Digital Commons Network^™

User-Centric Traffic Engineering In Software Defined Networks, Taimur Bakhshi

School of Engineering, Computing and Mathematics Theses

Software defined networking (SDN) is a relatively new paradigm that decouples individual network elements from the control logic, offering real-time network programmability, translating high level policy abstractions into low level device configurations. The framework comprises of the data (forwarding) plane incorporating network devices, while the control logic and network services reside in the control and application planes respectively. Operators can optimize the network fabric to yield performance gains for individual applications and services utilizing flow metering and application-awareness, the default traffic management method in SDN. Existing approaches to traffic optimization, however, do not explicitly consider user application trends. Recent SDN …

Abusive Text Detection Using Neural Networks, Hao Chen, Susan Mckeever, Sarah Jane Delany

Conference papers

eural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, …

Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon

College of Graduate Studies: Theses & Dissertations

Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most …

Integration And Evaluation Of Automated Pavement Distress Data In Indot’S Pavement Management System, Majed Alinizzi, Julie Yu Qiao, Amr Kandil, Hubo Cai, Samuel Labi

JTRP Technical Reports

This study was in two parts. The first part established and demonstrated a framework for pavement data integration. This is critical for fulfilling QC/QA needs of INDOT’s pavement management system, because the precision of the physical location references is a prerequisite for the reliable collection and interpretation of pavement data. Such consistency is often jeopardized because the data are collected at different years, and are affected by changes in the vendor, inventory, or referencing system or reference points. This study therefore developed a “lining-up” methodology to address this issue. The applicability of the developed methodology was demonstrated using 2012-2014 data …

Developing An Innovative Entity Extraction Method For Unstructured Data, Waleed A. Zaghloul, Silvana Trimi

Department of Management: Faculty Publications

The main goal of this study is to build high-precision extractors for entities such as Person and Organization as a good initial seed that can be used for training and learning in machine-learning systems, for the same categories, other categories, and across domains, languages, and applications. The improvement of entities extraction precision also increases the relationships extraction precision, which is particularly important in certain domains (such as intelligence systems, social networking, genetic studies, healthcare, etc.). These increases in precision improve the end users’ experience quality in using the extraction system because it lowers the time that users spend for training …

A Survey On Automated Food Monitoring And Dietary Management Systems, Vieira Bruno, Silva Resende, Juan Cui

School of Computing: Faculty Publications

Healthy diet with balanced nutrition is key to the prevention of life-threatening diseases such as obesity, cardiovascular disease, and cancer. Recent advances in smartphone and wearable sensor technologies have led to a proliferation of food monitoring applications based on automated food image processing and eating episode detection, with the goal to conquer drawbacks of the traditional manual food journaling that is time consuming, inaccurate, underreporting, and low adherent. In order to provide users feedback with nutritional information accompanied by insightful dietary advice, various techniques in light of the key computational learning principles have been explored. This survey presents a variety …

K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler

Graduate Student Theses, Dissertations, & Professional Papers

Biological sequence datasets are increasing at a prodigious rate. The volume of data in these datasets surpasses what is observed in many other fields of science. New developments wherein metagenomic DNA from complex bacterial communities is recovered and sequenced are producing a new kind of data known as metagenomic data, which is comprised of DNA fragments from many genomes. Developing a utility to analyze such metagenomic data and predict the sample class from which it originated has many possible implications for ecological and medical applications. Within this document is a description of a series of analytical techniques used to process …

Plantcv V2: Image Analysis Software For High-Throughput Plant Phenotyping, Malia A. Gehan, Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry, Steven T. Callen, Leonardo Chavez, Andrew N. Doust, Max J. Feldman, Kerrigan B. Gilbert, John G. Hodge, J. Steen Hoyer, Andy Lin, Suxing Liu, César Lizárraga, Argelia Lorence, Michael Miller, Eric Platon, Monica Tessman, Tony Sax

Department of Agronomy and Horticulture: Faculty Publications

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major …

Deep Learning Method Vs. Hand-Crafted Features For Lung Cancer Diagnosis And Breast Cancer Risk Analysis, Wenqing Sun

Open Access Theses & Dissertations

Breast cancer and lung cancer are two major leading causes of cancer deaths, and researchers have been developing computer aided diagnosis (CAD) system to automatically diagnose them for decades. In recent studies, we found that the techniques in CAD system can also be used for breast cancer risk analysis, like feature design and machine learning. Also we noticed that with the development of deep learning methods, the performance of CAD system can be improved by using computer automatically generated features. To explore these possibilities, we conducted a series of studies: the first two studies focused on transferring the original CAD …

Parkinson's Symptoms Quantification Using Wearable Sensors, Md Nafiul Alam

Theses and Dissertations

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, …

Evaluating Machine Learning Classifiers For Defensive Cyber Operations, Michael D. Rich, Robert F. Mills, Thomas E. Dube, Steven K. Rogers

Military Cyber Affairs

Today’s defensive cyber sensors are dominated by signature-based analytical methods that require continuous maintenance and lack the ability to detect unknown threats. Anomaly detection offers the ability to detect unknown threats, but despite over 15 years of active research, the operationalization of anomaly detection and machine learning for Defensive Cyber Operations (DCO) is lagging. This article provides an introduction to machine learning concepts with a focus on the unique challenges to using machine learning for DCO. Traditional machine learning evaluation methods are challenged in favor of a value-focused evaluation method that incorporates evaluator-specific weights for classifier and sensitivity threshold selection …

Global Gene Expression Profiling Of Healthy Human Brain And Its Application In Studying Neurological Disorders, Simarjeet K. Negi

Theses & Dissertations

The human brain is the most complex structure known to mankind and one of the greatest challenges in modern biology is to understand how it is built and organized. The power of the brain arises from its variety of cells and structures, and ultimately where and when different genes are switched on and off throughout the brain tissue. In other words, brain function depends on the precise regulation of gene expression in its sub-anatomical structures. But, our understanding of the complexity and dynamics of the transcriptome of the human brain is still incomplete. To fill in the need, we designed …

Integrative Approaches For Large-Scale Biomedical Data Analysis, Ashis Kumer Biswas

Computer Science and Engineering Dissertations - Archive

Advancement of the Next Generation Sequencing (NGS), also known as the High Throughput Sequencing (HTS) technologies allow researchers investigate genome, transcriptome, or epigenome of any organism from any perspective, thereby contributing to the enrichment of the biomedical data repositories for many of the lesser known phenomena. The regulatory activities inside genome by the non-coding RNAs (ncRNAs), the transcribed product of the long-neglected "junk DNA" molecules is one such phenomenon. While large-scale data about the ncRNAs are becoming publicly available, the computational challenges are being imposed to the bioinformaticians for efficient mining to get reliable answers to few subtle questions. Given …

Computational Models Of Visual Features: From Proto-Objects To Object Categories, Chen-Ping Yu

Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions)

The human visual perception is a complex system that excels in tasks such as object recognition and localization, face detection, object segmentation, action classification, and more. While we perform these everyday tasks with ease, little is known about the underlying process of the human visual system. In contrast, computer vision is a field of research that strives for better performance in the aforementioned tasks, which rely on carefully designed statistical machine learning based theories. As the field of computer vision and machine learning has matured over the past decade, there is an abundance of methods and theories that can be …

Predicting Malignant Nodules From Screening Ct Scans, Samuel Hawkins, Hua Wang, Ying Liu, Alberto Garcia, Olya Stringfield, Henry Krewer, Qiang Li, Dmitry Cherezov, Matthew Schabath, Lawrence O. Hall, Robert J. Gillies

Computer Science and Engineering Faculty Publications

Objectives

The aim of this study was to determine whether quantitative analyses (“radiomics”) of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer.

Methods

Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer.

Results

The best models used 23 stable features in a random forests classifier and could …

Examination And Utilization Of Rare Features In Text Classification Of Injury Narratives, Hsin-Ying Huang

Open Access Dissertations

Thanks to the advances in computing and information technology, analyzing injury surveillance data with statistical machine learning methods has grown in popularity, complexity, and quality over recent years. During that same time, researchers have recognized the limitations of statistical text analysis with limited training data. In response to the two primary challenges for statistical text analysis, dimensionality reduction and sparse data, many studies have focused on improving machine learning algorithms. Less research has been done, though, to examine and improve statistical machine learning methods in text classification from a linguistic perspective.

This study addresses this research gap by examining the …

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 …

Investigation Of Radiation Injury In The Esophagus From Definitive Chemoradiation Therapy Using Novel Imaging Biomarkers, Joshua S. Niedzielski

Dissertations and Theses (Open Access)

Radiation injury in the esophagus occurs with high frequency from the treatment of non-small cell lung cancer (NSCLC). Radiation esophagitis is an acute normal tissue toxicity that negatively affects treatment efficacy by limiting dose and potentially interrupting radiation therapy. Clinical quantification of this toxicity is typically achieved by utilizing physician grading scales, assigning complication severity on an ordinal scale of symptom presentation and/or physician chosen interventions. These criteria are subjective in nature, both from the physician assigning the grade and the patient reporting the symptom. Furthermore, radiation therapy planning guidelines for the esophagus are derived from toxicity prediction models utilizing …

Psychophysiological Analysis Of A Pedagogical Agent And Robotic Peer For Individuals With Autism Spectrum Disorders., Mohammad Nasser Saadatzi

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, …

Exploration Of Statistical Learning Strategies And Their Applications On Medical Image Data For Computer-Aided Diagnosis, Yifan Hu

Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions)

Machine learning addresses the question of how computer make decisions and predictions automatically through existing experiences and data, which has become an increasingly important topic with the advent of modern data science and automated big data analysis. Several algorithms are widely used in machine learning. However, each classifier, inevitably, has certain inadequacy for which we hope to compensate. To address these issues, this study first introduces the necessary theoretical background and principles for machine learning and those typical classifiers. Based on these classifiers, this paper attempts to (1) use bagging/boosting to improve the simple classifier, and, (2) find some combination …

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 …

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 …

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 …

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 …

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 …

Large-Scale Computational Screening And Machine Learning Approaches To Drug Discovery, Bryce K. Allen

Open Access Dissertations

Biological information continues to grow exponentially fueled by massive data generation projects such as the Human Genome Project, The Cancer Genome Atlas (TCGA), and the Library of Integrated Network-based Cellular Signatures (LINCS). Unprecedented amounts and varieties of data (big data) have the potential to bring enormous scientific advances. Such data-driven research relies on advanced computational approaches for data integration and analysis. While bioinformatics encompasses many fields, the focus of my research has been to predict small molecule chemicals that interact with protein targets of interest and could, ultimately, become therapeutically useful drugs. Drug resistance in newly diagnosed tumors is often …

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 …

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 …

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 …