Physical Sciences and Mathematics Commons^™

Ransomsoc: A More Effective Security Operations Center To Detect And Respond To Ransomware Attacks, Anthony Cheuk Tung Lai, Ping Fan Ke, Kelvin Chan, Siu Ming Yiu, Dongsun Kim, Wai Kin Wong, Shuai Wang, Joseph Muppala, Alan Ho

Research Collection School Of Computing and Information Systems

Ransomware remains a major threat for organizations. Despite a lot of research done, existing solutions still have at least two shortcomings. (I) Slow detection time: by the time we realize that the system is under ransomware attack, almost all files have been encrypted. (II) Without a ransomwareaware backup scheme: Most existing systems, in particular those in SMEs (small and medium enterprises), do not have a proper backup system. Even they have it, either it is not a remote-site backup (i.e., files in the backup system may also be encrypted) or it is not designed for ransomware attacks. In this paper, …

Self-Checking Deep Neural Networks For Anomalies And Adversaries In Deployment, Yan Xiao, Ivan Beschastnikh, Yun Lin, Rajdeep Singh Hundal, Xiaofei Xie, David S. Rosenblum, Jin Song Dong

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely adopted, yet DNN models are surprisingly unreliable, which raises significant concerns about their use in critical domains. In this work, we propose that runtime DNN mistakes can be quickly detected and properly dealt with in deployment, especially in settings like self-driving vehicles. Just as software engineering (SE) community has developed effective mechanisms and techniques to monitor and check programmed components, our previous work, SelfChecker, is designed to monitor and correct DNN predictions given unintended abnormal test data. SelfChecker triggers an alarm if the decisions given by the internal layer features of the model …

Clostridioides Difficile Biofilm And Spore Production In Response To Antibiotics And Immune Stress, Adenrele M. Oludiran

Chemistry & Biochemistry Theses & Dissertations

The development of new therapeutic options against Clostridioides difficile (C. difficile) infection is a critical public health concern, as the causative bacterium is highly resistant to multiple classes of antibiotics. C. difficile, an anaerobic spore-forming Gram-positive pathogenic bacterium, is a major cause of hospital-acquired infections. C. difficile persists in the environment and spreads the infection to new hosts in the form of dormant spores and can persist within hosts as surface-attached biofilms. These studies investigate bacterial vegetative cell survival, biofilm formation, and sporulation in response to stress. Antimicrobial host-defense peptides (HDPs) are highly effective at simultaneously modulating …

Optical And Computational Methods For Detection And Quantification Of Protein-Protein Interactions Using Single-Molecule Localization Microscopy, David James Schodt

Physics & Astronomy ETDs

Interactions between proteins residing on or near the membranes of biological cells trigger many of the signaling cascades that facilitate the transmission of information between the exterior and the interior of the cell. Normal function of these protein-protein interactions (PPIs) is required for the healthy functioning of complex single- and multi-cellular organisms. Abnormalities involving these interactions are implicated in several diseases, such as the progression and treatment resistance of many forms of cancer. Understanding both the normal and abnormal behaviors of these interactions is necessary to improve our understanding of such diseases, which in turn can drive drug development for …

Ultrashort Pulse Laser Filamentation Electrical And Optical Diagnostic Comparison, James E. Wymer

Optical Science and Engineering ETDs

Results presented here examine the effect of changing gas pressure on the radio frequency (RF) emissions of an ultrashort pulse laser filament plasma and how those emissions vary longitudinally in the laser focal region. We use a WR284 rectangular waveguide with a 1.5 cm hole that allows the beam through. A 3.2 GHz microwave signal is emitted in the waveguide, and signals are received through a waveguide-to-coax antenna connected to an HP8470B Schottky diode. By enabling and disabling the 3.2 GHz signal, we measure both the self-emitted RF from a USPL filament and subsequently the degree of attenuation a filament …

Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying Covid-19 Patients, Aaron Christopher Segura

Mathematics & Statistics ETDs

This study compared the performance of machine learning models in classifying COVID-19 patients using exhaled breath signals and simulated datasets. Ground truth classification was determined by the gold standard Polymerase Chain Reaction (PCR) test results. A residual bootstrapped method generated the simulated datasets by fitting signal data to Autoregressive Moving Average (ARMA) models. Classification models included neural networks, k-nearest neighbors, naïve Bayes, random forest, and support vector machines. A Recursive Feature Elimination (RFE) study was performed to determine if reducing signal features would improve the classification models performance using Gini Importance scoring for the two classes. The top 25% of …

Robust Uncertainty Quantification With Analysis Of Error In Standard And Non-Standard Quantities Of Interest, Zachary Stevens

Mathematics & Statistics ETDs

This thesis derives two Uncertainty Quantification (UQ) methods for differential equations that depend on random parameters: (\textbf{i}) error bounds for a computed cumulative distribution function (\textbf{ii}) a multi-level Monte Carlo (MLMC) algorithm with adaptively refined meshes and accurately computed stopping-criteria. Both UQ approaches utilize adjoint-based \textit{a posteriori} error analysis in order to accurately estimate the error in samples of numerically approximated quantities of interest. The adaptive MLMC algorithm developed in this thesis relies on the adjoint-based error analysis to adaptively create meshes and accurately monitor a stopping criteria. This is in contrast to classical MLMC algorithms which employ either a …

Optimal First Order Methods For Reducing Gradient Norm In Unconstrained Convex Smooth Optimization, Yunheng Jiang

All Theses

In this thesis, we focus on convergence performance of first-order methods to compute an $\epsilon$-approximate solution of minimizing convex smooth function $f$ at the $N$-th iteration.

In our introduction of the above research question, we first introduce the gradient descent method with constant step size $h=1/L$. The gradient descent method has a $\mathcal{O}(L^2\|x_0-x^*\|^2/\epsilon)$ convergence with respect to $\|\nabla f(x_N)\|^2$. Next we introduce Nesterov’s accelerated gradient method, which has an $\mathcal{O}(L\|x_0-x^*\|\sqrt{1/\epsilon})$ complexity in terms of $\|\nabla f(x_N)\|^2$. The convergence performance of Nesterov’s accelerated gradient method is much better than that of the gradient descent method but still not optimal. We also …

Galapagos-2/Galfitm/Gama – Multi-Wavelength Measurement Of Galaxy Structure: Separating The Properties Of Spheroid And Disk Components In Modern Surveys, Boris Häußler, Marina Vika, Steven P. Bamford, Evelyn J. Johnston, Sarah Brough, Sarah Casura, Benne Holwerda, Lee S. Kelvin, Cristina Popescu

Faculty and Staff Scholarship

Aims. We present the capabilities of GALAPAGOS-2 and GALFITM in the context of fitting two-component profiles – bulge–disk decompositions – to galaxies, with the ultimate goal of providing complete multi-band, multi-component fitting of large samples of galaxies in future surveys. We also release both the code and the fit results to 234 239 objects from the DR3 of the GAMA survey, a sample significantly deeper than in previous works.

Methods. We use stringent tests on both simulated and real data, as well as comparison to public catalogues to evaluate the advantages of using multi-band over single-band data.

Results. We show …

Phonon Dispersions Of Nonmagnetic Bcc Iron At High Pressures From Ab Initio Molecular Dynamics And Harmonic Ensemble Lattice Dynamics, Valeria Itzel Arteaga Muniz

Open Access Theses & Dissertations

Numerous computational and experimental studies on the crystal structure of metals nearthe melt line, indicate the body-centered cubic (bcc) structure can be favored over other crystal phases at lower temperatures [1]-[4] and even in cases in which other crystal structures are thermodynamically more stable, bcc may nucleate first from the melt, at rapid cooling rates [5]. Iron (Fe) is a polymorph metal, with a bcc ferromagnetic structure at ambient conditions. Even though the phase diagram of Fe is well known at relatively low pressures, there is currently no consensus on the crystal structure of Fe below the melt line at …

Continuous Field Sensor Authentication And Process Integrity Assurance Mechanisms In Critical National Infrastructures, Abel Osvaldo Gomez Rivera

Open Access Theses & Dissertations

The growing modernization of traditional Industrial Cyber-Physical Systems (ICPSs) has increased the probability and effectiveness of cyber attacks by integrating modern communication technologies that expose security vulnerabilities like lack of access control policies to adversaries. The exponential growth of cyber attacks has caught the attention of stakeholders that have proposed cybersecurity initiatives to protect ICPSs. ICPSs are part of the Critical National Infrastructures (CNIs) supporting the society's sustainability and national security. The cybersecurity initiatives aiming to address cyber attacks proposed by stakeholders must continuously authenticate constrained devices. Operational thresholds and process integrity assurance must also be maintained. A centralized ICPS …

Characterization Of A Flex-Fuel Oxy-Combustion System Through Experimental Investigation And Computational Modeling, Md Mohieminul I. Khan

Open Access Theses & Dissertations

Oxy-fuel combustion has the potential to offer high thermal efficiency and lower pollutants emission compared to other existing combustion technologies. More than 63% of the worldâ??s electricity is produced by fossil fuel combustion which also is the major reason for greenhouse gas emissions. Oxy combustion offers a solution to it. Oxy combustion uses pure oxygen as an oxidizer instead of air which eliminates the NOx and SOx products from the exhaust. In oxy-combustion flue gas mainly consists of CO2 and water vapor. Furthermore, combustion in presence of pure oxygen results in higher flame temperature and hence promises higher thermal efficiency. …

Neoglycoproteins As Potential Biomarkers And Vaccines For Chagas Disease, Elisa Garcia Carvajal

Open Access Theses & Dissertations

Trypanosoma cruzi, the causal agent of Chagas disease (CD), currently affects 6-8 million people worldwide, predominantly in Latin America, where the parasite is endemic. The treatment of CD is based on two drugs, benznidazole (BZN) and nifurtimox (NFX). These drugs cause adverse effects and are more effective in the acute phase of the disease. Conventional serology assays can be used for diagnosis, but they are not suitable for determining chemotherapy effectiveness because patients take approximately 10-20 years to exhibit negative seroconversion. Although CD has become a global health problem, specific biomarkers are still needed for the follow-up of chemotherapy and …

Diverse Effects Of Ev Charging Infrastructure On Electric Power Distribution Systems, Travis Michael Moore Newbolt

Open Access Theses & Dissertations

The advanced technology of today has allowed for an avenue into cleaner forms of energy that will not only protect our environment but also continue to advance our society. Among the many forms of clean energy, electric vehicles (EV) have the potential to mitigate our consumption of fossil fuels in vehicle transportation industries. In the U.S. for 2021, EVs account for approximately 700,000 registrations. That number is projected to increase to 2 million by 2030. Although EVs do reduce the number of emissions when compared to an internal combustion engine, they do however shift the responsibility to utility companies to …

Investigating The Effects Of Decoupling Cache And Core Speed On Power, Throughput, And Energy, David Daniel Pruitt

Open Access Theses & Dissertations

A variety of computer systems from HPC to mobile systems are power limited and performance sensitive. These systems use very similar components at different scales. Dynamic Voltage and Frequency Scaling (DVFS) features enable modulation of CPU performance and efficiency characteristics to power, energy and timing requirements.Programs have a variety of computational characteristics. If a CPU subsystem substantially limits a particular programâ??s execution progress, that programâ??s throughput will vary proportionally with the subsystemâ??s clock frequency. In contrast, if a CPU subsystem does not substantially limit throughput, the impact of a change in its clock frequency will result in a diminimus change …

Population Genetics Of Two Sympatric Species, The Round-Tailed Horned Lizard (Phrynosoma Modestum) And The Greater Earless Lizard (Cophosaurus Texanus), In The Indio Mountains Of West Texas, Logan Miles Horne

Open Access Theses & Dissertations

Population genetics of two sympatric species, the round-tailed horned lizard (phrynosoma modestum) and the greater earless lizard (cophosaurus texanus), in the Indio mountains of West Texas.

Evaluating Spatial-Temporal Patterns In Us Tornado Occurrence With Space Time Cube Analysis And Linear Kernel Density Estimation: 1950-2019, Darrell L. Wiser

Electronic Theses and Dissertations

This research estimated the spatial-temporal patterns of tornadoes in the continental United States from 1950-2019 using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. This study employed Space-Time Cube Analysis and Linear Kernel Density (Kernel Density Linear Process, (KDLP)) rather than the standard Kernel Density Estimation (KDE) approach; to evaluate whether tornado hotspot locations and intensities shift over time.

The first phase of the study utilized KDLP to map changes in tornado hotspots and qualitatively assess decadal shifts in hotspot locations and intensities by occurrence and magnitude between decades using ArcGIS Pro and CrimeStat. Next an …

Knowledge Representation And Management: Notable Contributions In 2021

Faculty, Staff and Student Publications

OBJECTIVES: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021.

METHODS: Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries.

RESULTS: A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, …

Charting The Proteome Landscape In Major Psychiatric Disorders: From Biomarkers To Biological Pathways Towards Drug Discovery

Faculty, Staff and Student Publications

Schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are major mental disorders that affect a significant proportion of the global population. Advancing our knowledge of the pathophysiology of these disorders and identifying biomarkers are urgent needs for developing objective diagnostic tests and new therapeutics. In this study, we performed a systematic review and then extracted, curated, and analyzed proteomics data from published studies, aiming to assess the proteome in peripheral blood of individuals with SZ, BD, or MDD. Then, we performed pathway and network analyses to illuminate the biological themes concatenated by the differentially expressed proteins by systematically …

Mathematical Modeling Of Seir Model With Generalized Incidence Function And The Extension To Covid-19 Model, Shymaa Mohammad Dadoa

Theses

The COVID-19 pandemic had shown the importance of the SEIR model in predicting the outcome of the disease spread and to find the best strategies to contain the pandemic. As this type of model has a limited number of compartments, many other models were derived from the SEIR model to cover, to the maximum, the complex dynamics of the disease spread. These extensions of the SEIR model bring natural validity questions: How can we validate these models? and how far/close are these extended models from giving us real insights into the pandemic?

This thesis investigates the SEIR epidemic model and …

Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar

Research Collection School Of Computing and Information Systems

Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and …

Neural-Progressive Hedging: Enforcing Constraints In Reinforcement Learning With Stochastic Programming, Supriyo Ghosh, Laura Wynter, Shiau Hong Lim, Duc Thien Nguyen

Research Collection School Of Computing and Information Systems

We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the execution of the policy, using probabilistic models of the state transitions to guide policy adjustments. The framework is particularly amenable to the class of sequential resource allocation problems since feasibility with respect to typical resource constraints cannot be enforced in a scalable manner. The NP framework provides an alternative that adds modest overhead during the online phase. …

Interpreting Trajectories From Multiple Views: A Hierarchical Self-Attention Network For Estimating The Time Of Arrival, Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Estimating the time of arrival is a crucial task in intelligent transportation systems. Although considerable efforts have been made to solve this problem, most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments. The segment view, though being able to depict the local traffic conditions straightforwardly, is insufficient to embody the intrinsic structure of trajectories on the road network. To overcome the limitation, this study proposes multi-view trajectory representation that comprehensively interprets a trajectory from the segment-, link-, and intersection-views. To fulfill the purpose, we design a hierarchical …

Individually Rational Collaborative Vehicle Routing Through Give-And-Take Exchanges, Tran Phong, Paul Tang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we are concerned with the automated exchange of orders between logistics companies in a marketplace platform to optimize total revenues. We introduce a novel multi-agent approach to this problem, focusing on the Collaborative Vehicle Routing Problem (CVRP) through the lens of individual rationality. Our proposed algorithm applies the principles of Vehicle Routing Problem (VRP) to pairs of vehicles from different logistics companies, optimizing the overall routes while considering standard VRP constraints plus individual rationality constraints. By facilitating cooperation among competing logistics agents through a Give-and-Take approach, we show that it is possible to reduce travel distance and …

Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv

Research Collection School Of Computing and Information Systems

Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order …

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning …

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning …

Structured Encryption For Knowledge Graphs, Yujie Xue, Lanxiang Chen, Yu Mi, Lingfang Zeng, Fatemeh Rezaeibagha, Robert H. Deng

Research Collection School Of Computing and Information Systems

We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents-symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational …

Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal …

P-Meta: Towards On-Device Deep Model Adaptation, Zhongnan Qu, Zimu Zhou, Yongxin Tong, Lothar Thiele

Research Collection School Of Computing and Information Systems

Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data samples, deploying the pre-trained model on IoT devices, and adapting the deployed model on the device with local data. Such an on-device adaption for deep learning empowered applications demands data and memory efficiency. However, existing gradient-based meta learning schemes fail to support memory-efficient adaptation. To this end, we propose p-Meta, a new meta learning method that enforces structure-wise partial parameter updates while ensuring fast generalization to unseen tasks. Evaluations on few-shot image classification and reinforcement …