Physical Sciences and Mathematics Commons^™

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. …

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 …

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

Masters Theses

No abstract provided.

Using Visual Media To Empower Citizen Scientists: A Case Study Of The Outsmart App, Megan E. Kierstead

Masters Theses

To be successful citizen science projects need to do two key things: (1) they need to meaningfully engage the public and they must also provide people with the tools, expertise, and/or training needed to participate in rigorous research that can be used by the scientific community. In some ways, these requirements are potentially at odds. Emphasis on rigor and expertise risks excluding members of the public who do not feel qualified to participate in esoteric or technically difficult scientific research. Conversely, projects that eschew rigorous methods in favor of wider participation might lead to bad data that cannot be used …

Developing 5gl Concepts From User Interactions, David Stuckless Meyer

Masters Theses

In the fulfilling of the contracts generated in Test Driven Development, a developer could be said to act as a constraint solver, similar to those used by a 5th Generation Language(5GL). This thesis presents the hypothesis that 5GL linguistic mechanics, such as facts, rules and goals, will be emergent in the communications of developer pairs performing Test Driven Development, validating that 5GL syntax is congruent with the ways that practitioners communicate. Along the way, nomenclatures and linguistic patterns may be observed that could inform the design of future 5GL languages.

Advanced Techniques For Improving Canonical Genetic Programming, Adam Tyler Harter

Masters Theses

"Genetic Programming (GP) is a type of Evolutionary Algorithm (EA) commonly employed for automated program generation and model identification. Despite this, GP, as most forms of EA's, is plagued by long evaluation times, and is thus generally reserved for highly complex problems. Two major impacting factors for the runtime are the heterogeneous evaluation time for the individuals and the choice of algorithmic primitives. The first paper in this thesis utilizes Asynchronous Parallel Evolutionary Algorithms (APEA) for reducing the runtime by eliminating the need to wait for an entire generation to be evaluated before continuing the search. APEA is applied to …

Impact Of Framing And Base Size Of Computer Security Risk Information On User Behavior, Xinhui Zhan

Masters Theses

"This research examines the impact of framing and base size of computer security risk information on users' risk perceptions and behavior (i.e., download intention and download decision). It also examines individual differences (i.e., demographic factors, computer security awareness, Internet structural assurance, self-efficacy, and general risk-taking tendencies) associated with users' computer security risk perceptions. This research draws on Prospect Theory, which is a theory in behavioral economics that addresses risky decision-making, to generate hypotheses related to users' decision-making in the computer security context. A 2 x 3 mixed factorial experimental design (N = 178) was conducted to assess the effect of …

Controlled Switching In Kalman Filtering And Iterative Learning Controls, He Li

Masters Theses

“Switching is not an uncommon phenomenon in practical systems and processes, for examples, power switches opening and closing, transmissions lifting from low gear to high gear, and air planes crossing different layers in air. Switching can be a disaster to a system since frequent switching between two asymptotically stable subsystems may result in unstable dynamics. On the contrary, switching can be a benefit to a system since controlled switching is sometimes imposed by the designers to achieve desired performance. This encourages the study of system dynamics and performance when undesired switching occurs or controlled switching is imposed. In this research, …

Evolved Parameterized Selection For Evolutionary Algorithms, Samuel Nathan Richter

Masters Theses

"Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual's genes survive, typically based on fitness. Various conventional fitness based selection functions exist, each providing a unique method of selecting individuals based on their fitness, fitness ranking within the population, and/or various other factors. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance is likely to be obtained by tuning an EA's selection …

Predictive Modeling Of Webpage Aesthetics, Ang Chen

Masters Theses

"Aesthetics plays a key role in web design. However, most websites have been developed based on designers' inspirations or preferences. While perceptions of aesthetics are intuitive abilities of humankind, the underlying principles for assessing aesthetics are not well understood. In recent years, machine learning methods have shown promising results in image aesthetic assessment. In this research, we used machine learning methods to study and explore the underlying principles of webpage aesthetics"--Abstract, page iii.

Design And Implementation Of Applications Over Delay Tolerant Networks For Disaster And Battlefield Environment, Karthikeyan Sachidanandam

Masters Theses

"In disaster/battlefield applications, there may not be any centralized network that provides a mechanism for different nodes to connect with each other to share important data. In such cases, we can take advantage of an opportunistic network involving a substantial number of mobile devices that can communicate with each other using Bluetooth and Google Nearby Connections API(it uses Bluetooth, Bluetooth Low Energy (BLE), and Wi-Fi hotspots) when they are close to each other. These devices referred to as nodes form a Delay Tolerant Network (DTN), also known as an opportunistic network. As suggested by its name, DTN can tolerate delays …

Agent Based Terrain Generator: Cruthú, Lawrence L. O'Boyle

Masters Theses

Terrain generation models are applied in different industries and fields of study. Current techniques assist in planning transportation networks, visualizing population migrations, conducting epidemiology research, and training self-driving cars and drones. Many current applications and research models generate complex realistic artifacts, e.g., trees, rivers, coastlines, populations, and even cities. Unfortunately, most of these techniques are described and implemented separately. Techniques do not work together to generate a complete holistic view. This thesis proposes a model, Cruthú (Gaelic for "creation"), that provides a novel platform allowing for complete world generation by integrating existing research and algorithms. The model is inspired by …

Exploring The Impact Of Pretrained Bidirectional Language Models On Protein Secondary Structure Prediction, Dillon G. Daudert

Masters Theses

Protein secondary structure prediction (PSSP) involves determining the local conformations of the peptide backbone in a folded protein, and is often the first step in resolving a protein's global folded structure. Accurate structure prediction has important implications for understanding protein function and de novo protein design, with progress in recent years being driven by the application of deep learning methods such as convolutional and recurrent neural networks. Language models pretrained on large text corpora have been shown to learn useful representations for feature extraction and transfer learning across problem domains in natural language processing, most notably in instances where the …

Deep Reinforcement Learning For Autonomous Search And Rescue, Juan Gonzalo Cárcamo Zuluaga

Masters Theses

Unmanned Aerial Vehicles (UAVs) are becoming more prevalent every day. In addition, advances in battery life and electronic sensors have enabled the development of diverse UAV applications outside their original military domain. For example, Search and Rescue (SAR) operations can benefit greatly from modern UAVs since even the simplest commercial models are equipped with high-resolution cameras and the ability to stream video to a computer or portable device. As a result, autonomous unmanned systems (ground, aquatic, and aerial) have recently been employed for such typical SAR tasks as terrain mapping, task observation, and early supply delivery. However, these systems were …

Peer Attention Modeling With Head Pose Trajectory Tracking Using Temporal Thermal Maps, Corey Michael Johnson

Masters Theses

Human head pose trajectories can represent a wealth of implicit information such as areas of attention, body language, potential future actions, and more. This signal is of high value for use in Human-Robot teams due to the implicit information encoded within it. Although team-based tasks require both explicit and implicit communication among peers, large team sizes, noisy environments, distance, and mission urgency can inhibit the frequency and quality of explicit communication. The goal for this thesis is to improve the capabilities of Human-Robot teams by making use of implicit communication. In support of this goal, the following hypotheses are investigated: …

A Survey Of Security And Privacy In Mobile Cloud Computing, Bhuvaneswari Rayapuri

Masters Theses

Cloud Computing is an emerging technology that provides shared processing resources and data to computers and other devices on demand. On the other hand, Mobile Computing allows transmission of data, voice and video. From these two there emerges a new concept Mobile Cloud Computing which not only overcomes the problems of Mobile Computing but also integrates Cloud Computing into Mobile Environments to overcome obstacles related to Performance, Security and Environment. This paper also provides a decent description on Security and Privacy, its related problems, threats and challenges. This paper first provides details on survey of Mobile Cloud Computing, then it …

Synthesis And Evaluation Of Acetylcholine Molecularly Imprinted Polymers, Nathaniel Donald Thiemann

Masters Theses

Polymers imprinted with acetylcholine during synthesis were prepared in order to evaluate their potential for implementation as a novel recognition element in acetylcholine biosensors. Biosensors, such as the glucose monitor, are used to rapidly detect and quantify a target analyte. Acetylcholine biosensors have already been produced using enzymatic recognition elements, but they are currently expensive and plagued by short viability. Molecularly imprinted polymers are not only cheap and durable, but have also been successfully used as a recognition element in biosensors for other analytes. Therefore, computational tools were used to rationally design acetylcholine molecularly imprinted polymers. Three chemicals, itaconic acid, …

Improved Crpd Analysis And A Secure Scheduler Against Information Leakage In Real-Time Systems, Ying Zhang

Masters Theses

"Real-time systems are widely applied to the time-critical fields. In order to guarantee that all tasks can be completed on time, predictability becomes a necessary factor when designing a real-time system. Due to more and more requirements about the performance in the real-time embedded system, the cache memory is introduced to the real-time embedded systems.

However, the cache behavior is difficult to predict since the data will be loaded either on the cache or the memory. In order to taking the unexpected overhead, execution time are often enlarged by a certain (huge) factor. However, this will cause a waste of …

Smart Augmented Reality Instructional System For Mechanical Assembly, Ze-Hao Lai

Masters Theses

"Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is …

Cloud Transactions And Caching For Improved Performance In Clouds And Dtns, Dileep Mardham

Masters Theses

"In distributed transactional systems deployed over some massively decentralized cloud servers, access policies are typically replicated. Interdependencies ad inconsistencies among policies need to be addressed as they can affect performance, throughput and accuracy. Several stringent levels of policy consistency constraints and enforcement approaches to guarantee the trustworthiness of transactions on cloud servers are proposed. We define a look-up table to store policy versions and the concept of "Tree-Based Consistency" approach to maintain a tree structure of the servers. By integrating look-up table and the consistency tree based approach, we propose an enhanced version of Two-phase validation commit (2PVC) protocol integrated …

Solidification Rate Detection Through Solid-Liquid Interface Tracking, Wei Luo

Masters Theses

”Studying the melt pool dynamics in laser powder bed fusion (LPBF) additive manufacturing is vital to predict the microstructure of the built part. High-speed synchrotron hard X-ray imaging is an advanced technique to monitor the LPBF process in situ and in real time. However, it is very challenging to track the solid-liquid interface due to the low image contrast, high image noise and unstable intensity of the image sequences.

In this paper, we propose an solid-liquid interface detector with human interaction to track the interface to compute the solidification rate of the melt pool. The proposed method includes six independent …

A Network Tomography Approach For Traffic Monitoring In Smart Cities, Ruoxi Zhang

Masters Theses

"Various urban planning and managing activities required by a Smart City are feasible because of traffic monitoring. As such, the thesis proposes a network tomography-based approach that can be applied to road networks to achieve a cost-efficient, flexible, and scalable monitor deployment. Due to the algebraic approach of network tomography, the selection of monitoring intersections can be solved through the use of matrices, with its rows representing paths between two intersections, and its columns representing links in the road network. Because the goal of the algorithm is to provide a cost-efficient, minimum error, and high coverage monitor set, this problem …

Analyzing Large Scale Trajectory Data To Identify Users With Similar Behavior, Tyler Clark Percy

Masters Theses

"In today's society, social networks are a popular way to connect with friends and family and share what's going on in your life. With the Internet connecting us all closer than ever before, it is increasingly common to use social networks to meet new friends online that share similar interests instead of only connecting with those you already know. For the problem of attempting to connect people with similar interests, this paper proposes the foundation for a Geo-social network that aims to extract the semantic meaning from users' location history and use this information to find the similarity between users. …

Classification Of Eeg Signals Of User States In Gaming Using Machine Learning, Chandana Mallapragada

Masters Theses

"In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and …

Reputation And Credit Based Incentive Mechanism For Data-Centric Message Delivery In Delay Tolerant Networks, Himanshu Jethawa

Masters Theses

"In a Data-centric Delay Tolerant Networks (DTNs), it is essential for nodes to cooperate in message forwarding in order to enable successful delivery of a message in an opportunistic fashion with nodes having their social interests defined. In the data-centric dissemination protocol proposed here, a source annotates messages (images) with keywords, and then intermediate nodes are presented with an option of adding keyword-based annotations in order to create higher content strength messages on path toward the destination. Hence, contents like images get enriched as there is situation evolution or learned by these intermediate nodes, such as in a battlefield, or …

An Approach For Formal Analysis Of The Security Of A Water Treatment Testbed, Sai Sidharth Patlolla

Masters Theses

"This thesis focuses on securing critical infrastructures such as chemical plants, manufacturing units, and power generating plants against attacks that disrupt the information flow from one component to another. Such systems are controlled by an Industrial Control System (ICS) that includes controllers communicating with each other, and with physical sensors and actuators, using a communications network.

Traditional security models partition the security universe into two worlds, secure and insecure, but in the real world the partitions often overlap and information is leaked even through the physical observation which makes it much harder to analyze a Cyber physical system (CPS). To …

Card: Concealed And Remote Discovery Of Iot Devices In Victims' Home Networks, Sammie Lee Bush

Masters Theses

"Smart devices are becoming more common in the standard households. They range from lights to refrigerators and their functionality and applications continues to grow with consumer demand. This increase in networked, complex devices has also brought an increase in vulnerabilities in the average consumer's home. There now exists an Internet of Things (IoT) ecosystem that creates new attack vectors for adversaries to spread malware, build botnets, and participate in other malicious activities. We will overview some of these new attack vectors as well as go over a framework that would allow an adversary to target a user's home network and …