Digital Commons Network^™

A Twenty-First Century Approach To Darius Milhaud's Saudades Do Brasil, Paige H. Crain

Theses and Dissertations--Music

French composer Darius Milhaud (1892-1974) lived in Rio de Janeiro, Brazil from February 1917 to November 1918. Milhaud’s experience in Rio was overwhelmingly positive, and several of his works were influenced by this period of his life, one being a suite for solo piano titled Saudades do Brasil, Op. 67. Inspired by Brazilian popular music, this piece is an example of exoticism, a trend of writing music based on cultures other than the composer’s own. Considering that exoticism historically has the potential to perpetuate harmful stereotypes or generate false information, this research delves into Saudades do Brasil, exploring …

Professional Experiences Of Disabled Music Therapists: An Adapted Multiple Case Study, August Lily Tuggle

Theses and Dissertations--Music

Literature about disability often focuses on disabled people as recipients of professional services and there is scant literature about disabled professionals. Although music therapists work primarily with disabled individuals, there is little research on or from the perspectives of disabled music therapists. A growing body of qualitative research, grey literature, and media platforms provides perspectives from the disability community regarding music therapy. Disabled individuals both within and outside the profession critique common music therapy attitudes and practices that conflict with the expressed needs of the disability community. However, much of this literature contains the perspectives of one individual or a …

Quakerism At Whittier College: Faded But Still Ever-Present, Damian Garcia

Whittier City & College Historical Research

No abstract provided.

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach, Ali Parsa, Neda Moghim, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Privacy-Preserved And Incentivized Knowledge Sharing For Reinforced-Learning Based Iot Platform Security, Weichao Wang, Md Morshed Alam, Yu Wang

School of Cybersecurity Faculty Publications

With the fast development and deep penetration of IoT devices and smart environments, using localized machine learning models to detect malicious activities has also been developed and deployed. However, these isolated learning models and results cannot be effectively federated together because of privacy concerns and lack of incentivization. This paper proposed several mechanisms to solve the problem. A verification method was designed for phased learning results to protect user privacy and prevent individual parties from manipulating the verification selection. The paper also presented an incentive method based on delay of distribution of the latest federated learning results. Extensive simulations were …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Problem-Centered Post-Secondary Computer Science Education: A Study Of The Private Artificial Intelligence Curriculum, Golnoush Haddadian, Prajwal Panzade, Daniel Takabi, Min Kyu Kim

School of Cybersecurity Faculty Publications

In response to the demand for Artificial Intelligence (AI) experts, this study introduced a curriculum development initiative. The aim was to design and implement a Private AI curriculum to understand the computer science (CS) students’ evaluations of the curricular activities and their levels of interest and motivation. Twenty-five students, a mix of undergraduates and graduates, were recruited and a scaled-down version of the curriculum was implemented. A parallel mixed-methods approach was employed. The results reinforced the significance of problem-centered curricula in CS context. Students rated the curricular activities highly and demonstrated strong motivation; however, graduates expressed more favorable view of …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

An Integrated Machine Learning Approach For Identifying Emergency Rescue Messages On Social Media During Natural Disasters, Wael Khallouli, Jiang Li, Jingwei Huang, Ghaith Rabadi, Samuel Kovacic

School of Cybersecurity Faculty Publications

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Domain Adapted Signal Classification For Simulated And Real-World Signals, Mohammad Ali

Theses and Dissertations

Signal classification models based on deep neural networks are trained on a set of data either simulated or over-the-air that are restricted to specific channel environments, varying distortion conditions such as SNR offsets. The consequence are models that unexpectedly do not do well in scenarios when the feature distribution between domains are too large such as when simulated models are deployed in real world environments. In this regard, we propose signal classification framework leveraging Domain-Adversarial Neural Networks to address SNR variability for simulated data Unsupervised Domain Adaptation frameworks for simulated to real world signals. Our first approach employs domain adversarial …

Great Expectations: How Gender Assigned At Birth Shapes Viewpoint Based Perceptual Disparities In Gender-Diverse People, Adelyn Emter

Theses and Dissertations

The purpose of this study was to examine disparities between personal beliefs and perceived societal ideologies about earning gendered traits and maintaining masculinity and femininity, with a focus on gender-diverse perspectives and the influence of gender identity on responses to social research. The current study recruited 334 gender-diverse participants to compare Viewpoint and Gendered Trait based on participant Gender Assigned at Birth, using twelve statements about earning and maintaining masculinity and femininity. The Traditional Egalitarian Sex Role (TESR) Inventory and Social Dominance Orientation (SDO) Scale were used as covariates which significantly contributed to the model. As expected, societal belief scores …

Beyond The Game: Designing An Nil First-Year Seminar Through Thematic Analysis Of Division 1 Student-Athletes Perspectives, Laura Dimattia

Theses and Dissertations

The 2021 relaxation of Name, Image, and Likeness (NIL) regulations by the NCAA has transformed collegiate athletics, granting student-athletes the opportunity to profit from their personal brands. These changes deliver financial and personal advancement opportunities but introduce new challenges. Student-athletes encounter challenges in managing responsibilities related to athletics, academics, and commercial activities amid evolving NIL compliance requirements. Universities struggle to manage multiple pressures including compliance requirements and recruitment strategies along with challenges from the Transfer Portal.

This study explores the motivational factors behind student-athletes' engagement with NIL and their learning preferences through a qualitative approach. The researcher conducted in-depth interviews …

Belongingness And Science Identity Of Underrepresented Students In Aerospace Sciences, Bella Hettich

Theses and Dissertations

Despite efforts to support underrepresented individuals—specifically those from racial, ethnic, and gender minority groups—they continue to face unique challenges that affect their belongingness in aerospace sciences, leading to issues with retention in educational programs and subsequent professional pathways. Such hindrances to personal achievement can also impede broader organizational goals. Addressing these challenges is critical, as supporting the success of underrepresented individuals fosters diverse perspectives, which are essential for driving innovation and maximizing organizational productivity. Just as importantly, ensuring that every individual, regardless of background, has access to opportunities and the support needed to succeed fulfills an ethical obligation. Upholding the …

A Federated Learning Solution For Secure And Scalable Edge Computing In Distributed Environments, Muhammad Hamza Karim

Theses and Dissertations

The growing penetration of Distributed Energy Resources (DERs) into the modern power system has introduced significant challenges related to data volume and cybersecurity. Centralized approaches struggle to process the large volume of data generated by inverters or other DER-related assets securely and efficiently. This problem raise concerns about the confidentiality, integrity, availability, and accountability (CIAA) properties of DER assets. Moreover, the large deployment of Internet of Things (IoT) and edge computing devices has introduced new security vulnerabilities. Malicious actors can exploit resource-constrained edge devices for unauthorized activities and resource consumption, threatening the performance and operational capabilities of the edge infrastructure. …

A Qualitative Study Exploring Best Practices And Applications Of Green, Eco, And Environmental Social Work In Canadian Social Work Practice, Ryley Kristanne Kowalski

Theses and Dissertations

Humanity and society are deeply connected to nature and the environment at their core. Through processes of colonization, the Industrial Revolution, urbanization, consumerism, and capitalism, people and societies have become increasingly disconnected from the natural environment. Introduced as a helping profession through the Industrial Revolution, social work began rooted in environment but quickly changed to understand human and societal wellbeing through the narrow scope of the social environment through the person-in-environment approach. Modern day issues of climate change, environmental degradation, environmental racism, and environmental justice movements have necessitated social work’s increased understanding of the natural environment. The purpose of this …

White Paper For Faculty Professional Development In Establishing, Addressing, And Informing, To Promote Mental Wellness For Health Careers Students, Betty J. Mcvay

Theses and Dissertations

This research utilized a qualitative case study to explore the need for faculty professional development in the health careers division at a public non-profit California community college, specifically Porterville College. Literature suggests that mental health is a crucial topic to address in contemporary higher education. College students, in general, are facing a notable deterioration in their overall mental well-being, accompanied by an increase in mental disorders. Furthermore, students in health careers programs frequently encounter even more intense stress and psychological challenges, stemming from their constant exposure to high-pressure, emotionally charged situations, and vicarious trauma. Although literature emphasizes the critical need …

Evaluating Human Factors In Isolation And Confinement During Analog Spaceflight: A Decadal Survey Assessing Ilmah Crew Psychology, Travis Michael Nelson

Theses and Dissertations

This study examined 13 crews participating in analog Martian surface simulations to investigate psychological experiences within the first collegiate planetary surface habitat. The University of North Dakota (UND) Integrated Lunar-Martian Analog Habitat (ILMAH) was utilized with high-performing participant crews (n=42) who were recognized and recruited for their skills. Common environmental similarities that each crew faced included isolation, confinement, time-latent communications, demanding work hours, monotonous tasks, physically strenuous Extravehicular Activities (EVA), personal psychological adaptations, and interpersonal psychosocial challenges. The primary objective of this study was to evaluate self-appraised crew performance measures across continuous phases of isolation and confinement lasting up to …

Artificial Intelligence And Machine Learning Models To Increase Colorectal Cancer Detection For American Samoa Adults, Va'atausili Tofaeono

Theses and Dissertations

Colorectal cancer (CRC) remains a leading cause of morbidity and mortality among Indigenous and Pacific Islander populations, particularly in American Samoa, where screening rates are critically low due to cultural barriers, limited healthcare infrastructure, and geographical isolation. This dissertation explores the potential of an Artificial Intelligence and Machine Learning (AI/ML) predictive model to enhance CRC screening and early detection among American Samoan adults. By leveraging widely available clinical data, such as complete blood count (CBC) results, age, and gender, the AI/ML model aims to identify high-risk individuals for targeted interventions, thereby improving early detection and treatment outcomes.The dissertation comprises three …

A Cross-Entropy-Based Framework Using Hypersoft Type-2 Neutrosophic Numbers For Innovation Capability Evaluation In Central Cities Of Emerging Economies, Xiangke Zhang, Shoukui He, Xu Chen

Neutrosophic Sets and Systems

The comprehensive evaluation of innovation capabilities in central cities of emerging countries is essential for promoting regional economic growth. This evaluation examines various factors, such as scientific activities, innovation infrastructure, talent resources, and policy environments, offering insights into the current innovation landscape and future potential. By guiding the allocation of resources and optimizing innovation policies, it fosters the transformation of technological achievements and accelerates urban economic transformation and sustainable development. Moreover, it helps cities attract investment and skilled talent, enhancing their competitiveness and influence. This evaluation is a multi-attribute decision-making (MADM) challenge, for which the cross-entropy method has been recently …

Utilizing Cocoso And Interval Neutrosophic Magdm With Superhypersoft For Competitiveness Assessment In Rural Tourism Of Surrounding Cities, Aiping Cui, Zhiyao Liu

Neutrosophic Sets and Systems

With rising living standards, rural tourism near cities has rapidly developed, playing a key role in the sustainable development of urban economies and societies. Factors like ecological environment, historical and cultural resources, and landscape significantly influence the competitiveness of rural tourism. However, current research on this topic is still in its early stages, with a focus on tourism benefits to cities, often overlooking other impacts. Rural tourism boosts employment, promotes rural industries, and increases farmers' incomes, accelerating rural urbanization and economic growth. Therefore, evaluating the competitiveness of rural tourism near cities is essential for its development. The evaluation of rural …

Dvnn-Cocoso: A Hybrid Neutrosophic Model For Evaluating Social And Economic Impacts Of Sports Event, Changshun Bai, Pute Lan

Neutrosophic Sets and Systems

The evaluation of the social benefits of major sports events is a multifaceted process, focusing on assessing the positive impacts such events bring to a host city and society. These benefits include promoting public health, fostering national pride and social cohesion, boosting local economies, and improving infrastructure. This evaluation process can be viewed as a multiple-attribute decision making (MADM) problem. This study introduces a novel double-valued neutrosophic number CoCoSo (DVNN-CoCoSo) technique for MADM, incorporating DVNN Hamming distance (DVNNHD) and DVNN Logarithmic distance (DVNNLD). This method effectively handles the uncertainty inherent in evaluating social benefits. A numerical example of social benefits …

On A Novel Topological Space Based On Partially Ordered Ring Of Weak Fuzzy Complex Numbers And Its Relation With The Partially Ordered Neutrosophic Ring Of Real Numbers, Raed Hatamleh

Neutrosophic Sets and Systems

This work is dedicated to studying for the first time a novel topological space generated by weak fuzzy complex intervals based on the partial ordered ring structure of weak fuzzy complex numbers, where we build a topology structure over these intervals. Also, we present many novel results as mathematical theorems, mathematical proofs, and many related examples. On the other hand, we use the AH-isometry to determine the relationship between the weak fuzzy complex intervals, and neutrosophic intervals.

Uncertain Labeling Graphs And Uncertain Graph Classes (With Survey For Various Uncertain Sets), Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Graph theory, a branch of mathematics, studies the relationships between entities using vertices and edges. Uncertain Graph Theory has emerged within this field to model the uncertainties present in real-world networks. Graph labeling involves assigning labels, typically integers, to the vertices or edges of a graph according to specific rules or constraints. This paper introduces the concept of the Turiyam Neutrosophic Labeling Graph, which extends the traditional graph framework by incorporating four membership values—truth, indeterminacy, falsity, and a liberal state—at each vertex and edge. This approach enables a more nuanced representation of complex relationships. Additionally, we discuss the Single-Valued Pentapartitioned …

Some Types Of Hyperneutrosophic Set (4): Cubic, Trapozoidal, Q-Rung Orthopair, Overset, Underset, And Offset, Florentin Smarandache, Takaaki Fujita

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundational work presented in [38–40]. The Neutrosophic Set provides a comprehensive mathematical framework for managing uncertainty, defined by three membership functions: truth, indeterminacy, and falsity. Recent advancements have introduced extensions such as the Hyperneutrosophic Set and the SuperHyperneutrosophic Set, which are specifically designed to address increasingly complex and multidimensional problems. The formal definitions of these sets are available in [30]. In this paper, we extend the Neutrosophic Cubic Set, Trapezoidal Neutrosophic Set, q-Rung Orthopair Neutrosophic Set, Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset using the frameworks of the Hyperneutrosophic Set and the SuperHyperneutrosophic Set. Furthermore, …

Some Types Of Hyperneutrosophic Set (3): Dynamic, Quadripartitioned, Pentapartitioned, Heptapartitioned, M-Polar, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundation established in [50, 51]. The Neutrosophic Set provides a robust mathematical framework for handling uncertainty, defined by three membership functions: truth, indeterminacy, and falsity. Recent developments have introduced extensions such as the Hyperneutrosophic Set and SuperHyperneutrosophic Set to tackle increasingly complex and multidimensional problems. In this study, we explore further extensions, including the Dynamic Neutrosophic Set, Quadripartitioned Neutrosophic Set, Pentapartitioned Neutrosophic Set, Heptapartitioned Neutrosophic Set, and m-Polar Neutrosophic Set, to address advanced challenges and applications.

Plithogenic Duplets And Plithogenic Triplets, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

A Neutrosophic Set is a mathematical framework that represents degrees of truth, indeterminacy, and falsehood to address uncertainty in membership values [41, 42]. In contrast, a Plithogenic Set extends this concept by incorporating attributes, their possible values, and the corresponding degrees of appurtenance and contradiction [50]. Among the related concepts of Neutrosophic Sets, Neutrosophic Duplets and Neutrosophic Triplets are well-known. This paper defines Plithogenic Duplets and Plithogenic Triplets as extensions of these concepts using the Plithogenic Set framework and briefly examines their relationship with existing concepts.