Digital Commons Network^™

Remote Onboarding Of Software Developers: Leveraging Virtual Reality And Ai Tools, James Dominic

All Dissertations

Software development teams add newcomers to accommodate the increasing demand, complexity of software solutions, and turnover. Onboarding newcomers is expensive and error-prone. It can take up to three years for a newcomer to become an expert on a project. Onboarding techniques described in the current literature focus on collocated teams. As more teams are adopting remote and distributed team structures, I address this research gap in understanding remote onboarding for software developers. I present my research on the use of Virtual Reality (VR) for remote software developer onboarding. I discuss a VR remote pair programming environment. With positive outcomes, pair …

On The Structure, Energy, And Segregation Behavior Of Grain Boundaries In Metallic Systems, Yasir Mahmood

All Dissertations

Nearly all structural metallic systems are multi-component polycrystalline aggregates; their microstructures are composed of crystalline grains that are internally joined at grain boundaries (GBs). This thesis focuses on GB structure, energy, and chemistry, as these greatly influence many material properties and processes, including boundary dynamics during processing treatments or under operating conditions.

Using atomistic simulations, we examine the impact of metastable GB structures on solute segregation. A wide range of GB geometries and metastable structures are used in our study. The Al-Mg alloy is used because it is of interest for light weighting. The atomistic simulation results are used to …

Exploring Equity In Introductory Biology Genetics Education, Sandy G. Phillips-Long

All Dissertations

The dissertation “Exploring Equity in Introductory Biology Genetics Education” delved into the critical examination of equity within undergraduate genetics education. Chapter One outlined the background, significance, research framework, and manuscripts included in this study. Chapter Two was a scoping review that provided an overview of the current pedagogical approaches in undergraduate genetics education. It identified six active learning pedagogies: Course-based Undergraduate Research Experience (CURE); group work; case-based learning; inquiry-based learning; Virtual Reality (VR); and Consider, Read, Elucidate the hypothesis, Analyze and interpret the data, and Think of the next Experiment (CREATE). Chapter Three was a systematic literature review investigating pedagogical …

Improving Academic Attitudes And Motivation Using Literacy Learning In A Rural Literacy Intervention Classroom, Dannie Wilson

All Dissertations

This qualitative study focuses on the importance of reading picture books aloud to improve students' social skills capacity by utilizing literacy and social skills in a rural setting. It is designed to address a problem of practice: the need for striving third, fourth, and fifth-grade students to gain and maintain motivation and positive attitudes toward learning. An overarching research question guided this study: “How does teaching interpersonal skills affect literacy learning attitudes and motivation in a rural fringe school?” The goal was to investigate strategies through improvement science. A short Plan-Do-Study-Act cycle was used as the structure for the research …

Some Experiments In Additive Number Theory, Yunan Wang

All Dissertations

This dissertation explores fundamental conjectures in number theory, focusing on the distribution patterns of representation functions in prime pairs. The work concentrates on twin primes, cousin primes, and primes separated by six units, offering a fresh heuristic interpretation of the Hardy-Littlewood correction factor. The analysis progresses to investigate the partition function for prime pairs in the form $(p, p+k)$, specifically for $k = 2, 4, 6$. The study culminates in the derivation of a general formula for prime pairs $(p, p+d)$, where $d$ is an even integer. Drawing on the insights gleaned from examining the correction factor, this dissertation proposes …

We Train Ai, Why Not Humans, Too? An Exploration Of Human-Ai Team Training For Future Workplace Viability, Caitlin M. Lancaster

All Dissertations

The integration of Artificial Intelligence (AI) in the workforce is transforming team dynamics, leading to the emergence of Human-AI Teams (HATs). These teams offer opportunities to capitalize on human strengths with AI's prowess, offering significant opportunities for innovation and efficiency. Effective HAT functioning requires aligning human expectations with AI capabilities and bridging knowledge gaps between teammates. Despite this potential, key integration challenges remain, such as developing shared mental models, addressing skill limitations, and overcoming negative AI perceptions. Existing training efforts often apply human-human teaming principles directly to HATs, overlooking AI's role as a teammate and limiting the development of HAT-specific …

Optimization Strategies For Political Redistricting, Blake Splitter

All Dissertations

Political redistricting has remained a hot-button issue in the United States for several decades. Every ten years, most states need to redraw their districts to account for changing populations. Sometimes, these district plans can be drawn with the malevolent intention of aiding one political party over another. This dissertation summarizes four distinct methods of drawing these districts using computer algorithms while keeping several objectives in mind. We test these approaches on the case study state of South Carolina, since it provides a sufficiently challenging problem for us to test various algorithms. We find that many of these approaches improve upon …

Capillary Phenomena In Flow Down Fibers: Beads, Bridges, Sheets, And Threads, Chase Tyler Gabbard

All Dissertations

Capillary phenomena are ubiquitous in everyday life with surface tension responsible for shaping liquids and deforming slender structures on length scales smaller than the capillary length, as seen in the formation of drops from a leaky faucet and in the coalescence of wet hair, respectively. Recently, thin film flow down fibers have received significant attention for their ability to produce bead-on-fiber patterns with associated high surface area-to-volume ratios that optimize retention times in heat and mass transfer applications. The presence of a base flow gives rise to complex dynamics including absolute and convective instabilities. The parallelization of these processes through …

Convex Approach To Data-Driven Optimal Control With Safety Constraints Using Linear Transfer Operator, Joseph Raphel Moyalan

All Dissertations

This thesis is concerned with the data-driven solution to the optimal control problem with safety constraints for a class of control-affine nonlinear systems. Designing optimal control satisfying safety constraints is a problem of interest in various applications, including robotics, power systems, transportation networks, and manufacturing. This problem is known to be non-convex. One of this thesis's main contributions is providing a convex formulation to this non-convex problem. The second main contribution is providing a data-driven framework for solving the control problem with safety constraints. The linear operator theoretic framework involving Perron-Frobenius and Koopman operators provides the convex formulation and associated …

Efficient First-Order Methods For Some Smooth Nonlinear Optimization Problems, Yunheng Jiang

All Dissertations

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Virtually Coached Nature-Based Activities For Perceived Stress In Rural-Dwelling Families With A Teenager With Autism Spectrum Disorder, Deborah R. Logan

All Dissertations

Families with teenagers with autism spectrum disorder (ASD) can experience more stress than families without teenagers with ASD due to individuals with ASD often exhibiting social behaviors that differ from social norms, and parents having to adjust their expectations and future goals for their child with ASD. Rural-dwelling families of teenagers with ASD may experience additional stress due to their having to travel to access therapeutic services. Research focused on family-based services aimed at decreasing stress among rural-dwelling families of teenagers with ASD is lacking. The purpose of this convergent mixed methods study was to explore the feasibility of an …

Physics-Informed Machine Learning Methods For Inverse Design Of Multi-Phase Materials With Targeted Mechanical Properties, Yunpeng Wu

All Dissertations

Advances in machine learning algorithms and applications have significantly enhanced engineering inverse design capabilities. This work focuses on the machine learning-based inverse design of material microstructures with targeted linear and nonlinear mechanical properties. It involves developing and applying predictive and generative physics-informed neural networks for both 2D and 3D multiphase materials.

The first investigation aims to develop a machine learning method for the inverse design of 2D multiphase materials, particularly porous materials. We first develop machine learning methods to understand the implicit relationship between a material's microstructure and its mechanical behavior. Specifically, we use ResNet-based models to predict the elastic …

Data-Driven Enhanced Energy Management System Applications For Energy Control Centers, Dulip Madurasinghe

All Dissertations

This dissertation explores the pressing needs of modern bulk power system operation and control and delves into enhancements to EMS applications with minimal infrastructure development. The dissertation investigates three main EMS application enhancements. The transmission network topology processing (TNTP) is a foundational application of the EMS. A physics-based hierarchical transmission network topology processing (H-TNTP), including substation configuration identification, to improve efficiency and reliability is proposed. Secondly, a multi-level distributed linear state estimation (D-LSE) approach solely based on PMUs is proposed utilizing H-TNTP as the network modeling tool. The D-LSE can conduct linear state estimation (LSE) at either substation, area or …

Integrating Artificial Intelligence And Augmented Reality For Enhanced Task Performance In The Construction Industry, Aasish Chandrika Bhanu

All Dissertations

The Architecture, Engineering, and Construction (AEC) sector, characterized by its complex tasks, often faces challenges in adopting cutting-edge technologies, a trend that can significantly hinder productivity improvements compared to other industries. This dissertation explores the emerging integration of Artificial Intelligence (AI) and Augmented Reality (AR) within the AEC domain, investigating how effectively this can be implemented to potentially revolutionize industry practices. AI could be used in the AEC domain for advanced analytical and decision-making processes, while AR could be used for training, visualization, and remote collaboration.

In scenarios where construction site workers require expert guidance, these technologies could be helpful. …

Opioid Overdose Epidemic Modeling, Chelsea Spence

All Dissertations

The opioid overdose crisis in the United States has led to thousands of lost lives and thousands more people struggling with opioid dependence. Disease modeling allows researchers to examine the course that the disease may take and to investigate policies to determine the effects they may have. Disease models can be used to model non-communicable diseases and have been used to study opioid use disorder. Many types of disease models exist with their own inherent benefits and drawbacks.

In this dissertation, we provide a scoping review of the disease models that have been used to study the opioid overdose epidemic. …

Analyzing The Two-Phase Heterogeneous And Batch Service Queuing System With Breakdown In Two-Phases, Feedback, And Vacation, S.P. Niranjan, S. Devi Latha

Baghdad Science Journal

Customers are arriving at the system in bulk according to the Poisson process with rate λ1. The batch service process is split into two phases called first essential service (FES) and second essential service (SES) with minimum server capacity ′a′ and maximum server capacity ′b′. Customer who requires feedback after SES completion will be taken immediately for service by the server with probability β.

Wavelet-Attention Swin For Automatic Diabetic Retinopathy Classification, Rasha Ali Dihin, Ebtesam N. Alshemmary, Waleed A. M. Al-Jawher

Baghdad Science Journal

Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes by damaging the blood vessels in the retina. High blood sugar levels can cause leakage or blockage of these vessels, leading to vision loss or blindness. Early detection of DR is crucial to prevent blindness, but manually analyzing fundus images can be time-consuming, especially with a large number of images. Swin-Transformers have gained popularity in medical image analysis, reducing calculations and yielding improved results. This paper introduces the WT Attention-Db5 Block, which focuses attention on the high-frequency domain using Discrete Wavelet Transform (DWT). This block extracts detailed information …

Continuous Flow Injection Analysis Method For The Determination Of A Drug Diphenhydramine Hydrochloride By Using Phosphomolybdic Acid, Muntadhar M. Jabbar, Elham N. Mezaal

Baghdad Science Journal

The study presented a new method for determining diphenhydramine hydrochloride (DPH) in its pure form and some pharmaceutical preparations, within CFIA technique. The method is simple, fast, sensitive, easy to operate, and of a low cost. It is based on the reaction of DPH with phosphomolybdic acid (PMA), in aqueous media, forming a white, slightly yellowish precipitate. The precipitate formed was studied using an Ayah 6S×1-ST-2D solar cell CFI analyzer, by the reflection of incident light off the surfaces of the deposited particles at (0–180°), expressed as the response measured in (mV) . Some chemical and physical variables were studied …

Development Of Iiot-Based Pd-Maas Using Rnn-Lstm Model With Jelly Fish Optimization In The Indian Ship Building Industry, Pnv Srinivasa Rao, Pvy Jayasree

Baghdad Science Journal

Building and repair of ships are considered the Evergreen industry nationally as well as globally. The ships are generally gone in by the periodic scheduled repairs by the Indian shipbuilding industries. Sometimes industries lack productivity and lack of modernization some modern methods should be followed. The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy. The selected data set is pre-processed and feature selection for the optimization for …

Injective Eccentric Domination In Graphs, Riyaz Ur Rehman A, A Mohamed Ismayil

Baghdad Science Journal

The concept of domination has inspired researchers which has contributed to a vast literature on domination. A subset of is said to be a dominating set, if every vertex not in is adjacent to at least one vertex in . The eccentricity of is the distance to a vertex farthest from . Thus . For a vertex each vertex at a distance from is an eccentric vertex. The eccentric set of a vertex is defined as . Let , then is known as an eccentric point set of if for every , has at least one vertex such that . …

Bridging The Gap: Ai And The Hidden Structure Of Consciousness, Emily Barnes, James Hutson

Faculty Scholarship

The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking …

Quantifying Relationships In Fuzzy Cognitive Maps Based On Content Analysis Of Unstructured Research Texts, Ahmed Alibage, Antonie J. Jetter, Elpiniki Papageorgiou

Engineering and Technology Management Faculty Publications and Presentations

FCM projects often rely on knowledge-based approaches, such as expert interviews, which can be challenging to conduct because they require extensive expert participation. We propose a novel alternative to create FCM models based on expert knowledge that is already codified in research texts and other publications. In this approach, thematic network analysis identifies FCM concepts and signed causal connections, while computation of t-coefficient is used to determine the weights of the identified edges. We introduce and evaluate the approach in the context of a real-world system modeling project that is based on 47 carefully selected, peer reviewed research publications on …

Larp: Language Audio Relational Pre‑Training For Cold‑Start Playlist Continuation, Rebecca Salganik, Xiaohao Liu, Yunshan Ma, Jian Kang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a personalized and musically cohesive manner, has become vital to the success of music streaming services. Currently, many existing playlist continuation approaches rely on collaborative filtering methods to perform their recommendations. However, such methods will struggle to recommend songs that lack interaction data, an issue known as the cold-start problem. Current approaches to this challenge design complex mechanisms for extracting relational signals from sparse collaborative signals and integrating them into content representations. However, these approaches …

Self-Chats From Large Language Models Make Small Emotional Support Chatbot Better, Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies …

G2face: High-Fidelity Reversible Face Anonymization Via Generative And Geometric Priors, Haoxin Yang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Jing Qin, Yi Wang, Pheng-Ann Heng, Shengfeng He

Research Collection School Of Computing and Information Systems

Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G 2 Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face …

Cross-Problem Learning For Solving Vehicle Routing Problems, Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, Senthilnath Jayavelu

Research Collection School Of Computing and Information Systems

Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP variants. This paper proposes the cross-problem learning to assist heuristics training for different downstream VRP variants. Particularly, we modularize neural architectures for complex VRPs into 1) the backbone Transformer for tackling the travelling salesman problem (TSP), and 2) the additional lightweight modules for processing problem-specific features in complex VRPs. Accordingly, we propose to pre-train the backbone Transformer for TSP, and then apply it in the process of fine-tuning the Transformer models for each target VRP variant. …

Hierarchical Neural Constructive Solver For Real-World Tsp Scenarios, Yong Liang Goh, Zhiguang Cao, Yining Ma, Yanfei Dong, Mohammed Haroon Dupty, Wee Sun Lee

Research Collection School Of Computing and Information Systems

Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learning task. However, their efficacy has primarily been demonstrated on entirely random problem instances that inadequately capture real-world scenarios. In this paper, we introduce realistic Traveling Salesman Problem (TSP) scenarios relevant to industrial settings and derive the following insights: (1) The optimal next node (or city) to visit often lies within proximity to the current node, suggesting the potential benefits of biasing choices based on current locations. (2) Effectively solving the TSP requires robust tracking of unvisited nodes and warrants succinct …

Planning Like Human : A Dual-Process Framework For Dialogue Planning, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Ming Liu, Zerui Chen, Bing Qin

Research Collection School Of Computing and Information Systems

In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature. Traditional approaches to enhance dialogue planning in LLMs, ranging from elaborate prompt engineering to the integration of policy networks, either face efficiency issues or deliver suboptimal performance. Inspired by the dual-process theory in psychology, which identifies two distinct modes of thinking—intuitive (fast) and analytical (slow), we propose the Dual-Process Dialogue Planning (DPDP) framework. DPDP embodies this theory through two complementary planning systems: an instinctive policy model for familiar …

Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy, Heng Yim Nicole Oo, Min Hun Lee, J. H. Lim

Research Collection School Of Computing and Information Systems

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes unstructured data (i.e. an image frame with facial line segments) and structured data (i.e. features of facial expressions) to detect facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 21 facial palsy patients. Our experimental results show that among various data modalities (i.e. unstructured data - RGB images …

Mapping The Single-Cell Differentiation Landscape Of Osteosarcoma, Danh D Truong, Corey Weistuch, Kevin A Murgas, Prasad Admane, Bridgette L King, Jes Chauviere Lee, Salah-E Lamhamedi-Cherradi, Jyothishmathi Swaminathan, Najat C Daw, Nancy Gordon, Vidya Gopalakrishnan, Richard G Gorlick, Neeta Somaiah, Joseph O Deasy, Antonios G Mikos, Allen Tannenbaum, Joseph Ludwig

Faculty, Staff and Student Publications

Purpose: The genetic intratumoral heterogeneity observed in human osteosarcomas poses challenges for drug development and the study of cell fate, plasticity, and differentiation, which are processes linked to tumor grade, cell metastasis, and survival.

Experimental design: To pinpoint errors in osteosarcoma differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs mesenchymal stem cells toward adipogenic and osteoblastic fates. Incorporating preexisting chondrocyte data, we applied trajectory analysis and non-negative matrix factorization to generate the first human mesenchymal differentiation atlas.

Results: This "roadmap" served as a reference to delineate the cellular composition of morphologically complex osteosarcoma tumors and quantify …