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Psychology And The Digital Everywhere: Artificial Intelligence, Cassie Van Stolk Assistant Professor Of Psychology, Department Of Psychology

Artificial Intelligence, 2024-25

This module within the PSYC 390: Psychology and the Digital Everywhere course investigates the the implications of AI on human experiences using a biopsychosocial lens. Topics covered include an exploration of AI as a tool versus as an autonomous mind, ethical considerations of AI usage, and the promises and pitfalls of AI as a tool within the field of psychology. This module aligns with the "Contemporary Global Challenges, Creativity and Innovation" Participation in a Global Society outcome within the Geneseo GLOBE Curriculum.

Real Time Pii Scanning, John David

Electronic Theses and Dissertations

The increased amount of web applications and internet software solutions utilizing cloud frameworks has contributed to large data sets of system log messages being generated constantly. These messages may contain sensitive data, creating an additional security risk for the systems and contributing to the need for analysis of such large volumes of data in real time. Large commercial data monitoring systems can solve for these analysis requirements, but they can be costly. We present a solution to analyzing web application log data which ingests it, processes it and visualizes sensitive data found within in real time. Our solution utilizes an …

Integrating Authentication Schemes In Augmented And Virtual Reality Classrooms, Naheem Noah

Electronic Theses and Dissertations

Augmented Reality and Virtual Reality (AR/VR) technologies are revolutionizing educational experiences, but their widespread adoption hinges on addressing critical security and usability challenges, particularly in the domain of user authentication. This research presents an investigation into the security landscape of AR/VR and explores a graphical authentication scheme called “Things” that enhances both security and usability in immersive learning environments. Through a systematic evaluation of popular AR/VR devices and applications, potential vulnerabilities and limitations were identified, such as high usage of pin/passwords which are susceptible to shoulder-surfing attacks, lack of multi-factor authentication, and unclear data-sharing practices. A review of existing knowledge-based …

Democratization Of Custom, High Quality Large Language Models, Pablo Lopez

College of Computing and Digital Media Dissertations

Large Language Models (LLMs) have shown exceptional performance in several natural language processing (NLP) tasks. Customizing LLMs boosts their performance in domain specific tasks but typically requires substantial resources and effort for training, such as supervised fine-tuning. This research proposes methods to achieve significant accuracy improvements given minimal resources, particularly focusing on open-ended question answering with a given piece of context. We utilize an LLM’s self-generated training data to fine-tune the LLM and partial fine-tuning with on-demand GPU to reduce practitioner training costs. The research shows that these methods give significant performance gains in a Retrieval Augmented Generation (RAG) based …

The Impact Of Model Variations On The Robustness Of Deep Learning Models In Adversarial Settings, Firuz Juraev, Mohammed Abuhamad, Simon S. Woo, George K. Thiruvathukal, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robots, and surveillance systems. These advancements include applying variations of sophisticated techniques that improve the performance of models. However, such models are not immune to adversarial manipulations, which can cause the system to misbehave and remain unnoticed by experts. The frequency of modifications to existing deep learning models necessitates thorough analysis to determine the impact on models’ robustness. In this work, we present an experimental evaluation of the effects of model modifications on deep learning model robustness using …

It Strategies For Social Media Implementation In Liberian Enterprises, Romeo Yleah

Walden Dissertations and Doctoral Studies

The rapid proliferation of social media platforms has globally transformed organizational operations, creating unique opportunities for direct consumer engagement. However, some Liberian organizations face challenges regarding effective social media implementation. Addressing this issue is critical for enabling Liberian businesses to compete internationally and maximize the potential of social media platforms. Grounded in the diffusion of innovations theory, the purpose of this qualitative pragmatic study was to explore effective IT strategies for integrating social media into Liberian enterprises. Data were collected using semi-structured interviews, observations, and field notes from eight IT managers in Monrovia who had integrated social media within the …

Evaluating Audio-Compression Techniques, Thien Tuong Duong

Theses and Dissertations

Perceptual quality of audio is the aural accuracy and fidelity perceived by the listener. It is how humans respond to the accuracy, intelligibility, or fidelity of aural media. High-fidelity audio represents high accuracy and minimal distortion and is the standard against which compression algorithms are assessed. We argue that maintaining high-fidelity audio is not just a technical requirement but is also about respecting the artistry of music production. The primary goal of this thesis is to compare the performance of popular audio codecs including file size and conversion speed of each codec when encoding the same file. A secondary goal …

Data Communication Over Power-Lines: A Review On Technical, And Applications Challenges, Abdi Abdalla

Tanzania Journal of Engineering and Technology (TJET)

This paper presents a review study on the data communication over power-lines, commonly referred to as power-line carrier, power-line communication (PLC), mains communications, or power-line digital subscriber line (PDSL). This study examines the technical and application advantages and challenges associated with adopting PLC as a preferred alternative technology for wideband or broadband data communication. The broader coverage area of the PLC network gives it a distinct advantage over other communication network technologies. Additionally, implementing a communication system using the existing power-line network is more cost-effective and less time-consuming compared to constructing a new network from scratch. However, the primary challenge …

Evaluation Of Business-Driven Reference Architecture For Big Data Analytics Implementation By Public Sector Organizations In Resource-Constrained Setting: A Case Study Of Uganda, Matendo Didas

Tanzania Journal of Engineering and Technology (TJET)

Big Data Analytics (BDA) is a new area at the nexus of agenda, public sector organizations, and government business. It may satisfy the growing need for trustworthy, cost-effective services in the public sector for better, more informed decision-making processes. BDA has been proposed on the planning schedules of several public sector organizations and the government. Therefore, from the previous work, using Uganda as a case study, specifically the Uganda Bureau of Statistics (UBOS), Ministry of Health (MoH), and Ministry of Education and Sports (MoES), this study aims to evaluate a designed Business-Driven Reference Architecture for Big Data Analytics Implementation (BRABDAI) …

Assessment Of Web Security Vulnerabilities For Common Open Source Virtualization Software, Said Ally

Tanzania Journal of Engineering and Technology (TJET)

Open-source hypervisors have emerged as an integral technology for virtualizing server resources in cloud and data center computing. Hypervisor security efficiency is determined by virtual machine isolation, which is a de facto adoption factor in the selection process, as well as its ability to respond to web attacks. This paper assesses the security performance of Proxmox VE and XenServer for type 1 hypervisors, and Kernel Virtual Machine and Oracle Virtual Box for type 2 hypervisors. Security analysis was conducted using common exposures extracted from vulnerability databases and mapped against the OWASP 2013 and 2017 projects. For clarity, experiments were carried …

Role Of Vegfa In Intervertebral Disc Pathoanatomy And Low Back Pain, Ryan Potter

McKelvey School of Engineering Graduate Student Theses & Dissertations

Chronic low back pain (LBP) affects more than 80% of Americans and causes significant disability and staggering public health costs. Despite this astonishing prevalence, there are currently no disease modifying therapies. Intervertebral disc (IVD) degeneration accounts for a considerable proportion of LBP, but the precise mechanisms driving the painful pathoanatomy are not understood. The degenerating IVD exhibits a complex array of inflammatory and physiologic changes, and many are associated with the presentation of LBP. For example, the presence of nerves and blood vessels in degenerate IVDs have been identified in patients and animals presenting with LBP symptoms. However, no studies …

Interventional Radiology's Exploration Into Artificial Intelligence, Raymond Nguyen

Master's Projects and Capstones

Background: Artificial intelligence (AI) has become more prominent in our daily lives in recent years. This includes various aspects of healthcare. Interventional radiology (IR) is one of these specialties that has taken strides in understanding how AI can be leveraged for patient care. This literature review aims to understand what areas will be most impacted by AI in IR and how it will influence both the patient and interventional radiologist.

Methods: Twenty-six publications from 2019-2024 were selected from PubMed and Scopus. Publications were sourced through a combination of keywords, subject headings (MeSH terms), and citation searching.

Results: This literature review …

A Platform For Integrating Internet Of Things, Machine Learning, And Big Data Practicum In Electrical Engineering Curricula, Nandana Jayachandran, Atef Abdrabou, Naod Yamane, Anwer Al-Dulaimi

All Works

The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. …

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, And Artificial Intelligence To Interrogate The Metabolic Evolution Of Glioblastoma, Kang Lin Hsieh, Qing Chen, Travis C Salzillo, Jian Zhang, Xiaoqian Jiang, Pratip K Bhattacharya, Shyan Shams

Faculty, Staff and Student Publications

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs …

Digital Scribes: A Possible Solution For Provider Burnout By Reducing Provider Workload, Shannon Storley

Theses and Graduate Projects

Background: Provider burnout is continuing to be a massive problem for our healthcare industry. One major contributor to provider burnout is burdensome administrative tasks associated with documentation of electronic medical records (EMR). This review aims to uncover the applications for artificially intelligent digital scribes as a solution to reduce EMR documentation burden. Purpose: Provider burnout has shown to increase the incidence of major mistakes and decreased patient safety grades. Digital scribes could be a solution in reducing provider burnout by reducing the administrative burden of EMR documentation. Methods: A comprehensive literature review was conducted using articles from PubMed using search …

Ethical Challenges And Solutions Of Generative Ai: An Interdisciplinary Perspective, Mousa Al-Kfairy, Dheya Mostafa, Nir Kshetri, Mazen Insiew, Omar Alfandi

All Works

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing …

Modular Approach To Soft Mobile Robots, Dimuthu Kodippili Arachchige

College of Computing and Digital Media Dissertations

Soft robot locomotion is a highly promising but under-researched subfield within the field of soft robotics. The compliant limbs and bodies of soft robots offer numerous benefits, including the ability to regulate impacts, tolerate falls, and navigate through tight spaces. These robots have the potential to be used for various applications, such as search and rescue, inspection, surveillance, and more. The state-of-the-art still faces many challenges, including limited degrees of freedom, a lack of diversity in gait trajectories, insufficient limb dexterity, limited payload capabilities, lack of control methods, etc. To address these challenges, this research introduces a modular approach to …

Ai-Based Methods For Detecting And Classifying Age-Related Macular Degeneration: A Comprehensive Review, Niveen Nasr El-Den, Mohamed Elsharkawy, Ibrahim Saleh, Mohammed Ghazal, Ashraf Khalil, Mohammad Z. Haq, Ashraf Sewelam, Hani Mahdi, Ayman El-Baz

All Works

This paper explores the advancements and achievements of artificial intelligence (AI) in computer vision (CV), particularly in the context of diagnosing and grading age-related macular degeneration (AMD), one of the most common leading causes of blindness and low vision that impact millions of patients globally. Integrating AI in biomedical engineering and healthcare has significantly enhanced the understanding and development of the CV application to mimic human problem-solving abilities. By leveraging AI-based models, ophthalmologists can improve the accuracy and speed of disease diagnosis, enabling early treatment and mitigating the severity of the conditions. This paper presents a comprehensive analysis of many …

A Learned Generalized Geodesic Distance Function-Based Approach For Node Feature Augmentation On Graphs, Amitoz Azad, Yuan Fang

Research Collection School Of Computing and Information Systems

Geodesic distances on manifolds have numerous applications in image processing, computer graphics and computer vision. In this work, we introduce an approach called 'LGGD' (Learned Generalized Geodesic Distances). This method involves generating node features by learning a generalized geodesic distance function through a training pipeline that incorporates training data, graph topology and the node content features. The strength of this method lies in the proven robustness of the generalized geodesic distances to noise and outliers. Our contributions encompass improved performance in node classification tasks, competitive results with state-of-the-art methods on real-world graph datasets, the demonstration of the learnability of parameters …

Artificial Intelligence, Work, And The Future Of Education, Daniel Brown

Library Presentations

No abstract provided.

Offensive Content Detection In Online Social Platforms, Ebuka Okpala

All Dissertations

Online social platforms enable users to connect with large, diverse audiences and the ability for a message or content to flow from one user to another user, user to followers, followers to user, and followers to followers. Of course, the advantages of this are apparent, and the dangers are also clearly obvious. The user-generated content could be abusive, offensive, or hateful to other users, possibly leading to adverse health effects or offline harm. As more of society's public discourse and interaction move online and these platforms grow and increase their reach, it is inherently important to protect the safety of …

Examining The Theory Of Planned Behavior As An Explanation For Why Some Creatives Learn To Use Generative Ai Tools, Navrose Bajwa

Theses, Dissertations and Culminating Projects

Artificial Intelligence (AI) can create art which earlier was restricted to humans. This involvement of AI in creating art poses risks of automation for the arts and design industry. One way with which artists can respond to this threat is to engage in proactive coping behavior and learn to use generative AI (GAI) for their work. Using an expanded version of Theory of Planned Behavior, this study looked at the predictors of graphic designer’s intentions to learn how to use GAI for art and design work. It was hypothesized that attitudes, social norms, perceived behavioral control and automation awareness would …

Enhancing Monthly Streamflow Prediction Using Meteorological Factors And Machine Learning Models In The Upper Colorado River Basin, Saichand Thota

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding and predicting streamflow along river basins is vital for planning future developments and ensuring safety, especially with climate change challenges. Our study focused on forecasting streamflow at Lees Ferry, a key location along the Colorado River in the Upper Colorado River Basin. We employed four machine learning models - Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal Auto-Regressive Integrated Moving Average; and combined historical streamflow data with meteorological factors such as snow water equivalent, temperature, and precipitation. Our analysis spanned 30 years of data from 1991 to 2020.

Our findings revealed that the Random Forest Regression …

Gamification In Erp Systems: A Study On Intrinsic Motivation And User Behavioral Intentions, Esi Adeborna, Fiona Fui-Hoon Nah, Luvai Motiwalla

Research Collection School Of Computing and Information Systems

In the evolving landscape of Enterprise Resource Planning (ERP) systems, integrating gamification shows promise for enhancing user training and education. However, empirical research on gamification's effect on intrinsic motivation in using ERP is limited. Hence, we examined the intrinsic motivational effects of a gamified ERP environment in this research. We created a Gamified Web Application linked to SAP ERP to investigate how gamification affects users' behavioral intention. Leveraging self-determination theory, we examined the influence of perceived competence, autonomy, and relatedness on usage intent. In a controlled experiment with 63 participants, data collected via survey revealed that gamification enhances perceived competence, …

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 …

Watme: Towards Lossless Watermarking Through Lexical Redundancy, Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Text watermarking has emerged as a pivotal technique for identifying machine-generated text. However, existing methods often rely on arbitrary vocabulary partitioning during decoding to embed watermarks, which compromises the availability of suitable tokens and significantly degrades the quality of responses. This study assesses the impact of watermarking on different capabilities of large language models (LLMs) from a cognitive science lens. Our finding highlights a significant disparity; knowledge recall and logical reasoning are more adversely affected than language generation. These results suggest a more profound effect of watermarking on LLMs than previously understood. To address these challenges, we introduce Watermarking with …

The Health Belief Model And Phishing: Determinants Of Preventative Security Behaviors, Jie Du, Andrew Kalafut, Gregory Schymik

Open Access Publishing Support Funded Articles

Email is frequently the attack vector of choice for hackers and is a large concern for campus IT organizations. This paper attempts to gain insight into what drives the email security behaviors of students, faculty, and staff at one midwestern public, master’s granting university. The survey relies on the health belief model as its theoretical basis and measures eight constructs including email security behavior, perceived barriers to practice, self-efficacy, cues to action, prior security experience, perceived vulnerability, perceived benefits, and perceived severity. Barriers to practice, self-efficacy, vulnerability, benefits, and prior experience variables were found to be significant determinants of self-reported …

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 …

Towards High Performance And Energy Efficient Data Centers., Bryan Harris

Electronic Theses and Dissertations

Our modern society has become ever more connected and reliant on ever larger quantities of data, which must be collected and processed over large geographic areas. The data centers that store, compute, and share this data have grown to such a scale that many data centers use as much electricity as a city. Priorities in research have shifted from not only seeking greater performance, but also towards greater energy efficiency. The control and management of computer systems has an impact on power consumption beyond the needs of individual components. In this dissertation, we examine techniques for improved performance and energy …

Applications Of Artificial Intelligence On Drought Impact Monitoring And Assessment, Beichen Zhang

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Drought, a prevalent and consequential natural disaster, poses widespread, indirect challenges across environmental and societal dimensions. Despite considerable focus on monitoring meteorological and hydrological drought and studying their characteristics, there is a gap in assessing its multifaceted impacts, especially on societal sectors. The dissertation comprises three research essays utilizing artificial intelligence to quantitatively study multi-dimensional drought impacts. The first essay leveraged deep learning and natural language processing to predict multi-dimensional drought impacts from textual datasets, including social media, news media, and citizen scientist reports. The findings demonstrate superior performance over traditional methods and unveil the spatial and temporal heterogeneity of …