Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, 2024 Singapore Management University
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang
Research Collection School Of Computing and Information Systems
In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …
Hierarchical Damage Correlations For Old Photo Restoration, 2024 Singapore Management University
Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Research Collection School Of Computing and Information Systems
Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …
A Nlp Approach To Automating The Generation Of Surveys For Market Research, 2024 Georgia Southern University
A Nlp Approach To Automating The Generation Of Surveys For Market Research, Anav Chug
Honors College Theses
Market Research is vital but includes activities that are often laborious and time consuming. Survey questionnaires are one possible output of the process and market researchers spend a lot of time manually developing questions for focus groups. The proposed research aims to develop a software prototype that utilizes Natural Language Processing (NLP) to automate the process of generating survey questions for market research. The software uses a pre-trained Open AI language model to generate multiple choice survey questions based on a given product prompt, send it to a targeted email list, and also provides a real-time analysis of the responses …
Detection Of Jamming Attacks In Vanets, 2024 East Tennessee State University
Detection Of Jamming Attacks In Vanets, Thomas Justice
Undergraduate Honors Theses
A vehicular network is a type of communication network that enables vehicles to communicate with each other and the roadside infrastructure. The roadside infrastructure consists of fixed nodes such as roadside units (RSUs), traffic lights, road signs, toll booths, and so on. RSUs are devices equipped with communication capabilities that allow vehicles to obtain and share real-time information about traffic conditions, weather, road hazards, and other relevant information. These infrastructures assist in traffic management, emergency response, smart parking, autonomous driving, and public transportation to improve roadside safety, reduce traffic congestion, and enhance the overall driving experience. However, communication between the …
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, 2024 Singapore Management University
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
Research Collection School Of Computing and Information Systems
The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …
Achieving Responsible Anomaly Detection, 2024 Utah State University
Achieving Responsible Anomaly Detection, Xiao Han
All Graduate Theses and Dissertations, Fall 2023 to Present
In the digital transformation era, safeguarding online systems against anomalies – unusual patterns indicating potential threats or malfunctions – has become crucial. This dissertation embarks on enhancing the accuracy, explainability, and ethical integrity of anomaly detection systems. By integrating advanced machine learning techniques, it improves anomaly detection performance and incorporates fairness and explainability at its core.
The research tackles performance enhancement in anomaly detection by leveraging few-shot learning, demonstrating how systems can effectively identify anomalies with minimal training data. This approach overcomes data scarcity challenges. Reinforcement learning is employed to iteratively refine models, enhancing decision-making processes. Transfer learning enables the …
Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, 2024 East Tennessee State University
Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji
Electronic Theses and Dissertations
Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and …
Decentralized Unknown Building Exploration By Frontier Incentivization And Voronoi Segmentation In A Communication Restricted Domain, 2024 Utah State University
Decentralized Unknown Building Exploration By Frontier Incentivization And Voronoi Segmentation In A Communication Restricted Domain, Huzeyfe M. Kocabas
All Graduate Theses and Dissertations, Fall 2023 to Present
Exploring unknown environments using multiple robots poses a complex challenge, particularly in situations where communication between robots is either impossible or limited. Existing exploration techniques exhibit research gaps due to unrealistic communication assumptions or the computational complexities associated with exploration strategies in unfamiliar domains. In our investigation of multi-robot exploration in unknown areas, we employed various exploration and coordination techniques, evaluating their performance in terms of robustness and efficiency across different levels of environmental complexity.
Our research is centered on optimizing the exploration process through strategic agent distribution. We initially address the challenge of city roadway coverage, aiming to minimize …
Pedestrian Pathing Prediction Using Complex Contextual Behavioral Data In High Foot Traffic Settings, 2024 Utah State University
Pedestrian Pathing Prediction Using Complex Contextual Behavioral Data In High Foot Traffic Settings, Laurel Bingham
All Graduate Theses and Dissertations, Fall 2023 to Present
Ensuring the safe integration of autonomous vehicles into real-world environments requires a comprehensive understanding of pedestrian behavior. This study addresses the challenge of predicting the movement and crossing intentions of pedestrians, a crucial aspect in the development of fully autonomous vehicles.
The research focuses on leveraging Honda's TITAN dataset, comprising 700 unique clips captured by moving vehicles in high-foot-traffic areas of Tokyo, Japan. Each clip provides detailed contextual information, including human-labeled tags for individuals and vehicles, encompassing attributes such as age, motion status, and communicative actions. Long Short-Term Memory (LSTM) networks were employed and trained on various combinations of contextual …
Proof-Of-Concept For Converging Beam Small Animal Irradiator, 2024 The Texas Medical Center Library
Proof-Of-Concept For Converging Beam Small Animal Irradiator, Benjamin Insley
Dissertations & Theses (Open Access)
The Monte Carlo particle simulator TOPAS, the multiphysics solver COMSOL., and
several analytical radiation transport methods were employed to perform an in-depth proof-ofconcept
for a high dose rate, high precision converging beam small animal irradiation platform.
In the first aim of this work, a novel carbon nanotube-based compact X-ray tube optimized for
high output and high directionality was designed and characterized. In the second aim, an
optimization algorithm was developed to customize a collimator geometry for this unique Xray
source to simultaneously maximize the irradiator’s intensity and precision. Then, a full
converging beam irradiator apparatus was fit with a multitude …
Generative Ai In Education From The Perspective Of Students, Educators, And Administrators, 2024 Utah State University
Generative Ai In Education From The Perspective Of Students, Educators, And Administrators, Aashish Ghimire
All Graduate Theses and Dissertations, Fall 2023 to Present
This research explores how advanced artificial intelligence (AI), like the technology that powers tools such as ChatGPT, is changing the way we teach and learn in schools and universities. Imagine AI helping to summarize thick legal documents into something you can read over a coffee break or helping students learn how to code by offering personalized guidance. We looked into how teachers feel about using these AI tools in their classrooms, what kind of rules schools have about them, and how they can make learning programming easier for students. We found that most teachers are excited about the possibilities but …
Analysis And Numerical Simulation Of Tumor Growth Models, 2024 University of Tennessee at Chattanooga
Analysis And Numerical Simulation Of Tumor Growth Models, Daniel Acosta Soba
Masters Theses and Doctoral Dissertations
In this dissertation we focus on the numerical analysis of tumor growth models. Due to the difficulty of developing physically meaningful approximations of such models, we divide the main problem into more simple pieces of work that are addressed in the different chapters. First, in Chapter 2 we present a new upwind discontinuous Galerkin (DG) scheme for the convective Cahn–Hilliard model with degenerate mobility which preserves the pointwise bounds and prevents non-physical spurious oscillations. These ideas are based on a well-suited piecewise constant approximation of convection equations. The proposed numerical scheme is contrasted with other approaches in several numerical experiments. …
Multi-Script Handwriting Identification By Fragmenting Strokes, 2024 University of South Alabama
Multi-Script Handwriting Identification By Fragmenting Strokes, Joshua Jude Thomas
<strong> Theses and Dissertations </strong>
This study tests the effectiveness of Multi-Script Handwriting Identification after simplifying character strokes, by segmenting them into sub-parts. Character simplification is performed through splitting the character by branching-points and end-points, a process called stroke fragmentation in this study. The resulting sub-parts of the character are called stroke fragments and are evaluated individually to identify the writer. This process shares similarities with the concept of stroke decomposition in Optical Character Recognition which attempts to recognize characters through the writing strokes that make them up. The main idea of this study is that the characters of different writing‑scripts (English, Chinese, etc.) may …
A Review Of Student Attitudes Towards Keystroke Logging And Plagiarism Detection In Introductory Computer Science Courses, 2024 Utah State University
A Review Of Student Attitudes Towards Keystroke Logging And Plagiarism Detection In Introductory Computer Science Courses, Caleb Syndergaard
All Graduate Theses and Dissertations, Fall 2023 to Present
The following paper addresses student attitudes towards keystroke logging and plagiarism prevention measures. Specifically, the paper concerns itself with changes made to the “ShowYourWork” plugin, which was implemented to log the keystrokes of students in Utah State University’s introductory Computer Science course, CS1400. Recent work performed by the Edwards Lab provided insights into students’ feelings towards keystroke logging as a measure of deterring plagiarism. As a result of that research, we have concluded that measures need to be taken to enable students to have more control over their data and assist students to feel more comfortable with keystroke logging. This …
Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, 2024 Singapore Management University
Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning
Research Collection School Of Computing and Information Systems
Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of “Zero-Shot Open-Set Recognition” (ZS-OSR), where a model is trained under the ZSL …
Diffusion-Based Negative Sampling On Graphs For Link Prediction, 2024 Singapore Management University
Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang
Research Collection School Of Computing and Information Systems
Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …
On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, 2024 Singapore Management University
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, 2024 Singapore Management University
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 …
An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, 2024 Singapore Management University
An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo
Research Collection School Of Computing and Information Systems
With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient …
Gpr-103 Personalized Pedagogy Through A Llm-Based Recommender System, 2024 Kennesaw State University
Gpr-103 Personalized Pedagogy Through A Llm-Based Recommender System, Mourya Teja Kunuku, Bharath Y Yadla
C-Day Computing Showcase
The educational domain is undergoing transformation due to the incorporation of Artificial Intelligence (AI), Large Language Models (LLMs), and generative AI technologies, raising the need for educators to integrate cutting-edge technological advancements and methodologies into their teaching approaches. Pedagogical Design Patterns (PDPs) have become prominent for their role in sharing effective educational practices and narrowing the divide between academic research and actual teaching methods. Despite their potential, the lack of widely accessible resources and the scattered nature of publishing outlets pose significant barriers to the broad application of PDPS. To address this issue, we propose the application of large language …