Physical Sciences and Mathematics Commons^™

A Framework To Study The Impact Of Interventions On Social Isolation During Pandemics Using Multi-Agent Simulation, Simranpreet Kaur

Electronic Theses and Dissertations

The spread of Coronavirus, widely known as COVID-19, has posed detrimental effects worldwide, affecting almost every primary sector. Due to its asymptomatic behavior and non-early diagnosis, government and health organizations implemented interventions such as physical distancing, lockdown, and quarantine, to mitigate the spread of the virus. Studies have shown that a connection exists between social isolation and health risks experienced by individuals. Thus, this research proposes an agent-based model to address the impact of varying interventions in our society. For simulation purposes, the SEIR model is followed, and agents are categorized into two classes based on their pace of movement, …

Variational Energies For The Rydberg P States Of Helium, Cody Mcleod

Electronic Theses and Dissertations

The aim of this work is to solve the quantum mechanical three-body problem for helium, and to obtain high precision eigenvalues for the higher-lying Rydberg states where previous methods have been of limited accuracy. A variational method in correlated Hylleraas coordinates is used involving three distinct distance scales, called a triple basis set. The eigenvalues and matrix elements of other operators are computed for P states of helium up to n = 15 using the varational method with a triple basis set in Hylleraas coordinates. The construction of the wave functions, as well as the behaviour of the asymptotic, intermediate …

Packing Non-Self-Crossing Edge-Disjoint Spanning Paths Into A Point Set, Rishav Chatterjee

Electronic Theses and Dissertations

The term packing refers to the arrangement of multiple geometrical structures or shapes such as circles, squares, triangles, or polygons into a fixed and finite set of points. The geometric structures to be packed can also be trees and paths. Packing is also possible in a 3-dimensional space with geometric structures such as spheres, cylinders, and cubes.

The concept of packing was introduced more than half a century ago. Since then, many researchers have studied the packing strategies of different geometric structures in different configurations of point-set. Packing strategies help to construct and arrange multiple geometric structures in a predetermined …

Traffic Sign And Light Detection Using Deep Learning For Automotive Applications, Humaira Naimi

Electronic Theses and Dissertations

Traffic sign and light detection are core components of Advanced Driver Assistance Systems (ADAS) and self-driving vehicles. The automotive industry is widely employing numerous approaches for automation through computer vision techniques. Object detection algorithms based on deep learning can be divided into two main categories, two stage and single stage detection algorithms. Two stage algorithms are designed to improve detection accuracy. While single stage algorithms are constructed to be faster, this increases their suitability for real time applications. This thesis presents a lightweight traffic sign and light detector by adapting a single stage, Single Shot Multibox Detection (SSD) algorithm by …

Content-Based Image Retrieval Using Hierarchical Decomposition Of Feature Descriptors, Eisa Adil

Electronic Theses and Dissertations

Due to modern technological advancements, the pervasiveness and complexity of images have remarkably increased. Searching databases for similar visual content, i.e., Content-Based Image Retrieval (CBIR), remains an open research problem. In this thesis, we propose a novel CBIR approach, in which each symbolic image has a quadtree representation consisting of SIFT-based orientational keypoints. Every quadrant node in the tree represents the dominant orientation of a region in the image. The quadtree image representation is used for bitwise signature indexing and image similarity measurement. Also, we convert each quadtree image representation to a trainable feature vector for use in the K-Nearest …

Neural Network-Based Multi-Task Learning For Product Opinion Mining, Manil Patel

Electronic Theses and Dissertations

Aspect Based Opinion Mining (ABOM) systems take user's reviews or posts as input from social media. The system aims to extract the aspect terms (e.g., pizza) and categories (e.g., food) and their polarities, to help the customers and identify product weaknesses. By solving these product weaknesses, companies can enhance customer satisfaction, increase sales, and boost revenues. Neural networks are widely used as classification algorithms for performing ABOM tasks for both the training (learning) phase from historical reviews to form class labels and the testing phase to predict the label for unknown data (new reviews). Neural network algorithms consist of artificial …

Novel Approaches To Cognitive Load Estimation In Automated Driving Systems, Prarthana Pillai

Electronic Theses and Dissertations

Automation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. However, the present-day ADS requires the human driver to remain vigilant at all times and be ready to take over whenever the driving task requires. Thus, continuous monitoring of the drivers is important for adopting ADS. Such monitoring can be done in ADS by measuring the cognitive load experienced by the drivers. Studies show various methods to estimate cognitive load, however, the state of the art in cognitive load estimation, …

Manufacturing: The Qualitative Study Of A Transition To A Green Facility, Brandon David Staves

Masters Theses & Specialist Projects

This qualitative study focused on answering three core questions: How have facilities reduce pollutions in regards to the quality of air, water, land, light and noise? Which Types of pollution reduction projects are more or less successful to implement? What types of pollution are facilities focusing on and why? The results of the 15 companies surveyed show a variety of projects that facilities have used to reduce pollution, it also shows that cost is a major factor in the unsuccessful projects, and that facilities are actively focused on reducing Air, Land, and Water pollution. While the data shows a variety …

Developing And Applying An Integrated Spatial Karst Evaluation And Management Priority Tool: Case Study In Tongass National Forest, Alaska, Usa, Christian Decelle

Masters Theses & Specialist Projects

Karst terrains are complex landscapes that are sensitive to human disturbance. Human activities have polluted and impacted many of these terrains around the world. To preserve these unique landscapes, many karst regions are protected and designated as national parks, geologic special areas, or UNESCO Biosphere Reserves. Despite the widespread general protection of karst landscapes globally, a review of each area’s management plan reveals there is no standardized method of cave and karst management or evaluation of karst impacts.

The non-standardization of karst management strategies may be due to the gap that exists between the needs of karst land managers and …

An Adaptive Hazard Planning And Mitigation Framework For Responding To Urban Contamination In Karst Aquifer Systems, James Edward Troxell

Masters Theses & Specialist Projects

Environmental hazards in karst regions are damaging and often go unnoticed until an issue has escalated to a point of affecting life or property. The field of emergency and environmental contamination response lacks planning or preparedness focused on remediating groundwater contamination in karst systems. A lack of preplanning before an incident can lead to confusion, delayed response, and the inability to remediate the contaminant. Due to the rapid movement of contaminants through urban karst groundwater aquifers, an efficient response plan that leverages localized data in a GIS should be developed and maintained in order to adequately respond. The objective of …

Investigating Epikarst Recharge Dynamics Under Agricultural Landuse Using Hydrometeorological And Isotopic Tracers, Austin Shane Deering

Masters Theses & Specialist Projects

Epikarst systems have complex recharge – discharge processes in telogenetic karst systems, including highly variable storage, flowpaths, and mixing dynamics. This research aimed to characterize the epikarst zone using hydrogen and oxygen isotopic tracers of these processes within south-central Kentucky’s Crumps Cave system located in the Pennyroyal Sinkhole Plain. Data and statistical analyses were applied to highresolution rainfall (RF), lysimeter (10 cm, 20 cm, 30 cm depths), and an epikarst Waterfall 1 (WF1) isotope data collected on a weekly basis between 2011-2018. These data were coupled with WF1 discharge measurements and weather station data collected at Crumps Cave Preserve during …

Supporting Renewable Energy Market Growth Through The Circular Integration Of End-Of-Use And End-Of-Life Photovoltaics, Erika Marsillac

Information Technology & Decision Sciences Faculty Publications

Energy demand continues to grow with the world’s burgeoning population. Meeting energy needs through renewable sources allows for market growth with limited environmental impact, but sourcing constraints can limit production, creating industrial and environmental problems. The exploitation of end-of-use and end-of-life photovoltaic (PV) options that are traditionally treated as waste offers a valuable opportunity to support renewable energy market growth with fewer sourcing constraints and minimal environmental impacts, but this circular investment has not yet been broadly implemented, nor is broad guidance widely available to aid its implementation. From a business perspective, this paper discusses the technical issues, assesses the …

Empirical Modeling Of Tilt-Rotor Aerodynamic Performance, Michael C. Stratton

Mechanical & Aerospace Engineering Theses & Dissertations

There has been increasing interest into the performance of electric vertical takeoff and landing (eVTOL) aircraft. The propellers used for the eVTOL propulsion systems experience a broad range of aerodynamic conditions, not typically experienced by propellers in forward flight, that includes large incidence angles relative to the oncoming airflow. Formal experiment design and analysis techniques featuring response surface methods were applied to a subscale, tilt-rotor wind tunnel test for three, four, five, and six blade, 16-inch diameter, propeller configurations in support of development of the NASA LA-8 aircraft. Investigation of low-speed performance included a maximum speed of 12 m/s and …

Geodetic Constraints On A 25-Year Magmatic Inflation Episode Near Three Sisters, Central Oregon, Robert Mccaffrey, Michael Lisowsk, Charles W. Wicks, Daniel Dzurisin

Geology Faculty Publications and Presentations

Crustal inflation near the Three Sisters volcanic center documented since the mid-1990s has persisted for more than two decades. We update past analyses of the event through 2020 by simultaneously inverting InSAR interferograms, GPS time series, and leveling data for time-dependent volcanic deformation source parameters. We explore several source models to estimate how the deformation rate varied through time and to identify parameters that can reproduce measured deformation. Our preferred model is a Mogi source 4.1 km below sea level (5.9 km below the surface) about 5 km west of the summit of South Sister. Inflation started in late 1995 …

Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of …

Mlcatchup: Automated Update Of Deprecated Machine-Learning Apis In Python, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Machine learning (ML) libraries are gaining vast popularity, especially in the Python programming language. Using the latest version of such libraries is recommended to ensure the best performance and security. When migrating to the latest version of a machine learning library, usages of deprecated APIs need to be updated, which is a time-consuming process. In this paper, we propose MLCatchUp, an automated API usage update tool for deprecated APIs of popular ML libraries written in Python. MLCatchUp automatically infers the required transformation to migrate usages of deprecated API through the differences between the deprecated and updated API signatures. MLCatchUp offers …

Target-Guided Emotion-Aware Chat Machine, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng

Research Collection School Of Computing and Information Systems

The consistency of a response to a given post at the semantic level and emotional level is essential for a dialogue system to deliver humanlike interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem and proposes a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leveraging target information to generate more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed …

The Efficacy Of Collaborative Authoring Of Video Scene Descriptions, Rosiana Natalie, Jolene Kar Inn Loh, Huei Suen Tan, Joshua Shi-Hao Tseng, Ian Luke Yi-Ren Chan, Ebrima H. Jarjue, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

The majority of online video contents remain inaccessible to people with visual impairments due to the lack of audio descriptions to depict the video scenes. Content creators have traditionally relied on professionals to author audio descriptions, but their service is costly and not readily-available. We investigate the feasibility of creating more cost-effective audio descriptions that are also of high quality by involving novices. Specifically, we designed, developed, and evaluated ViScene, a web-based collaborative audio description authoring tool that enables a sighted novice author and a reviewer either sighted or blind to interact and contribute to scene descriptions (SDs)—text that can …

Token Shift Transformer For Video Classification, Zhang Hao, Yanbin. Hao, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Transformer achieves remarkable successes in understanding 1 and 2-dimensional signals (e.g., NLP and Image Content Understanding). As a potential alternative to convolutional neural networks, it shares merits of strong interpretability, high discriminative power on hyper-scale data, and flexibility in processing varying length inputs. However, its encoders naturally contain computational intensive operations such as pair-wise self-attention, incurring heavy computational burden when being applied on the complex 3-dimensional video signals. This paper presents Token Shift Module (i.e., TokShift), a novel, zero-parameter, zero-FLOPs operator, for modeling temporal relations within each transformer encoder. Specifically, the TokShift barely temporally shifts partial [Class] token features back-and-forth …

Quantum Computing: Computational Excellence For Society 5.0, Paul R. Griffin, Michael Boguslavsky, Junye Huang, Robert J. Kauffman, Brian R. Tan

Research Collection School Of Computing and Information Systems

In this chapter, we consider which general business problems may be suitable for exploring the utilization of quantum computing and provide a framework for applying quantum computing. The characteristics of quantum computing systems are mapped into business problems to show the potential advantages of quantum computing. The framework shows how quantum computing can be applied in general, and a use case is offered for quantum machine learning (QML) related to the credit ratings of small and medium-size enterprises (SMEs).

Design And Supervision Model Of Group Projects For Active Learning, Yi Meng Lau, Kyong Jin Shim, Swapna Gottipati

Research Collection School Of Computing and Information Systems

This research paper presents a group project framework for a second-year programming course, which was conducted during the COVID-19 pandemic. The framework offers well defined stages of the group project which allow students to work on their choice of a real-world problem, integrate their learnings from previous courses, and present a working solution. In the group project, students actively participate, reflect, and contribute to achieving the goals set in the learning objectives of the course. Our framework incorporates key features from Kolb’s Experiential Learning Theory (1984) and principles of active learning from Barnes (1989) to achieve active and experiential learning …

Can Differential Testing Improve Automatic Speech Recognition Systems?, Muhammad Hilmi Asyrofi, Zhou Yang, Jieke Shi, Chu Wei Quan, David Lo

Research Collection School Of Computing and Information Systems

Due to the widespread adoption of Automatic Speech Recognition (ASR) systems in many critical domains, ensuring the quality of recognized transcriptions is of great importance. A recent work, CrossASR++, can automatically uncover many failures in ASR systems by taking advantage of the differential testing technique. It employs a Text-To-Speech (TTS) system to synthesize audios from texts and then reveals failed test cases by feeding them to multiple ASR systems for cross-referencing. However, no prior work tries to utilize the generated test cases to enhance the quality of ASR systems. In this paper, we explore the subsequent improvements brought by leveraging …

Deep Learning For Image Super-Resolution: A Survey, Zhihao Wang, Jian Chen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by …

Noahqa: Numerical Reasoning With Interpretable Graph Question Answering Dataset, Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects. First, we lack QA datasets covering complex questions that involve answers as well as the reasoning processes to get the answers. As a result, the state-of-the-art QA research on numerical reasoning still focuses on simple calculations and does not provide the mathematical expressions or evidences justifying the answers. Second, the QA community has contributed much effort to improving the interpretability of QA models. However, these models fail to explicitly show …

Occluded Person Re-Identification With Single-Scale Global Representations, Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen

Research Collection School Of Computing and Information Systems

Occluded person re-identification (ReID) aims at re-identifying occluded pedestrians from occluded or holistic images taken across multiple cameras. Current state-of-the-art (SOTA) occluded ReID models rely on some auxiliary modules, including pose estimation, feature pyramid and graph matching modules, to learn multi-scale and/or part-level features to tackle the occlusion challenges. This unfortunately leads to complex ReID models that (i) fail to generalize to challenging occlusions of diverse appearance, shape or size, and (ii) become ineffective in handling non-occluded pedestrians. However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians. To address these two …

Privacy-Preserving Voluntary-Tallying Leader Election For Internet Of Things, Tong Wu, Guomin Yang, Liehuang Zhu, Yulin Wu

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is commonly deployed with devices of limited power and computation capability. A centralized IoT architecture provides a simplified management for IoT system but brings redundancy by the unnecessary data traffic with a data center. A decentralized IoT reduces the cost on data traffic and is resilient to the single-point-of failure. The blockchain technique has attracted a large amount of research, which is redeemed as a perspective of decentralized IoT system infrastructure. It also brings new privacy challenges for that the blockchain is a public ledger of all digital events executed and shared among all participants. …

Self-Regulation For Semantic Segmentation, Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

In this paper, we seek reasons for the two major failure cases in Semantic Segmentation (SS): 1) missing small objects or minor object parts, and 2) mislabeling minor parts of large objects as wrong classes. We have an interesting finding that Failure-1 is due to the underuse of detailed features and Failure-2 is due to the underuse of visual contexts. To help the model learn a better trade-off, we introduce several Self-Regulation (SR) losses for training SS neural networks. By “self”, we mean that the losses are from the model per se without using any additional data or supervision. By …

Quantum-Inspired Algorithm For Vehicle Sharing Problem, Whei Yeap Suen, Chun Yat Lee, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Recent hardware developments in quantum technologies have inspired a myriad of special-purpose hardware devices tasked to solve optimization problems. In this paper, we explore the application of Fujitsu’s quantum-inspired CMOS-based Digital Annealer (DA) in solving constrained routing problems arising in transportation and logistics. More precisely in this paper, we study the vehicle sharing problem and show that the DA as a QUBO solver can potentially fill the gap between two common methods: exact solvers like Cplex and heuristics. We benchmark the scalability and quality of solutions obtained by DA with Cplex and with a greedy heuristic. Our results show that …

Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction to alleviate data sparsity is to leverage auxiliary information that may encode additional clues on how users consume items. Examples of such data (referred to as modalities) are social networks, item’s descriptive text, product images. The objective of this tutorial is to offer a comprehensive review of recent advances to represent, transform and incorporate the different modalities into recommendation models. Moreover, through practical hands-on sessions, we …

Covid-19 One Year On: Security And Privacy Review Of Contact Tracing Mobile Apps, Wei Yang Ang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

The ongoing COVID-19 pandemic caused 3.8 million deaths since December 2019. At the current vaccination pace, this global pandemic could persist for several years. Throughout the world, contact tracing (CT) apps were developed, which play a significant role in mitigating the spread of COVID-19. This work examines the current state of security and privacy landscape of mobile CT apps. Our work is the first attempt, to our knowledge, which provides a comprehensive analysis of 70 CT apps used worldwide as of year Q1 2021. Among other findings, we observed that 80% of them may have handled sensitive data without adequate …