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Real-Time Stealth Intervention For Motor Learning Using Player Flow-State, Ramin Tadayon, Ashish Amresh, Troy McDaniel, Sethuraman Panchanathan 2019 Arizona State University

Real-Time Stealth Intervention For Motor Learning Using Player Flow-State, Ramin Tadayon, Ashish Amresh, Troy Mcdaniel, Sethuraman Panchanathan

Ashish Amresh

We present a novel approach to real-time adaptation in serious games for at-home motor learning. Our approach assesses and responds to the “flow-state” of players by tracking and classifying facial emotions in real-time using the Kinect camera. Three different approaches for stealth assessment and adaptation using performance and flow-state data are defined, along with a case-study evaluation of these approaches based on their effectiveness at maintaining positive affective interaction in a subject.


Real-Time Rfi Mitigation In Radio Astronomy, Emily Ramey, Nick Joslyn, Richard Prestage, Michael Lam, Luke Hawkins, Tim Blattner, Mark Whitehead 2019 Washington University in St. Louis

Real-Time Rfi Mitigation In Radio Astronomy, Emily Ramey, Nick Joslyn, Richard Prestage, Michael Lam, Luke Hawkins, Tim Blattner, Mark Whitehead

Senior Honors Papers / Undergraduate Theses

As the use of wireless technology has increased around the world, Radio Frequency Interference (RFI) has become more and more of a problem for radio astronomers. Preventative measures exist to limit the presence of RFI, and programs exist to remove it from saved data, but the use of algorithms to detect and remove RFI as an observation is occurring is much less common. Such a method would be incredibly useful for observations in which the data must undergo several rounds of processing before being saved, as in pulsar timing studies. Strategies for real-time mitigation have been discussed and tested with ...


Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, Joe Pater 2019 Selected Works

Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, Joe Pater

Joe Pater

The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This paper traces the development of these two approaches to cognitive science, from their largely autonomous early development in their first thirty years, through their collision in the 1980s around the past tense debate (Rumelhart and McClelland 1986, Pinker and Prince 1988), and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines ...


Privileged Access Management, Anea Cobia 2019 La Salle University

Privileged Access Management, Anea Cobia

Economic Crime Forensics Capstones

Security breaches are becoming a common occurrence in society today. When breaches occur, people are often left wondering how they will be affected and what steps can be taken to protect them. The passing of stricter standards and regulations has not slowed would be hackers from crafting ways to breach networks. While there are many ways that a breach can occur, the focus of this paper will be to look at the usage of credentials and privileged accounts. Specifically, the idea of privilege access management and methods for protecting credentials will be examined.


An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine 2019 Southern Methodist University

An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine

SMU Data Science Review

In this paper, we present an evaluation of training size impact on validation accuracy for an optimized Convolutional Neural Network (CNN). CNNs are currently the state-of-the-art architecture for object classification tasks. We used Amazon’s machine learning ecosystem to train and test 648 models to find the optimal hyperparameters with which to apply a CNN towards the Fashion-MNIST (Mixed National Institute of Standards and Technology) dataset. We were able to realize a validation accuracy of 90% by using only 40% of the original data. We found that hidden layers appear to have had zero impact on validation accuracy, whereas the ...


Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran 2019 Southern Methodist University

Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran

SMU Data Science Review

In this paper, we present a performance comparison of machine learning algorithms executed on traditional and quantum computers. Quantum computing has potential of achieving incredible results for certain types of problems, and we explore if it can be applied to machine learning. First, we identified quantum machine learning algorithms with reproducible code and had classical machine learning counterparts. Then, we found relevant data sets with which we tested the comparable quantum and classical machine learning algorithm's performance. We evaluated performance with algorithm execution time and accuracy. We found that quantum variational support vector machines in some cases had higher ...


Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal 2019 Southern Methodist University

Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal

SMU Data Science Review

In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern ...


Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater 2019 Southern Methodist University

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide ...


Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks 2019 Southern Methodist University

Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks

SMU Data Science Review

In this paper, we present an approach to reducing bottom hole plunger dwell time for artificial lift systems. Lift systems are used in a process to remove contaminants from a natural gas well. A plunger is a mechanical device used to deliquefy natural gas wells by removing contaminants in the form of water, oil, wax, and sand from the wellbore. These contaminants decrease bottom-hole pressure which in turn hampers gas production by forming a physical barrier within the well tubing. As the plunger descends through the well it emits sounds which are recorded at the surface by an echo-meter that ...


Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard 2019 Yale University

Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard

Yale Day of Data

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim ...


Computer Organization With Mips, Seth D. Bergmann 2019 Rowan University

Computer Organization With Mips, Seth D. Bergmann

Open Educational Resources

This book is intended to be used for a first course in computer organization, or computer architecture. It assumes that all digital components can be constructed from fundamental logic gates. The book begins with number representation schemes and assembly language for the MIPS architecture, including assembler directives, pseudo-operations, and floating point instructions. It then describes the machine language instruction formats, and shows the student how to translate an assembly language program to machine language. This is followed by a chapter which describes how to construct an assembler for MIPS. This chapter may be omitted without loss of continuity. This is ...


A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas 2019 Southern Methodist University

A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas

SMU Data Science Review

There has been increasing growth in deployment of recommender systems across Internet sites, with various models being used. These systems have been particularly valuable for review sites, as they seek to add value to the user experience to gain market share and to create new revenue streams through deals. Hotels are a prime target for this effort, as there is a large number for most destinations and a lot of differentiation between them. In this paper, we present an evaluation of two of the most popular methods for hotel review recommender systems: collaborative filtering and matrix factorization. The accuracy of ...


Social Engineering In Call Centers And Ways To Reduce It, Maureen York 2019 La Salle University

Social Engineering In Call Centers And Ways To Reduce It, Maureen York

Economic Crime Forensics Capstones

Social engineering is the use of trickery, deception, persuasion, emotional manipulation, impersonation, and abuse of trust to gain information or access through the use of a human interface (Thompson, 2006). Social engineering relies on the human behavior in order to gain information or access. The technique of social engineering can be performed in numerous ways and has been proven to be an effective way for perpetrators to obtain valuable information.

This capstone project, I will focus on social engineering of call centers and the steps organizations can take to reduce it. For most organizations, the call centers or customer support ...


Development Of A Personal Logbook Android Application, John Byrnes 2019 La Salle University

Development Of A Personal Logbook Android Application, John Byrnes

Economic Crime Forensics Capstones

Development of a mobile app to keep up with animal information.


The Benefits Of Artificial Intelligence In Cybersecurity, Ricardo Calderon 2019 La Salle University

The Benefits Of Artificial Intelligence In Cybersecurity, Ricardo Calderon

Economic Crime Forensics Capstones

Cyberthreats have increased extensively during the last decade. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and botnets are almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques are able to mine data to detect botnets’ sources. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a ...


Accepting Blockchain Tech To Increase Bitcoin Acceptance, Oscar Nawrot 2019 La Salle University

Accepting Blockchain Tech To Increase Bitcoin Acceptance, Oscar Nawrot

Economic Crime Forensics Capstones

Cryptocurrencies, with Bitcoin leading the charge, are the beginning of something new because this ideology is attempting to change the way people store and use money - led by an influx of technological innovation and success. If executed accordingly, blockchain has the chance to completely change our world for the better. With Bitcoin being driven by advanced blockchain technology, this digital currency has the ability to decentralize one of the most important aspects of life - money. This in turn would allow for people to take direct control of their money without the need for being completely dependent on bank systems and ...


Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick 2019 University of Pennsylvania Law School

Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick

Faculty Scholarship at Penn Law

Across the Internet, mistaken and malicious routing announcements impose significant costs on users and network operators. To make routing announcements more reliable and secure, Internet coordination bodies have encouraged network operators to adopt the Resource Public Key Infrastructure (“RPKI”) framework. Despite this encouragement, RPKI’s adoption rates are low, especially in North America.

This report presents the results of a year-long investigation into the hypothesis—widespread within the network operator community—that legal issues pose barriers to RPKI adoption and are one cause of the disparities between North America and other regions of the world. On the basis of interviews ...


Rehab Tracker: Framework For Monitoring And Enhancing Nmes Patient Compliance, Timothy Stevens 2019 University of Vermont

Rehab Tracker: Framework For Monitoring And Enhancing Nmes Patient Compliance, Timothy Stevens

Graduate College Dissertations and Theses

We describe the development of a cyber-physical system (Rehab Tracker) for improving patient compliance with at-home physical rehabilitation using neuromuscular electrical stimulation (NMES) therapy. Rehab Tracker consists of three components: 1) hardware modifications to sense and store use data from an FDA-approved NMES therapy device and provide Bluetooth communication capability, 2) an iOS-based smartphone/tablet application to receive and transmit NMES use data and serve as a conduit for patient-provider interactions and 3) a back-end server platform to receive device use data, display compliance data for provider review and provide automated positive and remedial push notifications to patients to improve ...


When Human Cognitive Modeling Meets Pins: User-Independent Inter-Keystroke Timing Attacks, Ximing LIU, Yingjiu LI, Robert H. DENG, Bing CHANG, Shujun LI 2019 Singapore Management University

When Human Cognitive Modeling Meets Pins: User-Independent Inter-Keystroke Timing Attacks, Ximing Liu, Yingjiu Li, Robert H. Deng, Bing Chang, Shujun Li

Research Collection School Of Information Systems

This paper proposes the first user-independent inter-keystroke timing attacks on PINs. Our attack method is based on an inter-keystroke timing dictionary built from a human cognitive model whose parameters can be determined by a small amount of training data on any users (not necessarily the target victims). Our attacks can thus be potentially launched on a large scale in real-world settings. We investigate inter-keystroke timing attacks in different online attack settings and evaluate their performance on PINs at different strength levels. Our experimental results show that the proposed attack performs significantly better than random guessing attacks. We further demonstrate that ...


Automated Trading Systems Statistical And Machine Learning Methods And Hardware Implementation: A Survey, Boming Huang, Yuziang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou 2019 Old Dominion University

Automated Trading Systems Statistical And Machine Learning Methods And Hardware Implementation: A Survey, Boming Huang, Yuziang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou

Information Technology & Decision Sciences Faculty Publications

Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and ...


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