Physical Sciences and Mathematics Commons^™

Movie Reviews Sentiment Analysis Using Bert, Gibson Nkhata

Graduate Theses and Dissertations

Sentiment analysis (SA) or opinion mining is analysis of emotions and opinions from texts. It is one of the active research areas in Natural Language Processing (NLP). Various approaches have been deployed in the literature to address the problem. These techniques devise complex and sophisticated frameworks in order to attain optimal accuracy with their focus on polarity classification or binary classification. In this paper, we aim to fine-tune BERT in a simple but robust approach for movie reviews sentiment analysis to provide better accuracy than state-of-the-art (SOTA) methods. We start by conducting sentiment classification for every review, followed by computing …

Deciphering Surfaces Of Trans-Neptunian And Kuiper Belt Objects Using Radiative Scattering Models, Machine Learning, And Laboratory Experiments, Al Emran

Graduate Theses and Dissertations

Decoding surface-atmospheric interactions and volatile transport mechanisms on trans-Neptunian objects (TNOs) and Kuiper Belt objects (KBOs) involves an in-depth understanding of physical and thermal properties and spatial distribution of surface constituents – nitrogen (N2), methane (CH4), carbon monoxide (CO), and water (H2O) ices. This thesis implements a combination of radiative scattering models, machine learning techniques, and laboratory experiments to investigate the uncertainties in grain size estimation of ices, the spatial distribution of surface compositions on Pluto, and the thermal properties of volatiles found on TNOs and KBOs. Radiative scattering models (Mie theory and Hapke approximations) were used to compare single …

Carbon Cycling And Critical Zone Dynamics In An Urbanized Karst Groundwater System, Amy Hourigan

Masters Theses & Specialist Projects

Increasing atmospheric CO₂ concentrations are correlated to rising global temperatures. Investigating the cumulative global carbon cycling processes is important to understand and quantify the global carbon cycle. By investigating basic geochemical parameters, EpCO₂, DIC, and δ¹³C_DIC, at four sites along Lost River Cave (LRC), in Bowling Green, Kentucky, concentrations, fluxes and sources of C dissolved in groundwater were determined. Urban karst groundwater systems, compared to more natural karst landscapes, typically exhibit widespread impervious, heat-absorbing surfaces, urban heat island effects, and increased anthropogenic groundwater inputs and localized CO₂ emissions. Carbonate hydrogeochemical …

“This Isn't Working For Me. Can We Do It A Different Way?" The Lived Experiences Of Geoscience Students With Learning Disabilities, Nina Morris

Masters Theses

The purpose of this exploratory qualitative study is to better understand the lived experiences of geoscience students with learning disabilities in their geoscience classes. Students with learning disabilities bring a unique perspective while also facing unique challenges in post-secondary education. In the literature, there is limited information on what support and teaching strategies are beneficial to this population. Undergraduate and graduate geoscience students who identify as having a learning disability from colleges and universities in the United States were recruited. Six students consented to participate in the study, and each completed a semi-structured interview. Semi-structured interviews were designed to prompt …

Exploring The Effects Of Tree Roots On Infiltration Process, Nazife Onaral

Masters Theses

Effects of the root structures on soil infiltration dynamics are not clearly defined. Imaging the complex tree root structures and movement of water through the root ball is a challenging task to achieve without damaging the trees and roots by conventional methods. Commonly used methods are invasive, labor-intensive, and not easily accessible. Ground Penetrating Radar (GPR) has commonly been used to characterize soil profiles and it can be a reliable tool to map complex root structures with a novel high-resolution circular data collection technique. Electrical resistivity tomography (ERT) is another reliable geophysical method to image infiltration processes by the change …

Conflicts Between Ghg Accounting Methodologies In The Steel Industry, John Biberman, Perrine Toledano, Baihui Lei, Max Lulavy, Rohini Ram Mohan

Columbia Center on Sustainable Investment

Accurate, verifiable, and comparable greenhouse gas (GHG) emissions data throughout supply chains in the materials sector are necessary to drive decarbonization. This is particularly the case for the steel supply chain, a major source of GHG emissions with untapped potential for reduction. However, emissions accounting methods used by the steel industry suffer from gaps and misalignment, resulting in significant differences in reported GHG emissions. The result is a patchwork reporting landscape vulnerable to manipulation and miscommunication, generating little actionable data for policymakers, producers, customers, and investors. These shortcomings highlight the need for a harmonized carbon accounting framework for the steel …

Scaling Investment In Renewable Energy: Roadblocks And Drivers – Executive Summary, Mithatcan Aydos, Perrine Toledano, Martin Dietrich Brauch, Ladan Mehranvar, Theodoros Iliopoulos, Sunayana Sasmal

Columbia Center on Sustainable Investment

The zero-carbon energy transition is the solution to the 2022 energy crisis and a fundamental part of the solution to the global climate crisis. Yet, there are relatively low levels of investment in renewable energy in developing countries, hindering their achievement of the Sustainable Development Goals (SDGs) and contribution to the Paris Agreement goals.

In 2021, the Asia–Pacific region (excluding China) accounted for less than 8% of investments in energy transition technologies, Latin America and the Caribbean for less than 4%, and Africa and the Middle East for less than 2%. Annual investment in zero-carbon energy in developing economies other …

Dataset For Electronic And Optical Properties Of Y2o2s And Er Dopped Y2o2s Calculated Using Density Functional Theory And Simulated X-Ray Near Edge Spectra, N. Dimakis, Eric Baldemar Rodriguez Jr., Kofi Nketia Ackaah-Gyasi, Madhab Pokhrel

Physics and Astronomy Faculty Publications and Presentations

The computational data presented in this paper refer to the research article “Optical properties and simulated x-ray near edge spectra for Y2O2S and Er doped Y2O2S”. We present the data used to calculate the structural, electronic, and optical properties of the Y2O2S and its Er+3 doped counterparts at various concentrations using density functional theory (DFT) and simulated X-ray near edge (XANES) spectra. We report electronic information from DFT and DFT+U generated from the Vienna Ab initio Simulation Package (VASP) using PAW pseudopotentials. We also report VASP calculated optical properties for the host Y2O2S using the independent particle approximation (IPA), the …

Effective Detection Of Local Languages For Tourists Based On Surrounding Features, Tobenna Eze

Culminating Projects in Computer Science and Information Technology

The tourism industry is a trillion-dollar industry with many governments investing heavily in making their countries attractive enough to entice potential visitors. People engage in tourism due to different reasons which could range from business, education, leisure, medical or ancestral reasons. Communication between intending visitors and locals is essential, given the non-homogeneity that occurs across cultures and borders. In this paper, we focus on developing a cross-platform mobile application that listens to surrounding conversations, is able to pick certain keywords, automatically switch to the local language of its location and then offer translation capabilities to facilitate conversations. To implement this, …

Application Development Using Microservice Architecture, Tonmoy Saha, Tonmoy Saha

Culminating Projects in Computer Science and Information Technology

Application development has always been a complex process. An application, once developed, also needs to be maintained and enhanced to add new requirements. Traditionally the application has been a monolithic entity. Different components in the application are tightly coupled and making a change has always been challenging. Microservice architecture breaks away from this monolithic approach and arranges the different functionalities as services. In a microservice architecture, individual services are developed to perform one function only.

This report demonstrates the application development process using the Microservice architecture. It explains the design, development, and deployment of a Microservice-based application. Market Place is …

A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer

Research Collection School Of Computing and Information Systems

We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (QoS) constraints. The key challenge lies in the fact that the QoS functions have no closed-form and need to be approximated by simulation. In this paper we propose a new way to approximate the QoS functions by logistic functions and design a new algorithm that combines logistic regression, cut generations and logistic-based local search to efficiently find good staffing solutions. We report computational results using examples up to 65 call types and 89 agent groups …

Differentiate Metasploit Framework Attacks From Others, Gina Liu Ajero

Electronic Theses and Dissertations

Metasploit Framework is a very popular collection of penetration testing tools. From auxiliaries such as network scanners and mappers to exploits and payloads, Metasploit Framework offers a plethera of apparatuses to implement all the stages of a penetration test. There are two versions: both a free open-source community version and a commercial professional version called Metasploit Pro. The free version, Metasploit Framework, is heavily used by cyber crimininals to carry out illegal activities to gain unauthorized access to targets.

In this paper, I conduct experiments in a virtual environment to discover whether attacks originated from Metasploit Framework are marked with …

Turning Ligands On Their Side: Computational Investigation Into The Binding Of N2o And N2 In Transition Metal Complexes, Cole Donald

Electronic Theses and Dissertations

Common greenhouse gas nitrous oxide (N₂O) is a thermodynamically potent and environmentally benign oxidant, making it a desirable target for metal center activation. Unfortunately, N₂O is a poor ligand for transition metals due to its weak sigma-donating and pi-accepting properties; as a result, few transition metal complexes capable of interacting with N₂O have been found. As the primary source of all nitrogen in organisms, abundant gas dinitrogen (N₂) is a crucially important tiny molecule and an essential part of daily existence. However, due to its inertness, it has limited practical uses in …

Retrospective Varying Coefficient Association Analysis Of Longitudinal Binary Traits, Gang Xu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fails to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time.We developed several tests to fill the gap of analyzing time-varying genetic effects in longitudinal GWAS for binary traits. First, we propose a …

On Clinical Use Of Infrared Cameras For Video-Based Estimation Of 3d Facial Kinematics, William Mackenzie Harrington

Theses and Dissertations

Neurological and neurodegenerative disorders such as Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and stroke can cause speech and orofacial motor impairments with devastating effects on quality of life. Analysis of orofacial movement provides vital information for early diagnosis and tracking disease progression, but current clinical practice relies on perceptual assessments performed by clinicians, which are unreliable and insensitive to early symptoms. New methods in machine learning have enabled automatic and objective assessment of orofacial kinematics from color and depth videos, hence we introduce MEADepthCamera, a mobile application for RGB-D video and audio recording and automatic estimation of 3D facial …

Use Of Healthcare Utilization Records For Analyzing Trends In Clinical Toxoplasmosis: A Comparison Of Nevada And The United States, Elijah Kreutzer

UNLV Theses, Dissertations, Professional Papers, and Capstones

Toxoplasmosis, a zoonotic disease caused by the parasitic protist Toxoplasma gondii, is a ubiquitous, global public health concern with a wide variety of clinical manifestations. Surveillance for the disease is lacking even in developed countries, and what surveillance is present most often focuses on pregnant women. This research investigated trends in clinical toxoplasmosis in Nevada and nationally to address the lack of knowledge concerning how Nevada discharges compare to national discharges in cases of toxoplasmosis. Specifically, this research sought to determine what characterizes toxoplasmosis in Nevada across inpatient, outpatient, and emergency department settings, as well as how these cases differ …

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin Shar, Wei Minn, Nguyen Binh Duong Ta, Jianli Fan, Lingxiao Jiang, Daniel Wai Kiat Lim

Research Collection School Of Computing and Information Systems

Drones are increasingly popular and getting used in a variety of missions such as area surveillance, pipeline inspection, cinematography, etc. While the drone is conducting a mission, anomalies such as sensor fault, actuator fault, configuration errors, bugs in controller program, remote cyber- attack, etc., may affect the drone’s physical stability and cause serious safety violations such as crashing into the public. During a flight mission, drones typically log flight status and state units such as GPS coordinates, actuator outputs, accelerator readings, gyroscopic readings, etc. These log data may reflect the above-mentioned anomalies. In this paper, we propose a novel, deep …

Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati

Research Collection School Of Computing and Information Systems

In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements …

Measuring The Economic Impact Of Recurrent Flooding On Workforce Productivity And Property, Joshua G. Behr, Wie Yusuf, George Mcleod, Sarah Stafford, Derek Loftis, Afi Anuar, Rafael Diaz

Presentations, Lectures, Posters, Reports

From the Executive Summary:

This research draws upon expertise across multiple disciplines and fields. Leveraged are natural systems data and social-behavioral data. The high-level objective is to advance our understanding of how very recent recurrent flooding has impacted residents within the City of Portsmouth, and then forecast these impacts under projections of sea level rise. While this research draws upon data for the City of Portsmouth, the findings may be generalized to the broader Hampton Roads region.

Machine Learning-Based Methods For The Segmentation Of Scanning Electron Microscopy Images Of Fine-Grained Shale Samples, Binqian Yin

Earth & Environmental Sciences Dissertations

The segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological studies, as it will need to differentiate the boundaries for different mineral objects to facilitate subsequent analyses, such as porosity calculation. Recently, machine learning methods, especially convolutional neural networks (CNNs), have been explored to segment SEM images for fine-grained shale samples. However, existing methods fail to address two critical issues in the segmentation of shale rock images---insufficient labeled data and imbalanced objects. To this end, this dissertation has proposed a machine learning pipeline that consists of supervised, semi-supervised, and active learning stages to reduce manual efforts …

Qualitative Analysis For A Two-Component Peakon System With Cubic Nonlinearity, Shaojie Yang, Zhijun Qiao

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

This paper is devoted to studying a two-component peakon system with cubic nonlinearity, which is a two-component extension of the cubic Camassa–Holm equation. We first discuss the local well-posedness for the Cauchy problem of the system. Then, in light of a fine structure of the system, we present the precise blow-up scenario for strong solutions to the system and derive a new blow-up result with respect to initial data. Finally, peakon solutions are discussed as well.

Mysteerio: Multi-Regional Environmentally Extended Input-Output Model For United States, Yash Srivastava

All Theses

This research work titled My State Environmentally Extended Regional Input-Output (MYSTEERIO) is based on the concept of multi-regional environmentally extended input-output (MREEIO), which is a combination of lifecycle thinking and economics principles. Numerous studies have highlighted the advantages of using this methodology to determine the environmental impacts associated with economic activities for a region. However, no prior attempt has been made to conduct a state-level analysis for the US. This research work thus tries to estimate the impacts embodied due to the consumption of goods and services at the state level by using the MREEIO principles. Extensive coverage of the …

Non-Archimedean Quantum Mechanics Via Quantum Groups, Wilson A. Zuniga-Galindo

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

We present a new non-Archimedean realization of the Fock representation of the q-oscillator algebras where the creation and annihilation operators act on complex-valued functions, which are defined on a non-Archimedean local field of arbitrary characteristic, for instance, the field of p-adic numbers. This new realization implies that many quantum models constructed using q-oscillator algebras are non-Archimedean models, in particular, p-adic quantum models. In this framework, we select a q-deformation of the Heisenberg uncertainty relation and construct the corresponding q-deformed Schrödinger equations. In this way we construct a p-adic quantum mechanics which is a …

Singlish Checker: A Tool For Understanding And Analysing An English Creole Language, Lee-Hsun Hsieh, Nam Chew Chua, Agus Trisnajaya Kwee, Pei-Chi Lo, Yang-Yin Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

As English is a widely used language in many countries of different cultures, variants of English also known as English creoles have also been created. Singlish is one such English creole used by people in Singapore. Nevertheless, unlike English, Singlish is not taught in schools nor encouraged to be used in formal communications. Hence, it remains to be a low resource language with a lack of up-to-date Singlish word dictionary and computational tools to analyse the language. In this paper, we therefore propose Singlish Checker, a tool that is able to help detecting Singlish text, Singlish words and phrases. To …

Question-Attentive Review-Level Recommendation Explanation, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommendation explanations help to improve their acceptance by end users. The form of explanation of interest here is presenting an existing review of the recommended item. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance of each review to the recommendation objective. Our focus is on improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. …

Quote: Quality-Oriented Testing For Deep Learning Systems, Jialuo Chen, Jingyi Wang, Xingjun Ma, Youcheng Sun, Jun Sun, Peixin Zhang, Peng Cheng

Research Collection School Of Computing and Information Systems

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, i.e., given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model …

Appearance-Preserved Portrait-To-Anime Translation Via Proxy-Guided Domain Adaptation, Wenpeng Xiao, Cheng Xu, Jiajie Mai, Xuemiao Xu, Yue Li, Chengze Li, Xueting Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Converting a human portrait to anime style is a desirable but challenging problem. Existing methods fail to resolve this problem due to the large inherent gap between two domains that cannot be overcome by a simple direct mapping. For this reason, these methods struggle to preserve the appearance features in the original photo. In this paper, we discover an intermediate domain, the coser portrait (portraits of humans costuming as anime characters), that helps bridge this gap. It alleviates the learning ambiguity and loosens the mapping difficulty in a progressive manner. Specifically, we start from learning the mapping between coser and …

Pickup And Multi-Delivery Problem With Time Windows, Pham Tuan Anh, Aldy Gunawan, Vincent F. Yu, Tuan C. Chau

Research Collection School Of Computing and Information Systems

This paper addresses a new variant of Pickup and Delivery Problem with Time Windows (PDPTW) for enhancing customer satisfaction. In particular, a huge number of requests is served in the system, where each request includes a pickup node and several delivery nodes instead of a pair of pickup and delivery nodes. It is named Pickup and Multi-Delivery Problem with Time Windows (PMDPTW). A mixed-integer programming model is formulated with the objective of minimizing total travel costs. Computational experiments are conducted to test the correctness of the model with a newly generated benchmark based on the PDPTW benchmark instances. Results show …

Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing

Research Collection School Of Computing and Information Systems

Out-of-distribution (OOD) settings are used to measure a model’s performance when the distribution of the test data is different from that of the training data. NLU models are known to suffer in OOD settings (Utama et al., 2020b). We study this issue from the perspective of causality, which sees confounding bias as the reason for models to learn spurious correlations. While a common solution is to perform intervention, existing methods handle only known and single confounder, but in many NLU tasks the confounders can be both unknown and multifactorial. In this paper, we propose a novel interventional training method called …