Digital Commons Network^™

Machine Learning-Based Side-Channel Analysis On The Advanced Encryption Standard, Jack Edmonds, Tyler Moon

Electrical and Computer Engineering Senior Theses

Hardware security is essential in keeping sensitive information private. Because of this, it’s imperative that we evaluate the ability of cryptosystems to withstand cutting edge attacks. Doing so encourages the development of countermeasures and new methods of data protection as needed. In this thesis, we present our findings of an evaluation of the Advanced Encryption Standard, particularly unmasked and masked AES-128, implemented in software on an STM32F415 microcontroller unit (MCU), against machine learning-based side-channel analysis (MLSCA). 12 machine learning classifiers were used in combination with a side-channel leakage model in the context of four scenarios: profiling one device and key …

A Spatial Risk Prediction Model For Drug Overdose, Parisa Bozorgi

Theses and Dissertations

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy from 2015 to 2018. Overdose deaths, especially from opioids, have also been recognized in recent years as a significant public health issue. To address this public health problem, this study sought to identify neighborhood-level (e.g., block group) factors associated with drug overdose and develop a spatial model using machine learning (ML) algorithms to predict the likelihood or risk of drug overdoses across South Carolina. This study included block group level socio-demographic factors and drug use variables which …

New Aspects On Using Artificial Intelligence To Shape The Future Of Entrepreneurs, Saad Darwish, Abdulsalam Darwish, Venus Bunagan

Information Sciences Letters

One way for enterprises to be successful in today’s challenging market is to be agile and be flexible to handle market changes. Using a conceptual and operational framework for improving the enterprise and keeping their desired situation, is always required. In this paper, a service oriented decision support system-based framework is proposed. The framework seeks for service oriented architecture (SOA) governance and suggests the initial architecture of the enterprise to support agility and optimality. Also, for the stability purpose a structure including service platforms, analyzers and decision support systems are employed to analyze the enterprise and make better decisions.

Automatic Detection Of Vehicles In Satellite Images For Economic Monitoring, Cole Hill

USF Tampa Graduate Theses and Dissertations

With the growing supply of satellites capturing images of the planet, governments andinvestors are looking for ways in which these new images may be used to determine which businesses are struggling and thriving. Recent works have shown that parking lot fill rates can provide valuable information about businesses’ earnings, however, the task of manually annotating the number of vehicles in a parking lot is expensive and time-consuming. Systems which can automate this process are therefore valuable as they are faster and cheaper than human labor. In this thesis, the problem of detection of small objects in large low-resolution images is …

Scite: The Next Generation Of Citations, Sean Rife, Domenic Rosati, Joshua M. Nicholson

Faculty & Staff Research and Creative Activity

Key points

• While the importance of citation context has long been recognized, simple citation counts remain as a crude measure of importance.
• Providing citation context should support the publication of careful science instead of headline‐grabbing and salami‐sliced non‐replicable studies.
• Machine learning has enabled the extraction of citation context for the first time, and made the classification of citation types at scale possible.

Identifying, Analyzing, And Using Discriminatory Variables For Classification Of Neutrino Signal And Background Noise In Multivariate Analysis In The Askaryan Radio Array Experiment, Jesse Osborn

Honors Program: Senior Projects (Public)

The Askaryan Radio Array Experiment, located near the South Pole, works to pinpoint specific instances of neutrinos from outside the solar system interacting with nucleons inside the Antarctic ice, emitting radio waves. I have taken data from the ARA stations which is presumed to be background noise and compared it to simulated data meant to look like a neutrino signal. I developed a suite of variables for discrimination between the two data sets, using a computer algorithm to generate a single output variable which can be used to distinguish noise events from signal events. I maximized this discrimination process for …

Predicting Vasovagal Responses: A Model-Based And Machine Learning Approach, Theodore Raphan, Sergei B. Yakushi

Publications and Research

Vasovagal syncope (VVS) or neurogenically induced fainting has resulted in falls, fractures, and death. Methods to deal with VVS are to use implanted pacemakers or beta blockers. These are often ineffective because the underlying changes in the cardiovascular system that lead to the syncope are incompletely understood and diagnosis of frequent occurrences of VVS is still based on history and a tilt test, in which subjects are passively tilted from a supine position to 20◦ from the spatial vertical (to a 70◦ position) on the tilt table and maintained in that orientation for 10–15 min. Recently, is has been shown …

Daniel: Towards Automated Bug Discovery By Black Box Test Case Generation & Recommendation, Michael G. Peechatt

Theses

Finding and documenting bugs in software systems is an essential component of the software development process. A bug is defined as a series of steps that produces behavior which differs from the software specification and requirements. Finding steps to produce such behavior requires expert knowledge of the possible operations of the software in development as well as intuition and creativity. This thesis proposes the Directed Action Node Input Execution Language (DANIEL), a language that represents test cases as directed graphs, where each node represents an action, and possible input arguments for each action are represented along the incoming directed edges. …

Ordinal Statistical Models Of Physical Activity Levels From Accelerometer Data, Shafayet S. Hossain, Drew M. Lazar, Munni Begum

International Journal of Exercise Science

International Journal of Exercise Science 14(7): 338-357, 2021. Improvements in accelerometer technology has led to new types of data on which more powerful predictive models can be built to assess physical activity. This paper explains and implements ordinal random forest and partial proportional odds models which both take into account the ordinality of responses given explanatory accelerometer data. The data analyzed comes from 28 adults performing activities of daily living in two visits while wearing accelerometers on the ankle, hip, right and left wrist. The first visit provided training data and the second testing data so that an independent sample, …

Deep-Learning-Based Multivariate Pattern Analysis (Dmvpa): A Tutorial And A Toolbox, Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashtok Samal, Prahalada K. Rao, Matthew R. Johnson

Center for Brain, Biology, and Behavior: Faculty Publications

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on …

Spatial Mode Correction Of Single Photons Using Machine Learning, Narayan Bhusal, Sanjaya Lohani, Chenglong You, Mingyuan Hong, Joshua Fabre, Pengcheng Zhao, Erin M. Knutson, Ryan T. Glasser, Omar S. Magaña-Loaiza

Faculty Publications

Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum-photonic technologies. Unfortunately, this problem is exacerbated at the single-photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, the self-learning and self-evolving features of artificial neural networks are exploited to correct the complex spatial profile of distorted Laguerre–Gaussian modes …

Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson

Theses and Dissertations

The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into …

Performance Of Various Low-Level Decoder For Surface Codes In The Presence Of Measurement Error, Claire E. Badger

Theses and Dissertations

Quantum error correction is a research specialty within the area of quantum computing that constructs quantum circuits that correct for errors. Decoding is the process of using measurements from an error correcting code, known as error syndrome, to decide corrective operations to perform on the circuit. High-level decoding is the process of using the error syndrome to perform corrective logical operations, while low-level decoding uses the error syndrome to correct individual data qubits. Research on machine learning-based decoders is increasingly popular, but has not been thoroughly researched for low-level decoders. The type of error correcting code used is called surface …

Comparison Of Machine Learning Techniques On Trust Detection Using Eeg, James R. Elkins

Theses and Dissertations

Trust is a pillar of society and is a fundamental aspect in every relationship. With the use of automated agents in todays workforce exponentially growing, being able to actively monitor an individuals trust level that is working with the automation is becoming increasingly more important. Humans often have miscalibrated trust in automation and therefore are prone to making costly mistakes. Since deciding to trust or distrust has been shown to correlate with specific brain activity, it is thought that there are EEG signals which are associated with this decision. Using both a human-human trust and a human-machine trust EEG dataset …

Unsupervised Clustering Of Rf-Fingerprinting Features Derived From Deep Learning Based Recognition Models, Christian T. Potts

Theses and Dissertations

RF-Fingerprinting is focus of machine learning research which aims to characterize wireless communication devices based on their physical hardware characteristics. It is a promising avenue for improving wireless communication security in the PHY layer. The bulk of research presented to date in this field is focused on the development of features and classifiers using both traditional supervised machine learning models as well as deep learning. This research aims to expand on existing RF-Fingerprinting work by approaching the problem through the lens of an unsupervised clustering problem. To that end this research proposes a deep learning model and training methodology to …

Contract Information Extraction Using Machine Learning, Zachary E. Butcher

Theses and Dissertations

The Air Force Sustainment Center assisted by the Data Analytics Resource Team and the Defense Logistics Agency collected four million contracts onto one of the Air Force Research Laboratory’s high power computers. This thesis focuses on the effort to determine if parts are available through those contracts. Some information is extracted using machine learning in combination with natural language processing. Where machine learning methods are unsuccessful or inappropriate, text mining techniques, such as pattern recognition and rules, are used. Upon completion, the information is combined into a Gantt chart for quick evaluation. Only 21% of the contracts have their information …

Correction Of Back Trajectories Utilizing Machine Learning, Britta F. Gjermo Morrison

Theses and Dissertations

The goal of this work was to analyze 24-hour back trajectory performance from a global, low-resolution weather model compared to a high-resolution limited area weather model in particular meteorological regimes, or flow patterns using K-means clustering, an unsupervised machine learning technique. The duration of this study was from 2015-2019 for the contiguous United States (CONUS). Three different machine learning algorithms were tested to study the utility of these methods improving the performance of the CFS relative to the performance of the RAP. The aforementioned machine learning techniques are linear regression, Bayesian ridge regression, and random forest regression. These results mean …

A Machine Learning Approach As A Surrogate For A Finite Element Analysis: Status Of Research And Application To One Dimensional Systems, Poojitha Vurtur Badarinath, Maria Chierichetti, Fatemeh Davoudi Kakhki

Faculty Research, Scholarly, and Creative Activity

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution …

A Machine Learning Approach As A Surrogate For A Finite Element Analysis: Status Of Research And Application To One Dimensional Systems, Poojitha Vurtur Badarinath, Maria Chierichetti, Fatemeh Davoudi Kakhki

General Engineering

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution …

Behavior Modeling For Computer Generated Forces Based On Machine Learning, Zhang Qi, Junjie Zeng, Xu Kai, Qin Long, Quanjun Yin

Journal of System Simulation

Abstract: With the rapid development of Machine Learning, especially deep learning, it has become an important way of modeling Computer Generated Force (CGF) behavior by ML methods, which can overcome the challenges of traditional methods. The existing research and application of three typical learning methods in CGF behavior modeling are discussed, and the effects of introducing learning into different stages of the typical CGF applications are analyzed, and the function and performance requirements of CGF behavior modeling using machine learning are proposed. Four potential research directions in the field for future are proposed.

Liver Segmentation And Liver Cancer Detection Based On Deep Convolutional Neural Network: A Brief Bibliometric Survey, Kiran Malhari Napte Mr., Anurag Mahajan Dr.

Library Philosophy and Practice (e-journal)

Background: This study analyzes liver segmentation and cancer detection work, with the perspectives of machine learning and deep learning and different image processing techniques from the year 2012 to 2020. The study uses different Bibliometric analysis methods.

Methods: The articles on the topic were obtained from one of the most popular databases- Scopus. The year span for the analysis is considered to be from 2012 to 2020. Scopus analyzer facilitates the analysis of the databases with different categories such as documents by source, year, and county and so on. Analysis is also done by using different units of analysis such …

A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R

Library Philosophy and Practice (e-journal)

The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by …

Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study, Baozhong Wang, Jyotsna Sharma, Jianhua Chen, Patricia Persaud

Faculty Publications

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide …

Revman: Revenue-Aware Multi-Task Online Insurance Recommendation, Yu Li, Yi Zhang, Lu Gan, Gengwei Hong, Zimu Zhou, Qiang Li

Research Collection School Of Computing and Information Systems

Online insurance is a new type of e-commerce with exponential growth. An effective recommendation model that maximizes the total revenue of insurance products listed in multiple customized sales scenarios is crucial for the success of online insurance business. Prior recommendation models are ineffective because they fail to characterize the complex relatedness of insurance products in multiple sales scenarios and maximize the overall conversion rate rather than the total revenue. Even worse, it is impractical to collect training data online for total revenue maximization due to the business logic of online insurance. We propose RevMan, a Revenue-aware Multi-task Network for online …

Towards Advancing The Earthquake Forecasting By Machine Learning Of Satellite Data, Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huyui Zhou

Mathematics, Physics, and Computer Science Faculty Articles and Research

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for …

Holistic Control For Cyber-Physical Systems, Yehan Ma

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …

Creating A Machine Learning Model For The Prediction Of Refugee Flows, Esther Mead

Theses and Dissertations

The world-wide refugee problem has a long history, but continues to this day, and will unfortunately continue into the foreseeable future. Efforts to adequately anticipate, mitigate and prepare for refugee flows, however, are still lacking. There are many potential causes for refugee flows, but the published research has primarily focused on identifying ways to integrate already existing refugees into the various communities wherein they ultimately reside after having fled their home countries, rather than on the idea of figuring out ways to prevent the situations that initially caused them to flee. The model proposed in this dissertation uses a set …

Hybrid Modelling For Stroke Care: Review And Suggestions Of New Approaches For Risk Assessment And Simulation Of Scenarios, Tilda Herrgårdh, Vince I. Madai, John Kelleher, Rasmus Magnusson, Mika Gustafsson, Lili Milani, Peter Gennemark, Gunnar Cedersund

Articles

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose …

Integrating Multiscale Numerical Simulations With Machine Learning To Predict The Strain Sensing Efficiency Of Nano-Engineered Smart Cementitious Composites, Gideon A. Lyngdoh, Sumanta Das

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

Prediction of in-situ strain sensing efficiency of self-sensing cementitious composites using machine learning (ML) requires a large, representative, consistent, and accurate dataset. However, such large experimental dataset is not readily available. Moreover, the success of the ML approach depends on its ability to abide by the fundamental laws of physics. To address these challenges this paper synergistically integrates a validated finite element analysis (FEA)-based multiscale simulation framework with ML to predict the strain-sensing ability of self-sensing cementitious composites enabled by incorporating nano-engineered interfaces. The multiscale simulation framework is leveraged to develop a balanced, representative, complete, and consistent dataset containing 3000 …

A Review Of The Fractal Market Hypothesis For Trading And Market Price Prediction, Jonathan Blackledge, Marc Lamphiere

Articles

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be …