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Passive Quadrupedal Gait Synchronization For Extra Robotic Legs Using A Dynamically Coupled Double Rimless Wheel Model, Daniel J. Gonzalez, Harry Asada 2020 USMA West Point & MIT

Passive Quadrupedal Gait Synchronization For Extra Robotic Legs Using A Dynamically Coupled Double Rimless Wheel Model, Daniel J. Gonzalez, Harry Asada

West Point Research Papers

The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human's limbs to coordinate quadrupedal locomotion. Connecting the XRL to the human using a passive coupler, we have found that the two bipeds converge ...


A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik 2020 Augsburg College

A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik

Augsburg Honors Review

The intent of this work is to explore the interactions of artificial neural networks and digital games. It details the development of an artificial neural network trained upon a brick breaking game like the Atari game Breakout. This network was designed with the goals of not dropping the ball and maximizing the game score. Full game and network integration was not completed. However, two versions of the network were developed to move the paddle to the right or left based on the ball's point of impact on the paddle. In preliminary testing using manual inputs, these networks eventually learned ...


Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar 2020 Creighton University

Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar

SDSU Data Science Symposium

Data obtained from social media microblogging websites such as Twitter provide the unique ability to collect and analyze conversations of the public in order to gain perspective on the thoughts and feelings of the general public. Sentiment and volume analysis techniques were applied to the dataset in order to gain an understanding of the amount and level of sentiment associated with certain disaster-related tweets, including a topical analysis of specific terms. This study showed that disaster-type events such as a hurricane can cause some strong negative sentiment in the period of time directly preceding the event, but ultimately returns quickly ...


Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynalı 2020 Technological University Dublin

Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynalı

Dissertations

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an ...


Transactional Scripts In Contract Stacks, Shaanan Cohney, David A. Hoffman 2020 University of Pennsylvania

Transactional Scripts In Contract Stacks, Shaanan Cohney, David A. Hoffman

Faculty Scholarship at Penn Law

Deals accomplished through software persistently residing on computer networks—sometimes called smart contracts, but better termed transactional scripts—embody a potentially revolutionary contracting innovation. Ours is the first precise account in the legal literature of how such scripts are created, and when they produce errors of legal significance.

Scripts’ most celebrated use case is for transactions operating exclusively on public, permissionless, blockchains: such exchanges eliminate the need for trusted intermediaries and seem to permit parties to commit ex ante to automated performance. But public transactional scripts are costly both to develop and execute, with significant fees imposed for data storage ...


Acquisition Of Inflectional Morphology In Artificial Neural Networks With Prior Knowledge, Katharina Kann 2020 New York University

Acquisition Of Inflectional Morphology In Artificial Neural Networks With Prior Knowledge, Katharina Kann

Proceedings of the Society for Computation in Linguistics

How does knowledge of one language’s morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to-sequence architecture, which we train on different combinations of eight source and three target languages. A detailed analysis of the model outputs suggests the following conclusions: (i) if source and target language are closely related, acquisition of the target language’s inflectional morphology constitutes an easier task for the model; (ii) knowledge of a prefixing (resp. suffixing) language makes acquisition of a suffixing (resp. prefixing) language’s ...


The Role Of Linguistic Features In Domain Adaptation: Tag Parsing Of Questions, Aarohi Srivastava, Robert Frank, Sarah Widder, David Chartash 2020 Yale University

The Role Of Linguistic Features In Domain Adaptation: Tag Parsing Of Questions, Aarohi Srivastava, Robert Frank, Sarah Widder, David Chartash

Proceedings of the Society for Computation in Linguistics

The analysis of sentences outside the domain of the training data poses a challenge for contemporary syntactic parsing. The Penn Treebank corpus, commonly used for training constituency parsers, systematically undersamples certain syntactic structures. We examine parsing performance in Tree Adjoining Grammar (TAG) on one such structure: questions. To avoid hand-annotating a new training set including out-of-domain sentences, an expensive process, an alternate method requiring considerably less annotation effort is explored. Our method is based on three key ideas: First, pursuing the intuition that “supertagging is almost parsing” (Bangalore and Joshi, 1999), the parsing process is decomposed into two distinct stages ...


Towards A Framework For Certification Of Reliable Autonomous Systems, Michael Fisher, Viviana Mascardi, Kristin Yvonne Rozier, Bernd-Holger Schlingloff, Michael Winikoff, Neil Yorke-Smith 2020 University of Liverpool

Towards A Framework For Certification Of Reliable Autonomous Systems, Michael Fisher, Viviana Mascardi, Kristin Yvonne Rozier, Bernd-Holger Schlingloff, Michael Winikoff, Neil Yorke-Smith

Aerospace Engineering Publications

A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification ...


Machine Learning Prediction Of Glioblastoma Patient One-Year Survival, Andrew Du '20, Warren McGee, Jane Y. Wu 2020 Illinois Mathematics and Science Academy

Machine Learning Prediction Of Glioblastoma Patient One-Year Survival, Andrew Du '20, Warren Mcgee, Jane Y. Wu

Student Publications & Research

Glioblastoma (GBM) is a grade IV astrocytoma formed primarily from cancerous astrocytes and sustained by intense angiogenesis. GBM often causes non-specific symptoms, creating difficulty for diagnosis. This study aimed to utilize machine learning techniques to provide an accurate one-year survival prognosis for GBM patients using clinical and genomic data from the Chinese Glioma Genome Atlas. Logistic regression (LR), support vector machines (SVM), random forest (RF), and ensemble models were used to identify and select predictors for GBM survival and to classify patients into those with an overall survival (OS) of less than one year and one year or greater. With ...


Early Detection Of Fake News On Social Media, Yang Liu 2019 New Jersey Institute of Technology

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those ...


Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger 2019 Western University

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial ...


Stochastic Orthogonalization And Its Application To Machine Learning, yu hong 2019 Southern Methodist University

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.


Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia 2019 University of New Orleans

Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia

University of New Orleans Theses and Dissertations

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By ...


A Qualitative Representation Of Spatial Scenes In R2 With Regions And Lines, Joshua Lewis 2019 University of Maine

A Qualitative Representation Of Spatial Scenes In R2 With Regions And Lines, Joshua Lewis

Electronic Theses and Dissertations

Regions and lines are common geographic abstractions for geographic objects. Collections of regions, lines, and other representations of spatial objects form a spatial scene, along with their relations. For instance, the states of Maine and New Hampshire can be represented by a pair of regions and related based on their topological properties. These two states are adjacent (i.e., they meet along their shared boundary), whereas Maine and Florida are not adjacent (i.e., they are disjoint).

A detailed model for qualitatively describing spatial scenes should capture the essential properties of a configuration such that a description of the represented ...


Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur 2019 San Jose State University

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG ...


Ordinal Hyperplane Loss, Bob Vanderheyden 2019 Kennesaw State University

Ordinal Hyperplane Loss, Bob Vanderheyden

Analytics and Data Science Dissertations

This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize ...


Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg 2019 San Jose State University

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed ...


Graph Deep Learning: Methods And Applications, Muhan Zhang 2019 Washington University in St. Louis

Graph Deep Learning: Methods And Applications, Muhan Zhang

Engineering and Applied Science Theses & Dissertations

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning ...


Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi 2019 San Jose State University

Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi

Master's Projects

This study observes how an evidential reasoning approach can be used as a diagnostic tool for early detection of pancreatic cancer. The evidential reasoning model combines the output of a linear Support Vector Classifier (SVC) with factors such as smoking history, health history, biopsy location, NGS technology used, and more to predict the likelihood of the disease. The SVC was trained using genomic data of pancreatic cancer patients derived from the National Cancer Institute (NIH) Genomic Data Commons (GDC). To test the evidential reasoning model, a variety of synthetic data was compiled to test the impact of combinations of different ...


Image-Based Localization Of User-Interfaces, Riti Gupta 2019 San Jose Statte Universitty

Image-Based Localization Of User-Interfaces, Riti Gupta

Master's Projects

Image localization corresponds to translating the text present in the images from one language to other language. The aim of the project is to develop a methodology to translate the text in image captions from English to Hindi by taking context of the images into account. A lot of work has been done in this field [22], but our aim was to explore if the accuracy can be further improved by consideration of the additional information imparted by the images apart from the text. We have explored Deep Learning using neural networks for this project. In particular, Recurrent Neural Networks ...


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