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Articles 32761 - 32790 of 302623

Full-Text Articles in Physical Sciences and Mathematics

Surface Water/Groundwater Interactions And Hydrogeochemical Characterization Of The Elkhorn Mine And Mill, Beaverhead County, Montana, Tyler Kamp Apr 2022

Surface Water/Groundwater Interactions And Hydrogeochemical Characterization Of The Elkhorn Mine And Mill, Beaverhead County, Montana, Tyler Kamp

Graduate Theses & Non-Theses

The formation of acid mine drainage (AMD) and the contaminants associated with it have been described by some as the largest environmental problem facing the U.S. mining industry. Heavy metals associated with the drainage, such as copper, cadmium, and zinc affect the water quality of streams and can cause acute or chronic toxicity to invertebrates and fish (Martin, 1992& Padrillah et al., 2018). Elkhorn Creek in the Pioneer Mountains of southwest Montana is one of these impacted creeks. The historic Elkhorn Mine and Mill complex has historically attributed to the creek’s contamination and has undergone remediation. However, the problem has …

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Irregular Orbital Domination In Graphs, Peter E. Broe Apr 2022

Irregular Orbital Domination In Graphs, Peter E. Broe

Dissertations

In recent decades, domination in graphs has become a popular area of study due in large degree to its applications to modern society and the mathematical beauty of the topic. While this area evidently began with the work of Claude Berge in 1958 and of Oystein Ore in 1962, domination did not become an active area of research until 1977 with the appearance of a survey paper by Ernest Cockayne and Stephen Hedetniemi. Since then a large number of variations of domination have surfaced and provided numerous applications to different areas of science and real-life problems. Among these variations are …

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Weather-Sensitive Walmart Sales Modeling, Stella Rodriguez Apr 2022

Weather-Sensitive Walmart Sales Modeling, Stella Rodriguez

Honors Projects

Walmart operates thousands of stores in the U.S. and internationally with varying inventory and climates. During different weather conditions, demand for some products may increase or decrease, like umbrellas before a rainstorm. Since it is important for stores to not overstock or be out-of-stock of inventory, knowing how different weather conditions affect these weather-sensitive items would be vital for Walmart. This report details the process of data management, exploratory data analysis, modeling, and findings done on Walmart weather-sensitive sales.

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It Won’T Be Easy, Allison Arkush Apr 2022

It Won’T Be Easy, Allison Arkush

School of Art, Art History, and Design: Theses and Student Creative Work

Interdisciplinary artist Allison Arkush engages a wide range of materials, modalities, and research in her practice. In It Won’t Be Easy, Arkush places and piles her multimedia sculptures throughout the gallery to create installations that overlap ­with her writing and poetry, sometimes layering in (or extending out to) audio and video components. This approach facilitates the probing exploration of prevailing value systems through a flattening of hierarchies among and between humans, the other-than-human, and the inanimate—though no less lively. Her work meditates on and ‘vendiagrams’ things forsaken and sacred, the traumatic and nostalgic. The exhibition title acknowledges that the …

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The Effect Of Channel Stability On Fish Condition And Diet In Thompson Creek, La, Alexia Lagrone Apr 2022

The Effect Of Channel Stability On Fish Condition And Diet In Thompson Creek, La, Alexia Lagrone

Honors Theses

No abstract provided.

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2022 April - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University Apr 2022

2022 April - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

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Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques In Blue Tarpaulin Inspection, Madeline G. Miles Apr 2022

Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques In Blue Tarpaulin Inspection, Madeline G. Miles

Theses

This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are …

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Superfund And Society Benumbed: An In-Depth Look At Environmental Justice In South Carolina, Sydney A. Hampton Apr 2022

Superfund And Society Benumbed: An In-Depth Look At Environmental Justice In South Carolina, Sydney A. Hampton

Senior Theses

This thesis investigates the relationship between superfund sites in minority communities and their public health through the lens of social vulnerability. Various demographic parameters were used to assess the risk associated with minority communities and exposure to hazardous waste. After investigating the history of the Environmental Justice movement, three superfund sites of interest in South Carolina, and demographic and public health data; each community was analyzed to determine association between exposure to hazardous waste and minority status. Each examined community exhibited characteristics contributing to heightened social vulnerability, potentially causing increased susceptibility to negative health outcomes from exposure to hazardous waste.

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Destruction Is A Must-See: Coastal Heritage Site Erosion And Public Perception Of Climate Change, Haley Borowy Apr 2022

Destruction Is A Must-See: Coastal Heritage Site Erosion And Public Perception Of Climate Change, Haley Borowy

Senior Theses

Archaeological sites in South Carolina are vanishing. As sea level rise, and therefore coastal erosion, worsen, more sites will disappear. The questions of how erosion at these sites is measured and how the public perceives the effects of climate change have been studied separately, but not together. Here, the intersection of these is discussed, alongside how sites are portrayed affects how the public perceives them, and therefore their importance. Studies on measuring coastal erosion, local news reports, government documents, and public perception of coastal management and sea level rise illuminate how people eventually decide what is worth saving.

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Cost: Contrastive Learning Of Disentangled Seasonal-Trend Representations For Time Series Forecasting, Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi Apr 2022

Cost: Contrastive Learning Of Disentangled Seasonal-Trend Representations For Time Series Forecasting, Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step – we justify such a paradigm from a causal perspective. Following this principle, we propose a new time series representation learning framework for long sequence time …

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Rescuecastr: Exploring Photos And Live Streaming To Support Contextual Awareness In The Wilderness Search And Rescue Command Post, Brennon Jones, Anthony Tang, Carman Neustaedter Apr 2022

Rescuecastr: Exploring Photos And Live Streaming To Support Contextual Awareness In The Wilderness Search And Rescue Command Post, Brennon Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) is a command-and-control activity where a Command team manages field teams scattered across a large area looking for a lost person. The challenge is that it can be difficult for Command to maintain awareness of field teams and the conditions of the field. We designed RescueCASTR, an interface that explores the idea of deploying field teams with wearable cameras that stream live video or sequential photos periodically to Command that aid contextual awareness. We ran a remote user study with WSAR managers to understand the opportunities and challenges of such a system. We found that …

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Asteroids: Exploring Swarms Of Mini-Telepresence Robots For Physical Skill Demonstration, Jiannan Li, Maurício Sousa, Chu Li, Jessie Liu, Yan Chen, Ravin Balakrishnan, Tovi Grossman Apr 2022

Asteroids: Exploring Swarms Of Mini-Telepresence Robots For Physical Skill Demonstration, Jiannan Li, Maurício Sousa, Chu Li, Jessie Liu, Yan Chen, Ravin Balakrishnan, Tovi Grossman

Research Collection School Of Computing and Information Systems

Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor’s sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots’ viewpoints. Demonstrators can perceive the audiences’ …

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Learning Scenario Representation For Solving Two-Stage Stochastic Integer Programs, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang Apr 2022

Learning Scenario Representation For Solving Two-Stage Stochastic Integer Programs, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) based method to learn scenario representation for a class of SIP instances. Specifically, we design a graph convolutional network based encoder to embed each scenario with the deterministic part of its instance (i.e. context) into a low-dimensional latent space, from which a decoder reconstructs the scenario from its latent representation conditioned on the context. Such a design effectively captures the dependencies of …

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On Explaining Multimodal Hateful Meme Detection Models, Ming Shan Hee, Roy Ka-Wei Lee, Wen Haw Chong Apr 2022

On Explaining Multimodal Hateful Meme Detection Models, Ming Shan Hee, Roy Ka-Wei Lee, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful meme detection is a new multimodal task that has gained significant traction in academic and industry research communities. Recently, researchers have applied pre-trained visual-linguistic models to perform the multimodal classification task, and some of these solutions have yielded promising results. However, what these visual-linguistic models learn for the hateful meme classification task remains unclear. For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i.e., image and text) of the hateful memes. To fill this research gap, this paper propose three research questions to improve our understanding of these visual-linguistic …

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Learning For Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification, Cuong V. Nguyen, Khiem H. Le, Hong Quang Pham, Quang H. Pham, Binh T. Nguyen Apr 2022

Learning For Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification, Cuong V. Nguyen, Khiem H. Le, Hong Quang Pham, Quang H. Pham, Binh T. Nguyen

Research Collection School Of Computing and Information Systems

Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient. Our work explores beyond the common practice of transfer learning with a single pre-trained model. We focus on the task of Vietnamese sentiment classification and propose LIFA, a framework to learn a unified embedding from several pre-trained models. We further propose two more LIFA variants that encourage the pre-trained models to either cooperate or compete with one another. Studying these variants sheds light on the success of LIFA by showing that sharing knowledge among the …

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Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan Apr 2022

Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan

Research Collection School Of Computing and Information Systems

China has suffered overcapacity in coal power since 2016. With growing electricity demand and an economic crisis due to the Covid-19 pandemic, China faces a dilemma between easing restrictive policies for short-term growth in coal-fired power production and keeping restrictions in place for long-term sustainability. In this paper, we measure the risks faced by China's coal power units to become stranded in the next decade and estimate the associated economic costs for different shareholders. By implementing restrictive policies on coal power expansion, China can avoid 90% of stranded coal assets by 2025.

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Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong Fwa Apr 2022

Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propose automatic sensing of learners’ affective states in an online setting with web cameras capturing their facial landmarks and head poses. We postulate that the sparsely connected facial landmarks can be modelled using a Graph Neural Network. Using the publicly available in the wild DAiSEE dataset, we modelled both the spatial and temporal dimensions of the …

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Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li Apr 2022

Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li

Research Collection School Of Computing and Information Systems

Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been designed for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g., sequence, structure and network data. However, it is challenging to effectively integrate these data sources and automatically extract features for different link prediction tasks. Results: In …

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Algorithm Selection For The Team Orienteering Problem, Mustafa Misir, Aldy Gunawan, Pieter Vansteenwegen Apr 2022

Algorithm Selection For The Team Orienteering Problem, Mustafa Misir, Aldy Gunawan, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world problems like tourist trip planning. The complexity of the problem motivated to devise new algorithms. However, none of the existing algorithms came with the best performance across all the widely used benchmark instances. This fact suggests that there is a performance gap to fill. This gap can be targeted by developing more new algorithms as attempted by many researchers before. An alternative strategy is …

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Data Source Selection In Federated Learning: A Submodular Optimization Approach, Ruisheng Zhang, Yansheng Wang, Zimu Zhou, Ziyao Ren, Yongxin Tong, Ke Xu Apr 2022

Data Source Selection In Federated Learning: A Submodular Optimization Approach, Ruisheng Zhang, Yansheng Wang, Zimu Zhou, Ziyao Ren, Yongxin Tong, Ke Xu

Research Collection School Of Computing and Information Systems

Federated learning is a new learning paradigm that jointly trains a model from multiple data sources without sharing raw data. For the practical deployment of federated learning, data source selection is compulsory due to the limited communication cost and budget in real-world applications. The necessity of data source selection is further amplified in presence of data heterogeneity among clients. Prior solutions are either low in efficiency with exponential time cost or lack theoretical guarantees. Inspired by the diminishing marginal accuracy phenomenon in federated learning, we study the problem from the perspective of submodular optimization. In this paper, we aim at …

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Verifiable Searchable Encryption Framework Against Insider Keyword-Guessing Attack In Cloud Storage, Yinbin Miao, Robert H. Deng, Kim-Kwang Raymond Choo, Ximeng Liu, Hongwei Li Apr 2022

Verifiable Searchable Encryption Framework Against Insider Keyword-Guessing Attack In Cloud Storage, Yinbin Miao, Robert H. Deng, Kim-Kwang Raymond Choo, Ximeng Liu, Hongwei Li

Research Collection School Of Computing and Information Systems

Searchable encryption (SE) allows cloud tenants to retrieve encrypted data while preserving data confidentiality securely. Many SE solutions have been designed to improve efficiency and security, but most of them are still susceptible to insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. Also in existing SE solutions, a semi-honest-but-curious cloud server may deliver incorrect search results by performing only a fraction of retrieval operations honestly (e.g., to save storage space). To address these two challenging issues, we first construct the basic Verifiable SE Framework (VSEF), which can withstand …

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Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators, Shijia Li, Chunfu Jia, Pengda Qiu, Qiyuan Chen, Jiang Ming, Debin Gao Apr 2022

Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators, Shijia Li, Chunfu Jia, Pengda Qiu, Qiyuan Chen, Jiang Ming, Debin Gao

Research Collection School Of Computing and Information Systems

—Code virtualization is a well-known sophisticated obfuscation technique that uses custom virtual machines (VM) to emulate the semantics of original native instructions. Commercial VM-based obfuscators (e.g., Themida and VMProtect) are often abused by malware developers to conceal malicious behaviors. Since the internal mechanism of commercial obfuscators is a black box, it is a daunting challenge for the analyst to understand the behavior of virtualized programs. To figure out the code virtualization mechanism and design deobfuscation techniques, the analyst has to perform reverse-engineering on large-scale highly obfuscated programs. This knowledge learning process suffers from painful cost and imprecision. In this project, …

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On Size-Oriented Long-Tailed Graph Classification Of Graph Neural Networks, Zemin Liu, Qiheng Mao, Chenghao Liu, Yuan Fang, Jianling Sun Apr 2022

On Size-Oriented Long-Tailed Graph Classification Of Graph Neural Networks, Zemin Liu, Qiheng Mao, Chenghao Liu, Yuan Fang, Jianling Sun

Research Collection School Of Computing and Information Systems

The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multigraph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In particular, a large fraction of tail graphs usually have small sizes. Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a …

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Chatbot4qr: Interactive Query Refinement For Technical Question Retrieval, Neng Zhang, Qiao Huang, Xin Xia, Ying Zou, David Lo, Zhenchang Xing Apr 2022

Chatbot4qr: Interactive Query Refinement For Technical Question Retrieval, Neng Zhang, Qiao Huang, Xin Xia, Ying Zou, David Lo, Zhenchang Xing

Research Collection School Of Computing and Information Systems

Technical Q&A sites (e.g., Stack Overflow(SO)) are important resources for developers to search for knowledge about technical problems. Search engines provided in Q&A sites and information retrieval approaches have limited capabilities to retrieve relevant questions when queries are imprecisely specified, such as missing important technical details (e.g., the user's preferred programming languages). Although many automatic query expansion approaches have been proposed to improve the quality of queries by expanding queries with relevant terms, the information missed is not identified. Moreover, without user involvement, the existing query expansion approaches may introduce unexpected terms and lead to undesired results. In this paper, …

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Securead: A Secure Video Anomaly Detection Framework On Convolutional Neural Network In Edge Computing Environment, Hang Cheng, Ximeng Liu, Huaxiong Wang, Yan Fang, Meiqing Wang, Xiaopeng Zhao Apr 2022

Securead: A Secure Video Anomaly Detection Framework On Convolutional Neural Network In Edge Computing Environment, Hang Cheng, Ximeng Liu, Huaxiong Wang, Yan Fang, Meiqing Wang, Xiaopeng Zhao

Research Collection School Of Computing and Information Systems

Anomaly detection offers a powerful approach to identifying unusual activities and uncommon behaviors in real-world video scenes. At present, convolutional neural networks (CNN) have been widely used to tackle anomalous events detection, which mainly rely on its stronger ability of feature representation than traditional hand-crafted features. However, massive video data and high cost of CNN model training are a challenge to achieve satisfactory detection results for resource-limited users. In this paper, we propose a secure video anomaly detection framework (SecureAD) based on CNN. Specifically, we introduce additive secret sharing to design several calculation protocols for achieving safe CNN training and …

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Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc. Apr 2022

Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Causal Inference In Healthcare: Approaches To Causal Modeling And Reasoning Through Graphical Causal Models, Riddhiman Adib Apr 2022

Causal Inference In Healthcare: Approaches To Causal Modeling And Reasoning Through Graphical Causal Models, Riddhiman Adib

Dissertations (1934 -)

In the era of big data, researchers have access to large healthcare datasets collected over a long period. These datasets hold valuable information, frequently investigated using traditional Machine Learning algorithms or Neural Networks. These algorithms perform great in finding patterns out of datasets (as a predictive machine); however, the models lack extensive interpretability to be used in the healthcare sector (as an explainable machine). Without exploring underlying causal relationships, the algorithms fail to explain their reasoning. Causal Inference, a relatively newer branch of Artificial Intelligence, deals with interpretability and portrays causal relationships in data through graphical models. It explores the …

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All Pairs Routing Path Enumeration Using Latin Multiplication And Julia, Haochen Sun Apr 2022

All Pairs Routing Path Enumeration Using Latin Multiplication And Julia, Haochen Sun

Dissertations (1934 -)

Enumerating all routing paths among Autonomous Systems (ASes) at an Internet-scale is an intractable problem. The Border Gateway Protocol (BGP) is the standard exterior gateway protocol through which ASes exchange reachability information. Building an efficient path enumeration tool for a given network is an essential step toward estimating the resiliency of the network to cyber security attacks, such as routing origin and path hijacking. In our work, we use the matrix Latin multiplication method to compute all possible paths among all pairs of nodes. We parallelize this computation through the domain decomposition for matrix multiplication and implement our solution in …

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Acceleration Of Computational Geometry Algorithms For High Performance Computing Based Geo-Spatial Big Data Analysis, Anmol Paudel Apr 2022

Acceleration Of Computational Geometry Algorithms For High Performance Computing Based Geo-Spatial Big Data Analysis, Anmol Paudel

Dissertations (1934 -)

Geo-Spatial computing and data analysis is the branch of computer science that deals with real world location-based data. Computational geometry algorithms are algorithms that process geometry/shapes and is one of the pillars of geo-spatial computing. Real world map and location-based data can be huge in size and the data structures used to process them extremely big leading to huge computational costs. Furthermore, Geo-Spatial datasets are growing on all V’s (Volume, Variety, Value, etc.) and are becoming larger and more complex to process in-turn demanding more computational resources. High Performance Computing is a way to breakdown the problem in ways that …

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Epa Region 8 Qa Document Review Crosswalk, Nikia Greene Apr 2022

Epa Region 8 Qa Document Review Crosswalk, Nikia Greene

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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