scholarly journals Towards Large-Scale Data Annotation of Audio from Wearables: Validating Zooniverse Annotations of Infant Vocalization Types

2020 ◽  
Author(s):  
chiara semenzin ◽  
Lisa Hamrick ◽  
Amanda Seidl ◽  
Bridgette Lynne Kelleher ◽  
Alejandrina Cristia
Keyword(s):  
Large Scale ◽  
Strong Correlations ◽  
Data Annotation ◽  
Large Scale Data ◽  
Infant Vocalization ◽  
Recent Developments ◽  
Audio Data ◽  
Strategy Fit ◽  
Scale Data

Recent developments allow the collection of audio data from lightweight wearable devices, potentially enabling us to study language use from everyday life samples. However, extracting useful information from these data is currently impossible with automatized routines, and overly expensive with trained human annotators. We explore a strategy fit to the 21st century, relying on the collaboration of citizen scientists. A large dataset of infant speech was uploaded on a citizen science platform. The same data were annotated in the laboratory by highly trained annotators. We investigate whether crowdsourced annotations are qualitatively and quantitatively comparable to those produced by expert annotators in a dataset of children at high- and low-risk for language disorders. Our results reveal that classification of individual vocalizations on Zooniverse was overall moderately accurate compared to the laboratory gold standard. The analysis of descriptors defined at the level of individual children found strong correlations between descriptors derived from Zooniverse versus laboratory annotations.

scholarly journals Support Vector Machines in Big Data Classification: A Systematic Literature Review

2021 ◽  
Author(s):  
Mohammad Hassan Almaspoor ◽  
Ali Safaei ◽  
Afshin Salajegheh ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Support Vector ◽  
Research Areas ◽  
Large Scale Data ◽  
Training Samples ◽  
Big Data Classification ◽  
Scale Data

Abstract Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

scholarly journals An iterative method for classification of binary data

2020 ◽  
Author(s):  
Denali Molitor ◽  
Deanna Needell
Keyword(s):  
Binary Data ◽  
Large Scale ◽  
Support Vector ◽  
Large Scale Data ◽  
Classification Framework ◽  
Vector Machines ◽  
Inference Methods ◽  
Compressed Data ◽  
Scale Data

Abstract In today’s data-driven world, storing, processing and gleaning insights from large-scale data are major challenges. Data compression is often required in order to store large amounts of high-dimensional data, and thus, efficient inference methods for analyzing compressed data are necessary. Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application. As a side consequence, we show that the original framework can be used as a data preprocessing step to improve the performance of other methods, such as support vector machines. For several simple settings, we showcase the ability to obtain theoretical guarantees for the accuracy of the iterative classification method. The simplicity of the underlying classification framework makes it amenable to theoretical analysis.

Recent Developments on Security and Reliability in Large-Scale Data Processing with MapReduce

2016 ◽  
Vol 12 (1) ◽  
pp. 49-68 ◽  
Author(s):  
Christian Esposito ◽  
Massimo Ficco
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Programming Model ◽  
Big Data Analytics ◽  
Large Scale Data ◽  
Recent Developments ◽  
Security And Reliability ◽  
Large Scale Data Processing ◽  
The Right ◽  
Scale Data

The demand to access to a large volume of data, distributed across hundreds or thousands of machines, has opened new opportunities in commerce, science, and computing applications. MapReduce is a paradigm that offers a programming model and an associated implementation for processing massive datasets in a parallel fashion, by using non-dedicated distributed computing hardware. It has been successfully adopted in several academic and industrial projects for Big Data Analytics. However, since such analytics is increasingly demanded within the context of mission-critical applications, security and reliability in MapReduce frameworks are strongly required in order to manage sensible information, and to obtain the right answer at the right time. In this paper, the authors present the main implementation of the MapReduce programming paradigm, provided by Apache with the name of Hadoop. They illustrate the security and reliability concerns in the context of a large-scale data processing infrastructure. They review the available solutions, and their limitations to support security and reliability within the context MapReduce frameworks. The authors conclude by describing the undergoing evolution of such solutions, and the possible issues for improvements, which could be challenging research opportunities for academic researchers.

ANALISA POLA PEKERJAAN LULUSAN STMIK BUDI DARMA MENERAPKAN METODE C4.5

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Anisa Anisa ◽  
Mesran Mesran
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Large Data ◽  
Analysis Data ◽  
Mining Method ◽  
Training Set ◽  
Data Mining Method ◽  
Large Scale Data ◽  
Scale Data

Data mining is mining or discovery information to the process of looking for patterns or information that contains the search trends in a number of very large data in taking decisions on the future.In determining the patterns of classification techniques garnered record (Training set). The class attribute, which is a decision tree with method C 4.5 builds upon an algorithm of induction can be minimised.By utilizing data jobs graduates expected to generate information about interest & talent, work with benefit from graduate quisioner alumni. A pattern of work that sought from large-scale data and analyzed by various algorithms to compute the C 4.5 can do that work based on the pattern of investigation patterns that affect so that it found the rules are interconnected that can result from the results of the classification of objects of different classes or categories of attributes that influence to shape the patterns of work. The application used is software that used Tanagra data mining for academic and research purposes.That contains data mining method explored starting from the data analysis, and classification data mining.Keywords: analysis, Data Mining, method C 4.5, Tanagra, patterns of work

scholarly journals Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

2021 ◽  
Author(s):  
Noah F. Greenwald ◽  
Geneva Miller ◽  
Erick Moen ◽  
Alex Kong ◽  
Adam Kagel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Cell Segmentation ◽  
Tissue Imaging ◽  
Imaging Data ◽  
Whole Cell ◽  
Data Annotation ◽  
Large Scale Data ◽  
Level Performance ◽  
Scale Data

AbstractUnderstanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

scholarly journals Robust classification of protein variation using structural modelling and large-scale data integration

2016 ◽  
Vol 44 (6) ◽  
pp. 2501-2513 ◽  
Author(s):  
Evan H. Baugh ◽  
Riley Simmons-Edler ◽  
Christian L. Müller ◽  
Rebecca F. Alford ◽  
Natalia Volfovsky ◽  
...  
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Structural Modelling ◽  
Robust Classification ◽  
Protein Variation ◽  
Large Scale Data ◽  
Scale Data
Sign in / Sign up

Export Citation Format

Share Document