Digital Commons Network^™

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 …

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 …

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 …

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 …

A Trust-By-Learning Framework For Secure 6g Wireless Networks Under Native Generative Ai Attacks, Md Shirajum Munir, Sravanthi Proddatoori, Manjushree Muralidhara, Trinidad Mario Dena, Walid Saad, Zhu Han, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Sixth-generation (6G) wireless networks will become vulnerable due to native generative AI (GenAI)-driven intelligent poisoning attacks in both the radio unit and the core network. In particular, network parameters and metrics in cross-layer design pose fundamentally uncertain conditions and can be compromised through the native GenAI mechanism, which leverages data augmentation and reconstruction capabilities. This work investigates the capabilities of native GenAI to create novel poisoning attacks in wireless networks, while investigating their impact through uncertainty-informed root analysis. Then, detected attacks are mitigated by developing a trustworthy service aggregation in the wireless network. First, a joint decision problem is formulated …

Relationship Extraction Using Retrieval Augmented Generation For Biomedical Dataset, Jannat Jannat

Theses and Dissertations

With the increasing number of structured and unstructured data, obtaining reliable information effectively has become crucial. In the biomedical domain, extracting information from the scientific papers is crucial in order to stay up-to-date with accurate information, given the increased pace by which new research studies are published. This work focuses on identifying relationships between entities that are extracted from the abstracts and titles of biomedical research papers. In this work, we developed a Retrieval Augmented Generation (RAG) based system to automatically identify relations between biomedical entities. We evaluate multiple open source Large Language Models (LLMs) and the number of examples …

Revised Final Multi-Pathway Residential Metals Abatement Program Plan, Atlantic Richfield Company, Butte Silver Bow

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Silver Bow Creek Conservation Area Repository Data Gap Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

This Silver Bow Creek Conservation Area (SBCCA) Repository Data Gap Data Summary Report (DSR) presents the results from the soil sampling activities conducted as specified in the SBCCA Repository Data Gap Quality Assurance Project Plan (QAPP) (referred to herein as the SBCCA Repository Data Gap QAPP; Atlantic Richfield Company, 2024). The information presented in this DSR includes laboratory analytical results from subsurface soil sampling collected from Buffalo Gulch (BG), Butte Reduction Works (BRW), Diggings East (DE), and Northside Tailings (NST) within the SBCCA. This DSR was prepared by Pioneer Technical Services, Inc. (Pioneer), 1101 S. Montana Street, Butte, Montana 59701 …

Berkeley Pit And Discharge Pilot Project Quarterly Pilot Project Report Third Quarter 2024, Atlantic Richfield Company, Mark Thompson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

When Open-Source Information Backfires: Satellite Imagery And Privacy Breaches, Matin Pedram, Siena Chandler, Eugenia Georgiades

Vanderbilt Journal of Transnational Law

No abstract provided.

More Than Just A Game: What Higher Education Can Learn From Player Agency, Kyle Haiman

Dissertations and Theses @ UNI

Higher education presents students with the challenge of balancing institutional expectations for autonomy with the need for community and support. In a world that is becoming more individualized and less empathetic, agency, or engaging in intentional action,plays a crucial role in navigating this tension. Agency has been linked to academic success and real-world problem solving, yet its development among college-aged students remains an ongoing challenge. Games and game-based learning offer a unique lens through which to explore agency, as players regularly engage in decision making, problem solving, and self-directed learning.

This qualitative study examined how college students at a regional, …

Revealing The Hidden Benefits Of Social Emotional Learning: Unpacking What’S Really Going On?, Marissa Schweinfurth

Dissertations and Theses @ UNI

This study explores the role of Social Emotional Learning (SEL) in enhancing executive function (EF) skills among elementary school students, with a particular focus on those facing high levels of adversity. The research was driven by three key questions: (1) Can SEL improve EF skills in young students? (2) Does SEL lead to faster development of EF skills compared to typical age-related expectations? (3) Which specific SEL strategies are most effective in promoting EF skills? Utilizing the "Transactional Model of Stress and Coping" by Lazarus and Folkman as a conceptual framework, this study examined how SEL interventions act as coping …

Age As More Than A Number: An Applied Geography Of Elderly Vulnerability To Heat Events In The Southeastern United States, John Darko

Dissertations and Theses @ UNI

No abstract provided.

The Educational Perspective Of At-Risk Students: How Standards-Based Grading Affects Students In Alternative Education, Matthew John Secl

Dissertations and Theses @ UNI

This dissertation explored the widespread challenges within educational systems by investigating the transformative potential of Standards-Based Grading (SBG) for atrisk students in alternative education settings. In an epoch where traditional grading systems often perpetuate inequities and focus on punitive assessments that undermine student growth, this study will explore how SBG can redefine how students in alternative education settings perceive their academic success and understand their learning environments. By providing a fair and transparent system, SBG reassures students and educators alike, instilling confidence in the system. Through in-depth, semi-structured interviews with recent graduates from a Midwest alternative school, the research examined …

Taiwanese Elementary School Teachers’ Mindset Of Content And Language Integrated Learning (Clil) Instruction, Chin-Wen Chien

Journal of English Learner Education

Teachers need to undergo a mindset shift in language learning and teaching. This study used both qualitative and quantitative data to explore seven Taiwanese Content and language integrated learning (CLIL) teachers’ mindsets and led to the following major findings. First, the CLIL teachers had a stronger mindset regarding their instructional strategies, lesson preparations, and efficacy, compared to their more moderate mindset regarding their CLIL training and preparation. Secondly, the CLIL teachers had a different mindset regarding language use in their CLIL instruction due to their learners’ English proficiency levels. Finally, their lesson designs were lacking a 4Cs culture. Based on …

Digital Stories Of Resilience: A Case Study Of Urban Community College English Learners, Patricia George Hunter

Journal of English Learner Education

This study utilizes a qualitative case study approach to learn how project-based, digital storytelling (DST) voices the resilience of post-secondary English language learners (ELLs) attending a minority-serving, open-access, community college in New York City. Along with many studies that have examined the usefulness of DST in oral production, this research investigates whether project-based digital storytelling enhances students’ understanding of academic content related to resilience and conventional composition. Narrative inquiry (NI) research technique is the basis of the digital story project in an English as a Second Language (ESL) course. In this case, the study relies on observations and artifacts to …

Now I See: Video Supports For English Language Learners, Joanna C. Zimmerle, Hanrui He, Tara Hill

Journal of English Learner Education

General education teachers are often unprepared by teacher preparation programs and limited professional development opportunities to support English Language Learners (ELLs), despite the increasing number of ELLs entering US schools and the trend towards placing them in mainstream classrooms. Video resources offer one viable approach to supporting the language learning of ELLs, drawing upon theoretical frameworks such as dual-coding, constructivist theory, and sociocultural perspectives. This paper describes the use of video resources to assist general education teachers in effectively leveraging videos to support the language learning of ELLs in mainstream classrooms.

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Enhancing Image Classification Using A Convolutional Neural Network Model, Zena M. Saadi, Ahmed T. Sadiq, Omar Z. Akif, Marwa M. Eid

Journal of Soft Computing and Computer Applications

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

Foreword From Editor - 16th Edition: Toward An Inclusive Community Engagement, Yandi Andri Yatmo

ASEAN Journal of Community Engagement

This edition of AJCE defines and elaborates on the idea of inclusive community engagement as a means to involve the community in a meaningful process. ‘Inclusive’ refers to the principles of encompassing everyone, all individuals and groups alike, regardless of their identity, background, characteristics, needs, and perspectives, thereby ensuring that all voices are represented (Hodkinson, 2011). This practice extends beyond individuals with disabilities and embodies broader ideas of equality. Inclusive engagement plays a crucial part in fostering a constructive dialog that incorporates diverse perspectives within a community. Such engagements prioritize community participation in the decision-making process that affects their well-being …

Learning Paradigms For Rhythm Detection And Generation Using Mathematical Models, Biophysical And Artificial Neural Networks, Prianka Bose

Dissertations

Humans possess an inherent ability to recognize evenly-spaced rhythms, known as isochronous rhythms, owing to the brain's predisposition to entrain to external auditory stimuli with regular temporal intervals. The central focus of this research is to understand how the brain learns and retains rhythmic time intervals in the context of music. This dissertation studies rhythm detection and generation through mathematical models, biophysical networks, and artificial neural networks, addressing both isochronous and non-isochronous patterns.

A primary focus of the thesis is on isochronous rhythms. In particular, given a perturbation to an isochronous rhythm such as a tempo change or phase shift …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

Game-Based Learning With Augmented Reality, Kantida Nanon

Dissertations

Augmented Reality (AR) technology provides an effective and controllable learning environment, especially for complicated, potentially dangerous, or critical content. Effective implementation of interaction and platforms for educational tools can maximize learner engagement and optimize the learning experience. This dissertation explores the potential of AR technology to enhance learning experiences across multiple platforms and subject areas, particularly within chemistry and mathematics. The dissertation is grounded in the field of Human-Computer Interaction (HCI) and explores the potential of AR to increase learner engagement, improve learning outcomes, and transform traditional pedagogical practices.

The dissertation is divided into two parts. The first part examines …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Differential Item Functioning Of The Region-Based National Examinationequipment, Adi Setiawan, Gulzhaina Kuralbaevna Kassymova, Vianney Mbazumutima, Anggit Reviana Dewi Agustyani

REID (Research and Evaluation in Education)

This research aims to detect Differential Item Functioning (DIF) in the 2014/2015 National Examination Questions in mathematics of junior high schools and equivalent- level schools in the Yogyakarta region as a reference group and the South Kalimantan region as a focus group using the Likelihood Ratio Test (LRT) method, Area Measure Raju, and Lord. A sensitivity analysis was conducted to determine the most sensitive method. The data consisted of 5,465 National Examination papers of the students from the two regions who worked on type A questions. A sample of 1,000 exam papers for each region was established using the simple …

2024 Scholarly Productivity Report, Missouri University Of Science And Technology

Civil, Architectural and Environmental Engineering Scholarly Productivity Reports

No abstract provided.