Physical Sciences and Mathematics Commons^™

Scalable Algorithms And Hybrid Parallelization Strategies For Multivariate Integration With Paradapt And Cuda, Omofolakunmi Elizabeth Olagbemi

Dissertations

The evaluation of numerical integrals finds applications in fields such as High Energy Physics, Bayesian Statistics, Stochastic Geometry, Molecular Modeling and Medical Physics. The erratic behavior of some integrands due to singularities, peaks, or ridges in the integration region suggests the need for reliable algorithms and software that not only provide an estimation of the integral with a level of accuracy acceptable to the user, but also perform this task in a timely manner. We developed ParAdapt, a numerical integration software based on a classic global adaptive strategy, which employs Graphical Processing Units (GPUs) in providing integral evaluations. Specifically, ParAdapt …

Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu

Dissertations

For the ongoing advancement of the fields of Information Technology (IT) and Computer Science, machine learning-based approaches are utilized in different ways in order to solve the problems that belong to the Nondeterministic Polynomial time (NP)-hard complexity class or to approximate the problems if there is no known efficient way to find a solution. Problems that determine the proper set of reconfigurable parameters of parametric systems to obtain the near optimal performance are typically classified as NP-hard problems with no efficient mathematical models to obtain the best solutions. This body of work aims to advance the knowledge of machine learning …

Social Media Sentiment Analysis With A Deep Neural Network: An Enhanced Approach Using User Behavioral Information, Ahmed Sulaiman M. Alharbi

Dissertations

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data (including tweet length, spelling errors, abbreviations, and special characters), the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis constitutes a fundamental problem with many interesting applications, such as for Business Intelligence, Medical Monitoring, and National Security. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this research, we propose deep learning based frameworks that …

Predicting The Complexity Of Locality Patterns In Loop Nests In C Scientific Programs, Nasser M. Alsaedi

Dissertations

On modern computer systems, the performance of an application depends on its locality. Most existing locality measurements performed by compiler static analysis mainly target analyzing regular array references in loop nests. Measurements based on compiler static analysis have limited applicability when the loop bounds are unknown at compile time, when the control flow is dynamic, or when index arrays or pointer operations are used. In addition, compiler static analysis cannot adapt to input change.

Training-based locality analysis predicts the data reuse change across program inputs to provide run-time information. This analysis quantifies the number of unique memory locations accessed between …

Approximate Algorithms For Regulatory Motif Discovery In Dna, Hasnaa Imad Al-Shaikhli

Dissertations

Motif discovery is the problem of finding common substrings within a set of biological strings. Therefore it can be applied to finding Transcription Factor Binding Sites (TFBS) that have common patterns (motifs). A transcription factor molecule can bind to multiple binding sites in the promoter region of different genes to make these genes co-regulating. The Planted (l, d) Motif Problem (PMP) is a classic version of motif discovery where l is the motif length and d represents the maximum allowed mutation distance. The quorum Planted (l, d, q) Motif Problem (qPMP) is a version of PMP …

Exploring The Dynamics Of Scientific Research, Shilpa Lakhanpal

Dissertations

Scientific research papers present the research endeavors of numerous scientists around the world, and are documented across multitudes of technical conference proceedings, and other such publications. Given the plethora of such research data, if we could automate the extraction of key interesting areas of research, and provide access to this new information, it would make literature searches incredibly easier for researchers. This in turn could be very useful for them in furthering their research agenda. With this goal in mind, we have endeavored to provide such solutions through our research. Specifically, the focus of our research is to design, analyze …

High-Performance Quasi-Monte Carlo Integration And Applications, Ahmed Hassan H. Almulihi

Dissertations

While adaptive integration by region partitioning is generally effective in low dimensions, quasi-Monte Carlo methods can be used for integral approximations in moderate to high dimensions. Important application areas include high-energy physics, statistics, computational finance and stochastic geometry with applications in robotics, tessellations and imaging from medical data using tetrahedral meshes.

Lattice rule integration is a class of quasi-Monte Carlo methods, implemented by an equal-weight cubature formula and suited for fairly smooth functions. Successful methods to construct these rules are the component-by-component (CBC) algorithm by Sloan and Restsov (2001) and the fast algorithm for CBC by Nuyens and Cools (2006). …

The Standards Project, Dustin Robbins

Honors Theses

The Standards Project is a web app that is intended to assist United States K-12 students in meeting the academic standards each state has set out for their students. The app is intended to allow instructors to see how proficient incoming students are in standards set for the prior grade (e.g. 6th grade students’ 5th grade math skills would be shown) and launch “interventions”—be these online modules with educational content and quiz questions, after school activities, or some other form of instruction—in order to help students in problem areas while spending a minimum of class time on old material.

It …

Implementation Considerations For The Digital Bronco Id, Bryan Gilginas

Honors Theses

This paper aims to discuss the conditions and preferences of students that Western Michigan University should take if they ever implement a Digital Bronco ID. These conditions are found via an anonymous survey given to random students. These students were prompted to answer questions based on their preference and possible uses of the Digital Bronco ID. It was found that the respondents were significantly diverse in their answers. However, things such as gender, major, and age range played a significant role in patterns in which students chose their preferences. Within the paper, these patterns are interpreted and discussed for the …

High-Performance Reductive Strategies For Big Data From Lc-Ms/Ms Proteomics, Muaaz Gul Awan

Dissertations

Mass Spectrometry (MS)-based proteomics utilizes high performance liquid chromatography in tandem with high-throughput mass spectrometers. These experiments can produce MS data sets with astonishing speed and volume that can easily reach peta-scale level, creating storage and computational problems for large-scale systems biology studies. Each spectrum output by a mass spectrometer may consist of thousands of peaks, which must all be processed to deduce the corresponding peptide. However, only a small percentage of peaks in a spectrum are useful for further processing, as most of the peaks are either noise or are not useful. Our experiments have shown that 90 to …

Towards An Architecture For Secure Privacy-Preserving Opportunistic Resource Utilization Networks, Ahmed A. Al-Gburi

Dissertations

The paradigm of Opportunistic Resource Utilization Networks (oppnets) advances technology in the field of ad hoc networks. The salient feature of oppnets is their use of “helpers” to expand opportunistically when the need for more resources or capabilities arises. Like any other pervasive computing systems, oppnets face numerous security and privacy challenges. These challenges are addressed by utilizing two major ideas: Pervasive Trust Foundation (PTF) and Active Data Bundles (ADBs). The PTF paradigm makes trust the basis for security and privacy in pervasive computing systems, including oppnets. The ADBs are self-protecting data constructs that encapsulate together—in an inseparable way—sensitive data, …