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An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification., Khalil Damak May 2019

An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification., Khalil Damak

Electronic Theses and Dissertations

Streaming applications are now the predominant tools for listening to music. What makes the success of such software is the availability of songs and especially their ability to provide users with relevant personalized recommendations. State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction (listening to a song) using a memory-based deep learning structure that learns from temporal sequences of user actions. Despite advances in deep learning models for song recommendation systems, none has taken ...


A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab Dec 2018

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order ...


Network Science Algorithms For Mobile Networks., Heba Mohamed Elgazzar May 2018

Network Science Algorithms For Mobile Networks., Heba Mohamed Elgazzar

Electronic Theses and Dissertations

Network Science is one of the important and emerging fields in computer science and engineering that focuses on the study and analysis of different types of networks. The goal of this dissertation is to design and develop network science algorithms that can be used to study and analyze mobile networks. This can provide essential information and knowledge that can help mobile networks service providers to enhance the quality of the mobile services. We focus in this dissertation on the design and analysis of different network science techniques that can be used to analyze the dynamics of mobile networks. These techniques ...


Developing An Affect-Aware Rear-Projected Robotic Agent, Ali Mollahosseini Jan 2018

Developing An Affect-Aware Rear-Projected Robotic Agent, Ali Mollahosseini

Electronic Theses and Dissertations

Social (or Sociable) robots are designed to interact with people in a natural and interpersonal manner. They are becoming an integrated part of our daily lives and have achieved positive outcomes in several applications such as education, health care, quality of life, entertainment, etc. Despite significant progress towards the development of realistic social robotic agents, a number of problems remain to be solved. First, current social robots either lack enough ability to have deep social interaction with human, or they are very expensive to build and maintain. Second, current social robots have yet to reach the full emotional and social ...


Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi Jan 2018

Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi

Electronic Theses and Dissertations

The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage ...


Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles, Appala Narasimha Raju Chekuri Jan 2018

Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles, Appala Narasimha Raju Chekuri

Electronic Theses and Dissertations

Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly ...


A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki Dec 2017

A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki

Electronic Theses and Dissertations

Recent advances and widespread usage of online web services and social media platforms, coupled with ubiquitous low cost devices, mobile technologies, and increasing capacity of lower cost storage, has led to a proliferation of Big data, ranging from, news, e-commerce clickstreams, and online business transactions to continuous event logs and social media expressions. These large amounts of online data, often referred to as data streams, because they get generated at extremely high throughputs or velocity, can make conventional and classical data analytics methodologies obsolete. For these reasons, the issues of management and analysis of data streams have been researched extensively ...


Integrated Environment And Proximity Sensing For Uav Applications, Shawn S. Brackett Aug 2017

Integrated Environment And Proximity Sensing For Uav Applications, Shawn S. Brackett

Electronic Theses and Dissertations

As Unmanned Aerial Vehicle (UAV), or “drone” applications expand, new methods for sensing, navigating and avoiding obstacles need to be developed. The project applies an Extended Kalman Filter (EKF) to a simulated quadcopter vehicle though Matlab in order to estimate not only the vehicle state but the world state around the vehicle. The EKF integrates multiple sensor readings from range sensors, IMU sensors, and radiation sensors and combines this information to optimize state estimates. The result is an estimated world map to be used in vehicle navigation and obstacle avoidance.

The simulation handles the physics behind the vehicle flight. As ...


Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi Aug 2017

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race ...


A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener Aug 2017

A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener

Electronic Theses and Dissertations

This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment ...


Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani May 2017

Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani

Electronic Theses and Dissertations

This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.


Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami May 2017

Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami

Electronic Theses and Dissertations

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate ...


Optimized Multilayer Perceptron With Dynamic Learning Rate To Classify Breast Microwave Tomography Image, Chulwoo Pack Jan 2017

Optimized Multilayer Perceptron With Dynamic Learning Rate To Classify Breast Microwave Tomography Image, Chulwoo Pack

Electronic Theses and Dissertations

Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show promising performance using new format of data. The platform can have a flexibility on its input by adopting Artificial Neural Network (ANN) as a classifier. Among the various phases of CAD system, we have focused on optimizing the classification phase ...


A Reduced Labeled Samples (Rls) Framework For Classification Of Imbalanced Concept-Drifting Streaming Data., Elaheh Arabmakki Dec 2016

A Reduced Labeled Samples (Rls) Framework For Classification Of Imbalanced Concept-Drifting Streaming Data., Elaheh Arabmakki

Electronic Theses and Dissertations

Stream processing frameworks are designed to process the streaming data that arrives in time. An example of such data is stream of emails that a user receives every day. Most of the real world data streams are also imbalanced as is in the stream of emails, which contains few spam emails compared to a lot of legitimate emails. The classification of the imbalanced data stream is challenging due to the several reasons: First of all, data streams are huge and they can not be stored in the memory for one time processing. Second, if the data is imbalanced, the accuracy ...


Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo Jan 2016

Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo

Electronic Theses and Dissertations

Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing potential errors that can be made by fatigued or inexperienced physicians. This paper proposes a new shape metric based on the area ratio of a circle to classify mammographic images into benign and malignant class. Support Vector Machine is used as a machine learning tool for training and classification purposes. The improved ...


Product Authentication Using Hash Chains And Printed Qr Codes, Harshith R. Keni Jan 2016

Product Authentication Using Hash Chains And Printed Qr Codes, Harshith R. Keni

Electronic Theses and Dissertations

This thesis explores the usage of simple printed tags for authenticating products. Printed tags are a cheap alternative to RFID and other tag based systems and do not require specialized equipment. Due to the simplistic nature of such printed codes, many security issues like tag impersonation, server impersonation, reader impersonation, replay attacks and denial of service present in RFID based solutions need to be handled differently. An algorithm that utilizes hash chains to secure such simple tags while still keeping cost low is discussed. The security characteristics of this scheme as well as other product authentication schemes that use RFID ...


Fail-Safe Test Generation Of Safety Critical Systems, Salwa Elakeili Mar 2015

Fail-Safe Test Generation Of Safety Critical Systems, Salwa Elakeili

Electronic Theses and Dissertations

This dissertation introduces a technique for testing proper failure mitigation in safety critical systems. Unlike other approaches which integrate behavioral and failure models, and then generate tests from the integrated model, we build safety mitigation tests from an existing behavioral test suite, using an explicit mitigation model for which we generate mitigation paths which are then woven at selected failure points into the original test suite to create failure-mitigation tests (safety mitigation test).


Testing Data Vault-Based Data Warehouse, Connard N. Williams Jan 2015

Testing Data Vault-Based Data Warehouse, Connard N. Williams

Electronic Theses and Dissertations

Data warehouse (DW) projects are undertakings that require integration of disparate sources of data, a well-defined mapping of the source data to the reconciled data, and effective Extract, Transform, and Load (ETL) processes. Owing to the complexity of data warehouse projects, great emphasis must be placed on an agile-based approach with properly developed and executed test plans throughout the various stages of designing, developing, and implementing the data warehouse to mitigate against budget overruns, missed deadlines, low customer satisfaction, and outright project failures. Yet, there are often attempts to test the data warehouse exactly like traditional back-end databases and legacy ...


Human Action Recognition Via Fused Kinematic Structure And Surface Representation, Salah R. Althloothi Aug 2013

Human Action Recognition Via Fused Kinematic Structure And Surface Representation, Salah R. Althloothi

Electronic Theses and Dissertations

Human action recognition from visual data has remained a challenging problem in the field of computer vision and pattern recognition. This dissertation introduces a new methodology for human action recognition using motion features extracted from kinematic structure, and shape features extracted from surface representation of human body. Motion features are used to provide sufficient information about human movement, whereas shape features are used to describe the structure of silhouette. These features are fused at the kernel level using Multikernel Learning (MKL) technique to enhance the overall performance of human action recognition. In fact, there are advantages in using multiple types ...


Improving The Performance Of The Parallel Ice Sheet Model On A Large-Scale, Distributed Supercomputer, Timothy J. Morey May 2013

Improving The Performance Of The Parallel Ice Sheet Model On A Large-Scale, Distributed Supercomputer, Timothy J. Morey

Electronic Theses and Dissertations

In this thesis, we describe our work to understand and improve the performance and scalability of the Parallel Ice Sheet Model (PISM) on the Ranger supercomputer. PISM enables the simulation of large-scale ice sheets, such as those found in Greenland and Antarctica, which are of particular interest to climate scientists due to their potential to contribute to sea-level rise.

PISM has a unique parallel architecture that is designed to take advantage of the computational resources available on state-of-the-art supercomputers. The problem, however, is that even though PISM can run without modifcation on a supercomputer, it is generally unable to do ...


Reducing Communication Delay Variability For A Group Of Robots, Goncalo Martins Jan 2013

Reducing Communication Delay Variability For A Group Of Robots, Goncalo Martins

Electronic Theses and Dissertations

A novel architecture is presented for reducing communication delay variability for a group of robots. This architecture relies on using three components: a microprocessor architecture that allows deterministic real-time tasks; an event-based communication protocol in which nodes transmit in a TDMA fashion, without the need of global clock synchronization techniques; and a novel communication scheme that enables deterministic communications by allowing senders to transmit without regard for the state of the medium or coordination with other senders, and receivers can tease apart messages sent simultaneously with a high probability of success. This approach compared to others, allows simultaneous communications without ...


Accelerated Data Delivery Architecture, Michael L. Grecol Jan 2013

Accelerated Data Delivery Architecture, Michael L. Grecol

Electronic Theses and Dissertations

This paper introduces the Accelerated Data Delivery Architecture (ADDA). ADDA establishes a framework to distribute transactional data and control consistency to achieve fast access to data, distributed scalability and non-blocking concurrency control by using a clean declarative interface. It is designed to be used with web-based business applications. This framework uses a combination of traditional Relational Database Management System (RDBMS) combined with a distributed Not Only SQL (NoSQL) database and a browser-based database. It uses a single physical and conceptual database schema designed for a standard RDBMS driven application. The design allows the architect to assign consistency levels to entities ...


Simulation, Application, And Resilience Of An Organic Neuromorphic Architecture, Made With Organic Bistable Devices And Organic Field Effect Transistors, Robert A. Nawrocki Jan 2011

Simulation, Application, And Resilience Of An Organic Neuromorphic Architecture, Made With Organic Bistable Devices And Organic Field Effect Transistors, Robert A. Nawrocki

Electronic Theses and Dissertations

This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at ...


Preprocessing Of Microcirculatory Images., Mohamed S. Mansour Aug 1995

Preprocessing Of Microcirculatory Images., Mohamed S. Mansour

Electronic Theses and Dissertations

This thesis project consists of the development of a computerized image processing system to preprocess microvascular images of the mouse Latisimus Dorsi Muscle (LDM). This research has been conducted in association with the Division of Plastic and Reconstructive Surgery at the University of Louisville. The input to the system is a set of 35 or more overlapping microscopic fields or sub_images containing segments of the LDM microcirculation, the output is an adjacent single montage encompassing the entire LDM microvasculature. The developed system presents practical solutions to the problems of image registration, overlap resolution, and image segmentation, in addition, the results ...