Physical Sciences and Mathematics Commons^™

Studies On The Tempered Fractional Natural Decomposition Method, Nazek Ahmad Obeidat

Graduate College Dissertations and Theses

In Fractional Calculus (FC), the notion of fractional derivatives and integrals arise from convolutions with a power law, which, when multiplied by an exponential factor, one obtains tempered fractional derivatives and integrals.

The purpose of this dissertation is to develop theories and applications of a new technique in FC called the Tempered Fractional Natural Transform Method (TFNTM). This method can be used to solve a myriad of problems in linear and nonlinear ordinary and partial differential equations.

We present convergence analysis, give error estimates, and provide exact solutions, with graphical illustrations, to many well-known problems in tempered fractional differential equation, …

Disentangling The Separate And Intersecting Pathways Of Carbon And Nitrogen Response To Overlapping Drivers, Manya Ruckhaus

Graduate College Dissertations and Theses

Anthropogenic activities have drastically altered atmospheric composition — leading to unprecedented disturbances that might push ecosystems across thresholds where important ecosystem services, such as clean water and healthy soils are at risk. Such disturbances include increased heavy precipitation, rain on snow events, and longer-term shifts in rain composition and precipitation amount. Catchment response to such perturbations is widely variable, indicating that specific catchment characteristics may govern the resistance and resilience of the system. Forested catchments in the northeastern, U.S. have reported increasing dissolved organic carbon (DOC) concentrations in streams, and links to shifts in drivers—such as precipitation chemistry, season, and …

Examining The Relationship Between The Ereendavaa Metamorphic Core Complex And Duch Gol Basin In Northeast Mongolia In The Context Of The Mongol-Okhotsk Suture, Gavin Pirrie

Graduate College Dissertations and Theses

The Mongol-Okhotsk suture is integral in understanding the closure of the Mongol-Okhotsk Ocean, an ocean basin that once separated the Siberian craton from terranes of the Central Asian Orogenic Belt in southern Mongolia. However, the timing of closure and exact location of the suture are poorly known. This study examines two areas in northeastern Mongolia adjacent to the suture, the Ereendavaa Metamorphic Core Complex (EMCC) and the Duch Gol Basin, to aid in this understanding. The EMCC is an Early Cretaceous metamorphic core complex with an associated top-to-the-NW extensional detachment fault. Two sets of mineral lineations have previously been documented …

A Holistic Approach To River Restoration Design And Conservation Planning On The Reach And Basin Scales Using Hydraulic Modeling And Multi-Objective Optimization Tools, Lindsay Courtney Worley

Graduate College Dissertations and Theses

Flooding events around the world cost billions (USD) in damages each year. For decades, engineers have combated flood related damages by implementing flood mitigation controls such as channelization, levees or berms, and armoring. Recent advances in the study of river dynamics, however, have challenged the efficacy of these traditional flood mitigation techniques and pose that these structures are disconnecting channels from their floodplains, increasing flow rates, and contributing to more erosion. The effects of climate change combined with future predictions of increased storm frequency and intensity make it necessary to revise flood hazard mitigation strategies. A more nature-based alternative to …

Monitoring Of Suspended Sediment Mineralogy In Tropical Rivers: Effects Of Flowrate, Time, And Lithology, Trevor Joseph Mackowiak

Graduate College Dissertations and Theses

As a result of climate change, tropical storms will become more frequent and intense. Resulting changes in river flow dynamics have the potential to change the composition of suspended sediments in crucial tropical river ecosystems, possibly affecting their resiliency. This thesis investigates how changes river discharge and bedrock lithology affected the physiochemical nature of river suspended sediments over a typical year. Suspended sediment samples (n=73) on filter membranes and sediment grab samples (n=18) were collected from the riverbed and riverbank from three watersheds of differing lithology in the Luquillo Mountains, PR (R. Mameyes, R. Icacos, and Q. Sonadora). Suspended sediment …

Pymoocfd - A Multi-Objective Optimization Framework For Cfd, George Martin Cunningham Love

Graduate College Dissertations and Theses

Modern computational resource have solidified the use of computer modeling as an integral part of the engineering design process. This is particularly impressive when it comes to high-dimensional models such as computational fluid dynamics (CFD) models. CFD models are now capable of producing results with a level of confidence that would previously have required physical experimentation. Simultaneously, the development of machine learning techniques and algorithms has increased exponentially in recent years. This acceleration is also due to the widespread availability of modern computational resources. Thus far, the cross-over between these fields has been mostly focused on computer models with low …

Drones, Virtual Reality, And Modeling: Communicating Catastrophic Dam Failure, H. R. Spero, I. Vazquez-Lopez, K. Miller, R. Joshaghani, S. Cutchin, J. Enterkine

Computer Science Faculty Publications and Presentations

Dam failures occur worldwide and can be economically and ecologically devastating. Communicating the scale of these risks to the general public and decision-makers is imperative. Two-dimensional (2D) dam failure hydraulic models inform owners and floodplain managers of flood regimes but have limitations when shared with non-specialists. This study addresses these limitations by constructing a 3D Virtual Reality (VR) environment to display the 1976 Teton Dam disaster case study using a pipeline composed of (1) 2D hydraulic model data (extrapolated into 3D), (2) a 3D reconstructed dam, and (3) a terrain model processed from UAS (Uncrewed Airborne System) imagery using Structure …

Automatic Transformation Of Natural To Unified Modeling Language: A Systematic Review, Sharif Ahmed, Arif Ahmed, Nasir U. Eisty

Computer Science Faculty Publications and Presentations

Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches to UML generation, such as restrictions on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. Objective: This study aims to better understand …

Measuring Fairness In Ranked Results: An Analytical And Empirical Comparison, Amifa Raj, Michael D. Ekstrand

Computer Science Faculty Publications and Presentations

Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user's information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system's behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and …

Software Engineering Approaches For Tinyml Based Iot Embedded Vision: A Systematic Literature Review, Shashank Bangalore Lakshman, Nasir U. Eisty

Computer Science Faculty Publications and Presentations

Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces with Machine Learning (ML) to embed deep intelligence at the far edge. TinyML (Tiny Machine Learning) has enabled the deployment of ML models for embedded vision on extremely lean edge hardware, bringing the power of IoT and ML together. However, TinyML powered embedded vision applications are still in a nascent stage, and they are just starting to scale to widespread real-world IoT deployment. To harness the true potential of IoT and ML, …

Symbol And Communicative Grounding Through Object Permanence With A Mobile Robot, Josue Torres-Fonseca, Catherine Henry, Casey Kennington

Computer Science Faculty Publications and Presentations

Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration as it relates to symbol and communicative grounding in spoken dialogue systems. In this paper, we leverage SLAM as a module for tracking object permanence and use a robot platform to move around a scene where it discovers objects and learns how they are denoted. We evaluated by comparing our system’s effectiveness at learning words from human dialogue partners both with and without object permanence. …

Exploring Visitor Perceptions And Behaviours Related To Ticks And Lyme Disease Risk In An Ontario Protected Area, Ryan Brady

Theses and Dissertations (Comprehensive)

Lyme disease is the most common vector-borne zoonosis in North America and over the past decade, reported cases of the disease have been rapidly increasing in many regions throughout Canada. The relative novelty of this public health threat presents nature-based tourism and recreation organizations with a range of policy and management challenges. Currently, there is a limited understanding of public perceptions and behaviours associated with tick and Lyme disease risk, especially within a Canadian parks and protected areas visitation and visitor experience context. To address this practical and scholarly knowledge gap, this study utilized in-situ surveys to explore visitor perceptions, …

Actuarial Credibility Approach In Adjusting Initial Cost Estimates Of Transport Infrastructure Projects, Bartłomiej Rokicki, Krzysztof Ostaszewski

Faculty Publications – Mathematics

This paper presents a novel methodology based on the modified actuarial credibility approach. It allows for the adjustment of initial cost estimates of public infrastructure projects by accounting for the additional risk/uncertainty factor. Hence, it offers an interesting alternative to other existing forecasting methods. We test our approach by applying data for over 300 major infrastructure projects implemented in Poland between 2004 and 2020. We prove that, despite its simplicity, the actuarial credibility approach can deliver accurate cost estimates compared to more complex methods such as regression analysis (OLS) or machine learning (LASSO). In particular, we show that, although the …

The Role Of Interactive Features Within A Mathematics Storybook In Interpreting A Conflict And Conflict Resolution: The Case Of Three Fifth Graders, Mahtob Aqazade

Faculty Publications – Mathematics

Students often experience cognitive conflicts when trying to interpret negative numbers’ order and values because they do not correspond to their prior whole number knowledge. One way to trigger students’ cognitive conflicts and support their conflict resolution meaningfully is through stories. Thus, I used a temperature-related mathematics storybook—Temperature Turmoil—to highlight the cognitive conflict students often experience because of relying on the integers’ absolute value and introduce conflict resolution (i.e., integers have both absolute value and directed value). By incorporating interactive features, I used a multiple-case approach to describe three fifth graders’ cognitive conflict and conflict resolution experiences. Harry, Lola, and …

Air Quality: Assessment Of Pollutant Levels And Chemistry In Kitchener, On Using Multisensor Pods, Wisam Mohammed

Theses and Dissertations (Comprehensive)

Air quality is a growing concern amongst governmental bodies worldwide. A large number of scientific studies accumulated over the past 25 years suggest that poor ambient air quality is attributed to adverse health effects, especially in vulnerable communities that exhibit pre-existing conditions. The United Nations Children’s Fund (UNICEF) reported around 600 000 deaths globally in children under the age of 5 as a result of acute lower respiratory infections caused by poor air quality. With the current statistics on air quality impacts, it is clear that more needs to be done. This MSc work aims to put into perspective the …

Vims Marsh Migration Final Report + Metadata Sheets, Molly Mitchell, Karinna Nunez, Christine Tombleson, Julie Herman

Reports

Coastal marsh loss is a significant issue globally, due in part to rising sea levels and high levels of coastal human activity. Marshes have natural mechanisms to allow them to adapt to rising sea levels, however, migration across the landscape is one of those mechanisms and is frequently in conflict with human use of the shoreline. Ensuring the persistence of marshes into the future requires an understanding of where marshes are likely to migrate under sea level rise and targeting those areas for conservation and preservation activities. The goal of this project was to 1) compile existing datasets and information …

A Survey On Deep Learning For Software Engineering, Yanming Yang, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

In 2006, Geoffrey Hinton proposed the concept of training "Deep Neural Networks (DNNs)" and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in …

Secrets Behind The Science Of Symmetry: Evolution And Symmetry, Yeongseo Ko

The Synapse: Intercollegiate science magazine

No abstract provided.

New Political Ecologies Of Renewable Energy, Sarah Knuth, Ingrid Behrsin, Anthony Levenda, James Mccarthy

Geography

The critique of fossil fuel regimes has been a foundational concern for the field of political ecology, in its drives to expose the injustices and harms of energy extractivism and its early warnings of the climate crisis. However, it is increasingly evident that renewable energy sources and their infrastructures will carry their own costs and trade-offs, and that critique, resistance and alternative movement-building are needed to forge a truly just renewable energy transition. This theme issue underlines the many ways in which political ecology is well-positioned to lead critical and engaged scholarship in support of energy/climate justice. In this introduction …

Climate Adaptation For Tropical Island Land Stewardship: Adapting A Workshop Planning Process To Hawai'i, Ryan J. Longman, Courtney L. Peterson, Madeline Baroli, Abby G. Frazier, Zachary Cook, Elliott W. Parsons, Maude Dinan, Katie L. Kamelamela, Caitriana Steele, Reanna Burnett, Chris Swanston, Christian P. Giardina

Geography

No abstract provided.

Just-In-Time Defect Prediction On Javascript Projects: A Replication Study, Chao Ni, Xin Xia, David Lo, Xiaohu Yang, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Change-level defect prediction is widely referred to as just-in-time (JIT) defect prediction since it identifies a defect-inducing change at the check-in time, and researchers have proposed many approaches based on the language-independent change-level features. These approaches can be divided into two types: supervised approaches and unsupervised approaches, and their effectiveness has been verified on Java or C++ projects. However, whether the language-independent change-level features can effectively identify the defects of JavaScript projects is still unknown. Additionally, many researches have confirmed that supervised approaches outperform unsupervised approaches on Java or C++ projects when considering inspection effort. However, whether supervised JIT defect …

Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy

Research Collection School Of Computing and Information Systems

The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts (SCs) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and …

Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss, Cheng Yan, Guansong Pang, Xiao Bai, Changhong Liu, Xin Ning, Jun Zhou

Research Collection School Of Computing and Information Systems

Person Re-IDentification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key to accurate person ReID, because many identities can be differentiated only when looking into these fine-grained differences. However, most state-of-the-art person ReID approaches, typically driven by a triplet loss, fail to effectively learn the fine-grained features as they are focused more on differentiating large appearance differences. To address this issue, we introduce a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences …

Comparison Of The Mental Burden On Nursing Care Providers With And Without Mat-Type Sleep State Sensors At A Nursing Home In Tokyo, Japan: Quasi-Experimental Study, Sakiko Itoh, Hwee-Pink Tan, Kenichi Kudo, Yasuko Ogata

Research Collection School Of Computing and Information Systems

Background: Increasing need for nursing care has led to the increased burden on formal caregivers, with those in nursing homes having to deal with exhausting labor. Although research activities on the use of internet of things devices to support nursing care for older adults exist, there is limited evidence on the effectiveness of these interventions among formal caregivers in nursing homes. Objective: This study aims to investigate whether mat-type sleep state sensors for supporting nursing care can reduce the mental burden of formal caregivers in a nursing home. Methods: This was a quasi-experimental study at a nursing home in Tokyo, …

Secure Cloud Data Deduplication With Efficient Re-Encryption, Haoran Yuan, Xiaofeng Chen, Jin Li, Tao Jiang, Jianfeng Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users' sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware …

Transformer-Based Joint Learning Approach For Text Normalization In Vietnamese Automatic Speech Recognition Systems, The Viet Bui, Tho Chi Luong, Oanh Thi Tran

Research Collection School Of Computing and Information Systems

In this article, we investigate the task of normalizing transcribed texts in Vietnamese Automatic Speech Recognition (ASR) systems in order to improve user readability and the performance of downstream tasks. This task usually consists of two main sub-tasks: predicting and inserting punctuation (i.e., period, comma); and detecting and standardizing named entities (i.e., numbers, person names) from spoken forms to their appropriate written forms. To achieve these goals, we introduce a complete corpus including of 87,700 sentences and investigate conditional joint learning approaches which globally optimize two sub-tasks simultaneously. The experimental results are quite promising. Overall, the proposed architecture outperformed the …

On The Reproducibility And Replicability Of Deep Learning In Software Engineering, Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John C. Grundy, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge.Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility—whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability—whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a …

Codematcher: Searching Code Based On Sequential Semantics Of Important Query Words, Chao Liu, Xin Xia, David Lo, Zhiwei Liu, Ahmed E. Hassan, Shanping Li

Research Collection School Of Computing and Information Systems

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that …

Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu

Research Collection School Of Computing and Information Systems

User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of …

Face To Purchase: Predicting Consumer Choices With Structured Facial And Behavioral Traits Embedding, Zhe Liu, Xianzhi Wang, Lina Yao, Jake An, Lei Bai, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Predicting consumers’ purchasing behaviors is critical for targeted advertisement and sales promotion in e-commerce. Human faces are an invaluable source of information for gaining insights into consumer personality and behavioral traits. However, consumer's faces are largely unexplored in previous research, and the existing face-related studies focus on high-level features such as personality traits while neglecting the business significance of learning from facial data. We propose to predict consumers’ purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-N purchase …