Physical Sciences and Mathematics Commons^™

Assessment Of Phytoplankton Assemblages And Harmful Algal Blooms In New Jersey, Yaritza Acosta Caraballo

Theses, Dissertations and Culminating Projects

Urbanization, industrialization, and climate change have played a major role in the pollution of waterways, leading to a global increasing trend of harmful algal blooms (HAB) while jeopardizing water quality. Phytoplankton and HAB were evaluated within the highly urbanized and historically polluted state of New Jersey to help provide statewide baseline data for HAB and water quality management. A total of three studies were included in this dissertation. In the first study, phytoplankton communities were characterized in freshwaters of New Jersey during the cyanobacterial HAB season and their relationships to water quality at both statewide and ecoregion levels were examined. …

Seasonal Variations In Nutrient And Total Suspended Solids Sources And Fluxes In Rivers Of Northern New Jersey And Newark Bay, Usa, Jing Nie

Theses, Dissertations and Culminating Projects

In recent decades, aquatic systems have experienced major problems with water quality due to high nutrient concentrations from both point and non-point sources resulting from industrialization, urbanization, and population growth. While nutrient pollution due to land use change cannot be ignored, point sources such as combined sewer overflows and discharging sites have also contributed to the problem. Integrated hydrodynamic, chemical, and biological models have been developed to simulate nutrient transportation from both sources. This paper reviews and analyzes water quality data from published literature to evaluate nutrient pollution in aquatic systems and emphasizes the need for a continuously developed integrated …

The Fate Of Fraxinus Spp. In New Jersey : A Physioeconomic Evaluation Of The Forest System And Infrastructure Implications, Erik W. Lyttek

Theses, Dissertations and Culminating Projects

Though the impacts of pest infestation on forested ecosystems vary, the natural factors that facilitate outbreaks are of concern across multiple disciplines across the USA, affecting such diverse fields as economics, ecology, hydrology, atmospheric sciences, and pedology. Emerald Ash Borer (EAB) is a typical example of an aggressive invasive forest pest; it has proven difficult to eliminate, resistant to natural predators, and damaging to local flora. EAB has recently proliferated through the northern and mid-western forests of the USA, devastating the native population of ash trees (Fraxinus spp.), and has invaded an estimated 28% of all susceptible trees. In New …

Antarctic Peninsula Ice Sheet Dynamics In The Pliocene From Sedimentological Interpretation Of Ocean Drilling Program Site 1097, Ridley Joseph

Theses, Dissertations and Culminating Projects

Studying ice sheet behavior during the Pliocene Epoch is of great importance. The Early Pliocene was characterized by warm climatic conditions, similar carbon dioxide levels and high sea levels compared to the present. This time frame provides essential information on the foreseeable future of Earth’s cryosphere, particularly if climate change continues to persist. The sedimentary record of the Antarctic Peninsula Ice Sheet (APIS) is the focus of this study. At Ocean Drilling Program (ODP) Site 1097, cores were drilled into Pliocene strata at a shallow depth on the Antarctic Peninsula continental shelf. Sedimentary facies were interpreted, and twenty samples were …

Parameter Optimization For Excitable Cell Models, Amrit Parmar

Theses, Dissertations and Culminating Projects

The electrophysiology of nodose ganglia neurons is of great interest in the analysis of cell membrane currents and action potential behavior. This behavior was initially outlined in the Hodgkin-Huxley conductance model [1] using a system of nonlinear differential equations. Later, Schild et al. [2] developed an extension of the Hodgkin-Huxley model to provide a more exhaustive description of ion channels involved in nodose neuronal action potential activity. We consider a variety of methods to fit the parameters of both the Hodgkin-Huxley and Schild et al. models to an empirical stimulus response dataset. Our methods were validated using synthetic datasets, as …

Potential Of Increased Thermal Tolerance As A Response To Acute Heat Stress In A Disease-Transmitting Mosquito Species, Nicole Ashley Scanlon

Theses, Dissertations and Culminating Projects

Each year hundreds of millions of people are infected with mosquito-borne illnesses. West Nile Virus is the leading cause of mosquito-borne diseases in the United States (McDonald et al., 2021). The first case of West Nile Virus was detected in the United States in 1999 (Sejvar, 2003). Since that time the number of cases has dramatically risen. Mosquitoes are ectotherms which means their body temperature is dependent on the thermal conditions outside of their body. Because of this they are very sensitive to changes in their environment. The Earth’s average temperature has been slowly but steadily rising. This small but …

The Identification Of Rogue Access Points Using Channel State Information, Irene Mcginniss

Theses, Dissertations and Culminating Projects

Today's wireless networks (Wi-Fi) handle more significant numbers of connections, deploy efficiently, and provide increased reliability and high speeds at low cost. The ability of rogue access points (RAPs) to mimic legitimate APs makes them the most critical threat to wireless security. APs are found in coffee shops, supermarkets, stadiums, buses, trains, airports, hospitals, theaters, and shopping malls.

Rogue access points (RAP) are unauthorized devices that connect to legitimate access points and networks and bypass authorized security procedures. RAP detection has been attempted using hardware and software-based solutions requiring the developing of dedicated tools or beacon frame modification. (Arisandi, 2021). …

The Interconnected Roles Of Residues N189, E168, And S220 In M. Tuberculosis Indole-3-Glycerol Phosphate Synthase Catalysis, Erik Domlija

Theses, Dissertations and Culminating Projects

Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis that primarily attacks the lungs. Drug resistance for M. tuberculosis is emerging and therefore new drug targets need to be identified. One way to target TB is through the inhibition of the tryptophan biosynthetic pathway of M. tuberculosis since production of tryptophan is essential for the replication of M. tuberculosis. The enzyme Indole-3-glycerol phosphate synthase (MtIGPS) was chosen as a potential drug target due to its role in tryptophan biosynthesis. Residue N189 interacts with active site residues E168 and S220. It is inferred from previous studies done with …

First-Principal Investigations Of The Electronic, Magnetic, And Thermoelectric Properties Of Crtirhal Quaternary Heusler Alloy, Shuruq Alsayegh

Graduate Theses and Dissertations

Density functional theory calculations are performed to investigate the electrical electronic, magnetic, and thermoelectric properties of CrTiRhAl quaternary Heusler alloy (QHA). The type-I atomic configuration is found to be the most stable structure of this alloy. The CrTiRhAl QHA exhibits a half-metallic ferromagnetic structure with a narrow band gap at one spin channel (semiconductor), and a metallic behavior at the other spin channel. This corresponds to a 100% spin-polarization, making it ideal for potential spintronic applications. Applying the semi-classical Boltzmann theory, the Seebeck coefficient, electrical conductivity, and electronic thermal conductivity of CrTiRhAl alloy were calculated. The predicted figure of merit …

Achieving Causal Fairness In Recommendation, Wen Huang

Graduate Theses and Dissertations

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed …

Dpd Guided Insight On The Formation Process Of Polyethersulfone Membranes By Nonsolvent Induced Phase Separation And The Effects Of Additives, Eric Ledieu

Graduate Theses and Dissertations

Dissipative particle dynamics (DPD), a coarse grain simulation method, was applied to the membrane formation process of non-solvent induced phase separation (NIPS) to gain further insight on the mechanism of certain variables and how they affect the final morphology. NIPS involves two solutions, an organic polymer dissolved in an organic solvent colloquially called the dope and an aqueous coagulation bath, brought into contact with one another. The solvents then mix, causing the polymer to fall out of solution as an asymmetric membrane with a dense surface layer and a more open subsurface layer in response to the decreasing solubility. Polyethersulfone …

The Terroir Of Swiss Cheese: A Temporal And Geomorphological Investigation Of The Martian Co2 Sublimation Pits, Racine D. Cleveland

Graduate Theses and Dissertations

Observations by NASA Mars Global Surveyor showed evidence of rough topography on the South Pole of Mars. The topography is the result of CO2 sublimation processes that occur through the changing seasons on the red planet. These sublimation areas are known to scientists as Swiss Cheese Features (SCF). SCF are erosional degradation pits that have been studied for over two decades. Studies show that these SCF increase in area over time, but these values are collected by hand on a per feature basis. Models for the pit evolution have also played a part in understanding these SCF. This work is …

Asymptotic Properties And Separation Rates For Navier-Stokes Flows, Patrick Michael Phelps

Graduate Theses and Dissertations

In this dissertation, we investigate asymptotic properties of local energy solutions to the Navier-Stokes equations and develop an application which controls the separation of non-unique solutions in this class. Specifically, we quantify the rate at which two, possibly unique solutions evolving from the same data may separate pointwise away from a singularity. This is motivated by recent results on non-uniqueness for forced and unforced Navier-Stokes and analytical and numerical evidence suggesting non-uniqueness in the Leray class. Our investigation begins with discretely self-similar solutions known to exist globally in time and to be regular outside a space-time paraboloid. We prove decay …

Effects Of Landscape And Yard Features On Mammals In Residential Yards In Northwest Arkansas, Emily Johansson

Graduate Theses and Dissertations

The human footprint is rapidly expanding, and wildlife habitat is continuously being converted to human residential properties. Most wildlife residing in developing areas are displaced to nearby undeveloped areas. However, some animals can coexist with humans and acquire the necessary resources (food, water, shelter) within the human environment. This may be particularly true when development is low intensity, as in suburban yards. Due to the wide variety in how homeowners utilize their yards, they can be considered individually managed “greenspaces.” These yards can provide a range of food (e.g., bird feeders, compost, gardens), water (bird baths and garden ponds), and …

Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman

Graduate Theses and Dissertations

Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …

Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden

Computer Science Faculty Publications and Presentations

A proof of work (PoW) is an important cryptographic construct which enables a party to convince other parties that they have invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which have received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure, and thus the problem of interest is the feasibility of obtaining a sequence …

Subgroup Identification Via Interaction Tree And Mixed Model For Repeated Measures With Application To Alzheimer’S Disease, Zhichen Xu

Arts & Sciences Graduate Student Theses and Dissertations

No abstract provided.

Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …

Liloc: Enabling Precise 3d Localization In Dynamic Indoor Environments Using Lidars, Darshana Rathnayake, Meera Radhakrishnan, Inseok Hwang, Archan Misra

Research Collection School Of Computing and Information Systems

We present LiLoc, a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. The key differentiators in our work are: (a) First, unlike traditional localization approaches, our approach is robust to dynamically changing environmental conditions (e.g., varying crowd levels, object placement/layout changes); (b) Second, unlike prior work on visual and 3D SLAM, LiLoc is not dependent on a pre-built static map of the environment and instead works by utilizing dynamically updated point clouds captured from both infrastructural-mounted LiDARs and LiDARs equipped on individual mobile IoT devices. To achieve fine-grained, …

Are You Cloud-Certified? Preparing Computing Undergraduates For Cloud Certification With Experiential Learning, Eng Lieh Ouh, Benjamin Gan

Research Collection School Of Computing and Information Systems

Cloud Computing skills have been increasing in demand. Many software engineers are learning these skills and taking cloud certification examinations to be job competitive. Preparing undergraduates to be cloud-certified remains challenging as cloud computing is a relatively new topic in the computing curriculum, and many of these certifications require working experience. In this paper, we report our experiences designing a course with experiential learning to prepare our computing undergraduates to take the cloud certification. We adopt a university project-based experiential learning framework to engage industry partners who provide project requirements for students to develop cloud solutions and an experiential risk …

Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection, Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun

Research Collection School Of Computing and Information Systems

Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary prediction trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning …

Link Prediction On Latent Heterogeneous Graphs, Trung Kien Nguyen, Zemin Liu, Yuan Fang

Research Collection School Of Computing and Information Systems

On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal …

Graphprompt: Unifying Pre-Training And Downstream Tasks For Graph Neural Networks, Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang

Research Collection School Of Computing and Information Systems

Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune"and "pre-train, prompt"paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on …

Contrabert: Enhancing Code Pre-Trained Models Via Contrastive Learning, Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, Yang. Liu

Research Collection School Of Computing and Information Systems

Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code representation, they have been further applied in multiple downstream tasks such as clone detection, code search and code translation. However, it is also observed that these state-of-the-art pre-trained models are susceptible to adversarial attacks. The performance of these pre-trained models drops significantly with simple perturbations such as renaming variable names. This weakness may be inherited by their downstream models and thereby amplified at an unprecedented scale. To this end, we propose an approach namely ContraBERT that aims …

Widget Detection-Based Testing For Industrial Mobile Games, Xiongfei Wu, Jiaming Ye, Ke Chen, Xiaofei Xie, Ruochen Huang, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

The fast advances in mobile hardware and widespread smartphone usage have fueled the growth of global mobile gaming in the past decade. As a result, the need for quality assurance of mobile gaming has become increasingly pressing. While general-purpose testing methods have been developed for mobile applications, they become struggling when being applied to mobile games due to the unique characteristics of mobile games, such as dynamic loading and stunning visual effects. There comes a growing industrial demand for automated testing techniques with high compatibility (compatible with various resolutions, and platforms) and non-intrusive characteristics (without packaging external modules into the …

Neural Episodic Control With State Abstraction, Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.Generally, episodic control-based approaches are solutions that leveragehighly-rewarded past experiences to improve sample efficiency of DRL algorithms.However, previous episodic control-based approaches fail to utilize the latentinformation from the historical behaviors (e.g., state transitions, topological similarities,etc.) and lack scalability during DRL training. This work introducesNeural Episodic Control with State Abstraction (NECSA), a simple but effectivestate abstraction-based episodic control containing a more comprehensive episodicmemory, a novel state evaluation, and a multi-step state analysis. We evaluate ourapproach to the MuJoCo and Atari tasks in OpenAI gym domains. The experimentalresults indicate that NECSA achieves higher …

Multi-Lingual Multi-Partite Product Title Matching, Huan Lin Tay, Wei Jie Tay, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.

Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts in blockchains have been increasingly used for high-value business applications. It is essential to check smart contracts' reliability before and after deployment. Although various program analysis and deep learning techniques have been proposed to detect vulnerabilities in either Ethereum smart contract source code or bytecode, their detection accuracy and scalability are still limited. This paper presents a novel framework named MANDO-HGT for detecting smart contract vulnerabilities. Given Ethereum smart contracts, either in source code or bytecode form, and vulnerable or clean, MANDO-HGT custom-builds heterogeneous contract graphs (HCGs) to represent control-flow and/or function-call information of the code. It then …

Resale Hdb Price Prediction Considering Covid-19 Through Sentiment Analysis, Srinaath Anbu Durai, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or …

Generative Stresnet For Crime Prediction, Ba Phong Tran, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this work, we combine STResnet (Zhang et al., 2017) with VAE Kingma & Welling (2013) to generate crime distribution. The outputs can be used for downstream tasks such as patrol deployment planning Chase et al. (2021).