Physical Sciences and Mathematics Commons^™

Bias Detector Tool For Face Datasets Using Image Recognition, Jatin Vamshi Battu

Master's Projects

Computer Vision has been quickly transforming the way we live and work. One of its sub- domains, i.e., Facial Recognition has also been advancing at a rapid pace. However, the development of machine learning models that power these systems has been marred by social biases, which open the door to various societal issues. The objective of this project is to address these issues and ensure that computer vision systems are unbiased and fair to all individuals. To achieve this, we have created a web tool that uses three image classifiers (implemented using CNNs) to classify images into categories based on …

Video Sign Language Recognition Using Pose Extraction And Deep Learning Models, Shayla Luong

Master's Projects

Sign language recognition (SLR) has long been a studied subject and research field within the Computer Vision domain. Appearance-based and pose-based approaches are two ways to tackle SLR tasks. Various models from traditional to current state-of-the-art including HOG-based features, Convolutional Neural Network, Recurrent Neural Network, Transformer, and Graph Convolutional Network have been utilized to tackle the area of SLR. While classifying alphabet letters in sign language has shown high accuracy rates, recognizing words presents its set of difficulties including the large vocabulary size, the subtleties in body motions and hand orientations, and regional dialects and variations. The emergence of deep …

Navigating Classic Atari Games With Deep Learning, Ayan Abhiranya Singh

Master's Projects

Games for the Atari 2600 console provide great environments for testing reinforcement learning algorithms. In reinforcement learning algorithms, an agent typically learns about its environment via the delivery of periodic rewards. Deep Q-Learning, a variant of Q-Learning, utilizes neural networks which train a Q-function to predict the highest future reward given an input state and action. Deep Q-learning has shown great results in training agents to play Atari 2600 games like Space Invaders and Breakout. However, Deep Q-Learning has historically struggled with learning how to play games with greater emphasis on exploration and delayed rewards, like Ms. PacMan. In this …

3d Ar Reconstruction, Sneh Arvind Kothari

Master's Projects

The goal of the project is to improve the shopping experience for users by using augmented reality technology. People generally want opinions from others when buying shoes offline. Clicking and sending images of a shoe is not an ideal solution as it does not give the complete feel of the shoe. We developed the 3D AR Reconstruction app to make this process better. A user of our app clicks photos of the shoe. This image data is converted to form a mesh that can be shared. On receiving a model the user can open it in the app and interact …

Static Taint Analysis Via Type-Checking In Typescript, Abhijn Chadalawada

Master's Projects

With the widespread use of web applications across the globe, and the ad- vancements in web technologies in recent years, these applications have grown more ubiquitous and sophisticated than ever before. Modern web applications face the constant threat of numerous web security risks given their presence on the internet and the massive influx of data from external sources. This paper presents a novel method for analyzing taint through type-checking and applies it to web applications in the context of preventing online security threats. The taint analysis technique is implemented in TypeScript using its built-in type-checking features, and then integrated into …

Detecting Botnets Using Hidden Markov Model, Profile Hidden Markov Model And Network Flow Analysis, Rucha Mannikar

Master's Projects

Botnet is a network of infected computer systems called bots managed remotely by an attacker using bot controllers. Using distributed systems, botnets can be used for large-scale cyber attacks to execute unauthorized actions on the targeted system like phishing, distributed denial of service (DDoS), data theft, and crashing of servers. Common internet protocols used by normal systems for regular communication like hypertext transfer (HTTP) and internet relay chat (IRC) are also used by botnets. Thus, distinguishing botnet activity from normal activity can be challenging. To address this issue, this project proposes an approach to detect botnets using peculiar traits in …

Ml-Based User Authentication Through Mouse Dynamics, Sai Kiran Davuluri

Master's Projects

Increasing reliance on digital services and the limitations of traditional authentication methods have necessitated the development of more advanced and secure user authentication methods. For user authentication and intrusion detection, mouse dynamics, a form of behavioral biometrics, offers a promising and non-invasive method. This paper presents a comprehensive study on ML-Based User Authentication Through Mouse Dynamics.

This project proposes a novel framework integrating sophisticated techniques such as embeddings extraction using Transformer models with cutting-edge machine learning algorithms such as Recurrent Neural Networks (RNN). The project aims to accurately identify users based on their distinct mouse behavior and detect unauthorized access …

Steganographic Capacity Of Selected Machine Learning And Deep Learning Models, Lei Zhang

Master's Projects

As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic based attack, information would be hidden in a learning model, which might then be used to gain unauthorized access to a computer, or for other malicious purposes. In this research, we determine the steganographic capacity of various classic machine learning and deep learning models. Specifically, we determine the number of low-order bits of the trained parameters of a given model that can be altered without significantly affecting the performance of …

Classifying World War Ii Era Ciphers With Machine Learning, Brooke Dalton

Master's Projects

We examine whether machine learning and deep learning techniques can classify World War II era ciphers when only ciphertext is provided. Among the ciphers considered are Enigma, M-209, Sigaba, Purple, and Typex. For our machine learning models, we test a variety of features including the raw ciphertext letter sequence, histograms, and n-grams. The classification is approached in two scenarios. The first scenario considers fixed plaintext encrypted with fixed keys and the second scenario considers random plaintext encrypted with fixed keys. The results show that histograms are the best feature and classic machine learning methods are more appropriate for this kind …

Concept Drift Detection In Android Malware, Inderpreet Singh

Master's Projects

Machine learning and deep learning algorithms have been successfully applied to the problems of malware detection, classification, and analysis. However, most of such studies have been limited to applying learning algorithms to a static snapshot of malware, which fails to account for concept drift, that is, the non-stationary nature of the data. In practice, models need to be updated whenever a sufficient level of concept drift has occurred. In this research, we consider concept drift detection in the context of Android malware. We train a series of Support Vector Machines (SVM) over sliding windows of time and compare the resulting …

Leveraging Tweets For Rapid Disaster Response Using Bert-Bilstm-Cnn Model, Satya Pranavi Manthena

Master's Projects

Digital networking sites such as Twitter give a global platform for users to discuss and express their own experiences with others. People frequently use social media to share their daily experiences, local news, and activities with others. Many rescue services and agencies frequently monitor this sort of data to identify crises and limit the danger of loss of life. During a natural catastrophe, many tweets are made in reference to the tragedy, making it a hot topic on Twitter. Tweets containing natural disaster phrases but do not discuss the event itself are not informational and should be labeled as non-disaster …

Keystroke Dynamics And User Identification, Atharva Sharma

Master's Projects

We consider the potential of keystroke dynamics for user identification and authentication. We work with a fixed-text dataset, and focus on clustering users based on the difficulty of distinguishing their typing characteristics. After obtaining a confusion matrix, we cluster users into different levels of classification difficulty based on their typing patterns. Our goal is to create meaningful clusters that enable us to apply appropriate authentication methods to specific user clusters, resulting in an optimized balance between security and efficiency. We use a novel feature engineering method that generates image-like features from keystrokes and employ multiclass Convolutional Neural Networks (CNNs) to …

Real Time Panoramic Image Processing, Matthew Gerlits

Master's Projects

Image stitching algorithms are able to join sets of images together and provide a wider field of a vision when compared with an image from a single standard camera. Traditional techniques for accomplishing this are able to adequately produce a stitch for a static set of images, but suffer when differing lighting conditions exist between the two images. Additionally, traditional techniques suffer from processing times that are too slow for real time use cases. We propose a solution which resolves the issues encountered by traditional image stitching techniques. To resolve the issues with lighting difference, two blending schemes have been …

Web Traffic Time Series Forecasting, Summanth Redde Mulkkalla

Master's Projects

Online web traffic forecasting is one of the most crucial elements of maintaining and improving websites and digital platforms. Traffic patterns usually predict future online traffic, including page views, unique visitors, session duration, and bounce rates. However, it is challenging to forecast non-stationary online web traffic, particularly when the data has spikes or irregular patterns. This non-stationary property demands a more advanced forecasting technique. In this study, we provide a neural networkbased method, Spiking Neural Networks (SNNs), for dealing with the data spikes and irregular patterns in non-stationary data. In our study, we compared the forecasting results of SNNs with …

Forest Bathing Increases Adolescent Mental Well-Being And Connection To Nature: A Transformative Mixed Methods Study, Jennifer Keller

Antioch University Dissertations & Theses

Previous research has demonstrated that practicing forest bathing has significant positive effects on well-being. However, few studies have investigated whether forest bathing increases adolescent well-being despite the growing adolescent mental health crisis in the United States. Similarly, few studies have explored forest bathing’s impacts on connectedness to nature. Considering the ongoing environmental crisis, determining if forest bathing increases connectedness to nature is a critical expansion of forest bathing research, as connectedness to nature is linked to environmental care and concern. This study investigated the possibility that forest bathing, a nature-based mindfulness practice, could increase adolescent mental well-being and connectedness to …

Children Tell Landscape-Lore Among Perceptions Of Place: Relating Ecocultural Digital Stories In A Conscientizing/Decolonizing Exploration, Meredith Jean Bird Miller

Antioch University Dissertations & Theses

We know that when children feel a sense-of-relation within local natural environments, they are more prone to feel concern for them, while nurturing well-being and resilience in themselves and in lands/waters they inhabit. Positive environmental behaviors often follow into adulthood. Our human capacities for creating sustainable solutions in response to growing repercussions of global warming and climate change may grow if more children feel a sense of belonging in the wild natural world. As educators, if we listen to and learn from students’ voices about how they engage in nature, we can create pedagogical experiences directly relevant to their lives. …

Diving To New Depths: An Exploration Of Aquarium Visitors' Reflection At A Shark Exhibit, Nicole Leigh Conklin

Antioch University Dissertations & Theses

Zoos and aquariums (Z/As) are conservation-oriented free-choice learning institutions. In order to support their mission of advancing wildlife conservation, Z/As deliberately design opportunities and experiences to meaningfully engage visitors in understanding, caring for, and acting on behalf of exhibited species. Conservation psychologists and practitioners have applied values-based and models of human behavior to design and evaluate experiences aimed to influence myriad cognitive, affective, and behavioral outcomes. However, there is little research exploring the role of and opportunity for reflection within these institutions. Models of reflection and reflective practice, which are rooted in both theory and empirical data, stress the importance …

Connecting Antibiotic Resistance To The Environment (Care): Introducing A Novel Framework Integrating Chemical Cross-Resistance And Place-Based Engagement To The Blue Marsh Watershed In Reading, Pennsylvania, Jill Felker

Antioch University Dissertations & Theses

Antibiotic resistance is a serious health threat around the world. Millions of individuals are infected with antibiotic-resistant bacteria yearly, and thousands die from previously curable illnesses. Although antibiotic resistance occurs naturally, misuse of antibiotics accelerates the loss of their effectiveness. Public health campaigns focusing on antibiotic awareness have not effectively communicated and educated the public on this health crisis. New efforts to combat antibiotic resistance are urgently needed. This dissertation focuses on the ecological and public health components of antibiotic resistance research that must be addressed to decelerate antibiotic resistance. A new interdisciplinary theoretical framework was developed to Connect Antibiotic …

Eating Change: A Critical Autoethnography Of Community Gardening And Social Identity, Jessica Gerrior

Antioch University Dissertations & Theses

Community gardening efforts often carry a social purpose, such as building climate resilience, alleviating hunger, or promoting food justice. Meanwhile, the identities and motivations of community gardeners reflect both personal stories and broader social narratives. The involvement of universities in community gardening projects introduces an additional dimension of power and privilege that is underexplored in scholarly literature. This research uses critical autoethnography to explore the relationship of community gardening and social identity. Guided by Chang (2008) and Anderson and Glass-Coffin (2013), a systematic, reflexive process of meaning-making was used to compose three autoethnographic accounts. Each autoethnography draws on the author’s …

Examining The Effects Of Seed Mix Diversity And Composition, Biochar Application, Seeding Rate, Species Identity, And Topography On Palouse Prairie Restoration, Thurman Johnson

EWU Masters Thesis Collection

With over 99.9% of the Palouse prairie lost to land conversion, restoring native plant communities is crucial for ecological function, however, research on Palouse prairie restoration methods is sparse. Seed-based restoration uses a mix of seeded species to enhance competition against weeds, diversify vegetation, and adapt to environmental conditions. However, many factors can be varied, such as seed mix diversity and composition, the proportion of forbs to grasses, and seeding rate, and the most effective levels of each are not clear. Further, soil amendments, such as biochar, may benefit properties of tilled soils, but have not explored in Palouse Prairie …

Investigation Of Small Mammal Species Richness, Abundance, And Genetic Population Structure On And Around The Eastern Washington University Prairie Restoration Site, Sarah Deshazer

EWU Masters Thesis Collection

Small mammals are an ecologically important component of every landscape on Earth. They are a food source for higher trophic level animals, disperse plant seed and mycorrhizal fungi spore, engineer the landscape through burrowing and foraging activities, and alter plant community composition through selective predation of seed and grain. Studies have shown that small mammals may help facilitate the transition between successive stages in prairie restoration. Eastern Washington University has dedicated 120 acres of campus land to restoration of native prairie habitat. Small mammals can play both a positive and a negative role in restoration, therefore it is important to …

Digital Energy Platforms Considering Digital Privacy And Security By Design Principles, Umit Cali, Marthe Fogstad Dynge, Ahmed Idries, Sambeet Mishra, Ivanko Dmytro, Naser Hashemipour, Murat Kuzlu, Aleksandra Mileva (Ed.), Steffen Wendzel (Ed.), Virginia Franqueira (Ed.)

Engineering Technology Faculty Publications

The power system and markets have become increasingly complex, along with efforts to digitalize the energy sector. Accessing flexibility services, in particular, through digital energy platforms, has enabled communication between multiple entities within the energy system and streamlined flexibility market operations. However, digitalizing these vast and complex systems introduces new cybersecurity and privacy concerns, which must be properly addressed during the design of the digital energy platform ecosystems. More specifically, both privacy and cybersecurity measures should be embedded into all phases of the platform design and operation, based on the privacy and security by design principles. In this study, these …

Cybersecurity And Digital Privacy Aspects Of V2x In The Ev Charging Structure, Umit Cali, Murat Kuzlu, Onur Elma, Osman Gazi Gucluturk, Ahmet Kilic, Ferhat Ozgur Catak

Engineering Technology Faculty Publications

With the advancement of green energy technology and rising public and political acceptance, electric vehicles (EVs) have grown in popularity. Electric motors, batteries, and charging systems are considered major components of EVs. The electric power infrastructure has been designed to accommodate the needs of EVs, with an emphasis on bidirectional power flow to facilitate power exchange. Furthermore, the communication infrastructure has been enhanced to enable cars to communicate and exchange information with one another, also known as Vehicle-to-Everything (V2X) technology. V2X is positioned to become a bigger and smarter system in the future of transportation, thanks to upcoming digital technologies …

Defending Ai-Based Automatic Modulation Recognition Models Against Adversarial Attacks, Haolin Tang, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Automatic Modulation Recognition (AMR) is one of the critical steps in the signal processing chain of wireless networks, which can significantly improve communication performance. AMR detects the modulation scheme of the received signal without any prior information. Recently, many Artificial Intelligence (AI) based AMR methods have been proposed, inspired by the considerable progress of AI methods in various fields. On the one hand, AI-based AMR methods can outperform traditional methods in terms of accuracy and efficiency. On the other hand, they are susceptible to new types of cyberattacks, such as model poisoning or adversarial attacks. This paper explores the vulnerabilities …

Development Of Sensing And Programming Activities For Engineering Technology Pathways Using A Virtual Arduino Simulation Platform, Murat Kuzlu, Vukica Jovanovic, Otilia Popescu, Salih Sarp

Engineering Technology Faculty Publications

The Arduino platform has long been an efficient tool in teaching electrical engineering technology, electrical engineering, and computer science concepts in schools and universities and introducing new learners to programming and microcontrollers. Numerous Arduino projects are widely available through the open-source community, and they can help students to have hands-on experience in building circuits and programming electronics with a wide variety of topics that can make learning electrical prototyping fun. The educational fields of electrical engineering and electrical engineering technology need continuous updating to keep up with the continuous evolution of the computer system. Although the traditional Arduino platform has …

Integration Of Omnet++ Into A Networking Course In An Electrical Engineering Technology Program, Murat Kuzlu, Brian Emmanuel Tamayo, Salih Sarp, Otilia Popescu, Vukica M. Jovanovic

Engineering Technology Faculty Publications

Networking courses are an integral part of electrical engineering technology programs as the majority of electronics in the modern day are required to communicate with each other. They are also getting more attention in manufacturing engineering technology programs because of the development of emerging technologies in Industry 4.0 arena. From laptops, computers, cellphones, modern day vehicles and smart refrigerators, these devices require a certain level of networking in order to communicate with other devices, whether it be locally, or even across the other side of the world. The objective of networking courses in an electrical engineering program is to demonstrate …

Berriasian–Valanginian Geochronology And Carbon-Isotope Stratigraphy Of The Yellow Cat Member, Cedar Mountain Formation, Eastern Utah, Usa, Robert M. Joeckel, Celina A. Suarez, Noah M. Mclean, Andreas Möller, Gregory A. Ludvigson, Marina B. Suarez, James I. Kirkland, Joseph Andrew, Spencer Kiessling, Garrett A. Hatzell

Conservation and Survey Division

The Early Cretaceous Yellow Cat Member of the terrestrial Cedar Mountain Formation in Utah, USA. has been interpreted as a “time-rich” unit because of its dinosaur fossils, prominent paleosols, and the results of preliminary chemostratigraphic and geochronologic studies. Herein, we refine prior interpretations with: (1) a new composite C-isotope chemostratigraphic profile from the well-known Utahraptor Ridge dinosaur site, which exhibits δ¹³C features tentatively interpreted as the Valanginian double-peak carbon isotope excursion (the so-called “Weissert Event”) and some unnamed Berriasian features; and (2) a new cryptotephra zircon eruption age of 135.10 ± 0.30/0.31/0.34 Ma (2σ) derived from the CA-ID-TIMS …

Hydrogeologic Field Trip Of Northeast Nebraska, Sue Olafsen Lackey, Kathleen Cameron, Matt Marxsen

Conservation and Survey Division

No abstract provided.

2023 Nebraska Water Leaders Academy, Mark E. Burbach, Robert Matthew Joeckel

Conservation and Survey Division

Eighteen participants completed the 2023 Water Leaders Academy bringing the total number of graduates to 186 since the inception of the program in 2011. Assessments of participants’ transformational leadership skills, champion of innovation skills, water knowledge, engagement with water issues, civic capacity, entrepreneurial leadership behaviors, boundary spanning skills, and curiosity increased significantly over the course of the year, according to both the participants and their raters. Feedback from the participants was highly positive and constructive. Academy planners are addressing participant concerns. Results of the program assessment indicate that the curriculum is meeting the Academy’s objectives. Therefore, only minor changes are …

Telescopic Megafans On The High Plains, Usa Were Signal Buffers In A Major Source-To-Sink System, Jesse T. Korus Dr., Robert Matthew Joeckel

Conservation and Survey Division

No abstract provided.