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Analysis Of Pro-Drop Errors In L2 English By L1 Spanish Speakers, Marcos R. Ynoa 2020 The Graduate Center, City University of New York

Analysis Of Pro-Drop Errors In L2 English By L1 Spanish Speakers, Marcos R. Ynoa

All Dissertations, Theses, and Capstone Projects

Although educators and linguists have described and studied L2 learner errors, often from the perspective of positive and negative transfer (Krashen, 1981), there is relatively little empirical data on the frequencies of L2 error types. Learner error rankings do exist (Donahue, 2001) (Cambridge learner corpus; (Nicholls, 1999)), but these rankings are often too general and may overlook specifics when it comes to particular language learner groups. The goal of our work is to develop a tool that can be used to explore error patterns, address educator needs, and help answer research questions in L2 language learning. The tool we have ...


Heart Valve Isogeometric Sequentially-Coupled Fsi Analysis With The Space–Time Topology Change Method, Takuya Terahara, Kenji Takizawa, Tayfun E. Tezduyar, Yuri Bazilevs, Ming-Chen Hsu 2020 Waseda University

Heart Valve Isogeometric Sequentially-Coupled Fsi Analysis With The Space–Time Topology Change Method, Takuya Terahara, Kenji Takizawa, Tayfun E. Tezduyar, Yuri Bazilevs, Ming-Chen Hsu

Mechanical Engineering Publications

Heart valve fluid–structure interaction (FSI) analysis is one of the computationally challenging cases in cardiovascular fluid mechanics. The challenges include unsteady flow through a complex geometry, solid surfaces with large motion, and contact between the valve leaflets. We introduce here an isogeometric sequentially-coupled FSI (SCFSI) method that can address the challenges with an outcome of high-fidelity flow solutions. The SCFSI analysis enables dealing with the fluid and structure parts individually at different steps of the solutions sequence, and also enables using different methods or different mesh resolution levels at different steps. In the isogeometric SCFSI analysis here, the first ...


Milkweed (Asclepias Syriaca) Plant Detection Using Mobile Cameras, Koray Ozcan, Anuj Sharma, Steven P. Bradbury, Dana Schweitzer, Teresa Blader, Sue Blodgett 2020 Iowa State University

Milkweed (Asclepias Syriaca) Plant Detection Using Mobile Cameras, Koray Ozcan, Anuj Sharma, Steven P. Bradbury, Dana Schweitzer, Teresa Blader, Sue Blodgett

Natural Resource Ecology and Management Publications

Milkweed (Asclepias spp.) are host plants of monarch butterflies (Danaus plexippus). It is important to detect milkweed plant locations to assess the status and trends of monarch habitat in support of monarch conservation programs. In this paper, we describe autonomous detection of milkweed plants using cameras mounted to vehicles. For detection, we used both aggregated channel features (ACF) for running the detectors on embedded computing platforms with central processing unit and faster region‐based convolutional neural network (Faster R‐CNN) with a ResNet architecture‐based detector that is suitable for graphics processing unit optimized processing. The ACF‐based model produced ...


Attitudes Towards Teaching Computational Thinking And Computer Science: Insights From Educator Interviews And Focus Groups, Jorge Valenzuela 2019 Old Dominion University

Attitudes Towards Teaching Computational Thinking And Computer Science: Insights From Educator Interviews And Focus Groups, Jorge Valenzuela

Journal of Computer Science Integration

In the last three years, integration of both computational thinking (CT) and computer science (CS) into K-12 instruction has become a focus of many schools throughout the Commonwealth of Virginia and the United States. With this new widespread demand, educational leaders and educators are focusing efforts on understanding the core concepts and practices of CT and CS, looking for logical connections for integrating across curriculum, and seeking strategies for implementing a wide variety of educational technology tools (apps and devices). This phenomenological research study was designed to gather depth information from 14 K-16 educators through both semi-structured interviews and two ...


Numerical And Semi-Analytical Estimation Of Convective Heat Transfer Coefficient For Buildings In An Urban-Like Setting, Anwar Demsis Awol 2019 The University of Western Ontario

Numerical And Semi-Analytical Estimation Of Convective Heat Transfer Coefficient For Buildings In An Urban-Like Setting, Anwar Demsis Awol

Electronic Thesis and Dissertation Repository

Urban building arrangements such as packing density, orientation and size are known to influence the microclimate surrounding each building. Studies on the impact of urban microclimatic changes on convective heat transfer coefficient (CHTC) from a stock of buildings, however, have been rare in surveyed literature. The present study focuses on numerical and analytical investigation of CHTC from building-like models with homogeneous set of equal and unequal planar and frontal densities. Consequently, the study discusses the CHTC response in relation to broader changes in the urban surface form. Part of the process involves the development of a simplified one-dimensional semi-analytical CHTC ...


A Cfd Study On The Performance Of High Speed Planing Hulls, Mowgli J. Crosby 2019 Grand Valley State University

A Cfd Study On The Performance Of High Speed Planing Hulls, Mowgli J. Crosby

Masters Theses

Most high speed water craft are able to achieve high speeds through the use of a planing hull. Planing hulls use hydrodynamic forces to lift a portion of the vessel out of the water, reducing drag, and allowing for greater speeds. Determining the flow around such vessels is traditionally achieved using a scale model in a tow tank. The purpose of this study was to analyze the performance of a high speed planing hull determine the effects of several geometric features using computational fluid dynamics rather than traditional experimentation. The goal was to determine the best configuration to ensure the ...


Pneumothorax Radiograph Diagnosis Utilizing Deep Convolutional Neural Network, Ziqi Wang 2019 Georgia Southern University

Pneumothorax Radiograph Diagnosis Utilizing Deep Convolutional Neural Network, Ziqi Wang

Georgia Undergraduate Research Conference (GURC)

Pneumothorax is a life-threatening respiratory disease caused by physical trauma to the chest or as a complication of medical or surgical intervention (Zarogoulidis, 2014). Despite many available methods, a chest radiograph remains a primary method used for diagnosis. Although this technique is commonly used, it is challenging to diagnose based upon chest radiographs. Highly trained specialists are needed to review the chest radiographs which tends to create a large amount of additional work. Hence, in this study, our goal is to develop an algorithm using Deep Convolutional Neural Networks (DCNNs) to detect visual signs for pneumothorax in medical images and ...


Cyber Metaphors And Cyber Goals: Lessons From “Flatland”, Pierre Trepagnier 2019 Cyber Systems and Operations MIT Lincoln Laboratory Lexington, Massachusetts 02420

Cyber Metaphors And Cyber Goals: Lessons From “Flatland”, Pierre Trepagnier

Military Cyber Affairs

Reasoning about complex and abstract ideas is greatly influenced by the choice of metaphors through which they are represented. In this paper we consider the framing effect in military doctrine of considering cyberspace as a domain of action, parallel to the traditional domains of land, sea, air, and space. By means of the well-known Victorian science-fiction novella Flatland, we offer a critique of this dominant cyber metaphor. In Flatland, the problems of lower-dimensional beings comprehending additional dimensions are explored at some length. Inspired by Flatland, our suggested alternate metaphor for cyber is an additional (fourth) dimension. We then propose three ...


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

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 2019 Technological University Dublin

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 2019 Southern Methodist University

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 2019 Boise State University

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 2019 The University of Western Ontario

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 ...


An Efficient Framework For The Stochastic Verification Of Computation And Communication Systems Using Emerging Technologies, Zhen Zhang 2019 Utah State University

An Efficient Framework For The Stochastic Verification Of Computation And Communication Systems Using Emerging Technologies, Zhen Zhang

Funded Research Records

No abstract provided.


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.


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

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 ...


Cloneless: Code Clone Detection Via Program Dependence Graphs With Relaxed Constraints, Thomas J. Simko 2019 California Polytechnic State University, San Luis Obispo

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 ...


Fifth Aeon – A.I Competition And Balancer, William M. Ritson 2019 California Polytechnic State University, San Luis Obispo

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 ...


A Ulysses Pact With Artificial Systems. How To Deliberately Change The Objective Spirit With Cultured Ai, Bruno Gransche 2019 University of Siegen, Germany

A Ulysses Pact With Artificial Systems. How To Deliberately Change The Objective Spirit With Cultured Ai, Bruno Gransche

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The article introduces a concept of cultured technology, i.e. intelligent systems capable of interacting with humans and showing (or simulating) manners, of following customs and of socio-sensitive considerations. Such technologies might, when deployed on a large scale, influence and change the realm of human customs, traditions, standards of acceptable behavior, etc. This realm is known as the "objective spirit" (Hegel), which usually is thought of as being historically changing but not subject to deliberate human design. The article investigates the question of whether the purposeful design of interactive technologies (as cultured technologies) could enable us to shape modes of ...


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 ...


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