Digital Communications and Networking Commons^™

Comparative Analysis Of Rgb-Based Eye-Tracking For Large-Scale Human-Machine Applications, Brett Thaman, Trung Cao

Posters-at-the-Capitol

Gaze tracking has become an established technology that enables using an individual’s gaze as an input signal to support a variety of applications in the context of Human-Computer Interaction. Gaze tracking primarily relies on sensing devices such as infrared (IR) cameras. Nevertheless, in the recent years, several attempts have been realized at detecting gaze by acquiring and processing images acquired from standard RGB cameras. Nowadays, there are only a few publicly available open-source libraries and they have not been tested extensively. In this paper, we present the result of a comparative analysis that studied a commercial eye-tracking device using IR …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

Ggnb: Graph-Based Gaussian Naive Bayes Intrusion Detection System For Can Bus, Riadul Islam, Maloy K. Devnath, Manar D. Samad, Syed Md Jaffrey Al Kadry

Computer Science Faculty Research

The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks on critical vehicular electronic control units (ECUs) using controller area network (CAN). Besides, existing intrusion detection systems (IDSs) often propose to tackle a specific type of attack, which may leave a system vulnerable to numerous other types of attacks. A generalizable IDS that can identify a wide range of attacks within the shortest possible time has more practical value than attack-specific IDSs, which is not a trivial task to accomplish. In this …

Don't Bite The Bait: Phishing Attack For Internet Banking (E-Banking), Ilker Kara

Journal of Digital Forensics, Security and Law

Phishing attacks are based on obtaining desired information from users quickly and easily with the help of misdirecting, panicking, curiosity, or excitement. Most of the phishing web sites are designed on internet banking(e-banking) and the attackers can acquire financial information of misled users with the tactics and discourses they develop. Despite the increase of prevention techniques against phishing attacks day by day, an effective solution could not be found for this issue due to the human factor. Because of this reason, real phishing attack studies are essential to study and analyze the attackers’ attack techniques and strategies. This study focused …

Protection Of Patient Privacy On Mobile Device Machine Learning, Matthew Nguyen

Cybersecurity Undergraduate Research Showcase

An existing StudentLife Study mobile dataset was evaluated and organized to be applied to different machine learning methods. Different variables like user activity, exercise, sleep, study space, social, and stress levels are optimized to train a model that could predict user stress level. The different machine learning methods would test if both patient data privacy and training efficiency can be ensured.

Effects Of Cloud Computing In The Workforce, Kevin Rossi Acosta

Cybersecurity Undergraduate Research Showcase

In recent years, the incorporation of cloud computing and cloud services has increased in many different types of organizations and companies. This paper will focus on the philosophical, economical, and political factors that cloud computing and cloud services have in the workforce and different organizations. Based on various scholarly articles and resources it was observed that organizations used cloud computing and cloud services to increase their overall productivity as well as decrease the overall cost of their operations, as well as the different policies that were created by lawmakers to control the realm of cloud computing. The results of this …

Crest Or Trough? How Research Libraries Used Emerging Technologies To Survive The Pandemic, So Far, Scout Calvert

UNL Libraries: Faculty Publications

Introduction

In the first months of the COVID-19 pandemic, it was impossible to tell if we were at the crest of a wave of new transmissions, or a trough of a much larger wave, still yet to peak. As of this writing, as colleges and universities prepare for mostly in-person fall 2021 semesters, case counts in the United States are increasing again after a decline that coincided with easier access to the COVID vaccine. Plans for a return to campus made with confidence this spring may be in doubt, as we climb the curve of what is already the second …

What Is The Relationship Between Language And Thought?: Linguistic Relativity And Its Implications For Copyright, Christopher S. Yoo

All Faculty Scholarship

To date, copyright scholarship has almost completely overlooked the linguistics and cognitive psychology literature exploring the connection between language and thought. An exploration of the two major strains of this literature, known as universal grammar (associated with Noam Chomsky) and linguistic relativity (centered around the Sapir-Whorf hypothesis), offers insights into the copyrightability of constructed languages and of the type of software packages at issue in Google v. Oracle recently decided by the Supreme Court. It turns to modularity theory as the key idea unifying the analysis of both languages and software in ways that suggest that the information filtering associated …

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy

Research Collection School Of Computing and Information Systems

Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in the graph. Homophily estimates are unbiased when predictions of dyad categories are unbiased. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally produce unbiased predictions of dyad categories and therefore produce biased homophily estimates. Bias comes from three sources: sampling bias, correlation between model errors …

Development Of A Reference Design For Intrusion Detection Using Neural Networks For A Smart Inverter, Ammar Mohammad Khan

Graduate Theses and Dissertations

The purpose of this thesis is to develop a reference design for a base level implementation of an intrusion detection module using artificial neural networks that is deployed onto an inverter and runs on live data for cybersecurity purposes, leveraging the latest deep learning algorithms and tools. Cybersecurity in the smart grid industry focuses on maintaining optimal standards of security in the system and a key component of this is being able to detect cyberattacks. Although researchers and engineers aim to design such devices with embedded security, attacks can and do still occur. The foundation for eventually mitigating these attacks …

Vibransee: Enabling Simultaneous Visible Light Communication And Sensing, Ila Nitin Gokarn, Archan Misra

Research Collection School Of Computing and Information Systems

Driven by the ubiquitous proliferation of low-cost LED luminaires, visible light communication (VLC) has been established as a high-speed communications technology based on the high-frequency modulation of an optical source. In parallel, Visible Light Sensing (VLS) has recently demonstrated how vision-based at-a-distance sensing of mechanical vibrations (e.g., of factory equipment) can be performed using high frequency optical strobing. However, to date, exemplars of VLC and VLS have been explored in isolation, without consideration of their mutual dependencies. In this work, we explore whether and how high-throughput VLC and high-coverage VLS can be simultaneously supported. We first demonstrate the existence of …

Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley

Graduate Theses and Dissertations

Identifying freight patterns in transit is a common need among commercial and municipal entities. For example, the allocation of resources among Departments of Transportation is often predicated on an understanding of freight patterns along major highways. There exist multiple sensor systems to detect and count vehicles at areas of interest. Many of these sensors are limited in their ability to detect more specific features of vehicles in traffic or are unable to perform well in adverse weather conditions. Despite this limitation, to date there is little comparative analysis among Laser Imaging and Detection and Ranging (LIDAR) sensors for freight detection …

Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao

Graduate Theses and Dissertations

Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected.

Cloud computing has become more and more popular in …

Pier Ocean Pier, Brandon J. Nowak

Computer Engineering

Pier Ocean Peer is a weatherproof box containing a Jetson Nano, connected to a cell modem and camera, and powered by a Lithium Iron Phosphate battery charged by a 50W solar panel. This system can currently provide photos to monitor the harbor seal population that likes to haul out at the base of the Cal Poly Pier, but more importantly it provides a platform for future expansion by other students either though adding new sensors directly to the Jetson Nano or by connecting to the jetson nano remotely through a wireless protocol of their choice.

Iot Garden Frost Alarm, Andrew James

Honors Theses

Home gardeners are faced with yearly challenges due to spring frosts harming young plants. This is frequently mitigated by covering crops with frost blankets, but only on nights when a frost is predicted. In areas with less predictable climate, an unexpected frost can kill vulnerable plants, reducing the amount of food produced. A system is proposed and designed here to use internet of things (IoT) technology to enable a small weather station in the home garden to report current climate data and predict frosts, then alert the gardener in time for them to cover their plants.

The system as designed …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Ship-Gan: Generative Modeling Based Maritime Traffic Simulator, Chaithanya Shankaramurthy Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar

Research Collection School Of Computing and Information Systems

Modeling vessel movement in a maritime environment is an extremely challenging task given the complex nature of vessel behavior. Several existing multiagent maritime decision making frameworks require access to an accurate traffic simulator. We develop a system using electronic navigation charts to generate realistic and high fidelity vessel traffic data using Generative Adversarial Networks (GANs). Our proposed Ship-GAN uses a conditional Wasserstein GAN to model a vessel’s behavior. The generator can simulate the travel time of vessels across different maritime zones conditioned on vessels’ speeds and traffic intensity. Furthermore, it can be used as an accurate simulator for prior decision …

Improving Treatment Of Local Liver Ablation Therapy With Deep Learning And Biomechanical Modeling, Brian Anderson, Kristy Brock, Laurence Court, Carlos Eduardo Cardenas, Erik Cressman, Ankit Patel

Dissertations & Theses (Open Access)

In the United States, colorectal cancer is the third most diagnosed cancer, and 60-70% of patients will develop liver metastasis. While surgical liver resection of metastasis is the standard of care for treatment with curative intent, it is only avai lable to about 20% of patients. For patients who are not surgical candidates, local percutaneous ablation therapy (PTA) has been shown to have a similar 5-year overall survival rate. However, PTA can be a challenging procedure, largely due to spatial uncertainties in the localization of the ablation probe, and in measuring the delivered ablation margin.

For this work, we hypothesized …

Securing Fog Federation From Behavior Of Rogue Nodes, Mohammed Saleh H. Alshehri

Graduate Theses and Dissertations

As the technological revolution advanced information security evolved with an increased need for confidential data protection on the internet. Individuals and organizations typically prefer outsourcing their confidential data to the cloud for processing and storage. As promising as the cloud computing paradigm is, it creates challenges; everything from data security to time latency issues with data computation and delivery to end-users. In response to these challenges CISCO introduced the fog computing paradigm in 2012. The intent was to overcome issues such as time latency and communication overhead and to bring computing and storage resources close to the ground and the …

Lecture 08: Partial Eigen Decomposition Of Large Symmetric Matrices Via Thick-Restart Lanczos With Explicit External Deflation And Its Communication-Avoiding Variant, Zhaojun Bai

Mathematical Sciences Spring Lecture Series

There are continual and compelling needs for computing many eigenpairs of very large Hermitian matrix in physical simulations and data analysis. Though the Lanczos method is effective for computing a few eigenvalues, it can be expensive for computing a large number of eigenvalues. To improve the performance of the Lanczos method, in this talk, we will present a combination of explicit external deflation (EED) with an s-step variant of thick-restart Lanczos (s-step TRLan). The s-step Lanczos method can achieve an order of s reduction in data movement while the EED enables to compute eigenpairs in batches along with a number …

An Inside Vs. Outside Classification System For Wi-Fi Iot Devices, Paul Gralla

Dartmouth College Undergraduate Theses

We are entering an era in which Smart Devices are increasingly integrated into our daily lives. Everyday objects are gaining computational power to interact with their environments and communicate with each other and the world via the Internet. While the integration of such devices offers many potential benefits to their users, it also gives rise to a unique set of challenges. One of those challenges is to detect whether a device belongs to one’s own ecosystem, or to a neighbor – or represents an unexpected adversary. An important part of determining whether a device is friend or adversary is to …

Long Distance Bluetooth Low Energy Exploitation On A Wireless Attack Platform, Stephanie L. Long

Theses and Dissertations

In the past decade, embedded technology, known as the Internet of Things, has expanded for many uses. The smart home infrastructure has drastically grown to include networked refrigerators, lighting systems, speakers, watches, and more. This increase in the use of wireless protocols provides a larger attack surface for cyber actors than ever before. Wireless loT traffic is susceptible for sniffing by an attacker. The attack platform skypie is upgraded to incorporate Bluetooth Low Energy (BLE) beacon collection for pattern-of-life data, as well as device characteristic enumeration and potential characteristic modification. This platform allows an attacker to mount the skypie to …

Enumerating And Locating Bluetooth Devices For Casualty Recovery In A First-Responder Environment, Justin M. Durham

Theses and Dissertations

It is difficult for first-responders to quickly locate casualties in an emergency environment such as an explosion or natural disaster. In order to provide another tool to locate individuals, this research attempts to identify and estimate the location of devices that would likely be located on or with a person. A variety of devices, such as phones, smartwatches, and Bluetooth-enabled locks, are tested in multiple environments and at various heights to determine the impact that placement and interference played in locating the devices. The hypothesis is that most Bluetooth devices can be successfully enumerated quickly, but cannot be accurately located …

Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler

Engineering Technology Faculty Publications

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these …

The U-Net-Based Active Learning Framework For Enhancing Cancer Immunotherapy, Vishwanshi Joshi

Theses, Dissertations and Capstones

Breast cancer is the most common cancer in the world. According to the U.S. Breast Cancer Statistics, about 281,000 new cases of invasive breast cancer are expected to be diagnosed in 2021 (Smith et al., 2019). The death rate of breast cancer is higher than any other cancer type. Early detection and treatment of breast cancer have been challenging over the last few decades. Meanwhile, deep learning algorithms using Convolutional Neural Networks to segment images have achieved considerable success in recent years. These algorithms have continued to assist in exploring the quantitative measurement of cancer cells in the tumor microenvironment. …

Learning Adl Daily Routines With Spatiotemporal Neural Networks, Shan Gao, Ah-Hwee Tan, Rossi Setchi

Research Collection School Of Computing and Information Systems

The activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person’s habits, lifestyle, and wellbeing. Learning an individual’s ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for …

Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani

Electronic Theses and Dissertations

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold:

First, a Convolutional Neural Network (CNN)-based method for …

On The Usage And Vulnerabilities Of Api Systems, Conner D. Yu

Cybersecurity Undergraduate Research Showcase

To some, Application Programming Interface (API) is one of many buzzwords that seem to be blanketed in obscurity because not many people are overly familiar with this term. This obscurity is unfortunate, as APIs play a crucial role in today’s modern infrastructure by serving as one of the most fundamental communication methods for web services. Many businesses use APIs in some capacity, but one often overlooked aspect is cybersecurity. This aspect is most evident in the 2018 misuse case by Facebook, which led to the leakage of 50 million users’ records.1 During the 2018 Facebook data breach incident, threat actors …