimization Of Scheduling And Dispatching Cars On Demand, Vu Tran

Master's Projects

Taxicab is the most common type of on-demand transportation service in the city because its dispatching system offers better services in terms of shorter wait time. However, the shorter wait time and travel time for multiple passengers and destinations are very considerable. There are recent companies implemented the real-time ridesharing model that expects to reduce the riding cost when passengers are willing to share their rides with the others. This model does not solve the shorter wait time and travel time when there are multiple passengers and destinations. This paper investigates how the ridesharing can be improved by using the …

A Comparison Of Clustering Techniques For Malware Analysis, Swathi Pai

Master's Projects

In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering.

Malware Detection Using Dynamic Analysis, Swapna Vemparala

Master's Projects

In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach.

Clustering Versus Svm For Malware Detection, Usha Narra

Master's Projects

Previous work has shown that we can effectively cluster certain classes of mal- ware into their respective families. In this research, we extend this previous work to the problem of developing an automated malware detection system. We first compute clusters for a collection of malware families. Then we analyze the effectiveness of clas- sifying new samples based on these existing clusters. We compare results obtained using �-means and Expectation Maximization (EM) clustering to those obtained us- ing Support Vector Machines (SVM). Using clustering, we are able to detect some malware families with an accuracy comparable to that of SVMs. One …

Using Neural Networks For Image Classification, Tim Kang

Master's Projects

This paper will focus on applying neural network machine learning methods to images for the purpose of automatic detection and classification. The main advantage of using neural network methods in this project is its adeptness at fitting non­linear data and its ability to work as an unsupervised algorithm. The algorithms will be run on common, publically available datasets, namely the MNIST and CIFAR­10, so that our results will be easily reproducible.

Comparative Analysis Of Particle Swarm Optimization Algorithms For Text Feature Selection, Shuang Wu

Master's Projects

With the rapid growth of Internet, more and more natural language text documents are available in electronic format, making automated text categorization a must in most fields. Due to the high dimensionality of text categorization tasks, feature selection is needed before executing document classification. There are basically two kinds of feature selection approaches: the filter approach and the wrapper approach. For the wrapper approach, a search algorithm for feature subsets and an evaluation algorithm for assessing the fitness of the selected feature subset are required. In this work, I focus on the comparison between two wrapper approaches. These two approaches …

Using Hidden Markov Models To Detect Dna Motifs, Santrupti Nerli

Master's Projects

During the process of gene expression in eukaryotes, mRNA splicing is one of the key processes carried out by a complex called spliceosome. Spliceosome guarantees proper removal of introns and joining of exons before the translation process. Precise splicing is essential for the production of functional proteins. Spliceosome detects specific sequence motifs within an mRNA sequence called splice sites. Two of the splice sites are the 5’ and 3’ sites that border all the introns. Normal splicing process if disrupted by mutation may lead to fatal diseases. In this work, we predict splice sites in a human genome using hidden …

Using Probabilistic Graphical Models To Solve Np-Complete Puzzle Problems, Fengjiao Wu

Master's Projects

Probabilistic Graphical Models (PGMs) are commonly used in machine learning to solve problems stemming from medicine, meteorology, speech recognition, image processing, intelligent tutoring, gambling, games, and biology. PGMs are applicable for both directed graph and undirected graph. In this work, I focus on the undirected graphical model. The objective of this work is to study how PGMs can be applied to find solutions to two puzzle problems, sudoku and jigsaw puzzles. First, both puzzle problems are represented as undirected graphs, and then I map the relations of nodes to PGMs and Belief Propagation (BP). This work represents the puzzle grid …

Neuroscience-Inspired Dynamic Architectures, Catherine Dorothy Schuman

Doctoral Dissertations

Biological brains are some of the most powerful computational devices on Earth. Computer scientists have long drawn inspiration from neuroscience to produce computational tools. This work introduces neuroscience-inspired dynamic architectures (NIDA), spiking neural networks embedded in a geometric space that exhibit dynamic behavior. A neuromorphic hardware implementation based on NIDA networks, Dynamic Adaptive Neural Network Array (DANNA), is discussed. Neuromorphic implementations are one alternative/complement to traditional von Neumann computation. A method for designing/training NIDA networks, based on evolutionary optimization, is introduced. We demonstrate the utility of NIDA networks on classification tasks, a control task, and an anomaly detection task. There …

Using Genetic Learning In Weight-Based Game Ai, Dylan Anthony Kordsmeier

Computer Science and Computer Engineering Undergraduate Honors Theses

Human beings have been playing games for centuries, and over time, mankind has learned how to excel at these fun competitions. With the ever-growing interest in the field of Machine Learning and Artificial Intelligence (AI), developers have been finding ways to let the game compete against the player much like another human would. While there are many approaches to humanlike learning in machines, this article will focus on using Evolutionary Optimization as a method to develop different levels of pseudo-thinking inan AI used for ato effectively play the Connect Four game.