Artificial Intelligence and Robotics Commons^™

Transformer-Based Feature Fusion Approach For Multimodal Visual Sentiment Recognition Using Tweets In The Wild, Fatimah Alzamzami, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

We present an image-based real-time sentiment analysis system that can be used to recognize in-the-wild sentiment expressions on online social networks. The system deploys the newly proposed transformer architecture on online social networks (OSN) big data to extract emotion and sentiment features using three types of images: images containing faces, images containing text, and images containing no faces/text. We build three separate models, one for each type of image, and then fuse all the models to learn the online sentiment behavior. Our proposed methodology combines a supervised two-stage training approach and threshold-moving method, which is crucial for the data imbalance …

Integrating Ai Into Culinary Medicine: A Revolution In Nutrition And Home Cooking, Emeka Ikeakanam, Evan Curry, Terrence Mchugh, Jason Walker

Research Day

Introduction

With the growing popularity of the emerging field of culinary medicine, there is a growing understanding of the culinary barriers needed to be overcome to adopt healthier eating habits. Lack of confidence, low skills, and lack of time are some of the most common barriers that prevent individuals from cooking at home. However, integrating AI can offer personalized support for home cooking and help individuals overcome these barriers. AI-powered meal planning and recipe suggestions can guide healthy and nutritious food choices that cater to their dietary needs and preferences. Additionally, AI can modify recipes to accommodate individual health conditions …

Product Review Classification Using Machine Learning And Statistical Data Analysis, Kajal Singh

Independent Student Projects and Publications

The aim of the paper is to implement and analyze the machine learning models for product review dataset. The project focuses on binary classification, multi-class classification, and clustering approaches to analyze and categorize product reviews. The performance of the models over each of the five classification tasks is measured by the 5-fold cross-validation scores over the training data.

Soft Law 2.0: An Agile And Effective Governance Approach For Artificial Intelligence, Gary E. Marchant, Carlos Ignacio Gutierrez

Minnesota Journal of Law, Science & Technology

No abstract provided.

Digital Dna: The Ethical Implications Of Big Data As The World’S New-Age Commodity, Clark H. Dotson

Honors Theses

In the emerging digital world that we find ourselves in, it becomes apparent that data collection has become a staple of daily life, whether we like it or not. This research discussion aims to bring light to just how much one’s own digital identity is valued in the technologically-infused world of today, with distinct research and local examples to bring awareness to the ethical implications of your online presence. The paper in question examines anecdotal and research evidence of the collection of data, both through true and unjust means, as well as ethical implications of what this information truly represents. …

Programming An Autonomous Robot, Maxwell Brueggeman

Honors College Theses

Ravaged by hurricanes, Florida needed help restoring its natural beauty and returning its wildlife to their homes. This was the task for the IEEE SoutheastCon 2023 Hardware Competition. Florida’s restoration was simulated by returning various ducks and pillars that lay strewn across a game board to their proper places. Ducks needed to return to their pond, pillars needed to be stacked to create statues, and food needed to be placed in the manatee and alligator aquariums. Competing teams were challenged to create an autonomous robot capable of performing these tasks. During the first semester, sensor selection was tackled. Research was …

Fair Enough: Standardizing Evaluation And Model Selection For Fairness Research In Nlp, Xudong Han, Timothy Baldwin, Trevor Cohn

Natural Language Processing Faculty Publications

Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. However, current progress is hampered by a plurality of definitions of bias, means of quantification, and oftentimes vague relation between debiasing algorithms and theoretical measures of bias. This paper seeks to clarify the current situation and plot a course for meaningful progress in fair learning, with two key contributions: (1) making clear inter-relations among the current gamut of methods, and their relation to fairness theory; and (2) addressing the practical problem of model selection, which involves a trade-off between fairness and accuracy …

Analysis Of A Federated Learning Framework For Heterogeneous Medical Image Data: Privacy And Performance Perspective, Julia Brixey

Computer Science and Computer Engineering Undergraduate Honors Theses

The massive amount of data available in our modern world and the increase of computational efficiency and power have allowed for great advancements in several fields such as computer vision, image processing, and natural languages. At the center of these advancements lies a data-centric learning approach termed deep learning. However, in the medical field, the application of deep learning comes with many challenges. Some of the fundamental challenges are the lack of massive training datasets, unbalanced and heterogenous data between health applications and health centers, security and privacy concerns, and the high cost of wrong inference and prediction. One of …

Characterization Of 2d Quantum Materials Using Ai And Large-Scale Quantum Data Collection, Apoorva Bisht

Computer Science and Computer Engineering Undergraduate Honors Theses

2D materials like hexagonal boron nitride, graphene, and tungsten diselenide are widely utilized for studying their unique mechanical and opto-electronic properties to exploit them to make transistors and fabricating a variety of other devices. All these applications require that the 2D materials used be of specific uniform thickness. Until very recently, this process has been largely manual and tedious. However, few applications exploit the characteristic color-to-thickness correspondence of these near-transparent materials. To continue this effort, in this work we create a large-scale dataset for three different materials (hBN, graphene, and WSe$_2$) to train and test an image segmentation model along …

Physics-Based Human-In-The-Loop Machine Learning Combined With Genetic Algorithm Search For Multi-Criteria Optimization: Electrochemical Co2 Reduction Reaction, Naohiro Fujinuma, Samuel Lofland

College of Science & Mathematics Departmental Research

Machine learning (ML) can be a powerful tool to expedite materials research, but the deployment for experimental research is often hindered by data scarcity and model uncertainty. An human-in-the-loop procedure to tailor the implementation of ML for multicriteria optimization is described. The effectiveness of this procedure in the development of a nafion-based membrane electrode assembly for electrochemical CO2 reduction reaction (CO2RR) into CO for two targets is demonstrated: energy efficiency (EE) and partial current density for CO2RR (). Model-agnostic nonlinear correlation analyses identify the 11 features relevant to those targets. The three studied decision tree-based ML models yield similar cross-validation …

Improving The Performance Of Automatic Target Recognition Using Data Augmentation., Rolando Holmes

Electronic Theses and Dissertations

Automatic Target Recognition (ATR) is a task that aims to recognize targets based on data obtained from visual sensors such as Infrared cameras. Machine learning, in particular Deep Learning, has been the dominate approach to solve this task. However, in order to achieve high performance, Deep Learning requires a large amount of data that may not be available, which poses an issue when developing robust models. In this thesis, we propose the use of data augmentation from an automatic detector as an alternative source to supplement the need for more training data. We use a two stage approach to develop …

Modeling, Simulation And Control Of Microrobots For The Microfactory., Zhong Yang

Electronic Theses and Dissertations

Future assembly technologies will involve higher levels of automation in order to satisfy increased microscale or nanoscale precision requirements. Traditionally, assembly using a top-down robotic approach has been well-studied and applied to the microelectronics and MEMS industries, but less so in nanotechnology. With the boom of nanotechnology since the 1990s, newly designed products with new materials, coatings, and nanoparticles are gradually entering everyone’s lives, while the industry has grown into a billion-dollar volume worldwide. Traditionally, nanotechnology products are assembled using bottom-up methods, such as self-assembly, rather than top-down robotic assembly. This is due to considerations of volume handling of large …

The Six Emotional Dimension (6de) Model: A Multidimensional Approach To Analyzing Human Emotions And Unlocking The Potential Of Emotionally Intelligent Artificial Intelligence (Ai) Via Large Language Models (Llm), Jay Ratican, James Hutson

Faculty Scholarship

The rapid advancements in artificial intelligence (AI) research, particularly in training large language models (LLMs) such as OpenAI's ChatGPT 3.5 and 4, hold significant potential for future applications in education, healthcare, and assisted living. Emotionally intelligent AI systems can provide personalized and adaptive educational experiences, enhancing engagement and educational outcomes. In healthcare, they can offer empathetic mental health support, augmenting existing resources. In assisted living, AI companions can provide emotional support, cognitive stimulation, and monitoring services, promoting independence and safety. However, ethical considerations and privacy safeguards are crucial to ensure responsible deployment. Integrating emotionally intelligent AI in these domains has …

Understanding Societal Values Of Chatgpt, Yidan Tang

McKelvey School of Engineering Graduate Student Theses & Dissertations

As Large language models (LLMs) become increasingly pervasive in various domains, it is crucial to ensure that their outputs adhere to societal values and ethical considerations. In this thesis, we investigate the alignment of ChatGPT, a recent state-of-the-art large language model developed by OpenAI, with societal values. Specifically, we define the problem of societal values of LLMs and assemble a representative collection of 7 datasets covering 4 topics related to societal values. In-context learning techniques are applied and appropriate prompts are designed. The performance of each dataset is measured using a standardized evaluation system focused on accuracy. We then display …

Evaluating The Problem Solving Abilities Of Chatgpt, Fankun Zeng

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis addresses the need for a fair evaluation of language models' problem solving abilities by presenting a unified evaluation framework for ChatGPT on 16 problem solving datasets (e.g., NaturalQA, HellaSwag, MMLU, etc.). We evaluate the model's performance using F1, exact match, and quasi-exact match metrics and find that ChatGPT is highly accurate in solving tasks that require commonsense and knowledge. However, we also identify truncated text bias and few-shot scenarios as challenges that may impact ChatGPT's performance. Our research highlights the importance of standardizing datasets and developing a unified evaluation system for the fair evaluation of language models. Overall, …

Assessing Key Factors Influencing Fire-Induced Spalling Of Concrete Using Explainable Artificial Intelligence (Xai), Mohammad Khaled Gazi Albashiti

All Theses

This thesis adopts eXplainable Artificial Intelligence (XAI) to identify the key factors influencing the fire-induced spalling of concrete and to extract new insights into the fire-induced spalling phenomenon. In this pursuit, an XAI model was developed, validated, and then augmented with two explainability measures, namely, Shapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The proposed XAI model not only can predict the fire-induced spalling with high accuracy (i.e., >92 %) but can also articulate the reasoning behind its predictions (as in, the proposed model can specify the rationale for each prediction instance); thus, providing us with valuable insights …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Artificial: A Study On The Use Of Artificial Intelligence In Art, Hayden Ernst

Theses/Capstones/Creative Projects

In the past three to five years there have been significant improvements made in AI due to improvements in computing capacity, the collection and use of big data, and an increase in public interest and funding for research. Programs such as ChatGPT, DALL•E, and Midjourney have also gained tremendous popularity in a relatively short amount of time. This led me to this project in which I aimed to gain a deeper understanding of these art generator AI and where they fit into art as a whole. My goal was to give recommendations to museums and exhibits in Omaha on what …

Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …