Digital Commons Network^™

Deapsecure Computational Training For Cybersecurity Students: Improvements, Mid-Stage Evaluation, And Lessons Learned, Wirawan Purwanto, Yuming He, Jewel Ossom, Qiao Zhang, Liuwan Zhu, Karina Arcaute, Masha Sosonkina, Hongyi Wu

University Administration Publications

DeapSECURE is a non-degree computational training program that provides a solid high-performance computing (HPC) and big-data foundation for cybersecurity students. DeapSECURE consists of six modules covering a broad spectrum of topics such as HPC platforms, big-data analytics, machine learning, privacy-preserving methods, and parallel programming. In the second year of this program, to improve the learning experience, we implemented a number of changes, such as grouping modules into two broad categories, "big-data" and "HPC"; creating a single cybersecurity storyline across the modules; and introducing post-workshop (optional) "hackshops." Two major goals of these changes are, firstly, to effectively engage students to maintain …

Revisiting Absolute Pose Regression, Hunter Blanton

Theses and Dissertations--Computer Science

Images provide direct evidence for the position and orientation of the camera in space, known as camera pose. Traditionally, the problem of estimating the camera pose requires reference data for determining image correspondence and leveraging geometric relationships between features in the image. Recent advances in deep learning have led to a new class of methods that regress the pose directly from a single image.

This thesis proposes methods for absolute camera pose regression. Absolute pose regression estimates the pose of a camera from a single image as the output of a fixed computation pipeline. These methods have many practical benefits …

Quality Of Sql Code Security On Stackoverflow And Methods Of Prevention, Robert Klock

Honors Papers

This paper explores the frequency at which SQL/PHP posts on the website Stackoverflow.com contain code susceptible to SQL Injection, a common database vulnerability. Specifically, we analyze whether other users give notice of the vulnerability or provide an answer that is secure. The majority of questions analyzed were vulnerable to SQL Injection and were not corrected in their answers or brought to the attention of the original poster. To mitigate this, we present a machine learning bot which analyzes the poster’s code and alerts them of potential injection vulnerabilities, if necessary.

Machine Learning Model Optimization With Hyper Parameter Tuning Approach, Md. Riyad Hossain, Douglas Timmer

Manufacturing & Industrial Engineering Faculty Publications

Hyper-parameters tuning is a key step to find the optimal machine learning parameters. Determining the best hyper-parameters takes a good deal of time, especially when the objective functions are costly to determine, or a large number of parameters are required to be tuned. In contrast to the conventional machine learning algorithms, Neural Network requires tuning hyperparameters more because it has to process a lot of parameters together, and depending on the fine tuning, the accuracy of the model can be varied in between 25%-90%. A few of the most effective techniques for tuning hyper-parameters in the Deep learning methods are: …

Ex-Ante Temporal Optimization In Soybean Origination: An Overdetermined Approach Through Deep Learning, Noah Joseph Carlson

Agribusiness and Applied Economics

Digitization is influencing commodity trading and agricultural markets and as they transition towards extreme liquidity, agribusiness risk exposures increase, and traditional competitive advantages diminish. In commodity origination, logistics and destination basis comprise the most volatile and determinant influences of margin. To capture a consistently higher margin and represent narrowed interior basis, agribusiness firms must manage these risks by optimizing transformations. To accomplish this, ex-ante decision-making is often necessary as forward price clairvoyance is not always prevalent, is risky, or contains premium. Modeling this spatial equilibrium is difficult through traditional reductionist and essentialist application as the overdetermined and convoluted system presents …

View Synthesis Of Dynamic Scenes Based On Deep 3d Mask Volume, Kai-En Lin, Guowei Yang, Lei Xiao, Feng Liu, Ravi Ramamoorthi

Computer Science Faculty Publications and Presentations

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables …

Identification And Classification Of Radio Pulsar Signals Using Machine Learning, Di Pang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pul- sar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.

The research work presented in this dissertation is focused on development of ma- chine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named Single- Pulse Event Group IDentification (SPEGID), which automatically identifies and clas- …

A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita

All Works

The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine …

Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey

Browse all Theses and Dissertations

We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining …

Texture-Driven Image Clustering In Laser Powder Bed Fusion, Alexander H. Groeger

Browse all Theses and Dissertations

The additive manufacturing (AM) field is striving to identify anomalies in laser powder bed fusion (LPBF) using multi-sensor in-process monitoring paired with machine learning (ML). In-process monitoring can reveal the presence of anomalies but creating a ML classifier requires labeled data. The present work approaches this problem by printing hundreds of Inconel-718 coupons with different processing parameters to capture a wide range of process monitoring imagery with multiple sensor types. Afterwards, the process monitoring images are encoded into feature vectors and clustered to isolate groups in each sensor modality. Four texture representations were learned by training two convolutional neural network …

Texture-Driven Image Clustering In Laser Powder Bed Fusion, Alexander H. Groeger

Browse all Theses and Dissertations

The additive manufacturing (AM) field is striving to identify anomalies in laser powder bed fusion (LPBF) using multi-sensor in-process monitoring paired with machine learning (ML). In-process monitoring can reveal the presence of anomalies but creating a ML classifier requires labeled data. The present work approaches this problem by printing hundreds of Inconel-718 coupons with different processing parameters to capture a wide range of process monitoring imagery with multiple sensor types. Afterwards, the process monitoring images are encoded into feature vectors and clustered to isolate groups in each sensor modality. Four texture representations were learned by training two convolutional neural network …

Intrusion Detection With An Autoencoder And Anova Feature Selector, Rashmi Satyal

Computer Science

Intrusion detection systems are systems that aim at identifying malicious activities or violation of policies in a network. The problem of high dimensionality in intrusion detection systems is a barrier in processing data and analyzing network traffic. This work aims at tackling problems associated with high data dimensionality using a feature selection technique based on one way ANOVA F-test before the classification process. It also involves study of autoencoder as a classification technique for network data as opposed to the traditional use of autoencoders in image data. Experiments have been conducted using the popular NSL-KDD dataset and the results of …

Estimating Wildlife Strike Costs At Us Airports: A Machine Learning Approach, Levi Altringer, Jordan Navin, Michael J. Begier, Stephanie A. Shwiff, Aaron M. Anderson

United States Department of Agriculture Wildlife Services: Staff Publications

Current lower bound estimates of the economic burden of wildlife strikes make use of mean cost assignment to impute missing values in the National Wildlife Strike Database (NWSD). The accuracy of these estimates, however, are undermined by the skewed nature of reported cost data and fail to account for differences in observed strike characteristics—e.g., type of aircraft, size of aircraft, type of damage, size of animal struck, etc. This paper makes use of modern machine learning techniques to provide a more accurate measure of the strike-related costs that accrue to the US civil aviation industry. We estimate that wildlife strikes …

Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon

Theses and Dissertations

Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes …

Learning From Multi-Class Imbalanced Big Data With Apache Spark, William C. Sleeman Iv

Theses and Dissertations

With data becoming a new form of currency, its analysis has become a top priority in both academia and industry, furthering advancements in high-performance computing and machine learning. However, these large, real-world datasets come with additional complications such as noise and class overlap. Problems are magnified when with multi-class data is presented, especially since many of the popular algorithms were originally designed for binary data. Another challenge arises when the number of examples are not evenly distributed across all classes in a dataset. This often causes classifiers to favor the majority class over the minority classes, leading to undesirable results …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Unobtrusive Assessment Of Student Engagement Levels In Online Classroom Environment Using Emotion Analysis, Sasirekha Anbusegaran

College of Graduate Studies: Theses & Dissertations

Measuring student engagement has emerged as a significant factor in the process of learning and a good indicator of the knowledge retention capacity of the student. As synchronous online classes have become more prevalent in recent years, gauging a student's attention level is more critical in validating the progress of every student in an online classroom environment. This paper details the study on profiling the student attentiveness to different gradients of engagement level using multiple machine learning models. Results from the high accuracy model and the confidence score obtained from the cloud-based computer vision platform - Amazon Rekognition were then …

Methods For Developing A Machine Learning Framework For Precise 3d Domain Boundary Prediction At Base-Level Resolution, Spiro C. Stilianoudakis

Theses and Dissertations

High-throughput chromosome conformation capture technology (Hi-C) has revealed extensive DNA looping and folding into discrete 3D domains. These include Topologically Associating Domains (TADs) and chromatin loops, the 3D domains critical for cellular processes like gene regulation and cell differentiation. The relatively low resolution of Hi-C data (regions of several kilobases in size) prevents precise mapping of domain boundaries by conventional TAD/loop-callers. However, high resolution genomic annotations associated with boundaries, such as CTCF and members of cohesin complex, suggest a computational approach for precise location of domain boundaries.

We developed preciseTAD, an optimized machine learning framework that leverages a random …

Ordered Physical Human Activity Recognition Based On Ordinal Classification, Duygu Bağci Daş, Derya Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Human activity recognition (HAR) is a critical process for applications that focus on the classification of human physical activities such as jogging, walking, downstairs, and upstairs. Ordinal classification (OC) is a special type of supervised multi-class classification in which an inherent ordering among the classes exists, such as low, medium, and high. This study combines these two concepts and introduces an approach to ?human activity recognition based on ordinal classification? (HAROC). In the proposed approach, ordinal classification is applied to human activity recognition where the physical activities can be ordered by using their signals? band power values. This is the …

Deep Learning For Turkish Makam Music Composition, İsmai̇l Hakki Parlak, Yalçin Çebi̇, Ci̇han Işikhan, Derya Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we introduce a new deep-learning-based system that can compose structured Turkish makam music (TMM) in the symbolic domain. Presented artificial TMM composer (ATMMC) takes eight initial notes from a human user and completes the rest of the piece. The backbone of the composer system consists of multilayered long short-term memory (LSTM) networks. ATMMC can create pieces in Hicaz and Nihavent makams in Şarkı form, which can be viewed and played with Mus2, a notation software for microtonal music. Statistical analysis shows that pieces composed by ATMMC are approximately 84% similar to training data. ATMMC is an open-source …

A Novel Method For Soc Estimation Of Li-Ion Batteries Using A Hybrid Machinelearning Technique, Eymen İpek, Murat Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

The battery system is one of the key components of electric vehicles (EV) which has brought groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to be monitored and controlled to achieve safe and high-performance operation. Particularly, the battery management system (BMS) uses complex processing systems that perform measurements, estimation of the battery states, and protection of the system. State of charge (SOC) estimation is a major part of these processes which defines remaining capacity in the battery until the next charging operation as a proportion to the total battery capacity. Since SOC is not a parameter that …

A Dynamic Active Noise Control System For Live Music Attenuation, Elliot James Krueger

Graduate Research Theses & Dissertations

This thesis proposes a system design that will be suitable for applying active noise control (ANC) effectively to live musical instruments. The design consists of three parts: a signal separation section, an instrument classification section, and the active noise control section. The signal separation section will split up the music signals. The instrument classification section will identify the signals, and the ANC section will attenuate the music signal based on the previous information from the other sections. The two instruments of focus will be the trombone and tuba for their low frequency and ability to be quite loud in a …

A Theory-Supported Machine Learning Model For The Prediction Of Melt Pool Geometry And Optimal Process Window In Metal Additive Manufacturing, Sina Tayebati

Graduate Research Theses & Dissertations

Direct Energy Deposition (DED) is an additive manufacturing (AM) process capable of producing complicate-shaped or functionally graded components, and it is getting intense attention as a revolutionary technology to satisfy high demand in manufacturing process for the aerospace, automotive, and medical industries. However, the repeatability in geometries and properties of fabricated products is one of the most challenging issues for the DED process to be fully utilized, requiring comprehensive understanding of effect of processing conditions on the properties of fabricated parts, and development of relations among those conditions and properties. That is the motivation of this research. In this study, …

Applying Machine Learning Approaches To Suicide Prediction Using Healthcare Data: Overview And Future Directions, Edwin D Boudreaux, Elke Rundensteiner, Feifan Liu, Bo Wang, Celine Larkin, Emmanuel Agu, Samiran Ghosh, Joshua Semeter, Gregory Simon, Rachel E Davis-Martin

Faculty, Staff and Student Publications

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data.

Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can …

Disrupting The Perioperative Opioid Gateway: Identification Of Risk Factors For New Persistent Post-Surgical Opioid Use, Gia Marie Pittet

All ETDs from UAB

A large portion of the American Opioid Crisis is due to opioid naïve patients who become new persistent post-surgical opioid users, although the risk factors for the development of this addiction are not well studied. The objective of this study was to analyze multiple layers of pre-operative and procedural risk factors using an ecological perspective theoretical framework in adult patients undergoing invasive surgery. We performed a retrospective analysis of 13,970 opioid naïve adults in a mixed surgical cohort with data available at the University of California Los Angeles that was merged with narcotics data for the State of California (IRB#19-000625). …

The Application Of Machine Learning In Analyzing Organic Compounds From Nmr Spectral Data, Nicole Maia Powell

Senior Independent Study Theses

Nuclear magnetic resonance (NMR) is used in organic chemistry to identify unknown organic compounds. The data obtained from an NMR spectrometer are typically shown in the form of a spectrum, which is then analyzed by an analytical chemist. The action of analyzing a spectrum, especially one of a large and complex molecule, is a long and tedious process. In this project, Python is used to implement hierarchical clustering on NMR data obtained from an NMR spectrometer at the College of Wooster to explore its application in NMR analysis. MATLAB is used to build a decision tree from the same data, …

A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …

A New Approach: Semisupervised Ordinal Classification, Ferda Ünal, Derya Bi̇rant, Özlem Şeker

Turkish Journal of Electrical Engineering and Computer Sciences

Semisupervised learning is a type of machine learning technique that constructs a classifier by learning from a small collection of labeled samples and a large collection of unlabeled ones. Although some progress has been made in this research area, the existing semisupervised methods provide a nominal classification task. However, semisupervised learning for ordinal classification is yet to be explored. To bridge the gap, this study combines two concepts ?semisupervised learning? and "ordinal classification" for the categorical class labels for the first time and introduces a new concept of "semisupervised ordinal classification". This paper proposes a new algorithm for semisupervised learning …

Data-Driven Wildfire Risk Prediction In Northern California, Ashima Malik, Megha Rajam Rao, Nandini Puppala, Prathusha Koouri, Venkata Anil Kumar Thota, Qiao Liu, Sen Chiao, Jerry Gao

Faculty Research, Scholarly, and Creative Activity

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the …

A Coastal N₂ Fixation Hotspot At The Cape Hatteras Front: Elucidating Spatial Heterogeneity In Diazotroph Activity Via Supervised Machine Learning, Corday R. Selden, P. Dreux Chappell, Sophie Clayton, Alfonso Macías-Tapia, Peter W. Bernhardt, Margaret R. Mulholland

OES Faculty Publications

In the North Atlantic Ocean, dinitrogen (N₂) fixation on the western continental shelf represents a significant fraction of basin‐wide nitrogen (N) inputs. However, the factors regulating coastal N₂ fixation remain poorly understood, in part due to sharp physico‐chemical gradients and dynamic water mass interactions that are difficult to constrain via traditional oceanographic approaches. This study sought to characterize the spatial heterogeneity of N₂ fixation on the western North Atlantic shelf, at the confluence of Mid‐ and South Atlantic Bight shelf waters and the Gulf Stream, in August 2016. Rates were quantified using the ¹⁵N_{2 …}