Digital Commons Network^™

Exploring Mid-Level Teacher Use Of Behavior-Specific Praise And General Praise, Catherine Anderson

Curriculum and Instruction Undergraduate Honors Theses

Behavior-specific praise (BSP) is an evidence-based classroom management practice that can be used to increase desired behavior and decrease undesired behavior. Teachers use general praise (GP) more frequently even though it has proven to be less effective than BSP. This study extends previous research on frequency behavior-specific praise in mid-level classrooms. The expected outcome was that mid-level teacher rates of BSP would be much lower than GP. The proposed research questions include the following: What are middle school teacher’s rates of general praise vs. behavior-specific praise? Do teachers deliver behavior-specific praise more frequently to individual students or groups of students? …

Investigation Of K-2 Teachers' Self-Efficacy In One Rural School District, Crystal Schaefer

Curriculum and Instruction Undergraduate Honors Theses

In the past four years education has undergone many changes, whether it be shifting to distance learning in 2020 or changing student behaviors, and these changes have undeniable effects on teachers and their self-efficacy. The purpose of this study is to investigate teachers’ sense of self-efficacy. A 24-question Qualtrics survey was distributed through email for kindergarten through second grade teachers between two elementary schools in one rural Arkansas school district. These 24 questions were adapted from The Teachers’ Sense of Efficacy Scale. The survey collected 16 total responses. The survey results provided evidence that teacher efficacy in instructional strategies is …

School Readiness Social Questions And Generalization To Net Using Aac Devices For Early Childhood Students With Autism, Madison Leighr

Curriculum and Instruction Undergraduate Honors Theses

This study’s purpose was to provide intervention support with communication for participants on the autism spectrum between the ages of four and five years old before entering kindergarten using a handheld speech-generating device (SGD). With a multiple-baseline across-subjects approach, participants gained the skills to communicate their knowledge on kindergarten readiness questions whether verbally or through the assistance of a handheld speech-generating device (SGD) exposed to them during the intervention. This single-subject research study done during my internship taught school-readiness social questions to preschool students with autism with generalization to the natural environment teaching (NET). Using the natural environment teaching method …

Through Undertale To Interactive Storytelling: The Effect Of Game Design On Secondary Students’ Perceptions Of Creative Writing, Terrell Page Jr.

Curriculum and Instruction Undergraduate Honors Theses

Videogames have emerged as a globally influential storytelling medium, as evinced by the success of story-driven games like Undertale, The Legend of Zelda: Breath of the Wild, and The Last of Us II. This medium needs creative writers who can learn to think like game designers. But, recognizing that not all creative writers will pursue a career in game design, this project studied the impact of thinking like a game designer on the way creative writing students approach their craft. Drawing on a variety of videogames and genres, this project focused on the potential of world-building and player choice to …

Investigating Self-Perception Of Speech And Motivation For Return To Intervention For Post Cochlear Implantation In Young Adults, Madison Henry

Electronic Theses and Dissertations

Introduction: Cochlear Implants (CI) are designed to address severe to profound hearing loss. When implantation occurs after language develops, it is referred to as post-lingual (PL) implantation. Currently, no interventions have been identified to address the communication challenges that young adults with PL-CIs may face after they complete post-CI rehabilitation services. This study analyzes if an individualized intervention plan is beneficial for a person with PL-CIs when experiencing changing communication demands in the transition to college or the workforce. Methodology: Baseline assessments were conducted; the results of which allowed for the establishment of four co-constructed goals. These goals were targeted …

Carmina Anacreontea 1-20: Text, Translation, And Commentary, Bayla Kamens

Arts & Sciences Graduate Student Theses and Dissertations

No abstract provided.

Student Self-Efficacy And Attitude In Organic Chemistry: A Comparison Of Two Pedagogical Approaches, Matthew Autry

Arts & Sciences Graduate Student Theses and Dissertations

No abstract provided.

“It's More Than A Book Club”: Counterspaces And Bibliotherapic Wellness Among Black Undergraduate Women, Khrystal Johnson

Arts & Sciences Graduate Student Theses and Dissertations

Black students at Predominantly White Institutions (PWI) face heightened vulnerability to general and race-related stressors, necessitating the identification of culturally responsive and alternative care approaches to complement traditional talk therapy and clinical assessment, given the associated adverse academic and psychological outcomes. The current study explores the utility of a Black women-focused book club as an inclusive and identity-affirming space for Black women attending an elite and private PWI. Additionally, we consider how this bibliotherapic and group-based approach offered a critical counterspace for participants. Over the course of a four-month semester, two Black women co-facilitators held a monthly book club series …

Bidding Strategy For A Wind Power Producer In Us Energy And Reserve Markets, Anne Stratman

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Wind power is one of the world's fastest-growing renewable energy resources and has expanded quickly within the US electric grid. Currently, wind power producers (WPPs) may sell energy products in US markets but are not allowed to sell reserve products, due to the uncertain and intermittent nature of wind power. However, as wind’s share of the power supply grows, it may eventually be necessary for WPPs to contribute to system-wide reserves. This paper proposes a stochastic optimization model to determine the optimal offer strategy for a WPP that participates in the day-ahead and real-time energy and spinning reserve markets. The …

Vr Circuit Simulation With Advanced Visualization For Enhancing Comprehension In Electrical Engineering, Elliott Wolbach

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

As technology advances, the field of electrical and computer engineering continuously demands innovative tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. Through a comprehensive literature review, it was discovered that there was a gap in the current research on using VR technology to effectively visualize and comprehend non-observable electrical characteristics of electronic circuits. This thesis explores the integration of Virtual Reality (VR) technology and real-time electronic circuit simulation with enhanced visualization of non-observable concepts such as voltage distribution and current flow within these circuits. The primary objective is to develop an immersive educational platform that makes …

An Investigation Of Information Structures In Dna, Joel Mohrmann

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The information-containing nature of the DNA molecule has been long known and observed. One technique for quantifying the relationships existing within the information contained in DNA sequences is an entity from information theory known as the average mutual information (AMI) profile. This investigation sought to use principally the AMI profile along with a few other metrics to explore the structure of the information contained in DNA sequences.

Treating DNA sequences as an information source, several computational methods were employed to model their information structure. Maximum likelihood and maximum a posteriori estimators were used to predict missing bases in DNA sequences. …

Hemisphere:Visual Cultures Of The Americas Volume Xvi 2024

Hemisphere: Visual Cultures of the Americas

No abstract provided.

Wonders Of The Ancestral Puebloans: Astronomers, Engineers, And Magicians Of The Four Corners, Ashar Mobeen

Hemisphere: Visual Cultures of the Americas

No abstract provided.

Neither “Before” Nor “After”: Fat Ambivalence In Laura Aguilar’S Body (Of Work), Joe Baez

Hemisphere: Visual Cultures of the Americas

No abstract provided.

(Review) Caroline Winterer’S How The New World Became Old: The Deep Time Revolution In America, Emmanuel Ramos-Barajas

Hemisphere: Visual Cultures of the Americas

No abstract provided.

Towards Explainable Harmful Meme Detection Through Multimodal Debate Between Large Language Models, Hongzhan Lin, Ziyang Luo, Wei Gao, Jing Ma, Bo Wang, Ruichao Yang

Research Collection School Of Computing and Information Systems

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models …

Escaping Saddle Points In Heterogeneous Federated Learning Via Distributed Sgd With Communication Compression, Sijin Chen, Zhize Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

We consider the problem of finding second-order stationary points in the optimization of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of FL to achieve communication efficiency without compensating the learning accuracy, especially when local data are highly heterogeneous across different clients. Given this, we propose a novel algorithm PowerEF-SGD that only communicates compressed information via a novel error-feedback scheme. To our knowledge, PowerEF-SGD is the first distributed and compressed SGD algorithm that provably escapes saddle points …

Q-Learning Based Framework For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

Electrical and Electronic Equipment (EEE) has evolved into a gateway for accessing technological innovations. However, EEE imposes substantial pressure on the environment due to the shortened life cycles. E-waste encompasses discarded EEE and its components which are no longer in use. This study focuses on the e-waste collection problem and models it as a Vehicle Routing Problem with a heterogeneous fleet and a multi-period planning problem with time windows as well as stochastic travel times. Two different Q-learning-based methods are designed to enhance the search procedure for finding solutions. The first method involves utilizing the state-action value to determine the …

Ublade: Efficient Batch Processing For Uncertainty Graph Queries, Siyuan Yao, Yuchen Li, Shixuan Sun, Jiaxin Jiang, Bingsheng He

Research Collection School Of Computing and Information Systems

The study of uncertain graphs is crucial in diverse fields, including but not limited to protein interaction analysis, viral marketing, and network reliability. Processing queries on uncertain graphs presents formidable challenges due to the vast probabilistic space they encapsulate. While existing systems employ batch processing to address these challenges, their performance is often compromised by the suboptimal selection of parallel graph traversal methods, the excessive costs in random number generation, and additional sampling-loads intrinsic to batch processing. In this paper, we introduce uBlade, an efficient batch-processing framework for uncertain graph queries on multi-core CPUs. uBlade utilizes the work-efficient graph traversal, …

Enabling Roll-Up And Drill-Down Operations In News Exploration With Knowledge Graphs For Due Diligence And Risk Management, Sha Wang, Yuchen Li, Hanhua Xiao, Zhifeng Bao, Yanfei Dong

Research Collection School Of Computing and Information Systems

Efficient news exploration is crucial in real-world applications, particularly within the financial sector, where numerous control and risk assessment tasks rely on the analysis of public news reports. The current processes in this domain predominantly rely on manual efforts, often involving keyword-based searches and the compilation of extensive keyword lists. In this paper, we introduce NCEXPLORER, a framework designed with OLAP-like operations to enhance the news exploration experience. NCEXPLORER empowers users to use roll-up operations for a broader content overview and drill-down operations for detailed insights. These operations are achieved through integration with external knowledge graphs (KGs), encompassing both fact-based …

Text-Attributed Graph Representation Learning : Methods, Applications, And Challenges, Ce Zhang, Menglin Yang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Text documents are usually connected in a graph structure, resulting in an important class of data named text-attributed graph, e.g., paper citation graph and Web page hyperlink graph. On the one hand, Graph Neural Networks (GNNs) consider text in each document as general vertex attribute and do not specifically deal with text data. On the other hand, Pre-trained Language Models (PLMs) and Topic Models (TMs) learn effective document embeddings. However, most models focus on text content in each single document only, ignoring link adjacency across documents. The above two challenges motivate the development of text-attributed graph representation learning, combining GNNs …

Factored Mdp Based Moving Target Defense With Dynamic Threat Modeling, Megha Bose, Praveen Paruchuri, Akshat Kumar

Research Collection School Of Computing and Information Systems

Moving Target Defense (MTD) has emerged as a proactive defense framework to counteract ever-changing cyber threats. Existing approaches often make assumptions about attacker-side knowledge and behavior, potentially resulting in suboptimal defense. This paper introduces a novel MTD approach, leveraging a Markov Decision Process (MDP) model that eliminates the need for prior knowledge about attacker intentions or payoffs. Our framework seamlessly integrates real-time attacker responses into the defender's MDP using a dynamic Bayesian network. We use a factored MDP model to enable a more comprehensive and realistic representation of the system having multiple switchable aspects and also accommodate incremental updates of …

Fashionregen: Llm‑Empowered Fashion Report Generation, Yujuan Ding, Yunshan Ma, Wenqi Fan, Yige Yao, Tat‑Seng Chua, Qing Li

Research Collection School Of Computing and Information Systems

Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports. It is traditionally performed by fashion professionals based on their expertise and experience, which requires high labour cost and may also produce biased results for relying heavily on a small group of people. In this paper, to tackle the Fashion Report Generation (FashionReGen) task, we propose an intelligent Fashion Analyzing and Reporting system based the advanced Large Language Models (LLMs), debbed as GPT-FAR. Specifically, it tries to deliver FashionReGen based on effective …

Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia

Research Collection School Of Computing and Information Systems

Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap …

On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao

Research Collection School Of Computing and Information Systems

Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We …

Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …

Vibmilk: Non-Intrusive Milk Spoilage Detection Via Smartphone Vibration, Yuezhong Wu, Wei Song, Yanxiang Wang, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

Quantifying the chemical process of milk spoilage is challenging due to the need for bulky, expensive equipment that is not user-friendly for milk producers or customers. This lack of a convenient and accurate milk spoilage detection system can cause two significant issues. First, people who consume spoiled milk may experience serious health problems. Secondly, milk manufacturers typically provide a “best before” date to indicate freshness, but this date only shows the highest quality of the milk, not the last day it can be safely consumed, leading to significant milk waste. A practical and efficient solution to this problem is proposed …

Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude

Research Collection School Of Computing and Information Systems

The recent surge in the integration of Large Language Models (LLMs) like ChatGPT into qualitative research in software engineering, much like in other professional domains, demands a closer inspection. This vision paper seeks to explore the opportunities of using LLMs in qualitative research to address many of its legacy challenges as well as potential new concerns and pitfalls arising from the use of LLMs. We share our vision for the evolving role of the qualitative researcher in the age of LLMs and contemplate how they may utilize LLMs at various stages of their research experience.

Social Balance On Networks: Local Minima And Best-Edge Dynamics, Krishnendu Chatterjee, Jakub Svoboda, Dorde Zikelic, Andreas Pavlogiannis, Josef Tkadlec

Research Collection School Of Computing and Information Systems

Structural balance theory is an established framework for studying social relationships of friendship and enmity. These relationships are modeled by a signed network whose energy potential measures the level of imbalance, while stochastic dynamics drives the network toward a state of minimum energy that captures social balance. It is known that this energy landscape has local minima that can trap socially aware dynamics, preventing it from reaching balance. Here we first study the robustness and attractor properties of these local minima. We show that a stochastic process can reach them from an abundance of initial states and that some local …

The Impact Of Ai On Ux: Challenges And Opportunities, Susan Stephanie Wells

Theses

Integrating artificial intelligence (AI) in user experience (UX) design is reshaping the field of UX, offering new opportunities and challenges for designers. This thesis project explores the multifaceted relationship between AI and UX design, focusing on the challenges, opportunities, and skills demanded of UX designers in the age of AI. Through a review of academic research and real-world experiences, this project studies the impact of AI on web design processes, UX testing, and data analysis. Key findings highlight the transformative potential of AI in enhancing user experiences, from suggesting website structures to facilitating UX testing and data analysis.

Comparative analysis …