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6,452 results for “Application”
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figire 1. Screenshots of the Virtual Campus mobile application (a, b, c)
<p>Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 4. Statistical Metrics distribution of model on RIO as response of Terasort application
<p>Figure 4 shows the statistical metrics distribution of regression model on read rate as the response of Terasort application. The filled triangle point-up indicates the minimum stable sampling time for statistical metrics. The top-half of figure 4 shows the residual standard error (RSE) distribution as training data size increase. The remaining half is for the distribution of R2.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 3. Estimate coefficients distribution of model on read rate as response of Terasort application
<p>Figure 3 shows estimate coefficients distribution of model on read rate as the response of Terasort application. The filled triangle point-up indicates the position of the minimum stable sampling time for the corresponding estimate coefficients as well as the number above it shows the exact position value. The dashed line represents the average estimated coefficient of the regression model.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 1. ACF and PACF plot of TeraSort application
<p>In Figure 1, the autocorrelation plots of all resource usage parameters reveal non- randomness because of the corresponding autocorrelations, denoted by the circle, violate the dashed lines (95% confidence boundary) and are statistically significant for lags up to 100. The filled triangle point-up in partial autocorrelation plots marks the largest partial autocorrelation of usage parameters as well as the corresponding lag number.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 6. RSE and R2 of regression models of MapReduce applications
<p>Figure 6 shows the fit quality of regression models. It is following.The left panel and the right panel of figure 6 show the residual standard error (RSE) distribution and R2 distribution of each application. The good fit quality corresponds to a taller R2 bar and a shorter RSE bar. The R2 almost 1 and small RSE show the best fit quality of the regression models on memory usage as the response. The overall higher RSE and lower R2 of regression models on CPU as the response show the worse quality of fitting goodness. The regression models on read rate as the response also show a moderate fitting quality. For the regression models on write rate as the response, Terasort application exhibits the best quality and Teragen application as well. Others show the worse fitting quality. The results show that the regression models on intensive usage parameters as response exhibit the good fitting quality.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 2. ACF plot of Residuals of Regression Model of Terasort
<p>, are uncorrelated. If the error term is uncorrelated, it proves that there exists strong randomness in residuals of the model and provides the evidence for the unbiased estimate for the true standard error. The autocorrelation plot is used to check this assumption. Figure 2 shows the autocorrelation plot of residuals of regression models of TeraSort application. In Figure 2, the horizontal axis represents lag time and the vertical axis indicates the autocorrelation between residual at time t and residual at other lag time. At lag 0, autocorrelation is always equal to 1 and represents time series itself. Most of the autocorrelation at other lag time fall into the 95% confidence interval, only few of them violate the dashed line. Such a shape of ACF plot proves that residuals are uncorrelated and respects to the independent assumption of the linear regression model residuals.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 8. Minimum sample time of statistical metrics of MapReduce applications
<p>Figure 8 presents the minimum sampling time distribution of statistic metrics which ensures the stable modeling. Overall, the minimum sampling time of statistic metrics is smaller than sampling time of estimated coefficients. For different applications, a time-consuming application like Terasort needs the largest sampling time to tend to be stable. The Pi application shows the smallest minimum sampling time to reach stability.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 7. Minimum sampling time of MapReduce applications on various response variable
<p>Pi application needs the smallest sampling time. The remaining applications need the similar minimum sampling time. Overall, the stable regression models on memory usage as response show the least need for sampling time. The results show that various applications have different minimum sampling time to get stable. The application which performs more read/write operations shows larger sampling time need.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary
<p>In Figure 5, the positive dependency of different strength between each resource usage parameter and the corresponding previous usage parameter is exhibited for all MapReduce applications. It indicates that all current resource usage parameters are positively dependent on the previous values to some extent degree. Except for these common dependencies, there exist some special dependencies for different applications. On the top-left panel of Figure 5, CPU usage of Pi application shows the strongest positive dependency to lagged CPU usage, the Teragen application had the weakest positive dependency, and others exhibit the moderate positive dependency. </p>
Voice-activated applications and Multipath TCP: A good match?
<p>The attachment is the dataset of the paper "Voice-activated applications and Multipath TCP: A good match?"<br> which has been accepted in Workshop on Mobile Network Measurement 2018.</p> <p>Homepage: https://www.info.ucl.ac.be/~tranviet/</p> <p>LKL-MPTCP stack: https://github.com/hoang-tranviet/mptcp/tree/lkl_4.13-mptcp_v0.93_API<br> Simulated voice traffic program: https://github.com/hoang-tranviet/iperf-siri<br> Client Docker script: https://github.com/hoang-tranviet/lkl-docker-monroe</p>
Companion for "Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources"
<p>This is the companion data for the VECPAR2018 submission paper entitled: Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources by Lucas Leandro Nesi, Lucas Mello Schnorr, and Philippe Olivier Alexandre Navaux. All the data, source code, and images generation scripts used in the paper are present here.</p>
A new and highly robust light-responsive Azo-UiO-66 for highly selective and low energy post-combustion CO2 capture and its application in a mixed matrix membrane for CO2/N2 separation
<p>Supporting information for publication in Journal of Materials Chemistry A, <a href="https://dx.doi.org/10.1039/C8TA03553A">https://dx.doi.org/10.1039/C8TA03553A </a></p>
Research data supporting "Post-polymerisation functionalisation of conjugated polymer backbones and its application in multi-functional emissive nanoparticles"
<p>Research data supporting the publication:</p> <p>Creamer A. et al., "Post-polymerisation functionalisation of conjugated polymer backbones and its application in multi-functional emissive nanoparticles",<em> Nature Communications</em><strong>, 9</strong>:3237 (2018).</p>
Large uptake of atmospheric OCS observed at a moist old growth forest: Controls and implications for carbon cycle applications
<p>This repository contains all data for the manuscript by Rastogi et al., titled "<strong>Large uptake of atmospheric OCS observed at a moist old growth forest: Controls and implications for carbon cycle applications</strong>". This manuscript has been accepted for publication in the Journal of Geophysical Research: Biogeosciences</p> <p> </p>
Dataset for "The AllScale Runtime Application Model" publication
<p>The experimental data collected for the associated paper.</p> <p>Each file covers the throughput measures for the three evaluated applications. The first column is the number of nodes, the second the throughput achieved by the AllScale runtime system, and the last column the throughput of a reference MPI version.</p>
An Ultra Low-Loss Silicon-Micromachined Waveguide Filter for D-Band Telecommunication Applications
<p>The dataset contains S-parameter measurements between 110-170 GHz, for a silicon micromachined filter. It also contains quality factor data for the filter and the complex propagation constant data.</p>
The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews (data repository)
<p>These are the data we generated in our evaluation of the graphical user interface.</p> <p>Please see our publication on Wellcome Open Research for information about the evaluations.</p>
Spark application traces of the run of 9 differents instance of BigDataBench applications
<p>Spark application logs of the run of 9 different instances of BigDataBench applications. Each run was done with an input size in GB of 32, 64, 128 and with 4, 8, 16 executor respectively. There were 2 executors per node; so, it runs on 2, 4, and 8 nodes + a master node for Hadoop services.</p> <p>The input data were generated with the Data generator provided by BigDataBench using this procedure:</p> <p>https://gitlab.inria.fr/mmercier/bebida/blob/master/experiments/generate_dataset/journal.md</p> <p>List of the applications and their parameters:</p> <ul> <li>Grep: <ul> <li> parameter: "word"</li> </ul> </li> <li>WordCount <ul> <li> no parameters</li> </ul> </li> <li>Kmean: <ul> <li> parameter: "4 3" input size in GB: "32, 64, 128"</li> </ul> </li> </ul> <p>BigDataBench implementation can be downloaded here: http://prof.ict.ac.cn/download.html</p> <p>It was run on Debian 8, with Spark 2.1.0, on top of Hadoop 2.7.1 with Yarn and HDFS, using openjdk-7-jre-headless.</p> <p>All the details of the environment can be found here:</p> <p>https://gitlab.inria.fr/mmercier/bebida/blob/master/environments/bebida-slave.yaml</p> <p>The experiment in itself is described here:</p> <p>https://gitlab.inria.fr/mmercier/bebida/tree/master/experiments/run_big_data_workload</p> <p>All nodes Hardware description of the nodes:</p> <p>https://public-api.grid5000.fr/stable/sites/nancy/clusters/graphene/nodes.json?pretty=1</p> <p>They can be visualized using the Spark History server.</p>
Data for figures in "Reproducibility in Benchmarking Parallel Fast Fourier Transform based Applications"
<p>FFT benchmark data and Python plotting programs</p>
Supporting data for: "Diaphysator: an online application for the exhaustive cartography and user-friendly statistical analysis of long bone diaphyses"
<p>Example of dataset to be used with the R-shiny application “Diaphysator”, composed of right tibiae and femora.</p> <p>These data file have been published in: Lacoste Jeanson, A., Santos, F., Villa, C., Banner, J., & Bruzek, J. (2018). Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT. <em>American Journal of Physical Anthropology</em>, 167, 813– 826. doi: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23713">10.1002/ajpa.23713</a></p> <p>This zip file contains:</p> <ul> <li>an “Information file” in CSV format</li> <li>various data files for human femora and tibiae in CSV format</li> </ul> <p>For all CSV files, the field separator is the comma “,” and the character used for decimal points is the dot “.”</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.