Digital Commons Network^™

The Gavel And The Camera: Environmental Law And Photojournalism's Relationship To Appalachian Identity, Emily S. Patton

Honors Theses

Both the federal government and photojournalism have played a substantial role in the “othering” of Appalachia while simultaneously attempting to save it. Examining both institutions demonstrates the exploitative processes that alter the perception of rural communities and their surrounding environment. Stacey Kranitz’s archival exhibit, As It Was Give(n) To Me, internalizes this precarious role of the camera in a region heavily journalized. By inhabiting the area for years, establishing close relationships, and researching the documented history of Appalachia, Kranitz’s work becomes her livelihood in the dissolution of profession vs. personal. She is critical of past projects whose objective portrayal of …

Reinforcement Tuning For Detecting Stances And Debunking Rumors Jointly With Large Language Models, Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Ling, Bo Wang

Research Collection School Of Computing and Information Systems

Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim level, which are difficult to obtain. To address this issue, we leverage large language models (LLMs) as the foundation annotators for the joint stance detection (SD) and rumor verification (RV) tasks, dubbed as JSDRV. We introduce a novel reinforcement tuning framework to enhance the joint predictive capabilities of LLM-based SD and RV components. Specifically, we devise a policy for selecting LLM-annotated data at the two levels, employing a hybrid reward mechanism …

Empathyear : An Open-Source Avatar Multimodal Empathetic Chatbot, Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria

Research Collection School Of Computing and Information Systems

This paper introduces EmpathyEar, a pioneering open-source, avatar-based multimodal empathetic chatbot, to fill the gap in traditional text-only empathetic response generation (ERG) systems. Leveraging the advancements of a large language model, combined with multimodal encoders and generators, EmpathyEar supports user inputs in any combination of text, sound, and vision, and produces multimodal empathetic responses, offering users, not just textual responses but also digital avatars with talking faces and synchronized speeches. A series of emotion-aware instruction-tuning is performed for comprehensive emotional understanding and generation capabilities. In this way, EmpathyEar provides users with responses that achieve a deeper emotional resonance, closely emulating …

Artificial Intelligence And Administrative Justice: An Analysis Of Predictive Justice In France, Zouhaier Nouri, Walid Ben Salah, Nayel Al Omrane

All Works

This article critically analyzes the ethical and legal implications of adopting predictive analytics by the French administrative justice system. It raises a key question: Is it wise to integrate artificial intelligence into the administrative justice system, considering its potential benefits, despite the associated risks, ethical dilemmas, and legal challenges? The research employs a method based on an extensive literature review, a qualitative analysis of the adoption by the French administrative justice of predictive analytics tools, and a critical evaluation of the benefits and issues these tools bring. The study finds that AI can make the administrative justice system more efficient, …

Characterizing Gsk3Β Interaction And Kinetics Via Isothermal Titration Calorimetry, W A Bhagya De Silva

UNLV Theses, Dissertations, Professional Papers, and Capstones

Glycogen synthase kinase 3β (GSK3β) is a multifunctional serine/threonine kinase involved in several key signaling pathways, including glycogen metabolism, WNT/β-catenin, and Hedgehog signaling. Hyperactivity of GSK3β has been linked to Alzheimer’s disease, bipolar disorder, type II diabetes, and some cancers. Therefore, GSK3β is of interest as a target for therapeutics. Lithium ion (Li+) is a classical inhibitor of GSK3β. Structurally similar beryllium ion (Be2+) is ~1000-fold more potent. Lithium and beryllium have demonstrated pathway-specific and cell type-specific inhibition of GSK3β, prompting an investigation into the inhibitory mechanisms and thermodynamic characteristics of the metal-enzyme interaction.Isothermal titration calorimetry (ITC) is a label-free …

Sparse Neural Network To Enhance Performance Under Limited Parameter Constraints., Nailah Rawnaq

Graduate Theses and Dissertations

Over the past decade, the widespread adoption of deep neural networks has been a breakthrough driven by significant computational advancements. Additionally, the number of parameters of those models is exponentially increasing for performing complex tasks and achieving better performance. However, in most practical cases, often there are constraints in the number of parameters due to limited resources in storage size and computational cost. Network pruning can lead to an optimal solution to this problem. In this thesis, I present supporting evidence to the hypothesis that higher sparsity leads to better performance for a convolution-based neural network. I perform performance studies …

High Fat Diet & Social Isolation: Interactive Effects On Pain, Cognition, & Neuroinflammation, Ian M. Campuzano

Research Psychology Theses

Prior research has established a role for both social isolation and exposure to high fat Western diets in altering a range of behaviors from reduced memory performance to increased depression-like behaviors. The present study scrutinizes the interplay among these variables during the peri-adolescent developmental phase, utilizing Long-Evans rats as the experimental model. Our overarching hypothesis is that rats exposed to either social isolation, a high-fat diet, or both will result in heightened pain sensitivity, diminished cognitive flexibility, and increased neuroinflammatory responses within brain regions implicated in sociability, cognition, memory, and pain processing. Behavioral flexibility will be assessed using a maze-based …

An Application Of An In-Depth Advanced Statistical Analysis In Exploring The Dynamics Of Depression, Sleep Deprivation, And Self-Esteem, Muslihat Gaffari

Electronic Theses and Dissertations

Depression, intertwined with sleep deprivation and self-esteem, presents a significant challenge to mental health worldwide. The research shown in this paper employs advanced statistical methodologies to unravel the complex interactions among these factors. Through log-linear homogeneous association, multinomial logistic regression, and generalized linear models, the study scrutinizes large datasets to uncover nuanced patterns and relationships. By elucidating how depression, sleep disturbances, and self-esteem intersect, the research aims to deepen understanding of mental health phenomena. The study clarifies the relationship between these variables and explores reasons for prioritizing depression research. It evaluates how statistical models, such as log-linear, multinomial logistic regression, …

Emotionality Stigma Scale: Measurement Development, Reliability, And Validity., Hayley D. Seely

Electronic Theses and Dissertations

Emotions are biological responses to stimuli that allow individuals to derive meaning, appraise experiences, and prepare to respond. However, individuals perceive emotions differently based on emotion socialization which not only dictates the way emotions are viewed and managed but also has been directly linked with mental health outcomes. Furthermore, research shows emotion socialization is informed by demographic variables such as gender such that the expectations of emotionality differ; where women are taught to express emotions, men are taught to conceal. Given the societal rules regarding emotionality, it is possible that emotionality stigma – the stigma around the experience and expression …

Advancing Telehealth Through Artificial Intelligence: Incorporating Emotional Intelligence And Addressing Cybersecurity Challenges, Mahima Rajendra Pulgaonkar

Electronic Theses, Projects, and Dissertations

This culminating experience project explores the integration of Emotional Artificial Intelligence (Emotional AI) into telehealth systems, addressing the dual challenges of enhancing patient care and mitigating cybersecurity risks. The research questions are: (Q1) How can Emotionally Intelligent AI improve telehealth systems' ability to recognize and respond to mental health symptoms? and (Q2) What are the specific cybersecurity challenges associated with AI in telehealth and how can they be mitigated? The findings for each question are: Q1: Emotionally Intelligent AI can significantly enhance telehealth by providing personalized, empathetic interactions that improve patient engagement, adherence to treatment plans, and early detection of …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

Neural Networks For Decisions Under Uncertainty, Edwin Tomy George

Open Access Theses & Dissertations

Neural networks are used in many real-world applications, ranging from classification tasks to medical diagnostics. For each task, a neural network is typically able to make predictions due to its ability to extract meaningful patterns from processing large amounts of data. Thus, given the increases in available data in recent decades, the performance of neural networks in making accurate predictions has greatly increased. However, this data often comes with ingrained uncertainties due to measurement errors or the inherent variability of individual data points. Neural networks can learn despite the errors in the overall data, but what if we want them …

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 …

A Two-Part Validation Study Of The Sexual Minority Identity Emotion Scale, Henrietta Kadi Tettey-Tawiah

Graduate Theses and Dissertations

A number of existing studies indicate that there is some correlation between pride and shame and various behavioral health outcomes like anxiety, depression, and self-harm in the general population. Research also confirms that this relationship is true among sexual minorities as well. This study sets itself up to interrogate this assertion vis-à-vis sexual minority adolescents (SMAs). Consequently, confirmatory factor analysis (CFA) and differential item functioning (DIF) detection analysis were performed on the 35-item Sexual Minority Identity Emotion Scale (SMIES) for SMAs. A tree-based modeling technique, specifically conditional inference trees (CIT), was then employed to explore and make predictions about the …

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 …

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 …

Tool-Sensed Object Information Effectively Supports Vision For Multisensory Grasping, Ivan Camponogara, Alessandro Farnè, Robert Volcic

All Works

Tools enable humans to extend their sensing abilities beyond the natural limits of their hands, allowing them to sense objects as if they were using their hands directly. The similarities between direct hand interactions with objects (hand-based sensing) and the ability to extend sensory information processing beyond the hand (tool-mediated sensing) entail the existence of comparable processes for integrating tool- and hand-sensed information with vision, raising the question of whether tools support vision in bimanual object manipulations. Here, we investigated participants' performance while grasping objects either held with a tool or with their hand and compared these conditions with visually …

Mining Gambling Data For Modeling Gambling Behavior Patterns, Piyush Aniruddha Puranik

UNLV Theses, Dissertations, Professional Papers, and Capstones

Understanding player behavior for responsible gambling research is a difficult task due to the lack of data on players’ activities. Past studies in this area are largely limited to publicly available behavioral data or aggregated players data. Problem gambling in gamblers is typically identified only after they have already been addicted or have already been engaging in problematic gambling behavior. Furthermore, “risky” gambling behavior has historically been difficult to define due to the varying patterns of gambling activity that could potentially be attributed to it.In this dissertation we illustrate the methodology and algorithms used to engineer financial data for further …

We're Swarming Again! Swarming, Collectivity, And Trope: The Case Of Extinction Rebellion, Tyler J. Behymer

Department of Communication Studies: Dissertations, Theses, and Student Research

This thesis explores the rhetoric of the eco-movement Extinction Rebellion, focusing on the use of swarming and nature tropes to mobilize collective action and revivify contemporary notions of collectivity. Drawing on rhetoric of social movement scholarship, cultural studies, and psychoanalysis, this essay theorizes swarming as a tropological economy that expands the conditions of propriety in the context of collectivity. Through an analysis of Extinction Rebellion’s discourse, this study demonstrates how the naturalization of swarming tropes works in various ways to rewild conventional political discourse, galvanize disruptive collective assembly, and challenge green neoliberalism.

Advisor: Casey Ryan Kelly

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 …

Firm-Level Ai Ethical Awareness: Measurement And Effects, Yan Ma, Nan Hu

Research Collection School Of Computing and Information Systems

With rapid integration of AI technologies into business operation, ethical issues associated with AI development, implementation, and deployment, has become increasingly conspicuous. An effective measure of firm-level AI Ethical Awareness (FAIEA) is crucial for both academics and practitioners to assess FAIEA efficiently. In this study, based on qualitative information in earning conference call, we construct and validate a firm specific measure of FAIEA. Upon establishing a reliable measure, we explore the determinants and consequence of FAIEA. We find Corporate Social Responsibility (CSR) engagement and the presence of a Chief Information Officer (CIO) as key determinants of higher FAIEA, suggesting the …

Unifying Global-Local Representations In Salient Object Detection With Transformers, Sucheng Ren, Nanxuan Zhao, Qiang Wen, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

The fully convolutional network (FCN) has dominated salient object detection for a long period. However, the locality of CNN requires the model deep enough to have a global receptive field and such a deep model always leads to the loss of local details. In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers. With the global view in very shallow layers, the transformer encoder preserves more local representations to recover the spatial details in final saliency maps. Besides, as each layer can capture …

Speaker Verification In Agent-Generated Conversations, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general and special purpose dialogue tasks. However, the ability to personalize the generated utterances to speakers, whether conducted by human or LLM, has not been well studied. To bridge this gap, our study introduces a novel evaluation challenge: speaker verification in agent-generated conversations, which aimed to verify whether two sets of utterances originate from the same speaker. To this end, we assemble a large dataset collection encompassing …

Causvsr: Causality Inspired Visual Sentiment Recognition, Xinyue Zhang, Zhaoxia Wang, Hailing Wang, Jing Xiang, Chunwei Wu, Guitao Cao

Research Collection School Of Computing and Information Systems

Visual Sentiment Recognition (VSR) is an evolving field that aims to detect emotional tendencieswithin visual content. Despite its growing significance, detecting emotions depicted in visual content,such as images, faces challenges, notably the emergence of misleading or spurious correlationsof the contextual information. In response to these challenges, we propose a causality inspired VSRapproach, called CausVSR. CausVSR is rooted in the fundamental principles of Emotional Causalitytheory, mimicking the human process from receiving emotional stimuli to deriving emotional states.CausVSR takes a deliberate stride toward conquering the VSR challenges. It harnesses the power of astructural causal model, intricately designed to encapsulate the dynamic causal …

Automatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Modelsautomatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Models, Yang Ren, Yuqi Wu, Jungwei W Fan, Aditya Khurana, Sunyang Fu, Dezhi Wu, Hongfang Liu, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.

MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural …

Predicting Personality Or Prejudice? Facial Inference In The Age Of Artificial Intelligence, Shilpa Madan, Gayoung Park

Research Collection Lee Kong Chian School Of Business

Facial inference, a cornerstone of person perception, has traditionally been studied through human judgments about personality traits and abilities based on people's faces. Recent advances in artificial intelligence (AI) have introduced new dimensions to this field, employing machine learning algorithms to reveal people's character, capabilities, and social outcomes based just on their faces. This review examines recent research on human and AI-based facial inference across psychology, business, computer science, legal, and policy studies to highlight the need for scientific consensus on whether or not people's faces can reveal their inner traits, and urges researchers to address the critical concerns …

Book Review: How To Expect The Unexpected: The Science Of Making Predictions -- And The Art Of Knowing When Not To By Kit Yates, Mark Huber

Journal of Humanistic Mathematics

Humans think about the future all the time. Prediction is a part of how we prepare for the coming of both good and bad events in our lives. Kit Yates' book, How to expect the unexpected, concentrates primarily on the question of why prediction is difficult, and what mental shortcuts people take in prediction that can lead to incorrect results. Unfortunately, a lack of concern for details and several omissions undermine the quality of the book.

The Value Of Adding Nothing: A Call For Reform-Oriented Polynomial Division, Jonathan Clark, Jeneva Clark

Journal of Humanistic Mathematics

The call to implement reform practices in schools reflects the historical turn away from the behaviorist theory of learning in education. Yet the praxis of this turn remains a significant challenge, particularly within mathematics classrooms where procedural memorization is emphasized. In this article, we show one means of how to advance our pursuit of meaningful mathematics into polynomial division. Building on the literature for reform-based division methods, an alternative to the long division algorithm will be explored that relies solely on adding zero and fundamental algebraic principles.

Bootstraps And Scaffolds: What A Cognitive-Historical Analysis Of The Complex Number System Reveals About Numerical Cognition, Charles R. Card, Gary G. Miller

Journal of Humanistic Mathematics

The following investigation is a cognitive-historical analysis of the conceptual development of complex numbers. The history of this development spans nearly two millennia, from the earliest appearance of the square root of a negative quantity in the calculations of Heron of Alexandra (1st Century CE) to the full flowering of complex numbers in the first half of the 19th Century. The approach used for this analysis is Nersessian's, including her formulations of model-based reasoning and mental models. Additional aspects of the analysis feature the prominent roles played by process representations, including object-process complementarities, and by core numerical systems. Our analysis …

Building Communities Of Care For Equity, Justice, And Culturally Responsive Practice In Mathematics Education, Nicole Fletcher, B Waid

Journal of Humanistic Mathematics

Teaching is widely considered one of the “caring professions,” but conceptualizations of care and how care is put into practice in education are not universal. In this article, we draw from a range of perspectives on care that integrate supportive interpersonal relationships, high expectations, and culturally relevant theories of critical care, as well as Queer Theory and Disability Justice, to explore the application of these ideas in mathematics education. We identify key elements for building communities of care in mathematics education contexts: co-constructing community agreements, redefining participation, shifting traditional power structures, collaborative problem solving, and building networks of care beyond …