Computer Sciences Commons^™

Motion Synthesis And Control For Autonomous Agents Using Generative Models And Reinforcement Learning, Pei Xu

All Dissertations

Imitating and predicting human motions have wide applications in both graphics and robotics, from developing realistic models of human movement and behavior in immersive virtual worlds and games to improving autonomous navigation for service agents deployed in the real world. Traditional approaches for motion imitation and prediction typically rely on pre-defined rules to model agent behaviors or use reinforcement learning with manually designed reward functions. Despite impressive results, such approaches cannot effectively capture the diversity of motor behaviors and the decision making capabilities of human beings. Furthermore, manually designing a model or reward function to explicitly describe human motion characteristics …

Ber Analysis Of Bpsk Modulation Scheme For Multiple Combining Schemes Over Flat Fading Channel, Zuhaib Nishtar, Jamil Afzal

Neutrosophic Systems with Applications

Focus of the study was to provide error-free communication in mobile communication with higher data rates, spectral efficiency, and energy efficient. Basically, work was done to investigate the performance of the Binary Phase Shift Keying (BPSK) modulation technique for multiple combining schemes and the behavior of signal in wireless communication where multipath propagation and uncertainty in the system. We use the Multiple-input Multiple-output (MIMO) and antenna diversity to get many copies of the same signal; some of them were faded, but some had sufficient information. Then the next step was to combine or select the best signal to achieve the …

Generative Pre-Trained Transformers (Gpt) And Space Health: A Potential Frontier In Astronaut Health During Exploration Missions, Ethan Waisberg, Joshua Ong, Mouayad Masalkhi, Nasif Zaman, Sharif Amit Kamran, Prithul Sarker, Andrew G Lee, Alireza Tavakkoli

Faculty, Staff and Student Publications

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

Facial Expression Recognition Using Convolutional Neural Networks (Cnns) And Generative Adversarial Networks (Gans) For Data Augmentation And Image Generation, Shekhar Singh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Facial expressions play a crucial role in human communication, serving as a powerful means to convey emotions. However, classifying facial expressions using artificial intelligence (AI) can be challenging, especially with small datasets and images. Facial Expression Recognition (FER) is an active area of research, with Convolutional Neural Networks (CNNs) being widely employed for classification. In this research, we propose a CNN-based approach for FER that utilizes both original and augmented datasets to enhance classification accuracy. Experimental results on the FER2013 dataset show test accuracies of 63.39% and 64.59% for the original and augmented datasets, respectively, in a seven-class classification task. …

Constrained Route Optimization With Fleet Considerations For Electrified Heavy-Duty Freight Vehicles, Zarin Subah Shamma

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Almost 75% of traffic-related emissions are caused by heavy-duty freight trucks and significantly impact neighborhoods, schools, and communities around shipping and distribution lines. With poor air quality and respiratory health, many children in at-risk and disadvantaged communities experience high rates of asthma, lower attendance in school, and lower concentration. This research creates to improve the impacts of heavy-duty electric freight by improving the route efficiency (in terms of energy, time, or route distance) of EV trucks. Our software and algorithms are tested in a simulation environment using data from several thousand fleet trucks operating in the Salt Lake City area. …

Comparative Study Of Clustering Techniques On Eye-Tracking In Dynamic 3d Virtual Environments, Scott Johnson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Eye-tracking has been used for decades to understand how and why an individual focuses on particular objects, areas, and elements of space. A vast body of knowledge exists on how eye-tracking is measured. However, historically, eye-tracking has been predominately studied using 2D environments, with limited work in 3D environments. The purpose of this study is to identify which methods most accurately represent the areas that have captured the participant’s visual attention within a 3D dynamic environment. This will be completed by evaluating different clustering methods of fixations using a customized virtual reality tool that collects eye-tracking data. There exist several …

Generalizing Deep Learning Methods For Particle Tracing Using Transfer Learning, Shubham Gupta

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Particle tracing is a very important method for scientific visualization of vector fields, but it is computationally expensive. Deep learning can be used to speed up particle tracing, but existing deep learning models are domain-specific. In this work, we present a methodology to generalize the use of deep learning for particle tracing using transfer learning. We demonstrate the performance of our approach through a series of experimental studies that address the most common simulation design scenarios: varying time span, Reynolds number, and problem geometry. The results show that our methodology can be effectively used to generalize and accelerate the training …

Generative Artificial Intelligence And Metaverse: Future Of Work, Future Of Society, And Future Of Humanity, Yuxin Liu, Keng Siau

Research Collection School Of Computing and Information Systems

The rapid development of Generative Artificial Intelligence (GenAI) and the emergence of the Metaverse are dynamically reshaping our lives and societies. GenAI can enhance the development of Metaverse and empower the applications in Metaverse. Metaverse is also an excellent environment for GenAI to demonstrate its power and usefulness. This interwoven relationship fuels the potential of integrating GenAI and Metaverse. The paper discusses the integration potential of GenAI and Metaverse from four aspects. We further investigate how GenAI, Metaverse, and the integration of GenAI and Metaverse can reshape our future across the realms of work, society, and humanity. This paper offers …

Flood: A Flexible Invariant Learning Framework For Out-Of-Distribution Generalization On Graphs, Yang Liu, Xiang Ao, Fuli Feng, Yunshan Ma, Kuan Li, Tat‑Seng Chua, Qing He

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have achieved remarkable success in various domains but most of them are developed under the in-distribution assumption. Under out-of-distribution (OOD) settings, they suffer from the distribution shift between the training set and the test set and may not generalize well to the test distribution. Several methods have tried the invariance principle to improve the generalization of GNNs in OOD settings. However, in previous solutions, the graph encoder is immutable after the invariant learning and cannot be adapted to the target distribution flexibly. Confronting the distribution shift, a flexible encoder with refinement to the target distribution can …

Diffusion Variational Autoencoder For Tackling Stochasticity In Multi-Step Regression Stock Price Prediction, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem …

The Future Of Cryptocurrency And Blockchain Technology In Finance, Wanyi Wong, Alan @ Ali Madjelisi Megargel

Research Collection School Of Computing and Information Systems

Cryptocurrencies have been all the rage in recent years, with many being drawn to their appeal as speculative investment assets. Its proponents also champion the secure and decentralised nature of the technology it is based on, called the blockchain. Given the secure nature of blockchain technology, the idea of adopting cryptocurrencies as legal tender currency has also been mooted and experimented with – with the most famous example being the Central American nation of El Salvador’s bold move to adopting the cryptocurrency Bitcoin as legal tender in September 2021. In theory, this would provide a solution to the high transaction …

Decoding The Underlying Meaning Of Multimodal Hateful Memes, Ming Shan Hee, Wen Haw Chong, Roy Ka-Wei Lee

Research Collection School Of Computing and Information Systems

Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme …

Transferable Curricula Through Difficulty Conditioned Generators, Sidney Tio, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial "Experts" to humans remain a significant challenge. A promising avenue for such transfer would be the use of curricula. Recent methods in curricula generation focuses on training RL agents efficiently, yet such methods rely on surrogate measures to track student progress, and are not suited for training robots in the real world (or more ambitiously humans). In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents …

Generalization Through Diversity: Improving Unsupervised Environment Design, Wenjun Li, Pradeep Varakantham, Dexun Li

Research Collection School Of Computing and Information Systems

Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e.g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board). Due to this dependence, small changes in the environment (e.g., positions of obstacles in the maze, size of the board) can severely affect the effectiveness of the policy learned by the agent. To that end, existing work has proposed training RL agents on an adaptive curriculum of environments (generated automatically) to improve performance on out-of-distribution (OOD) test scenarios. Specifically, existing research has employed the potential for the …

Learning To Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching And Rescheduling Via Reinforcement Learning, Waldy Joe, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. Our approach utilizes Multi-Agent Value Function Approximation (MAVFA) with a rescheduling heuristic to learn dispatching and rescheduling policies jointly. Often, police operations are divided into multiple sectors for more effective and efficient operations. In a dynamic setting, incidents occur throughout the day across different sectors, disrupting initially-planned patrol schedules. To maximize policing effectiveness, police agents from different sectors cooperate by sending reinforcements to support one another in their incident response and even routine patrol. This poses an interesting research challenge …

Camper: An Effective Framework For Privacy-Aware Deep Entity Resolution, Yuxiang Guo, Lu Chen, Zhengjie Zhou, Baihua Zheng, Ziquan Fang, Zhikun Zhang, Yuren Mao, Yunjun Gao

Research Collection School Of Computing and Information Systems

Entity Resolution (ER) is a fundamental problem in data preparation. Standard deep ER methods have achieved state-of-the-art efectiveness, assuming that relations from diferent organizations are centrally stored. However, due to privacy concerns, it can be difcult to centralize data in practice, rendering standard deep ER solutions inapplicable. Despite eforts to develop rule-based privacy-preserving ER methods, they often neglect subtle matching mechanisms and have poor efectiveness as a result. To bridge efectiveness and privacy, in this paper, we propose CampER, an efective framework for privacy-aware deep entity resolution. Specifcally, we frst design a training pair self-generation strategy to overcome the absence …

Document-Level Relation Extraction Via Separate Relation Representation And Logical Reasoning, Heyan Huang, Changsen Yuan, Qian Liu, Yixin Cao

Research Collection School Of Computing and Information Systems

Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, we propose a novel model, SRLR, using Separate Relation Representation and Logical Reasoning considering the indirect relation representation and complex reasoning of evidence sentence problems. Specifically, we first expand the judgment of relational facts from the entity-level to the mention-level, highlighting fine-grained information to capture the relation representation for the entity pair. Second, we propose a logical reasoning module to identify evidence sentences and conduct …

Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce Zhang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a …

An Adaptive Large Neighborhood Search For Heterogeneous Vehicle Routing Problem With Time Windows, Minh Pham Kien Nguyen, Aldy Gunawan, Vincent F. Yu, Mustafa Misir

Research Collection School Of Computing and Information Systems

The heterogeneous vehicle routing problem with time windows (HVRPTW) employs various vehicles with different capacities to serve upcoming pickup and delivery orders. We introduce a HVRPTW variant for reflecting the practical needs of crowd-shipping by considering the mass-rapid-transit stations, as the additional terminal points. A mixed integer linear programming model is formulated. An Adaptive Large Neighborhood Search based meta-heuristic is also developed by utilizing a basic probabilistic selection strategy, i.e. roulette wheel, and Simulated Annealing. The proposed approach is empirically evaluated on a new set of benchmark instances. The computational results revealed that ALNS shows its clear advantage on the …

Grasp Based Metaheuristic To Solve The Mixed Fleet E-Waste Collection Route Planning Problem, Aldy Gunawan, Dang V.A. Nguyen, Pham K.M. Nguyen, Pieter. Vansteenwegen

Research Collection School Of Computing and Information Systems

The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …

Deep Weakly-Supervised Anomaly Detection, Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e., seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two …

Survey On Sentiment Analysis: Evolution Of Research Methods And Topics, Jingfeng Cui, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates …

A Certificateless Designated Verifier Sanitizable Signature, Yonghua Zhan, Bixia Yi, Yang Yang, Renjie He, Rui Shi

Research Collection School Of Computing and Information Systems

Sanitizable Signature is a digital signature variant that enables modification operations, allowing sanitizers to alter the signed data in a regulated manner without requiring any interaction with the original signer. It is widely used in scenarios such as healthcare data privacy protection, social networks, secure routing, etc. In existing sanitizable signature schemes, anyone can verify the validity and authenticity of the sanitized message, which results in costly certificate management overhead or complicated key escrow problems. To address these challenges, a designated verifier certificateless sanitizable signature scheme is proposed. This scheme introduces the concept of a designated verifier into sanitizable signatures, …

Mastering Stock Markets With Efficient Mixture Of Diversified Trading Experts, Shuo Sun, Xinrun Wang, Wanqi Xue, Xiaoxuan Lou, Bo An

Research Collection School Of Computing and Information Systems

Quantitative stock investment is a fundamental financial task that highly relies on accurate prediction of market status and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, the performance of existing DL methods is unstable with sensitivity to network initialization and hyperparameter selection. One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage …

Corporate Trade War Uncertainty And Patent Bubble, Xu Yang, Nan Hu, Peng Liang

Research Collection School Of Computing and Information Systems

This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a machine learning approach called Word2Vec from computational linguistics, we construct and validate a measure of firm-level managers’ perceived trade war uncertainty (TWU). We find that TWU has a positive effect on the number of total patent applications, but this positive effect is totally driven by low-quality patents instead of high-quality patents. Moreover, we document that firms have stronger incentives for such strategic innovation behavior when the underlying firms are more financially constrained, and/or when the management is more …

Web Based Management System For Housing Society, Likhitha Reddy Eddala

Electronic Theses, Projects, and Dissertations

Web Based Management System for Housing Society plays a major role in our day-to-day life. We develop a global web dependent application using AngularJS, Node JS and MySQL, with Xampp as the server to make an effective management system. This system is designed to provide a user-friendly and efficient platform for managing all the details of daily notices, monthly meetings, events, payments, maids etc., This system mainly consists of three modules, they are: Admin, User and Security. Each module here serves specific features and functionalities present within society. Admin module provides the features for managing user, houses, security, maids, notices, …

Geospatial Wildfire Risk Prediction Using Deep Learning, Abner Alberto Benavides

Electronic Theses, Projects, and Dissertations

This report introduces a thorough analysis of wildfire prediction using satellite imagery by applying deep learning techniques. To find wildfire-prone geographical data, we use U-Net, a convolutional neural network known for its effectiveness in biomedical image segmentation. The input to the model is the Sentinel-2 multispectral images to supply a complete view of the terrain features.

We evaluated the wildfire risk prediction model’s performance using several metrics. The model showed high accuracy, with a weighted average F1 score of 0.91 and an AUC-ROC score of 0.972. These results suggest that the model is exceptionally good at predicting the location of …

Contactless Food Ordering System, Rishivar Kumar Goli

Electronic Theses, Projects, and Dissertations

Contactless food ordering has revolutionized the way a customer interacts with restaurants by allowing them to place orders and make transactions. Through these web-based platforms, customers can now browse menus, customize orders, and make payments seamlessly. By scanning the restaurant’s QR code, customers can reserve a table. If the table is available, then automatically it will be reserved. However, if the table is occupied the customer will be added to the waiting list. Once the customer selects desired food then they can securely make payments based on ordered food items. The food will be delivered straight to the customer's table. …

Evaluating Learning Geometric Concepts To Generate Predicate Abstract Domains In Static Program Analysis, Patrick Chadbourne

Boise State University Theses and Dissertations

Accuracy of static analysis over predicate abstract domains depends on the partitions of predicates. More precise predicates approximate concrete values of program variables resulting in more accurate analysis. Manual reasoning about these partitions must be done on a case-by-case basis and is time consuming and difficult.

This work explores learning geometric concepts to automate discovery of predicate domain candidates.

The proposed framework uses run-time data from program executions to gather training data for a PAC-learner to generate separating hyperplanes that can be projected onto predicate domains.

The thesis implements the framework and performs evaluations of it effectiveness on a set …

Robust Digital Nucleic Acid Memory, Golam Md Mortuza

Boise State University Theses and Dissertations

The rapid growth of data generation from electronic devices has created a critical demand for efficient and sustainable data storage solutions. Traditional storage systems face challenges regarding reliability, energy consumption, and scalability, necessitating the exploration of alternative technologies. This dissertation explores the potential of Deoxyribonucleic Acid (DNA) as an alternative storage medium, along with the associated challenges and potential solutions.

This dissertation focuses on Digital Nucleic Acid Memory (dNAM), which utilizes Single Molecule Localization Microscopy (SMLM) to encode and store data within DNA structures called DNA origami. SMLM surpasses the limitations of light’s diffraction limit, enabling the imaging of biological …