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The Perils And Promises Of Artificial General Intelligence, Brian S. Haney 2019 Notre Dame Law School

The Perils And Promises Of Artificial General Intelligence, Brian S. Haney

Journal of Legislation

No abstract provided.


A Machine Learning Approach To Predicting Alcohol Consumption In Adolescents From Historical Text Messaging Data, Adrienne Bergh 2019 Chapman University

A Machine Learning Approach To Predicting Alcohol Consumption In Adolescents From Historical Text Messaging Data, Adrienne Bergh

Computational and Data Sciences (MS) Theses

Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train ...


Download Bmp Trains 2020 Newest Version, Martin Wanielista 2019 University of Central Florida

Download Bmp Trains 2020 Newest Version, Martin Wanielista

BMP Trains

This is version 1.2.11. BMP Trains 2020 is a computer program used to assess the average annual removal effectiveness of nutrients. It is based on research from the State of Florida and uses design criteria in the State. It is accepted by regulatory agencies in the State. The development was supported by the Florida Department of Transportation. It is an improvement from all previous versions – most notably because of the departure from the Microsoft Excel platform to being programmed and compiled using the C# programming language. The entire user interface and much of the internal function has been ...


Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams 2019 Iowa State University

Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams

Derrick K Rollins, Sr.

Understanding stress-strain behavior of asphalt pavement under repetitive traffic loading is of critical importance to predict pavement performance and service life. For viscoelastic materials, the stress-strain relationship can be represented by the dynamic modulus. The dynamic modulus test in indirect tension mode can be used to measure the modulus of each specific layer of asphalt pavements using representative samples. Dynamic modulus is a function of material properties, loading, and environmental conditions. Developing predictive models for dynamic modulus is efficient and cost effective. This article focuses on developing an accurate Finite Element (FE) model using mixture elastic modulus and asphalt binder ...


Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams 2019 Iowa State University

Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams

R. Christopher Williams

Understanding stress-strain behavior of asphalt pavement under repetitive traffic loading is of critical importance to predict pavement performance and service life. For viscoelastic materials, the stress-strain relationship can be represented by the dynamic modulus. The dynamic modulus test in indirect tension mode can be used to measure the modulus of each specific layer of asphalt pavements using representative samples. Dynamic modulus is a function of material properties, loading, and environmental conditions. Developing predictive models for dynamic modulus is efficient and cost effective. This article focuses on developing an accurate Finite Element (FE) model using mixture elastic modulus and asphalt binder ...


Analytical And Numerical Modeling Of Cavity Expansion In Anisotropic Poroelastoplastic Soil, Kai Liu 2019 Louisiana State University and Agricultural and Mechanical College

Analytical And Numerical Modeling Of Cavity Expansion In Anisotropic Poroelastoplastic Soil, Kai Liu

LSU Doctoral Dissertations

Cavity expansion/contraction problems have attracted intensive attentions over the past several decades due to its versatile applications, such as the interpretation of pressuremeter/piezocone penetration testing results and the modelling of pile installation/tunnel excavation in civil engineering, and the prediction of critical mud pressure required to maintain the wellbore stability in petroleum engineering. Despite the fact that various types of constitutive models have been covered in the literature on this subject, the soils and/or rocks were usually treated as isotropic geomaterials.

In recognition of the above fact, this research makes a substantial extension of the fundamental cavity ...


Using Transfer Learning In Network Markets, Kai Cai 2019 The Graduate Center, City University of New York

Using Transfer Learning In Network Markets, Kai Cai

All Dissertations, Theses, and Capstone Projects

Mechanism design is the sub-field of microeconomics and game theory, which considers agents have their own private information and are self-interested and tries to design systems that can produce desirable outcomes. In recent years, with the development of internet and electronic markets, mechanism design has become an important research field in computer science. This work has largely focused on single markets. In the real world, individual markets tend to connect to other markets and form a big “network market”, where each market occupies a node in the network and connections between markets reflect constraints on traders in the markets. So ...


Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene 2019 University of Louisville

Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. Several studies have been conducted in the past years to develop a better understanding of the disease and therefore a better diagnosis and a better treatment by analyzing diverse data sets consisting of behavioral surveys and tests, phenotype description, and brain imagery. However, data analysis is challenged by the diversity, complexity and heterogeneity of patient cases and by the need for integrating diverse data sets to reach a better understanding of ASD. The aim of our study is to mine homogeneous groups of patients from a heterogeneous ...


Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi 2019 University of Louisville

Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi

Electronic Theses and Dissertations

Recommender systems are becoming widely used in everyday life. They use machine learning algorithms which learn to predict our preferences and thus influence our choices among a staggering array of options online, such as movies, books, products, and even news articles. Thus what we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated predictions made by learning machines. Similarly, the predictive accuracy of these learning machines heavily depends on the feedback data, such as ratings and clicks, that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown ...


Forecasting Model For Disease Propensity Using Ehr Data, soodabeh sarafrazi, Omar Sharif, Matthew Domingo, Jie Han, Michael Chang, Omid Khazaie, Anil Kemisetti 2019 The University of San Francisco

Forecasting Model For Disease Propensity Using Ehr Data, Soodabeh Sarafrazi, Omar Sharif, Matthew Domingo, Jie Han, Michael Chang, Omid Khazaie, Anil Kemisetti

Creative Activity and Research Day - CARD

Many diseases such as diabetes and cardiovascular diseases are actionable, i.e. they are preventable by early intervention. One to two years of early warning would represent a huge advance in dealing with these conditions and could help prevent further complications such as heart disease, stroke, kidney failure, blindness, and amputation. In this project, we are developing an extensible condition forecasting model to assess the risk of diabetes and heart problems in patients in advance. Using TensorFlow, Elastic MapReduce (EMR), and AWS Sagemaker, we are training a Wide and Deep Neural Network on a dataset of more than 170 million ...


Deep Learning, Medical Physics And Cargo Cult Science., Miguel Romero PhD, Gilmer Valdes PhD, Timothy Solberg PhD, Yannet Interian PhD 2019 University of San Francisco

Deep Learning, Medical Physics And Cargo Cult Science., Miguel Romero Phd, Gilmer Valdes Phd, Timothy Solberg Phd, Yannet Interian Phd

Creative Activity and Research Day - CARD

Deep learning algorithms have become widely popular, with considerable success in fields where datasets have hundreds of thousands or million points. As deep learning is increasingly applied to the fields of medical physics and radiation oncology, a reasonable question follows: are these techniques the best approach, given the unique conditions in our field? In this study, we investigate the dependence of dataset size on the performance of deep learning algorithms compared with more traditional radiomics-based methods.


Using Principle Component Analysis To Analyze Tertiary And Quaternary Spectral Mixtures, David Burnett 2019 Olivet Nazarene University

Using Principle Component Analysis To Analyze Tertiary And Quaternary Spectral Mixtures, David Burnett

Scholar Week 2016 - present

CRISM images from Mars are expected to contain carbonates such as magnesite. Prior research has been successfully able to determine the approximate percent composition of phyllosilicates in binary lab mixtures using Principle Component Analysis (PCA). In order to expand this model to work on CRISM images, one of preliminary steps is allowing the algorithm to work on mixtures with more than two components, which was the primary purpose of this research.


An Application Of Artificial General Intelligence In Board Games, Nathan Skalka 2019 University of Nebraska at Omaha

An Application Of Artificial General Intelligence In Board Games, Nathan Skalka

Computer Science Graduate Research Workshop

No abstract provided.


A Machine Learning Approach To Network Intrusion Detection System Using K Nearest Neighbor And Random Forest, Ilemona S. Atawodi 2019 University of Southern Mississippi

A Machine Learning Approach To Network Intrusion Detection System Using K Nearest Neighbor And Random Forest, Ilemona S. Atawodi

Master's Theses

The evolving area of cybersecurity presents a dynamic battlefield for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date.

A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented ...


Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam 2019 Purdue University

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of ...


Using Gleaned Computing Power To Forecast Emerging-Market Equity Returns With Machine Learning, Xida Ren 2019 William & Mary

Using Gleaned Computing Power To Forecast Emerging-Market Equity Returns With Machine Learning, Xida Ren

Undergraduate Honors Theses

This paper examines developing machine learning and statistic models to build forecast models for equity returns in an emergent market, with an emphasis on computing. Distributed systems were pared with random search and Bayesian optimization to find good hyperparameters for neural networks. No significant results were found.


Advances In Design Methodology In Swelling Shale Rock In Southern Ontario, Thomas R.A. Lardner 2019 The University of Western Ontario

Advances In Design Methodology In Swelling Shale Rock In Southern Ontario, Thomas R.A. Lardner

Electronic Thesis and Dissertation Repository

As infrastructure requirements increase in southern Ontario, excavations within swelling rock formations will become more frequent and larger. The objective of this study is to advance design capability for structures in swelling rock through three aspects: i) developing a practical swelling model for design engineers, ii) investigate two crushable/compressible materials for the mitigation of swelling rock effects, and iii) observe and analyze the behaviour of swelling rock to current excavation techniques.

A swelling rock constitutive model has been developed. The swelling parameters include the horizontal and vertical free swell potential, threshold stress, and critical stress as well as a ...


Recipe For Disaster, Zac Travis 2019 University of New Mexico

Recipe For Disaster, Zac Travis

MFA Thesis Exhibit Catalogs

Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.

The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as ...


Modern Yard Sale Application, Lauren Epling, Matthew Piasecki 2019 California Polytechnic State University, San Luis Obispo

Modern Yard Sale Application, Lauren Epling, Matthew Piasecki

Computer Science and Software Engineering

YardSail is a modern application that provides users a place to post and view local Yard Sales. There is an astounding need for a safe space where users can comfortably post their yard sale address and items for all locals to easily see (without needing to drive down a specific street to find out). Currently, there does not exist an application for users that accomplishes what we set out to accomplish. As a team, we truly believe YardSail could be a popular application that helps users sail through the experience of hosting or visiting a yard sale.


Chip-Off Success Rate Analysis Comparing Temperature And Chip Type, Choli Ence, Joan Runs Through, Gary D. Cantrell 2019 St George Police Department

Chip-Off Success Rate Analysis Comparing Temperature And Chip Type, Choli Ence, Joan Runs Through, Gary D. Cantrell

Journal of Digital Forensics, Security and Law

Throughout the digital forensic community, chip-off analysis provides examiners with a technique to obtain a physical acquisition from locked or damaged digital device. Thermal based chip-analysis relies upon the application of heat to remove the flash memory chip from the circuit board. Occasionally, a flash memory chip fails to successfully read despite following similar protocols as other flash memory chips. Previous research found the application of high temperatures increased the number of bit errors present in the flash memory chip. The purpose of this study is to analyze data collected from chip-off analyses to determine if a statistical difference exists ...


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