Statistical Approaches For Estimation And Comparison Of Brain Functional Connectivity,
2021
Virginia Commonwealth University
Statistical Approaches For Estimation And Comparison Of Brain Functional Connectivity, Jifang Zhao
Theses and Dissertations
Drug addiction can lead to many health-related problems and social concerns. Functional connectivity obtained from functional magnetic resonance imaging (fMRI) data promotes a variety of fundamental understandings in such association. Due to its complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computationally efficient algorithm to estimate the parameters. Our method is used to identify functional connectivity and …
Proposed Data Governance Framework For Small And Medium Scale Enterprises (Smes),
2021
Minnesota State University, Mankato
Proposed Data Governance Framework For Small And Medium Scale Enterprises (Smes), Rejoice Okoro
All Graduate Theses, Dissertations, and Other Capstone Projects
Data governance is not a one size fits all, instead, it should be an evolutionary process that can be started small and measurable along the way. This research aims at proposing a data governance framework by ensuring data management processes, data security and control are compliant with laws and policies. This article also presents the first results of a comparative analysis between three data privacy laws and outlines five components which together form a data governance framework for SMEs. The data governance model documents data quality roles and their type of interaction with data quality management activities exploring how data …
Modelling Acoustics In Ancient Maya Cities: Moving Towards A Synesthetic Experience Using Gis & 3d Simulation,
2021
University of California, Merced
Modelling Acoustics In Ancient Maya Cities: Moving Towards A Synesthetic Experience Using Gis & 3d Simulation, Graham Goodwin, Heather Richards-Rissetto
Department of Anthropology: Faculty Publications
Archaeological analyses have successfully employed 2D and 3D tools to measure vision and movement within cityscapes; however, built environments are often designed to invoke synesthetic experiences. GIS and Virtual Reality (VR) now enable archaeologists to also measure the acoustics of ancient spaces. To move toward an understanding of synesthetic experience in ancient Maya cities, we employ GIS and 3D modelling to measure sound propagation and reverberation using the main civic-ceremonial complex in ancient Copán as a case study. For the ancient Maya, sight and sound worked in concert to create ritually-charged atmospheres and architecture served to shape these experiences. Together …
An Ensemble Approach For Annotating Source Code Identifiers With Part-Of-Speech Tags,
2021
Bowling Green State University
An Ensemble Approach For Annotating Source Code Identifiers With Part-Of-Speech Tags, Christian D. Newman,, Michael J. Decker, Reem S. Alsuhaibani, Anthony Peruma, Mohamed Wiem Mkaouer, Satyajit Mohapatra, Tejal Vishnoi, Marcos Zampieri, Timothy Sheldon, Emily Hill
Articles
This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to …
Convolutional Audio Source Separation Applied To Drum Signal Separation,
2021
Colby College
Convolutional Audio Source Separation Applied To Drum Signal Separation, Marius Orehovschi
Honors Theses
This study examined the task of drum signal separation from full music mixes via both classical methods (Independent Component Analysis) and a combination of Time-Frequency Binary Masking and Convolutional Neural Networks. The results indicate that classical methods relying on predefined computations do not achieve any meaningful results, while convolutional neural networks can achieve imperfect but musically useful results. Furthermore, neural network performance can be improved by data augmentation via transposition – a technique that can only be applied in the context of drum signal separation.
Big Data Management In The Shipping Industry : Examining Strengths Vs Weaknesses And Highlighting Relevant Business Opportunities,
2021
World Maritime University
Big Data Management In The Shipping Industry : Examining Strengths Vs Weaknesses And Highlighting Relevant Business Opportunities, Dimitrios Dalaklis, Georgios Vaitsos, Nikitas Nikitakos, Dimitrios Papachristos, Angelo Dalaklis, Esslam Hassan
Conference Papers
History testifies that there is a dialectic relationship between humans and technology. Especially during the last couple of decades, the shipping industry has benefitted from a very extended number of advanced technology innovations. Today, all systems supporting the conduct of navigation and the various information technology (IT) applications related to ship management activities are heavily reliant upon (almost) real-time information to safely/effectively fulfil their allocated tasks. As a result, truly vast quantities of data -which are often described as “Big Data” in the wider literature- are created and the issue of how to effectively manage all the associated information is …
Reliable And Interpretable Machine Learning For Modeling Physical And Cyber Systems,
2021
Virginia Commonwealth University
Reliable And Interpretable Machine Learning For Modeling Physical And Cyber Systems, Daniel L. Marino Lizarazo
Theses and Dissertations
Over the past decade, Machine Learning (ML) research has predominantly focused on building extremely complex models in order to improve predictive performance. The idea was that performance can be improved by adding complexity to the models. This approach proved to be successful in creating models that can approximate highly complex relationships while taking advantage of large datasets. However, this approach led to extremely complex black-box models that lack reliability and are difficult to interpret. By lack of reliability, we specifically refer to the lack of consistent (unpredictable) behavior in situations outside the training data. Lack of interpretability refers to the …
Use Of Lymesim 2.0 To Assess The Potential For Single And Integrated Management Methods To Control Blacklegged Ticks (Ixodes Scapularis; Acari: Ixodidae) And Transmission Of Lyme Disease Spirochetes,
2021
Hollins University
Use Of Lymesim 2.0 To Assess The Potential For Single And Integrated Management Methods To Control Blacklegged Ticks (Ixodes Scapularis; Acari: Ixodidae) And Transmission Of Lyme Disease Spirochetes, Shravani Chitineni, Elizabeth R. Gleim, Holly D. Gaff
Undergraduate Honors Theses
Annual Lyme disease cases continue to rise in the U.S. making it the most reported vector-borne illness in the country. The pathogen (Borrelia burgdorferi) and primary vector (Ixodes scapularis; blacklegged tick) dynamics of Lyme disease are complicated by the multitude of vertebrate hosts and varying environmental factors, making models an ideal tool for exploring disease dynamics in a time- and cost-effective way. In the current study, LYMESIM 2.0, a mechanistic model, was used to explore the effectiveness of three commonly used tick control methods: habitat-targeted acaricide (spraying), rodent-targeted acaricide (bait boxes), and white-tailed deer targeted acaricide (4-poster …
Assessing And Forecasting Chlorophyll Abundances In Minnesota Lake Using Remote Sensing And Statistical Approaches,
2021
Minnesota State University, Mankato
Assessing And Forecasting Chlorophyll Abundances In Minnesota Lake Using Remote Sensing And Statistical Approaches, Ben Von Korff
All Graduate Theses, Dissertations, and Other Capstone Projects
Harmful algae blooms (HABs) can negatively impact water quality, lake aesthetics, and can harm human and animal health. However, monitoring for HABs is rare in Minnesota. Detecting blooms which can vary spatially and may only be present briefly is challenging, so expanding monitoring in Minnesota would require the use of new and cost efficient technologies. Unmanned aerial vehicles (UAVs) were used for bloom mapping using RGB and near-infrared imagery. Real time monitoring was conducted in Bass Lake, in Faribault County, MN using trail cameras. Time series forecasting was conducted with high frequency chlorophyll-a data from a water quality sonde. Normalized …
Public Interest Technology – Exploring Covid-19 Health Data,
2021
CUNY College of Staten Island
Public Interest Technology – Exploring Covid-19 Health Data, Sarah Zelikovitz
Open Educational Resources
This module is part of a Introduction to Data Science course that covers the different parts of the data science process: data acquisition, cleaning, exploratory data analysis, and modeling. The COVID-19 pandemic has created much interest in public health data, as well as interest in visualization of all types of data. Public health data has a set of challenges that is unique to health data, with HIPAA laws, and real time collection of data. With COVID-19, the challenges are particularly amplified, as data collection and statistics collected are constantly changing in response to feedback from labs, hospitals, drug companies, and …
Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak,
2021
Singapore Management University
Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam
Research Collection School Of Computing and Information Systems
The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of …
Question Answering By Bert,
2021
Minnesota State University Moorhead
Question Answering By Bert, Suman Karanjit
Student Academic Conference
No abstract provided.
Modeling Multivariate Hopfield-Transformer Hawkes Process: Application To Sovereign Credit Default Swaps,
2021
Wilfrid Laurier University
Modeling Multivariate Hopfield-Transformer Hawkes Process: Application To Sovereign Credit Default Swaps, Mohsen Bahremani
Theses and Dissertations (Comprehensive)
Hawkes process was evolved so that the past events contribute to the occurrence time of future events by self-exciting or mutually exciting. However, many real-world data do not follow the Hawkes process's assumptions (i.e., positivity, additivity, and exponential decay) and become more complex to be modeled by the traditional Hawkes processes, so the neural Hawkes process was developed to tackle the challenges. However, Recurrent Neural Networks (RNN) fail to capture long-term dependencies among multiple point processes, and Transformer Hawkes processes only address temporal characteristics of Hawkes processes. In this thesis, we proposed a combination of neural networks and Hawkes processes …
Change Detection And Landscape Similarity Comparison Using Computer Vision Methods,
2021
Wilfrid Laurier University
Change Detection And Landscape Similarity Comparison Using Computer Vision Methods, Karim Malik Wilfrid Laurier University
Theses and Dissertations (Comprehensive)
Human-induced disturbances of terrestrial and aquatic ecosystems continue at alarming rates. With the advent of both raw sensor and analysis-ready datasets, the need to monitor ecosystem disturbances is now more imperative than ever; yet the task is becoming increasingly complex with increasing sources and varieties of earth observation data. In this research, computer vision methods and tools are interrogated to understand their capability for comparing spatial patterns. A critical survey of literature provides evidence that computer vision methods are relatively robust to scale and highlights issues involved in parameterization of computer vision models for characterizing significant pattern information in a …
Red Drum And Spotted Seatrout Live-Release Tournament Mortality And Dispersal,
2021
University of South Alabama
Red Drum And Spotted Seatrout Live-Release Tournament Mortality And Dispersal, T. Reid Nelson, Crystal Hightower, Sean P. Powers
University Faculty and Staff Publications
Although catch-and-release fishing tournaments undoubtedly reduce mortality of target species, postrelease mortality and fish stockpiling at release sites remain common concerns related to these tournaments. The impacts of liverelease tournaments on freshwater species have been widely studied. However, research on estuarine sport fishes is lacking even though catch-and-release tournaments targeting these species are prevalent and popular recreational fisheries exist. Therefore, we estimated the post-weigh-in mortality and dispersal of Red Drum Sciaenops ocellatus and Spotted Seatrout Cynoscion nebulosus released from the 2016–2018 Alabama Deep Sea Fishing Rodeo live-weigh-in categories using acoustic telemetry. To concurrently estimate overall post-weigh-in mortality and dispersal, we …
Automatic Hierarchy Expansion For Improved Structure And Chord Evaluation,
2021
Smith College
Automatic Hierarchy Expansion For Improved Structure And Chord Evaluation, Katherine M. Kinnaird, Brian Mcfee
Statistical and Data Sciences: Faculty Publications
No abstract provided.
Moving Ethnography: Infrastructuring Doubletakes And Switchbacks In Experimental Collaborative Methods,
2021
Indian Institute of Technology Hyderabad
Moving Ethnography: Infrastructuring Doubletakes And Switchbacks In Experimental Collaborative Methods, Aalok Khandekar, Brandon Costelloe-Kuehn, Lindsay Poirier, Alli Morgan, Alison Kenner, Kim Fortun, Mike Fortun
Statistical and Data Sciences: Faculty Publications
In this article, we describe how our work at a particular nexus of STS, ethnography, and critical theory—informed by experimental sensibilities in both the arts and sciences—transformed as we built and learned to use collaborative workflows and supporting digital infrastructure. Responding to the call of this special issue to be “ethnographic about ethnography,” we describe what we have learned about our own methods and collaborative practices through building digital infrastructure to support them. Supporting and accounting for how experimental ethnographic projects move—through different points in a research workflow, with many switchbacks, with project designs constantly changing as the research develops—was …
A Comparison Of Exhaustive And Non-Lattice-Based Methods For Auditing Hierarchical Relations In Gene Ontology,
2021
The Texas Medical Center Library
A Comparison Of Exhaustive And Non-Lattice-Based Methods For Auditing Hierarchical Relations In Gene Ontology, Rashmie Abeysinghe, Fengbo Zheng, Licong Cui
Faculty, Staff and Student Publications
Uncovering and fixing errors in biomedical terminologies is essential so that they provide accurate knowledge to downstream applications that rely on them. Non-lattice-based methods have been applied to identify various kinds of inconsistencies in different biomedical terminologies. In previous work, we have introduced two inference-based approaches that were applied in an exhaustive manner to audit hierarchical relations in the Gene Ontology: (1) Lexical-based inference framework, and (2) Subsumption-based sub-term inference framework. However, it is unclear how effective these exhaustive approaches perform compared with their corresponding non-lattice-based approaches. Therefore, in this paper, we implement the non-lattice versions of these two exhaustive …
Neither “Post-War” Nor Post-Pregnancy Paranoia: How America’S War On Drugs Continues To Perpetuate Disparate Incarceration Outcomes For Pregnant, Substance-Involved Offenders,
2021
Pitzer College
Neither “Post-War” Nor Post-Pregnancy Paranoia: How America’S War On Drugs Continues To Perpetuate Disparate Incarceration Outcomes For Pregnant, Substance-Involved Offenders, Becca S. Zimmerman
Pitzer Senior Theses
This thesis investigates the unique interactions between pregnancy, substance involvement, and race as they relate to the War on Drugs and the hyper-incarceration of women. Using ordinary least square regression analyses and data from the Bureau of Justice Statistics’ 2016 Survey of Prison Inmates, I examine if (and how) pregnancy status, drug use, race, and their interactions influence two length of incarceration outcomes: sentence length and amount of time spent in jail between arrest and imprisonment. The results collectively indicate that pregnancy decreases length of incarceration outcomes for those offenders who are not substance-involved but not evenhandedly -- benefitting white …
Multi-Stream Longitudinal Data Analysis Using Deep Learning,
2021
University of Kentucky
Multi-Stream Longitudinal Data Analysis Using Deep Learning, Sajjad Fouladvand
Theses and Dissertations--Computer Science
Longitudinal healthcare data encompasses all tasks where patients information are collected at multiple follow-up times. Analyzing this data is critical in addressing many real world problems in healthcare such as disease prediction and prevention. In this thesis, technical challenges in analyzing longitudinal administrative claims data are addressed and novel deep learning based models are proposed for multi-stream data analysis and disease prediction tasks. These algorithms and frameworks are assessed mainly on substance use disorders prediction tasks and specifically designed to tackled these disorders. Substance use disorder is a public health crisis costing the US an estimated $740 billion annually in …
