scholarly journals Performance and impact of dynamic data placement in ATLAS

2019 ◽  
Vol 214 ◽  
pp. 04025
Author(s):  
Thomas Maier ◽  
Thomas Beermann ◽  
Günter Duckeck ◽  
Mario Lassnig ◽  
Federica Legger ◽  
...  
Keyword(s):  
Distributed Computing ◽  
Data Placement ◽  
Machine Learning Techniques ◽  
Data Management System ◽  
Distributed Data ◽  
Dynamic Data ◽  
Learning Techniques ◽  
Wide Availability ◽  
Popularity Prediction ◽  
The Impact

For high-throughput computing the efficient use of distributed computing resources relies on an evenly distributed workload, which in turn requires wide availability of input data that is used in physics analysis. In ATLAS, the dynamic data placement agent C3PO was implemented in the ATLAS distributed data management system Rucio which identifies popular data and creates additional, transient replicas to make data more widely and more reliably available. This proceedings presents studies on the performance of C3PO and the impact it has on throughput rates of distributed computing in ATLAS. Furthermore, results of a study on popularity prediction using machine learning techniques are presented.

scholarly journals C3PO - A Dynamic Data Placement Agent for ATLAS Distributed Data Management

2017 ◽  
Vol 898 ◽  
pp. 062012 ◽  
Author(s):  
T Beermann ◽  
M Lassnig ◽  
M Barisits ◽  
C Serfon ◽  
V Garonne ◽  
...  
Keyword(s):  
Data Management ◽  
Data Placement ◽  
Distributed Data ◽  
Dynamic Data ◽  
Distributed Data Management

scholarly journals Machine learning analysis of lifeguard flag decisions and recorded rescues

2019 ◽  
Vol 19 (11) ◽  
pp. 2541-2549
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Nathan Cherry ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Rip Currents ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 369 ◽  
Author(s):  
Semin Ryu ◽  
Seung-Chan Kim
Keyword(s):  
Intelligent System ◽  
Surface Characteristics ◽  
Machine Learning Techniques ◽  
Test Accuracy ◽  
Learning Approaches ◽  
Well Test ◽  
Mechanical Responses ◽  
Learning Techniques ◽  
The Impact ◽  
Mechanical Impulse

Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study.

scholarly journals Integrating a dynamic data federation into the ATLAS distributed data management system

2019 ◽  
Vol 214 ◽  
pp. 07009
Author(s):  
Frank Berghaus ◽  
Tobias Wegner ◽  
Mario Lassnig ◽  
Marcus Ebert ◽  
Cedric Serfon ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Data Management System ◽  
Distributed Data ◽  
Dynamic Data ◽  
Distributed Data Management ◽  
Data Federation ◽  
Other Information ◽  
Multiple Copies ◽  
Set Up

Input data for applications that run in cloud computing centres can be stored at remote repositories, typically with multiple copies of the most popular data stored at many sites. Locating and retrieving the remote data can be challenging, and we believe that federating the storage can address this problem. In this approach, the closest copy of the data is used based on geographical or other information. Currently, we are using the dynamic data federation, Dynafed, a software solution developed by CERN IT. Dynafed supports several industry standard interfaces, such as Amazon S3, Microsoft Azure and HTTP with WebDAV extensions. Dynafed functions as an abstraction layer under which protocol-dependent authentication details are hidden from the user, requiring the user to only provide an X509 certificate. We have set up an instance of Dynafed and integrated it into the ATLAS distributed data management system, Rucio. We report on the challenges faced during the installation and integration.

Convolutional Recurrent Neural Networks Based Speech Emotion Recognition

2020 ◽  
Vol 17 (8) ◽  
pp. 3786-3789
Author(s):  
P. Gayathri ◽  
P. Gowri Priya ◽  
L. Sravani ◽  
Sandra Johnson ◽  
Visanth Sampath
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Machine Learning Techniques ◽  
Speech Emotion Recognition ◽  
Emotional Information ◽  
Feature Representations ◽  
Emotional Factors ◽  
Learning Techniques ◽  
The Impact

Recognition of emotions is the aspect of speech recognition that is gaining more attention and the need for it is growing enormously. Although there are methods to identify emotion using machine learning techniques, we assume in this paper that calculating deltas and delta-deltas for customized features not only preserves effective emotional information, but also that the impact of irrelevant emotional factors, leading to a reduction in misclassification. Furthermore, Speech Emotion Recognition (SER) often suffers from the silent frames and irrelevant emotional frames. Meanwhile, the process of attention has demonstrated exceptional performance in learning related feature representations for specific tasks. Inspired by this, propose a Convolutionary Recurrent Neural Networks (ACRNN) based on Attention to learn discriminative features for SER, where the Mel-spectrogram with deltas and delta-deltas is used as input. Finally, experimental results show the feasibility of the proposed method and attain state-of-the-art performance in terms of unweighted average recall.

scholarly journals COVID-19 in China: Risk Factors and R0 Revisited

2020 ◽  
Author(s):  
Irtesam Mahmud Khan ◽  
Wenyi Zhang ◽  
Sumaira Zafar ◽  
Yong Wang ◽  
Junyu He ◽  
...  
Keyword(s):  
Virus Transmission ◽  
Geographic Region ◽  
Machine Learning Techniques ◽  
Transmission Model ◽  
Health Crisis ◽  
Global Pandemic ◽  
Learning Techniques ◽  
Visual Aid ◽  
Spatiotemporal Clustering ◽  
The Impact

The COVID-19 epidemic had spread rapidly through China and subsequently has proliferated globally leading to a pandemic situation around the globe. Human-to-human transmissions, as well as asymptomatic transmissions of the infection, have been confirmed. As of April 3rd, public health crisis in China due to COVID-19 is potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020 (excluding Wuhan from our analysis due to missing data). Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. The prefectures were grouped based on several relevant features using unsupervised machine learning techniques. We performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions for prevention planning in an ongoing global pandemic. Finally, our results indicate that the impact of temperature and demographic (different age group percentage compared to the total population) factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help prioritize segments of a given community/ region for action and provide a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

scholarly journals A Survey on Prevention of Overfitting in Convolution Neural Networks Using Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 177 ◽  
Author(s):  
Dr M.R.Narasinga Rao ◽  
V Venkatesh Prasad ◽  
P Sai Teja ◽  
Md Zindavali ◽  
O Phanindra Reddy
Keyword(s):  
Neural Networks ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Test Time ◽  
Document Type ◽  
The Neural Network ◽  
Learning Techniques ◽  
Extreme Problem ◽  
Regularization Techniques ◽  
The Impact

Deep neural nets with a vast quantity of parameters are very effective machine getting to know structures. However, overfitting is an extreme problem in such networks. Massive networks are also sluggish to use, making it difficult to cope with overfitting by combining the predictions of many distinct large neural nets at test time. Dropout is a method for addressing this problem. The important thing concept is to randomly drop units (at the side of their connections) from the neural network for the duration of education. This prevents units from co-adapting an excessive amount of. during schooling, dropout samples from an exponential quantity of various "thinned" networks. At take a look at the time, it is simple to precise the impact of averaging the predictions of plenty of these thinned networks through in reality using a single unthinned network that has smaller weights. This considerably minimize overfitting and provides fundamental enhancements over other regularization techniques. We show that dropout enhance the overall performance of neural networks on manage gaining knowledge of obligations in imaginative and prescient, speech reputation, document type and computational biology, acquiring today's effects on many benchmark facts sets.  

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