Digital Commons Network^™

Metric Learning Via Linear Embeddings For Human Motion Recognition, Byoungdoo Kong

Masters Theses

We consider the application of Few-Shot Learning (FSL) and dimensionality reduction to the problem of human motion recognition (HMR). The structure of human motion has unique characteristics such as its dynamic and high-dimensional nature. Recent research on human motion recognition uses deep neural networks with multiple layers. Most importantly, large datasets will need to be collected to use such networks to analyze human motion. This process is both time-consuming and expensive since a large motion capture database must be collected and labeled. Despite significant progress having been made in human motion recognition, state-of-the-art algorithms still misclassify actions because of characteristics …

Confronting Wicked Crypto: Wicked Problems, Encryption Policy, And Exceptional Access Technology, Kevin Nicholas Kredit

Masters Theses

Public debate has resumed on the topic of exceptional access (EA), which refers to alternative means of decryption intended for law enforcement use. The resumption of this debate is not a renege on a resolute promise made at the end of the 1990s “crypto war”; rather, it represents a valid reassessment of optimal policy in light of changing circumstances. The imbalance between privacy, access, and security in the context of constantly changing society and technology is a wicked problem that has and will continue to evade a permanent solution. As policymakers consider next steps, it is necessary that the technical …

Towards Development Of A Remote Charting System For Connected Healthcare, Alex Bodurka

Masters Theses

Health Care Providers play a crucial role in a patients well-being. While their primary role is to treat the patient, it is also vital to ensure that they can spend adequate time with the patient to create a unique treatment plan and build a personal relationship with their patients to help them feel comfortable during their treatment. Health Care Providers are frequently required to manually record patient data to track their healthcare progress during their hospital stay. However, with hospitals continuously trying to optimize their workflows, this crucial one-on-one time with the patient is often not practical.

As a solution, …

Random Search Plus: A More Effective Random Search For Machine Learning Hyperparameters Optimization, Bohan Li

Masters Theses

Machine learning hyperparameter optimization has always been the key to improve model performance. There are many methods of hyperparameter optimization. The popular methods include grid search, random search, manual search, Bayesian optimization, population-based optimization, etc. Random search occupies less computations than the grid search, but at the same time there is a penalty for accuracy. However, this paper proposes a more effective random search method based on the traditional random search and hyperparameter space separation. This method is named random search plus. This thesis empirically proves that random search plus is more effective than random search. There are some case …

Information Theory Problem Description Parser, Gary Brent Hurst

Masters Theses

Data corruption and data loss create huge problems when they occur, so naturally safeguards are usually in place to recover lost data. This often involves allowing less space for data in order to allow space for an encoding that can be used to recover any data that might be lost. The question arises, then, about how to most efficiently implement these safeguards with respect to storage, network bandwidth, or some linear combination of those two things. This work has two main goals for the information theory community: to produce an intuitive-to-use problem description parser that facilitates research in the area, …

A Framework To Support Automatic Certification For Self-Adaptive Systems, Ioannis Nearchou

Masters Theses

Presently, cyber-physical systems are increasingly being integrated into societies, from the economic sector to the nuclear energy sector. Cyber-physical systems are systems that combine physical, digital, human, and other components, which operate through physical means and software. When system errors occur, the consequences of malfunction could negatively impact human life. Academic studies have relied on the MAPE-K feedback loop model to develop various system components to satisfy the self-adaptive features, such that violation of the safety requirements can be minimized. Assurance of system requirement satisfaction is argued through an industrial standard form, called an assurance case, which is usually applied …

A Privacy Evaluation Of Nyx, Savannah A. Norem

Masters Theses

For this project, I will be analyzing the privacy leakage in a certain DDoS mitigation system. Nyx has been shown both in simulation and over live internet traffic to mitigate the effects of DDoS without any cooperation from downstream ASes and without any modifications to current routing protocols. However it does this through BPG-poisoning, which can unintentionally advertise information. This project explores what the traffic from Nyx looks like and what information can be gathered from it. Specifically, Nyx works by defining a deployer/critical relationship whose traffic is moved to maintain even under DDoS circumstances, and I will be evaluating …

Improving Convolutional Neural Network Robustness To Adversarial Images Through Image Filtering, Natalie E. Bogda

Masters Theses

The field of computer vision and deep learning is known for its ability to recognize images with extremely high accuracy. Convolutional neural networks exist that can correctly classify 96\% of 1.2 million images of complex scenes. However, with just a few carefully positioned imperceptible changes to the pixels of an input image, an otherwise accurate network will misclassify this almost identical image with high confidence. These perturbed images are known as \textit{adversarial examples} and expose that convolutional neural networks do not necessarily "see" the world in the way that humans do. This work focuses on increasing the robustness of classifiers …

Compound Effects Of Clock And Voltage Based Power Side-Channel Countermeasures, Jacqueline Lagasse

Masters Theses

The power side-channel attack, which allows an attacker to derive secret information from power traces, continues to be a major vulnerability in many critical systems. Numerous countermeasures have been proposed since its discovery as a serious vulnerability, including both hardware and software implementations. Each countermeasure has its own drawback, with some of the highly effective countermeasures incurring large overhead in area and power. In addition, many countermeasures are quite invasive to the design process, requiring modification of the design and therefore additional validation and testing to ensure its accuracy. Less invasive countermeasures that do not require directly modifying the system …

Values Of Artificial Intelligence In Marketing, Yingrui Xi

Masters Theses

“Artificial Intelligence (AI) is causing radical changes in marketing and emerging as a competent assistant supporting all areas of the marketing field. The influences and impacts AI has created in various marketing segments have aroused much interest among marketing professionals and academic scholars. Comprehensive and systematic studies on the values of AI in marketing, however, are still lacking and the existing literature fragmented. This research provides a comprehensive review of the existing literature in the relevant fields as well as a series of systematic interviews using the Value-Focused Thinking approach to understand the values of AI in marketing. This research …

On Predicting Stopping Time Of Human Sequential Decision-Making Using Discounted Satisficing Heuristic, Mounica Devaguptapu

Masters Theses

“Human sequential decision-making involves two essential questions: (i) "what to choose next?", and (ii) "when to stop?". Assuming that the human agents choose an alternative according to their preference order, our goal is to model and learn how human agents choose their stopping time while making sequential decisions. In contrary to traditional assumptions in the literature regarding how humans exhibit satisficing behavior on instantaneous utilities, we assume that humans employ a discounted satisficing heuristic to compute their stopping time, i.e., the human agent stops working if the total accumulated utility goes beyond a dynamic threshold that gets discounted with time. …

A Study On Real-Time Database Technology And Its Applications, Geethmi Nimantha Dissanayake

Masters Theses

No abstract provided.

Attack Detection And Mitigation In Mobile Robot Formations, Arnold Fernandes

Masters Theses

"A formation of cheap and agile robots can be deployed for space, mining, patrolling, search and rescue applications due to reduced system and mission cost, redundancy, improved system accuracy, reconfigurability, and structural flexibility. However, the performance of the formation can be altered by an adversary. Therefore, this thesis investigates the effect of adversarial inputs or attacks on a nonholonomic leader-follower-based robot formation and introduces novel detection and mitigation schemes.

First, an observer is designed for each robot in the formation in order to estimate its state vector and to compute the control law. Based on the healthy operation of the …

Computer Vision Based Deep Learning Models For Cyber Physical Systems, Muhammad Monjurul Karim

Masters Theses

“Cyber-Physical Systems (CPSs) are complex systems that integrate physical systems with their counterpart cyber components to form a close loop solution. Due to the ability of deep learning in providing sensor data-based models for analyzing physical systems, it has received increased interest in the CPS community in recent years. However, developing vision data-based deep learning models for CPSs remains critical since the models heavily rely on intensive, tedious efforts of humans to annotate training data. Besides, most of the models have a high tradeoff between quality and computational cost. This research studies deep learning algorithms to achieve affordable and upgradable …