Digital Technology Enables Modernization Of National Statistics, 2022 School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
Digital Technology Enables Modernization Of National Statistics, Zongben Xu, Yanyun Zhao, Liping Zhu, Guang Chen, Hongyun Zhang
Bulletin of Chinese Academy of Sciences (Chinese Version)
The modernization of national statistics is part of the modernization of national governance. Digital technology has provided power for the transformation of statistical production mode, the improvement of statistical productivity, and the reconstruction of statistical production relations. Digital technology has become an important prerequisite for the promotion of statistical modernization reform. This study summarizes the international experience of digital technology enabling government statistics, the top-level design of national statistical legal system, and the importance of digital technology in promoting the modernization of statistics. This study also analyzes the main challenges existing in the current national statistics and data work. Finally, …
Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, 2022 Louisiana State University and Agricultural and Mechanical College
Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu
LSU Doctoral Dissertations
In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of …
Utilizing Remote Sensing Technology To Relocate Lubra Village And Visualize Flood Damages, 2022 Macalester College
Utilizing Remote Sensing Technology To Relocate Lubra Village And Visualize Flood Damages, Ronan Wallace
Mathematics, Statistics, and Computer Science Honors Projects
As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community and habitat-conscious solutions. In Himalayan Mustang, Nepal, indigenous Lubra village faces threats of increasing flash flooding. After every flood, residual concrete-like sediment hardens across the riverbed, causing the riverbed elevation to rise. As elevation increases, sediment encroaches on Lubra’s agricultural fields and homes, magnifying flood vulnerability. In the last monsoon season alone, the village witnessed floods swallowing several fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as eight centuries …
Pyseg: A Python Package For 2d Material Flake Localization, Segmentation, And Thickness Prediction, 2022 University of New Hampshire, Durham
Pyseg: A Python Package For 2d Material Flake Localization, Segmentation, And Thickness Prediction, Diana B. Horangic
Student Research Projects
Thin materials are of interest for their extraordinary physical, mechanical, thermal, electrical, and optical properties. Monolayers and bilayers of 2D materials can be manufactured through a variety of exfoliation methods. To determine layer thickness, Raman spectroscopy or other methods like Rayleigh scattering are used. These methods are, however, slow, and they require equipment beyond an optical microscope. A Python package that automates flake identification processes was built, with access solely to RGB data from an optical microscope assumed. My package, pyseg, localizes flakes on a substrate and then makes a rough estimate of their thickness from first principles. It can …
Fairness And Privacy In Machine Learning Algorithms, 2022 Kennesaw State University
Fairness And Privacy In Machine Learning Algorithms, Neha Bhargava
Master of Science in Computer Science Theses
Roughly 2.5 quintillion bytes of data is generated daily in this digital era. Manual processing of such huge amounts of data to extract useful information is nearly impossible but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of machine learning algorithms there has always been concerns about the ethical issues that may arise from the use of this modern technology. While achieving high accuracies, …
Discourse, Power Dynamics, And Risk Amplification In Disaster Risk Management In Canada, 2022 The University of Western Ontario
Discourse, Power Dynamics, And Risk Amplification In Disaster Risk Management In Canada, Martins Oluwole Olu-Omotayo
Electronic Thesis and Dissertation Repository
The domain of disaster risk management is rife with discursive contentions, whereby dominant discourses amplify the powers of risk actors to precipitate and reinforce political, economic, and environmental inequalities that predispose different sections of the population to unequal disaster risk vulnerabilities. This thesis identified important actors (government, risk experts, media, and NGOs) that shape the power dynamics in disaster risk management in Canada and explained their roles, influences, and the dimensions in which their powers negotiate each other through risk discourses. The patterns of these power dynamics in the three aspects of power –communication, assessment, and social trust –were also …
Spatial Validation Of Agent-Based Models, 2022 Claremont Graduate University
Spatial Validation Of Agent-Based Models, Kristoffer Wikstrom, Hal T. Nelson
Public Administration Faculty Publications and Presentations
This paper adapts an existing techno–social agent-based model (ABM) in order to develop a new framework for spatially validating ABMs. The ABM simulates citizen opposition to locally unwanted land uses, using historical data from an energy infrastructure siting process in Southern California. Spatial theory, as well as the model’s design, suggest that adequate validation requires multiple tests rather than relying solely on a single test-statistic. A pattern-oriented modeling approach was employed that first mapped real and simulated citizen comments across the US Census tract. The suite of spatial tests included Global Moran’s I, complemented with bivariate correlations, as well as …
Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, 2022 Washington University in St. Louis
Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James
McKelvey School of Engineering Theses & Dissertations
Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …
Examining The Relationship Between Stomiiform Fish Morphology And Their Ecological Traits, 2022 Nova Southeastern University
Examining The Relationship Between Stomiiform Fish Morphology And Their Ecological Traits, Mikayla L. Twiss
All HCAS Student Capstones, Theses, and Dissertations
Trait-based ecology characterizes individuals’ functional attributes to better understand and predict their interactions with other species and their environments. Utilizing morphological traits to describe functional groups has helped group species with similar ecological niches that are not necessarily taxonomically related. Within the deep-pelagic fishes, the Order Stomiiformes exhibits high morphological and species diversity, and many species undertake diel vertical migration (DVM). While the morphology and behavior of stomiiform fishes have been extensively studied and described through taxonomic assessments, the connection between their form and function regarding their DVM types, morphotypes, and daytime depth distributions is not well known. Here, three …
A Maturity Model Of Data Modeling In Self-Service Business Intelligence Software, 2022 Kennesaw State University
A Maturity Model Of Data Modeling In Self-Service Business Intelligence Software, Anna Kurenkov
Master of Science in Information Technology Theses
Although Self-Service Business Intelligence (SSBI) is continually being adopted in various industries, there is a lack of research focused on data modeling in SSBI. This research aims to fill that research gap and propose a maturity model for SSBI data modeling which is generalizeable between different software and applicable for users of all technical backgrounds. Through extensive literature review, a five-tier maturity model was proposed, explained, and instantiated in PowerBI and Tableau. The testing of the model was found to be simple and intuitive, and the research concludes that the model is applicable to enterprise SSBI environments. This research is …
Industry 4.0, 2022 Western Michigan University
Industry 4.0, Zachary Zeitler
Honors Theses
The ultimate goal of this project is to automate quality control processes on various machines which include 3D printers, welders, routers, CNCs, and more. Using the “Digital Twin” approach, we want to automate monitoring, data collection, data analysis, and corrective action. Our involvement begins with building a new piece of software that can perform two specific functions and collect data from cameras. We are not attempting to analyze the data, make corrective action, or design a final version of the physical attributes. Our software provides the user with the ability to capture an image set or a 3D scan and …
Critically Observing The Challenges And Changes: An Analysis On Covid-19’S Impact With An Emphasis On Students In Higher Education, 2022 University of Mississippi
Critically Observing The Challenges And Changes: An Analysis On Covid-19’S Impact With An Emphasis On Students In Higher Education, Landon Perkins
Honors Theses
This project involves comparing different visualizations related to COVID-19 and higher education in order to determine key impacts of the COVID-19 pandemic on students in higher education, as well as higher education as a whole. The main metrics used to determine the impact were mental health indicators for anxiety or depressive disorders, enrollment numbers by control type (public, private non-profit, or private for-profit) and state for 2020 and 2021, and state mandate lift dates for a variety of mandates implemented across the United States. These metrics were analyzed both individually and against each other to determine if they had any …
The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection, 2022 ADAPT Centre, Trinity College Dublin
The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection, Matthew Nicholson, Rahul Agrahari, Clare Conran, Haythem Assem, John D. Kelleher
Articles
This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in the …
Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis, 2022 Northern Illinois University
Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis, Nicholas Podlesak
Honors Capstones
In this research project, natural language processing techniques’ ability to accurately classify medical text was measured to reinforce the relevance of artificial intelligence in the medical field. Sentiment analyses (analyses to determine whether the text was positive or negative) were performed on the prescription drug reviews in an open-source dataset using four different models: lexical, a neural network, a support vector machine, and a logistic regression model. Each model’s effectiveness was gauged by its ability to correctly classify unlabeled drug reviews (i.e., a percentage representing accuracy). The machine learning models were able to accurately classify the text, while the lexical …
Safe Sharing For Sensitive Data, 2022 Western University
Safe Sharing For Sensitive Data, Kristi Thompson
Western Libraries Presentations
This workshop focused on the question of when and how human subjects' data can be safely shared. It introduced the basics of data anonymization and discussed how to tell if a dataset has been de-identified. Case studies of successful anonymization and some spectacular failures were shared
Enhancing The Performance Of The Mtcnn For The Classification Of Cancer Pathology Reports: From Data Annotation To Model Deployment, 2022 University of Tennessee, Knoxville
Enhancing The Performance Of The Mtcnn For The Classification Of Cancer Pathology Reports: From Data Annotation To Model Deployment, Kevin De Angeli
Doctoral Dissertations
Information contained in electronic health records (EHR) combined with the latest advances in machine learning (ML) have the potential to revolutionize the medical sciences. In particular, information contained in cancer pathology reports is essential to investigate cancer trends across the country. Unfortunately, large parts of information in EHRs are stored in the form of unstructured, free-text which limit their usability and research potential. To overcome this accessibility barrier, cancer registries depend on expert personnel who read, interpret, and extract relevant information. Naturally, as the number of stored pathology reports increases every day, depending on human experts presents scalability challenges. Recently, …
Denoising And Deconvolving Sperm Whale Data In The Northern Gulf Of Mexico Using Fourier And Wavelet Techniques, 2022 University of New Orleans, New Orleans
Denoising And Deconvolving Sperm Whale Data In The Northern Gulf Of Mexico Using Fourier And Wavelet Techniques, Kendal Mccain Leftwich
University of New Orleans Theses and Dissertations
The use of underwater acoustics can be an important component in obtaining information from the oceans of the world. It is desirable (but difficult) to compile an acoustic catalog of sounds emitted by various underwater objects to complement optical catalogs. For example, the current visual catalog for whale tail flukes of large marine mammals (whales) can identify even individual whales from their individual fluke characteristics. However, since sperm whales, Physeter microcephalus, do not fluke up when they dive, they cannot be identified in this manner. A corresponding acoustic catalog for sperm whale clicks could be compiled to identify individual …
From Computer Curriculum That Works For The Use Of Computer Intellignece Computer Science, 2022 CUNY New York City College of Technology
From Computer Curriculum That Works For The Use Of Computer Intellignece Computer Science, Malachi B. Bacchus
Publications and Research
Computer interconnection can link different networks by using electrical artificial flow ways that can travel through different connections. these are called data network which travels through different sectors of the network simulation of service computer network using artificial intelligence to enhanced further understanding the computations, I've also demonstrated knowing by using the network to get better understanding of how ethical computing can be learned through universities and collegiate that can help established knowledge and healthy computer information. The main tools for the research are using data networking, ethical learning and translation towards different computer systems.
The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., 2022 University of Louisville
The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah
Electronic Theses and Dissertations
Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …
Identity Term Sampling For Measuring Gender Bias In Training Data, 2022 Technological University Dublin
Identity Term Sampling For Measuring Gender Bias In Training Data, Nasim Sobhani, Sarah Jane Delany
Conference Papers
Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure …