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Full-Text Articles in Computational Engineering

Download Bmp Trains 2020 Newest Version, Martin Wanielista Oct 2019

Download Bmp Trains 2020 Newest Version, Martin Wanielista

BMP Trains

This is version 2.0.2. 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.

This version increases the reporting decimal places for TP removed in the Summary and Complete Reports A well as changes from fraction to % in exfiltration worksheet..

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 ...


Size Matters: The Impact Of Training Size In Taxonomically-Enriched Word Embeddings, Alfredo Maldonado, Filip Klubicka, John D. Kelleher Oct 2019

Size Matters: The Impact Of Training Size In Taxonomically-Enriched Word Embeddings, Alfredo Maldonado, Filip Klubicka, John D. Kelleher

Articles

Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching ...


Modelling The Addition Of Limestone In Cement Using Hydcem, Niall Holmes, Denis Kelliher, Mark Tyrer Sep 2019

Modelling The Addition Of Limestone In Cement Using Hydcem, Niall Holmes, Denis Kelliher, Mark Tyrer

Conference papers

Hydration models can aid in the prediction, understanding and description of hydration behaviour over time as the move towards more sustainable cements continues.

HYDCEM is a new model to predict the phase assemblage, degree of hydration and heat release over time for cements undergoing hydration for any w/c ratio and curing temperatures up to 450C. HYDCEM, written in MATLAB, complements more sophisticated thermodynamic models by predicting these properties over time using user-friendly inputs within one code. A number of functions and methods based on up to date cement hydration behaviour from the literature are hard-wired into the code along ...


Improve Image Classification Using Data Augmentation And Neural Networks, Shanqing Gu, Manisha Pednekar, Robert Slater Aug 2019

Improve Image Classification Using Data Augmentation And Neural Networks, Shanqing Gu, Manisha Pednekar, Robert Slater

SMU Data Science Review

In this paper, we present how to improve image classification by using data augmentation and convolutional neural networks. Model overfitting and poor performance are common problems in applying neural network techniques. Approaches to bring intra-class differences down and retain sensitivity to the inter-class variations are important to maximize model accuracy and minimize the loss function. With CIFAR-10 public image dataset, the effects of model overfitting were monitored within different model architectures in combination of data augmentation and hyper-parameter tuning. The model performance was evaluated with train and test accuracy and loss, characteristics derived from the confusion matrices, and visualizations of ...


Development Of A Statistical Shape-Function Model Of The Implanted Knee For Real-Time Prediction Of Joint Mechanics, Kalin Gibbons Aug 2019

Development Of A Statistical Shape-Function Model Of The Implanted Knee For Real-Time Prediction Of Joint Mechanics, Kalin Gibbons

Boise State University Theses and Dissertations

Outcomes of total knee arthroplasty (TKA) are dependent on surgical technique, patient variability, and implant design. Non-optimal design or alignment choices may result in undesirable contact mechanics and joint kinematics, including poor joint alignment, instability, and reduced range of motion. Implant design and surgical alignment are modifiable factors with potential to improve patient outcomes, and there is a need for robust implant designs that can accommodate patient variability. Our objective was to develop a statistical shape-function model (SFM) of a posterior stabilized implant knee to instantaneously predict output mechanics in an efficient manner. Finite element methods were combined with Latin ...


Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez Jul 2019

Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez

Electronic Thesis and Dissertation Repository

Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make ...


The Perils And Promises Of Artificial General Intelligence, Brian S. Haney Jun 2019

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

Journal of Legislation

No abstract provided.


Machine Learning With Multi-Class Regression And Neural Networks: Analysis And Visualization Of Crime Data In Seattle, Erkin David George Jun 2019

Machine Learning With Multi-Class Regression And Neural Networks: Analysis And Visualization Of Crime Data In Seattle, Erkin David George

Honors Projects

This article examines the implications of machine learning algorithms and models, and the significance of their construction when investigating criminal data. It uses machine learning models and tools to store, clean and analyze data that is fed into a machine learning model. This model is then compared to another model to test for accuracy, biases and patterns that are detected in between the experiments. The data was collected from data.seattle.gov and was published by the City of Seattle Data Portal and was accessed on September 17, 2018. This research will be looking into how machine learning models can ...


Fifth Aeon - A.I Competition And Balancer, William M. Ritson Jun 2019

Fifth Aeon - A.I Competition And Balancer, William M. Ritson

Master's Theses and Project Reports

Collectible Card Games (CCG) are one of the most popular types of games in both digital and physical space. Despite their popularity, there is a great deal of room for exploration into the application of artificial intelligence in order to enhance CCG gameplay and development. This paper presents Fifth Aeon a novel and open source CCG built to run in browsers and two A.I applications built upon Fifth Aeon. The first application is an artificial intelligence competition run on the Fifth Aeon game. The second is an automatic balancing system capable of helping a designer create new cards that ...


Cloneless: Code Clone Detection Via Program Dependence Graphs With Relaxed Constraints, Thomas J. Simko Jun 2019

Cloneless: Code Clone Detection Via Program Dependence Graphs With Relaxed Constraints, Thomas J. Simko

Master's Theses and Project Reports

Code clones are pieces of code that have the same functionality. While some clones may structurally match one another, others may look drastically different. The inclusion of code clones clutters a code base, leading to increased costs through maintenance. Duplicate code is introduced through a variety of means, such as copy-pasting, code generated by tools, or developers unintentionally writing similar pieces of code. While manual clone identification may be more accurate than automated detection, it is infeasible due to the extensive size of many code bases. Software code clone detection methods have differing degree of success based on the analysis ...


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

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 ...


Analytical And Numerical Modeling Of Cavity Expansion In Anisotropic Poroelastoplastic Soil, Kai Liu May 2019

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 ...


University Of Rhode Island Course Information Assistant, Daniel Gauthier May 2019

University Of Rhode Island Course Information Assistant, Daniel Gauthier

Senior Honors Projects

Personal voice-interactive systems have become ubiquitous in daily life. There are many of these digital assistants such as Siri, Alexa, and Google Assistant. The chances are high you have access to one right now. This technology has reached a point where the context of a conversation can be maintained, which is a vast improvement over earlier technology. Interactions without conversational context can limit interactions greatly and this was the case for previous digital assistants. Every time someone would say something to an assistant, it was like they were constantly changing operators on a customer service line. The assistants can now ...


Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene May 2019

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 May 2019

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 ...


Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde May 2019

Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde

Electronic Theses and Dissertations

In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and ...


Using Transfer Learning In Network Markets, Kai Cai May 2019

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 ...


Forecasting Model For Disease Propensity Using Ehr Data, Soodabeh Sarafrazi, Omar Sharif, Matthew Domingo, Jie Han, Michael Chang, Omid Khazaie, Anil Kemisetti Apr 2019

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 Apr 2019

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.


Multi-Objective Bayesian Optimization Of Super Hydrophobic Coatings On Asphalt Concrete Surfaces, Ali Nahvi, Mohammad Kazem Sadoughi, Ali Arabzadeh, Alireza Sassani, Chao Hu, Halil Ceylan, Sunghwan Kim Apr 2019

Multi-Objective Bayesian Optimization Of Super Hydrophobic Coatings On Asphalt Concrete Surfaces, Ali Nahvi, Mohammad Kazem Sadoughi, Ali Arabzadeh, Alireza Sassani, Chao Hu, Halil Ceylan, Sunghwan Kim

Ali Nahvi

Conventional snow removal strategies add direct and indirect expenses to the economy through profit lost due to passenger delays costs, pavement durability issues, contaminating the water runoff, and so on. The use of superhydrophobic (super-water-repellent) coating methods is an alternative to conventional snow and ice removal practices for alleviating snow removal operations issues. As an integrated experimental and analytical study, this work focused on optimizing superhydrophobicity and skid resistance of hydrophobic coatings on asphalt concrete surfaces. A layer-by-layer (LBL) method was utilized for spray depositing polytetrafluoroethylene (PTFE) on an asphalt concrete at different spray times and variable dosages of PTFE ...


Using Principle Component Analysis To Analyze Tertiary And Quaternary Spectral Mixtures, David Burnett Apr 2019

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 Apr 2019

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 Apr 2019

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 Apr 2019

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 Apr 2019

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 Mar 2019

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 Mar 2019

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 ...


Ordinary Differential Equation Neuralnetworks: Mathematics And Application Using Diffeqflux.Jl, Muhammad Moiz Saeed Mar 2019

Ordinary Differential Equation Neuralnetworks: Mathematics And Application Using Diffeqflux.Jl, Muhammad Moiz Saeed

Senior Capstone Theses

This paper has two objectives. 1. It simplifies the Mathematics behind a simple Neural Network. Furthermore, it explores how Neural Networks can be modeled using Ordinary Differential Equations(ODE). 2. It implements a simple example of an ODE Neural network using diffeqflux.jl library. My paper is based on the paper "Neural Ordinary Differential equations"[1] paper and contains multiple extracts from this paper and hence the work in chapter 4 should not be considered original work as it aims to explain the mathematics in the original paper and all credit is due to the authors of the paper [1 ...


Modern Yard Sale Application, Lauren Epling, Matthew Piasecki Mar 2019

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 Feb 2019

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 ...