Theory and Algorithms Commons^™

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

A Reasonable Bias Approach To Gerrymandering: Using Automated Plan Generation To Evaluate Redistricting Proposals, Bruce E. Cain, Wendy K. Tam Cho, Yan Y. Liu, Emily R. Zhang

William & Mary Law Review

No abstract provided.

Quantum Chemical Analysis Of Stable Noble Gas Cations For Astrochemical Detection, Carlie M. Novak

Honors College Theses

The search for possible, natural, noble gas molecules has led to quantum chemical, spectroscopic analysis of NeCCH+, ArNH+ ArCCH+, and ArCN+. Each of these systems have been previously shown to be a stable minimum on its respective potential energy surface. However, no spectroscopic data are available for laboratory detection or interstellar observation of these species, and the interstellar medium may be the most likely place, in nature, where these noble gas cations are found. The bent shape of NeCCH+ is confirmed here with a fairly large dipole moment and a bright C -- H stretching frequency at 3101.9 cm^{-1 …}

Modular Scheduling System For Westside School District, Tyler Bienhoff

Honors Program: Senior Projects (Public)

Westside School district offers a modular scheduling system for their high school that is more similar to a college schedule than the typical high school system. Due to the complexity of their master schedule each semester, there are no commercially available products that can assist in creating a schedule. Hence, this thesis discusses a scheduling algorithm and management system that was built specifically for Westside High School with the potential to be expanded for use by other interested schools. The first part of the paper is focused on gathering input from students and faculty for which courses and how many …

Quantum Attacks On Modern Cryptography And Post-Quantum Cryptosystems, Zachary Marron

Senior Honors Theses

Cryptography is a critical technology in the modern computing industry, but the security of many cryptosystems relies on the difficulty of mathematical problems such as integer factorization and discrete logarithms. Large quantum computers can solve these problems efficiently, enabling the effective cryptanalysis of many common cryptosystems using such algorithms as Shor’s and Grover’s. If data integrity and security are to be preserved in the future, the algorithms that are vulnerable to quantum cryptanalytic techniques must be phased out in favor of quantum-proof cryptosystems. While quantum computer technology is still developing and is not yet capable of breaking commercial encryption, these …

Multiple Sclerosis Identification Based On Fractional Fourier Entropy And A Modified Jaya Algorithm, Shui-Hua Wang, Hong Cheng, Preetha Phillips, Yu-Dong Zhang

Publications and Research

Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, …

Similarity Based Classification Of Adhd Using Singular Value Decomposition, Taban Eslami, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for …

Compressed Sensing For Few-View Multi-Pinhole Spect With Applications To Preclinical Imaging, Benjamin Michael Rizzo

Dissertations (1934 -)

Single Photon Emission Computed Tomography (SPECT) can be used to identify and quantify changes in molecular and cellular targets involved in disease. A radiopharmaceutical that targets a specific metabolic function is administered to a subject and planar projections are formed by imaging emissions at different view angles around the subject. The reconstruction task is to determine the distribution of radioactivity within the subject from the projections. We present a reconstruction approach that utilizes only a few view angles, resulting in a highly underdetermined system, which could be used for dynamic imaging applications designed to quantify physiologic processes altered with disease. …

Artificial Intelligence: An Analysis Of Alan Turing’S Role In The Conception And Development Of Intelligent Machinery, Erika L. Furtado

Selected Honors Theses

The purpose of this thesis is to follow the thread of Alan Turing’s ideas throughout his decades of research and analyze how his predictions have come to fruition over the years. Turing’s Computing Machinery and Intelligence is the paper in which the Turing Test is described as an alternative way to answer the question “can machines think?” (Turing 433). Since the development of Turing’s original paper, there has been a tremendous amount of advancement in the field of artificial intelligence. The field has made its way into art classification as well as the medical industry. The main concept researched in …

Application Of Huffman Data Compression Algorithm In Hashing Computation, Lakshmi Narasimha Devulapalli Venkata,

Masters Theses & Specialist Projects

Cryptography is the art of protecting information by encrypting the original message into an unreadable format. A cryptographic hash function is a hash function which takes an arbitrary length of the text message as input and converts that text into a fixed length of encrypted characters which is infeasible to invert. The values returned by the hash function are called as the message digest or simply hash values. Because of its versatility, hash functions are used in many applications such as message authentication, digital signatures, and password hashing [Thomsen and Knudsen, 2005].

The purpose of this study is to apply …

Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems

Computer Science and Engineering Theses and Dissertations

Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …

Sok: Towards The Science Of Security And Privacy In Machine Learning, Nicolas Papernot, Patrick Mcdaniel, Arunesh Sinha, Michael Wellman

Research Collection School Of Computing and Information Systems

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive—new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community’s understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We …

Distributed Multi-Task Classification: A Decentralized Online Learning Approach, Chi Zhang, Peilin Zhao, Shuji Hao, Yeng Chai Soh, Bu Sung Lee, Chunyan Miao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Although dispersing one single task to distributed learning nodes has been intensively studied by the previous research, multi-task learning on distributed networks is still an area that has not been fully exploited, especially under decentralized settings. The challenge lies in the fact that different tasks may have different optimal learning weights while communication through the distributed network forces all tasks to converge to an unique classifier. In this paper, we present a novel algorithm to overcome this challenge and enable learning multiple tasks simultaneously on a decentralized distributed network. Specifically, the learning framework can be separated into two phases: (i) …

Demo Abstract: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve battery-less wearable system. Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts, which suggests that energy harvesting and sensing can be performed concurrently. However, realization of simultaneous energy harvesting and sensing (SEHS) is challenging as the energy harvesting process distorts the sensing signal. In this demo, we propose a novel SEHS architecture prototyped in the form factor of an insole, which combines …

A Sliding-Window Framework For Representative Subset Selection, Yanhao Wang, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. A common approach is to model RSS as the submodular maximization problem because the utility of extracted representatives often satisfies the "diminishing returns" property. To capture the data recency issue and support different types of constraints in real-world problems, we formulate RSS as maximizing a submodular function subject to a d-knapsack constraint (SMDK) over sliding windows. Then, we propose a novel KnapWindow framework for SMDK. Theoretically, KnapWindow is 1-ε/1+d - approximate for SMDK and achieves sublinear complexity. Finally, we evaluate the efficiency and effectiveness …

Mitigating The Effects Of Boom Occlusion On Automated Aerial Refueling Through Shadow Volumes, Zachary C. Paulson

Theses and Dissertations

In flight refueling of Unmanned Aerial Vehicles (UAVs) is critical to the United States Air Force (USAF). However, the large communication latency between a ground-based operator and his/her remote UAV makes docking with a refueling tanker unsafe. This latency may be mitigated by leveraging a tanker-centric stereo vision system. The vision system observes and computes an approaching receiver's relative position and orientation offering a low-latency, high frequency docking solution. Unfortunately, the boom -- an articulated refueling arm responsible for physically pumping fuel into the receiver -- occludes large portions of the receiver especially as the receiver approaches and docks with …

Integrity Monitoring For Automated Aerial Refueling: A Stereo Vision Approach, Thomas R. Stuart

Theses and Dissertations

Unmanned aerial vehicles (UAVs) increasingly require the capability to y autonomously in close formation including to facilitate automated aerial refueling (AAR). The availability of relative navigation measurements and navigation integrity are essential to autonomous relative navigation. Due to the potential non-availability of the global positioning system (GPS) during military operations, it is highly desirable that relative navigation can be accomplished without the use of GPS. This paper develops two algorithms designed to provide relative navigation measurements solely from a stereo image pair. These algorithms were developed and analyzed in the context of AAR using a stereo camera system modeling that …

Methods Of Reverse Engineering A Bitstream For Field Programmable Gate Array Protection, Daniel J. Celebucki

Theses and Dissertations

Field Programmable Gate Arrays (FPGAs) are found in numerous industries including consumer electronics, automotive, military and aerospace, and critical infrastructure. The ability to be reprogrammed as well as large computational power and relatively low price make them a good fit for low-volume applications that cannot justify the Non-Recurring Engineering (NRE) costs associated with producing Application-Specific Integrated Circuits (ASICs). FPGAs however, have seen a variety of security issues stemming from the fact that their configuration files are not inherently protected. This research assesses the feasibility of reverse engineering the bitstream format for a previously unexplored FPGA, as well as the utilization …

Special Perturbations On The Jetson Tx1 And Tx2 Computers, Tyler M. Moore

Theses and Dissertations

Simplified General Perturbations Number 4 (SGP4) has been the traditional algorithm for performing Orbit Determination (OD) onboard orbiting spacecraft. However, the recent rise of high-performance computers with low Size, Weight, and Power (SWAP) factors has provided the opportunity to use Special Perturbations (SP), a more accurate algorithm to perform onboard OD. This research evaluates the most efficient way to implement SP on NVIDIA’s Jetson TX series of integrated Graphical Processing Units (GPUs). An initial serial version was implemented on the Jetson TX1 and TX2's Central Processing Units (CPUs). The runtimes of the initial version are the benchmark that the runtimes …

Rss-Based Device-Free Passive Detection And Localization Using Home Automation Network Radio Frequencies, Tiffany M. Phan

Theses and Dissertations

This research provided a proof of concept for a device-free passive (DfP) system capable of detecting and localizing a target through exploitation of a home automation network’s radio frequency (RF) signals. The system was developed using Insteon devices with a 915 MHz center frequency. Without developer privileges, limitations of the Insteon technology like no intrinsic received signal strength (RSS) field and silent periods between messages were overcome by using software-defined radios to simulate Insteon devices capable of collecting and reporting RSS, and by creating a message generation script and implementing a calibrated filter threshold to reduce silent periods. Evaluation of …