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A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela 2020 Fordham University

A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela

Faculty Publications

We propose a novel approach to the ’reality gap’ problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions ...


Cache-Enabled In Cooperative Cognitive Radio Networks For Transmission Performance, Jiachen Yang, Houbing Song, Chaofan Ma, Jiabao Man, Huifang Xu, Gan Zheng 2020 Tianjin University

Cache-Enabled In Cooperative Cognitive Radio Networks For Transmission Performance, Jiachen Yang, Houbing Song, Chaofan Ma, Jiabao Man, Huifang Xu, Gan Zheng

Publications

The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay ...


Geometric State Observers For Autonomous Navigation Systems, Miaomiao Wang 2020 The University of Western Ontario

Geometric State Observers For Autonomous Navigation Systems, Miaomiao Wang

Electronic Thesis and Dissertation Repository

The development of reliable state estimation algorithms for autonomous navigation systems is of great interest in the control and robotics communities. This thesis studies the state estimation problem for autonomous navigation systems. The first part of this thesis is devoted to the pose estimation on the Special Euclidean group $\SE(3)$. A generic globally exponentially stable hybrid estimation scheme for pose (orientation and position) and velocity-bias estimation on $\SE(3)\times \mathbb{R}^6$ is proposed. Moreover, an explicit hybrid observer, using inertial and landmark position measurements, is provided.

The second part of this thesis is devoted to the problem ...


A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir HaghighatiMaleki 2020 The University of Western Ontario

A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki

Electronic Thesis and Dissertation Repository

Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data ...


Retrospective Study Of Fmol Health System Utilization Using Geospatial Information, Deekshith Mandala 2020 Louisiana State University and Agricultural and Mechanical College

Retrospective Study Of Fmol Health System Utilization Using Geospatial Information, Deekshith Mandala

LSU Master's Theses

Medicaid Expansion and closing of Emergency Departments (ED) like Earl K. Long, Baton Rouge General Mid-City ED, and Champion Medical Center changed the health care landscape in East Baton Rouge Parish (EBRP). In this research study, a Geographical Information System (GIS) is used to analyze the impact of the expansion of Medicaid and the inauguration of Our Lady of the Lake North Baton Rouge ED (OLOL NBR ED) over the utilization of Franciscan Missionaries of our Lady Health System (FMOLHS) for both emergency and non-emergency health care services. This study is performed across the 58 neighborhoods of EBRP. Overutilization of ...


Privacy Concerns Regarding Wearable Iot Devices: How It Is Influenced By Gdpr?, Chinju Paul, Kevin P. Scheibe, Sree Nilakanta 2020 Iowa State University

Privacy Concerns Regarding Wearable Iot Devices: How It Is Influenced By Gdpr?, Chinju Paul, Kevin P. Scheibe, Sree Nilakanta

Supply Chain and Information Management Conference Papers, Posters and Proceedings

Internet of Things (IoT) devices have implications for health and fitness. Fitness wearables can promote healthy behavior and improve an individual’s overall health and quality of life. Even though fitness wearables have various benefits, privacy concerns regarding the data collected remain as a major barrier to adoption of fitness wearables. Intrinsic factors like disposition to value privacy and extrinsic factors like privacy policies and General Data Protection Regulation (GDPR) can influence users’ privacy concerns. This research uses experimental design to understand how these factors influence privacy concerns. The results suggest that GDPR reduces the average privacy concerns of users ...


Teaching Autonomous Systems At 1/10th-Scale, Abhijeet Agnihotri, Matthew O'Kelly, Rahul Mangharam, Houssam Abbas 2020 Oregon State University

Teaching Autonomous Systems At 1/10th-Scale, Abhijeet Agnihotri, Matthew O'Kelly, Rahul Mangharam, Houssam Abbas

Real-Time and Embedded Systems Lab (mLAB)

Teaching autonomous systems is challenging because it is a rapidly advancing cross-disciplinary field that requires theory to be continually validated on physical platforms. For an autonomous vehicle (AV) to operate correctly, it needs to satisfy safety and performance properties that depend on the operational context and interaction with environmental agents, which can be difficult to anticipate and capture. This paper describes a senior undergraduate level course on the design, programming and racing of 1/10th-scale autonomous race cars. We explore AV safety and performance concepts at the limits of perception, planning, and control, in a highly interactive and competitive environment ...


Design Of Subsurface Phased Array Antennas For Digital Agriculture Application, Abdul Salam 2020 Purdue University

Design Of Subsurface Phased Array Antennas For Digital Agriculture Application, Abdul Salam

Faculty Publications

With the advancement in subsurface communications technology, an overarching solution to a underground phased array antenna design for digital agriculture requires interdisciplinary research involving topics ranging from insights on the constitutive parameters of the soil medium and impact of soil moisture on the array factor to antenna measurements and subsurface communication system design. In this paper, based on the analysis of underground radio wave propagation in subsurface radio channel, a phased array antenna design is presented that uses water content information and beam steering mechanisms to improve efficiency and communication range of wireless underground communications. It is shown the subsurface ...


Physical Randomness Can Help In Computations, Olga Kosheleva, Vladik Kreinovich 2020 University of Texas at El Paso

Physical Randomness Can Help In Computations, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Can we use some so-far-unused physical phenomena to compute something that usual computers cannot? Researchers have been proposing many schemes that may lead to such computations. These schemes use different physical phenomena ranging from quantum-related to gravity-related to using hypothetical time machines. In this paper, we show that, in principle, there is no need to look into state-of-the-art physics to develop such a scheme: computability beyond the usual computations naturally appears if we consider such a basic notion as randomness.


An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram 2020 Technological University Dublin

An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram

Dissertations

In a world where anybody can share their views, opinions and make it sound like these are facts about the current situation of the world, Fake News poses a huge threat especially to the reputation of people with high stature and to organizations. In the political world, this could lead to opposition parties making use of this opportunity to gain popularity in their elections. In the medical world, a fake scandalous message about a medicine giving side effects, hospital treatment gone wrong or even a false message against a practicing doctor could become a big menace to everyone involved in ...


Classification Of Animal Sound Using Convolutional Neural Network, Neha Singh 2020 Technological University Dublin

Classification Of Animal Sound Using Convolutional Neural Network, Neha Singh

Dissertations

Recently, labeling of acoustic events has emerged as an active topic covering a wide range of applications. High-level semantic inference can be conducted based on main audioeffects to facilitate various content-based applications for analysis, efficient recovery and content management. This paper proposes a flexible Convolutional neural network-based framework for animal audio classification. The work takes inspiration from various deep neural network developed for multimedia classification recently. The model is driven by the ideology of identifying the animal sound in the audio file by forcing the network to pay attention to core audio effect present in the audio to generate Mel-spectrogram ...


Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone 2020 Technological University Dublin

Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone

Dissertations

The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO ...


A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas 2020 Technological University Dublin

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas

Dissertations

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study ...


An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson de Castro 2020 Technological University Dublin

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope ...


3d Convolutional Neural Networks For The Diagnosis Of 6 Unique Pathologies On Head Ct, Travis Clarke, Paras Lakhani, MD 2020 Thomas Jefferson University

3d Convolutional Neural Networks For The Diagnosis Of 6 Unique Pathologies On Head Ct, Travis Clarke, Paras Lakhani, Md

Phase 1

Introduction: Head CT scans are a standard first-line tool used by physicians in the diagnosis of neurological pathologies. Recently, the development of deep learning models such as convolutional neural networks (CNNs) has allowed the rapid identification of bleeds and other pathologies on CT scans. This study aims to show that by training 3D CNNs with a larger, curated dataset, a more comprehensive list of potential diagnoses can be included in the detailed model.

Methods: A retrospective study was performed using a dataset of 66,000 head CT studies from the Thomas Jefferson University health system. Studies were acquired using a ...


Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev 2020 Technological University Dublin

Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev

Dissertations

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of ...


Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy 2020 Technological University Dublin

Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy

Dissertations

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are ...


Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar 2020 Technological University Dublin

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially ...


Customer Churn Prediction, Deepshikha Wadikar 2020 Technological University Dublin

Customer Churn Prediction, Deepshikha Wadikar

Dissertations

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the ...


Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher 2020 Technological University Dublin

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female samples; Male ...


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