Theory and Algorithms Commons^™

A Machine Learning Recommender Model For Ride Sharing Based On Rider Characteristics And User Threshold Time, Govind Pramod Yatnalkar

Theses, Dissertations and Capstones

In the present age, human life is prospering incredibly due to the 4^th Industrial Revolution or The Age of Digitization and Computing. The ubiquitous availability of the Internet and advanced computing systems have resulted in the rapid development of smart cities. From connected devices to live vehicle tracking, technology is taking the field of transportation to a new level. An essential part of the transportation domain in smart cities is Ride Sharing. It is an excellent solution to issues like pollution, traffic, and the rapid consumption of fuel. Even though Ride Sharing has several benefits, the current usage is …

Radically Simplifying Gated Recurrent Architectures Without Loss Of Performance, Jonathan Boardman, Ying Xie

Published and Grey Literature from PhD Candidates

Long Short-Term Memory (LSTM) units are a family of Recurrent Neural Network (RNN) architectures that have proven incredibly effective at learning from sequence data. They are also extremely complex, making them expensive to train and difficult to understand. A recent trend towards simplification has produced the Gated Recurrent Unit (GRU) and the Minimal Gated Unit (MGU), both of which perform as well as the LSTM (or better) on a variety of tasks. The MGU is one of the simplest gated recurrent architectures at the moment. Our study demonstrates that it is possible to radically simplify the MGU without significant loss …

Squared Distance Matrix Of A Weighted Tree, Ravindra B. Bapat

Journal Articles

Let T be a tree with vertex set f1;: :: ; ng such that each edge is assigned a nonzero weight. The squared distance matrix of T; denoted by is the n n matrix with (i; j)-element d(i; j)2; where d(i; j) is the sum of the weights of the edges on the (ij)-path. We obtain a formula for the determinant of A formula for 1 is also obtained, under certain conditions. The results generalize known formulas for the unweighted case.

Statistical Analysis Of Tandem Queues With Markovian Passages In Porous Mediums, Gboyega David Adepoju

Theses, Dissertations and Capstones

Queuing theory is the mathematical study of queues or waiting lines. A queue is formed whenever the demand for service exceeds the capacity to provide service at that point in time. In this thesis, the birth-and-death process is used to model the movement of customers or units into and out of a network of queues in tandem. We start with the theoretical analysis of M/M/1 queues with Poisson arrival and exponential service time with first-come first-served (FCFS) discipline and one service station. We derive the global balance equation for each network. Using both the iterative and the probability generating function, …

Android Application For Mnist Handwritten Digits Classification, Mina Gabriel

Project Topics and Ideas

Use Neural Network architecture to classify MNIST handwritten digits dataset, student/s should implement a phone application (Android) to demonstrate their work, application will then be published to the app store for other students and CISC faculty members for evaluation and feedback.

Optimal Gateway Placement In Low-Cost Smart Cities, Oluwashina Madamori

Theses and Dissertations--Computer Science

Rapid urbanization burdens city infrastructure and creates the need for local governments to maximize the usage of resources to serve its citizens. Smart city projects aim to alleviate the urbanization problem by deploying a vast amount of Internet-of-things (IoT) devices to monitor and manage environmental conditions and infrastructure. However, smart city projects can be extremely expensive to deploy and manage partly due to the cost of providing Internet connectivity via 5G or WiFi to IoT devices. This thesis proposes the use of delay tolerant networks (DTNs) as a backbone for smart city communication; enabling developing communities to become smart cities …

Distributed Multi-Label Learning On Apache Spark, Jorge Gonzalez Lopez

Theses and Dissertations

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes …

Who Should Be Invited To My Party: A Size-Constrained K-Core Problem In Social Networks, Yu-Liang Ma, Ye Yuan, Feida Zhu, Guo-Ren Wang, Jing Xiao, Jian-Zong Wang

Research Collection School Of Computing and Information Systems

In this paper, we investigate the problem of a size-constrained k-core group query (SCCGQ) in social networks, taking both user closeness and network topology into consideration. More specifically, SCCGQ intends to find a group of h users that has the highest social closeness while being a k-core. SCCGQ can be widely applied to event planning, task assignment, social analysis, and many other fields. In contrast to existing work on the k-core detection problem, which aims to find a k-core in a social network, SCCGQ not only focuses on k-core detection but also takes size constraints into consideration. Although the conventional …

Development Of A Sensing System For Underground Optic Fiber Cable Conduit Mapping, Sherif Bakr

All Graduate Theses, Dissertations, and Other Capstone Projects

The motivation of this research is to obtain an accurate three-dimensional (3D) layout of an underground conduit, which may be beneficial to optic fiber cable installers and engineers. A newly designed algorithm for 3D position tracking with the help of an inertial sensor and an encoder has been developed. Two types of representations (Euler angle and Quaternion) for orientation and rotation are also introduced, followed by several data pre-processing procedures. A sensing fusion method is utilized to overcome the accumulated errors introduced by the sensor drifting. Considering the application of 3D underground duct mapping in this research, a sensing system …

A Simulation Model For Estimating Human Error Probability, Nitisha Reddy Boyapati

Theses and Dissertations

This report describes the system dynamics architecture of a simulation model which estimates human error probability for humans performing certain tasks in a given scenario. Human error probability is estimated as a function of the type of tasks performed and the number of performance shaping factors. In this work, the Standardized Plant Analysis Risk-Human (SPAR-H) reliability analysis method is utilized for estimating the probability of human error. The system dynamics simulation model captures the cause and effect relationships of the SPAR-H defined performance shaping factors that affect human error and uses them to assess the overall human error probability of …

Modeling Stochastically Intransitive Relationships In Paired Comparison Data, Ryan Patrick Alexander Mcshane

Statistical Science Theses and Dissertations

If the Warriors beat the Rockets and the Rockets beat the Spurs, does that mean that the Warriors are better than the Spurs? Sophisticated fans would argue that the Warriors are better by the transitive property, but could Spurs fans make a legitimate argument that their team is better despite this chain of evidence?

We first explore the nature of intransitive (rock-scissors-paper) relationships with a graph theoretic approach to the method of paired comparisons framework popularized by Kendall and Smith (1940). Then, we focus on the setting where all pairs of items, teams, players, or objects have been compared to …

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …

High Dimensional Outlier Detection, Omid Khormali

Graduate Student Theses, Dissertations, & Professional Papers

In statistics and data science, outliers are data points that differ greatly from other observations in a data set. They are important attributes of the data because they can dramatically influence patterns and relationships manifested by non-outliers. It is therefore very important to detect and adequately deal with outliers. Recently, a novel algorithm, the ROMA algorithm, has been proposed [11]. In this paper, we propose a modification of the ROMA algorithm that reduces its computational complexity from $O(n^2 m)$ to $O((n/(2^m-o(1)))^2 m)$ where $n$ is the number of data points and $m$ is the dimension of the space. And as …

Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data, Christopher A. Ramezan

Graduate Theses, Dissertations, and Problem Reports (ETD)

High spatial resolution (HR) (1m – 5m) remotely sensed data in conjunction with supervised machine learning classification are commonly used to construct land-cover classifications. Despite the increasing availability of HR data, most studies investigating HR remotely sensed data and associated classification methods employ relatively small study areas. This work therefore drew on a 2,609 km2, regional-scale study in northeastern West Virginia, USA, to investigates a number of core aspects of HR land-cover supervised classification using machine learning. Issues explored include training sample selection, cross-validation parameter tuning, the choice of machine learning algorithm, training sample set size, and feature selection. A …

Agent-Based Modeling And Simulation Approaches In Stem Education Research, Shanna R. Simpson-Singleton, Xiangdong Che

Journal of International Technology and Information Management

The development of best practices that deliver quality STEM education to all students, while minimizing achievement gaps, have been solicited by several national agencies. ABMS is a feasible approach to provide insight into global behavior based upon the interactions amongst agents and environments. In this review, we systematically surveyed several modeling and simulation approaches and discussed their applications to the evaluation of relevant theories in STEM education. It was found that ABMS is optimal to simulate STEM education hypotheses, as ABMS will sensibly present emergent theories and causation in STEM education phenomena if the model is properly validated and calibrated.

Randomized Algorithms For Preconditioner Selection With Applications To Kernel Regression, Conner Dipaolo

HMC Senior Theses

The task of choosing a preconditioner M to use when solving a linear system Ax=b with iterative methods is often tedious and most methods remain ad-hoc. This thesis presents a randomized algorithm to make this chore less painful through use of randomized algorithms for estimating traces. In particular, we show that the preconditioner stability || I - M^-1A ||_F, known to forecast preconditioner quality, can be computed in the time it takes to run a constant number of iterations of conjugate gradients through use of sketching methods. This is in spite of folklore which …

Learning To Map The Visual And Auditory World, Tawfiq Salem

Theses and Dissertations--Computer Science

The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabilistic models and a weakly-supervised, multi-task training …

Revisiting The Isoperimetric Graph Partitioning Problem, Sravan Danda, Aditya Challa, B. S. Daya Sagar, Laurent Najman

Journal Articles

Isoperimetric graph partitioning, which is also known as the Cheeger cut, is NP-hard in its original form. In the literature, multiple modifications to this problem have been proposed to obtain approximation algorithms for clustering applications. In the context of image segmentation, a heuristic continuous relaxation to this problem introduced by Leo Grady and Eric L. Schwartz has yielded good quality results. This algorithm is based on solving a linear system of equations involving the Laplacian of the image graph. Furthermore, the same algorithm applied to a maximum spanning tree (MST) of the image graph was shown to produce similar results …

Transdimensional Transformation Based Markov Chain Monte Carlo, Moumita Das, Sourabh Bhattacharya

Journal Articles

Variable dimensional problems, where not only the parameters, but also the number of parameters are random variables, pose serious challenge to Bayesians. Although in principle the Reversible Jump Markov Chain Monte Carlo (RJMCMC) methodology is a response to such challenges, the dimension-hopping strategies need not be always convenient for practical implementation, particularly because efficient “move-types” having reasonable acceptance rates are often difficult to devise. In this article, we propose and develop a novel and general dimension-hopping MCMC methodology that can update all the parameters as well as the number of parameters simultaneously using simple deterministic transformations of some low-dimensional (often …

@Yourlocation: A Spatial Analysis Of Geotagged Tweets In The Us, Ocean Mckinney

CMC Senior Theses

This project examines the spatial network properties observable from geo-located tweet data. Conventional exploration examines characteristics of a variety of network attributes, but few employ spatial edge correlations in their analysis. Recent studies have demonstrated the improvements that these correlations contribute to drawing conclusions about network structure. This thesis expands upon social network research utilizing spatial edge correlations and presents processing and formatting techniques for JSON (JavaScript Object Notation) data.