Physical Sciences and Mathematics Commons^™

Using Concurrent Functional Regression To Reconstruct River Stage Data During Flood Events And Identify Influential Functional Measurements, Ryan Pittman

Theses and Dissertations

On October 4, 2015, the Cedar Creek gage at Congaree National Park stopped reporting stages, and the readings did not resume until approximately two weeks later because of record-breaking rainfall that led to some of the worst flooding in South Carolina history. Our goal is to reconstruct the Cedar Creek stage during this missing two-week window. The Congaree River gage in Congaree National Park remained functioning throughout the October 2015 flood, when the stage reached its maximum recorded crest. The stages from the two gages are directly related during floods as water travels through the local spillways and flood planes …

Multi-Objective Routing For Distributed Controllers, Konstantin Y. Rubin

Theses and Dissertations

A long-term goal of future naval shipboard power systems is the ability to manage energy flow with sufficient flexibility to accommodate future platform requirements such as better survivability, continuity, and support of pulsed and other demanding loads. To facilitate scalable, low-latency global distributed system control, each control module can include an integrated network interface connected through multiple channels onto a direct, multi-hop network topology. In this work, we focus on a 2D Torus, in which control nodes are arranged in a regular 2D grid, with each node connected through point-to-point connections to its four immediate neighbors. An important advantage of …

Ultra-Low Frequency Waves At Middle Latitudes During Substorms: Observations And Modeling, Mergen Alimaganbetov

Doctoral Dissertations and Master's Theses

This doctoral dissertation presents the results of investigation of the Ultra-Low Frequency (ULF) waves at middle latitudes during substorms. The dissertation consists of two major parts, observations and simulations. The research in this dissertation proposes that the main role in the generation of ULF waves at middle latitudes during substorm belongs to the plasmapause.

The first part of the dissertation presents results of the data analysis of 84 intense substorm events as well as an overview of space observation programs such as CRRES, Van Allen Probes and DMSP. Data used in this study are from the ACE satellite taken measurements …

Identification Of Factors Associated With Fume Events Using Text Mining And Data Mining Methods, Mary B. O'Connor

Doctoral Dissertations and Master's Theses

Pilots, flight attendants, and passengers can be exposed to toxic compounds when the bleed air that supplies the cabin and flight deck is contaminated with pyrolyzed hydraulic fluid or oil from turbine jet engines. These fume events occur sporadically and can result in acute or chronic exposure in air crews and can have catastrophic consequences if flight crew members become impaired or incapacitated. The purpose of this research was to explore unstructured textual data and identify important factors associated with these events. Models using machine learning algorithms were developed and tested using variables gleaned from the text mining process and …

Solar Cell Brdf Measurement And Modeling With Out-Of-Plane Data, Todd V. Small, Samuel D. Butler, Michael A. Marciniak

Faculty Publications

In this work, a CCD-augmented complete angle scatter instrument (CASI) with a visible red laser source was used to measure the BRDF of a commercially available solar cell designed for small satellites, simultaneously capturing both in-plane and out-of-plane data with high angular resolution surrounding the specular direction. The measurements exhibited three distinct scatter features: a central specular peak, an offset specular peak, and a diffraction pattern. The two peaks were caused by different material surfaces with slightly different normal directions, and the diffraction pattern arose from periodically-spaced metal conducting bars running in one direction across the solar cell surface. The …

Reinvigorating A Technical Countering Weapons Of Mass Destruction Distance Learning Graduate Certificate Program, James C. Petrosky, Gaiven Varshney, Jeremy Slagley, Sara Shaghaghi

Faculty Publications

Current Countering Weapons of Mass Destruction (CWMD) demands can be divided broadly into policy and science. The science of chemical, biological, and radiological/nuclear weapons informs the limits of development, production, employment, operation, detection, risk characterization, human and material protection, and medical intervention. In short, the science of weapons of mass destruction (WMD) should precede and inform the development of policy. It is to this end that the Air Force Institute of Technology (AFIT) CWMD program was re-established, providing a technical educational option for practitioners to understand the science behind a very technically challenging subject.

Assessment Of Heavy Metals In Sharks From The Arabian Gulf Waters, The United Arab Emirates, Noor Alali

Theses

Metals occur naturally in the environment however anthropogenic activities have contributed to their increase in the marine ecosystem. As elements have the ability to bioaccumulate, biomagnify and transfer between biological compartments throughout the food web, they pose a great threat to living biota. The characteristics of the Arabian Gulf make it a harsh environment for marine species especially when multiple stressors are acting together. Rhizoprionodon acutus (milk shark) and Carcharhinus sorrah (spot-tail shark) are essential compartments of the Arabian Gulf ecosystem. As they are found up in the food chain and considered a predator species they are under the threat …

The Red Palm Weevil In The Uae: Morphological Diversity And Rnai-Mediated Gene Silencing Of Two Cuticle-Related Genes, Safa Hashem Mohammed Musaed

Theses

Red Palm Weevil (RPW), Rhynchophorus ferrugineus (Coleoptera: Curculionidae), threatens palm trees worldwide. A better understanding of this insect can help with designing an adequate management strategy. This study aimed to a better understand the morphological diversity of RPW and examined RNAi-mediated gene silencing of two cuticle-related genes, vestigial (vg) gene, and Laccase (Lac2) gene by injecting the last larval stage with double-stranded RNA (dsRNA). For the morphological diversity study, adults of RPW were collected and classified by their prothoracic spots. Additional morphological characters were measured such as Pronotum Length (PL), Pronotum Width (PW), Elytra Length (EL), Elytra Width (EW), and …

Soliton Based All-Optical Data Processing In Waveguides, Amaria Javed

Dissertations

The growing demand for higher data processing speed and capacity motivates the replacement of the current electronic data processing by optical data processing in analogy with the successful replacement of electronic data communication by optical data communication. In a quest to achieve comprehensive optical data processing we aim at using solitons in waveguide arrays to perform all-optical data processing operations. Solitons are special nonlinear waves appreciated for their ability to conserve their shape and velocity before and after scattering. They are observed naturally in diverse fields of science, namely, nonlinear physics, mathematics, hydrodynamics, biophysics, and quantum field theory, etc. with …

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 …

Improving The Performance Of Transportation Networks: A Semi-Centralized Pricing Approach, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Liujiang Kang, Xuexi Zhang, Qilun Wu

Research Collection School Of Computing and Information Systems

Improving the performance of transportation network is a crucial task in traffic management. In this paper, we start with a cooperative routing problem, which aims to minimize the chance of road network breakdown. To address this problem, we propose a subgradient method, which can be naturally implemented as a semi-centralized pricing approach. Particularly, each road link adopts the pricing scheme to calculate and adjust the local toll regularly, while the vehicles update their routes to minimize the toll costs by exploiting the global toll information. To prevent the potential oscillation brought by the subgradient method, we introduce a heavy-ball method …

Uncovering Patterns In Reviewers' Feedback To Scene Description Authors, Rosiana Natalie, Jolene Kar Inn Loh, Huei Suen Tan, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions (ADs) can increase access to videos for blind people. Researchers have explored different mechanisms for generating ADs, with some of the most recent studies involving paid novices; to improve the quality of their ADs, novices receive feedback from reviewers. However, reviewer feedback is not instantaneous. To explore the potential for real-time feedback through automation, in this paper, we analyze 1,120 comments that 40 sighted novices received from a sighted or a blind reviewer. We find that feedback patterns tend to fall under four themes: (i) Quality; commenting on different AD quality variables, (ii) Speech Act; the utterance or …

A First Look At Accessibility Issues In Popular Github Projects, Tingting Bi, Xin Xia, David Lo, Aldeida Aleti

Research Collection School Of Computing and Information Systems

Accessibility design elements allow people to access software products and services independent of their different abilities. However, accessibility is challenging to handle and whether accessibility is widely considered in software projects is unclear. In this work, we aim to understand if accessibility is a prevalent consideration in practice, what accessibility issues are discussed in GitHub projects, what potential reasons cause accessibility issues, and what solutions (e.g., tools and standards) are applied for addressing accessibility issues. In this work, we collect 11,820 accessibility issues and their threads discussed by developers in popular GitHub projects. We manually analyzed and grouped the collected …

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 …

Online Learning: A Comprehensive Survey, Steven C. H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao

Research Collection School Of Computing and Information Systems

Online learning represents a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time. The goal of online learning is to maximize the accuracy/correctness for the sequence of predictions/decisions made by the online learner given the knowledge of correct answers to previous prediction/learning tasks and possibly additional information. This is in contrast to traditional batch or offline machine learning methods that are often designed to learn a model from the entire training data set at once. Online …

Eargate: Gait-Based User Identification With In-Ear Microphones, Andrea Ferlini, Dong Ma, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of earworn wearables (Earables) as the new frontier of wearable devices, we investigate the feasibility of earable-based gait identification. Specifically, we look at gait-based identification from the sounds induced by walking and propagated through the musculoskeletal system in the body. Our system, EarGate, leverages an in-ear facing microphone which exploits the earable’s occlusion effect to reliably detect the user’s gait from inside the ear canal, without impairing the general usage of earphones. With data collected from 31 subjects, we show that EarGate …

Weakly-Supervised Video Anomaly Detection With Contrastive Learning Of Long And Short-Range Temporal Features, Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, …

Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual labelling of abnormal samples and the subsequent issues involved in training with extremely class-imbalanced data. Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns. One significant challenge faced …

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 …

Burst-Induced Multi-Armed Bandit For Learning Recommendation, Rodrigo Alves, Antoine Ledent, Marius Kloft

Research Collection School Of Computing and Information Systems

In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where each timestamp corresponds to a visit by a user and a corresponding decision regarding recommendation. The main novelty is that we model the reward distribution as a consequence of variations in the intensity of the activity, and thereby we assist the exploration/exploitation dilemma by exploring the temporal dynamics of the …

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 …

Missing Data Imputation For Solar Yield Prediction Using Temporal Multi-Modal Variational Auto-Encoder, Meng Shen, Huaizheng Zhang, Yixin Cao, Fan Yang, Yonggang Wen

Research Collection School Of Computing and Information Systems

The accurate and robust prediction of short-term solar power generation is significant for the management of modern smart grids, where solar power has become a major energy source due to its green and economical nature. However, the solar yield prediction can be difficult to conduct in the real world where hardware and network issues can make the sensors unreachable. Such data missing problem is so prevalent that it degrades the performance of deployed prediction models and even fails the model execution. In this paper, we propose a novel temporal multi-modal variational auto-encoder (TMMVAE) model, to enhance the robustness of short-term …

The Robust Inverse Scattering Method For Focusing Ablowitz–Ladik Equation On The Non-Vanishing Background, Yiren Chen, Bao-Feng Feng, Liming Ling

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we consider the robust inverse scattering method for the Ablowitz–Ladik (AL) equation on the non-vanishing background, which can be used to deal with arbitrary-order poles on the branch points and spectral singularities in a unified way. The Darboux matrix is constructed with the aid of loop group method and considered within the framework of robust inverse scattering transform. Various soliton solutions are constructed without using the limit technique. These solutions include general soliton, breathers, as well as high order rogue wave solutions.

Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a “dog” model is learned within “grass+dog” and “road+dog” respectively, so the “grass” and …

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 …

Using Role Play To Develop An Empathetic Mindset In Executive Education, Siu Loon Hoe, Tamsin Greulich-Smith

Research Collection School Of Computing and Information Systems

The purpose of this article is to discuss the importance of a role play activity as part of an experiential instructional strategy to develop an empathetic mindset among professionals, managers, and executives (PMEs) attending an executive education program in change management. This article provides an approach and process for management educators and facilitators of executive education programs to introduce and teach role play for the busy executives to learn about empathy. Role play is a useful teaching method that helps adult learners understand the importance of seeing things from another person’s point of view especially within a short period of …

Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available …

Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research addresses a new variant of the vehicle routing problem, called the two-echelon vehicle routing problem with time windows, covering options, and occasional drivers (2E-VRPTW-CO-OD). In this problem, two types of fleets are available to serve customers, city freighters and occasional drivers (ODs), while two delivery options are available to customers, home delivery and alternative delivery. For customers choosing the alternative delivery, their demands are delivered to one of the available covering locations for them to pick up. The objective of 2E-VRPTW-CO-OD is to minimize the total cost consisting of routing costs, connection costs, and compensations paid to ODs …

Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns …

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 …