a research data about campaign participation in Surabaya City 2015

Categories:
87 Views

This dataset was collected for research conducted within the project AN.ON-Next funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371.

Instructions: 

The data are primarily gathered with 7-point Likert scales (exceptions are shown in the documentation). Thus, the analysis requires statistical approaches which are applicable to ordinal data. Examples of how the dataset was used in prior research can be found in the documentation.

Categories:
268 Views

This dataset was collected for research conducted within the project AN.ON-Next funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371.

Instructions: 

The data are primarily gathered with 7-point Likert scales (exceptions are shown in the documentation). Thus, the analysis requires statistical approaches which are applicable to ordinal data. Examples of how the dataset was used in prior research can be found in the documentation.

Categories:
392 Views

The water consumption from different house holds recorded for a period of one year

Instructions: 

Water consumption in different time instances

Categories:
331 Views

Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists.  This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.

Instructions: 

When using this dataset, please use the following citation:

@article{doi:10.1121/10.0001567,
author = {Roberts,Timothy and Paliwal,Kuldip K. },
title = {A time-scale modification dataset with subjective quality labels},
journal = {The Journal of the Acoustical Society of America},
volume = {148},
number = {1},
pages = {201-210},
year = {2020},
doi = {10.1121/10.0001567},
URL = {https://doi.org/10.1121/10.0001567},
eprint = {https://doi.org/10.1121/10.0001567}
}

 

Audio files are named using the following structure: SourceName_TSMmethod_TSMratio_per.wav and split into multiple zip files.For 'TSMmethod', PV is the Phase Vocoder algorithm, PV_IPL is the Identity Phase Locking Phase Vocoder algorithm, WSOLA is the Waveform Similarity Overlap-Add algorithm, FESOLA is the Fuzzy Epoch Synchronous Overlap-Add algorithm, HPTSM is the Harmonic-Percussive Separation Time-Scale Modification algorithm and uTVS is the Mel-Scale Sub-Band Modelling Filterbank algorithm. Elastique is the z-Plane Elastique algorithm, NMF is the Non-Negative Matrix Factorization algorithm and FuzzyPV is the Phase Vocoder algorithm using Fuzzy Classification of Spectral Bins.TSM ratios range from 33% to 192% for training files, 20% to 200% for testing files and 22% to 220% for evaluation files.

  • Train: Contains 5280 processed files for training neural networks
  • Test: Contains 240 processed files for testing neural networks
  • Ref_Train: Contains the 88 reference files for the processed training files
  • Ref_Test: Contains the 20 reference files for the processed testing files
  • Eval: Contains 6000 processed files for evaluating TSM methods.  The 20 reference test files were processed at 20 time-scales using the following methods:
    • Phase Vocoder (PV)
    • Identity Phase-Locking Phase Vocoder (IPL)
    • Scaled Phase-Locking Phase Vocoder (SPL)
    • Phavorit IPL and SPL
    • Phase Vocoder with Fuzzy Classification of Spectral Bins (FuzzyPV)
    • Waveform Similarity Overlap-Add (WSOLA)
    • Epoch Synchronous Overlap-Add (ESOLA)
    • Fuzzy Epoch Synchronous Overlap-Add (FESOLA)
    • Driedger's Identity Phase-Locking Phase Vocoder (DrIPL)
    • Harmonic Percussive Separation Time-Scale Modification (HPTSM)
    • uTVS used in Subjective testing (uTVS_Subj)
    • updated uTVS (uTVS)
    • Non-Negative Matrix Factorization Time-Scale Modification (NMFTSM)
    • Elastique.

 

TSM_MOS_Scores.mat is a version 7 MATLAB save file and contains a struct called data that has the following fields:

  • test_loc: Legacy folder location of the test file.
  • test_name: Name of the test file.
  • ref_loc: Legacy folder location of reference file.
  • ref_name: Name of the reference file.
  • method: The method used for processing the file.
  • TSM: The time-scale ratio (in percent) used for processing the file. 100(%) is unity processing. 50(%) is half speed, 200(%) is double speed.
  • MeanOS: Normalized Mean Opinion Score.
  • MedianOS: Normalized Median Opinion Score.
  • std: Standard Deviation of MeanOS.
  • MeanOS_RAW: Mean Opinion Score before normalization.
  • MedianOS_RAW: Median Opinion Scores before normalization.
  • std_RAW: Standard Deviation of MeanOS before normalization.

 

TSM_MOS_Scores.csv is a csv containing the same fields as columns.

Source Code and method implementations are available at www.github.com/zygurt/TSM

Please Note: Labels for the files will be uploaded after paper publication.

Categories:
471 Views

Five well-known Border Gateway Anomalies (BGP) anomalies:
WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, occurred in May 2017, May 2005, January 2003, September 2001, and July 2001, respectively.
The Reseaux IP Europeens (RIPE) BGP update messages are publicly available from the Network Coordination Centre (NCC) and contain:
WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, and regular data: https://www.ripe.net/analyse/.

Instructions: 

Raw data from the "route collector rrc 04" are organized in folders labeled by the year and month of the collection date.
Complete datasets for WannaCrypt, Moscow blackout, Slammer, Nimda, and Code Red I are available from the RIPE route collector rrc 04 site:
RIPE NCC: https://www.ripe.net
Analyze: https://www.ripe.net/analyse
Internet Measurements: https://www.ripe.net/analyse/internet-measurements
Routing Information Service (RIS): https://www.ripe.net/analyse/internet-measurements/routing-information-s...
RIS Raw Data: https://www.ripe.net/analyse/internet-measurements/routing-information-s...
rrc04.ripe.net: data.ris.ripe.net/rrc04/
The date of last modification and the size of the datasets are also included.

BGP update messages are originally collected in multi-threaded routing toolkit (MRT) format.
"Zebra-dump-parser" written in Perl is used to extract to ASCII the BGP updated messages.
The 37 BGP features were extracted using a C# tool to generate uploaded datasets (csv files).
Labels have been added based on the periods when data were collected.

Categories:
564 Views

Since there is no image-based personality dataset, we used the ChaLearn dataset for creating a new dataset that met the characteristics we required for this work, i.e., selfie images where only one person appears and his face is visible, labeled with the person's apparent personality in the photo.

Instructions: 

Portrait Personality dataset of selfies based on the ChaLearn dataset First Impressions. This dataset consists of 30,935 selfies labeled with apparent personality. Each selfie file was named with the prefix of the original video followed by the frame's number. "bigfive_labels.csv" contains the labels for each trait of the Big Five model, using the prefix (name of the original video). Video frames and models are available at https://github.com/miguelmore/personality.

Categories:
1451 Views

These datasets are used to detect Intrusions in Controller Area Network (CAN) bus. Intrusions are detected using various Machine Learning and Deep Learning algorithms.

.

Categories:
1044 Views

The data made available are the simulations of a time-resolved Monte Carlo model for use in quantitative as well as qualitative analysis of different types of particle atmospheres.

Instructions: 

1. Set the geometry

2. Define the atmosphere

   2.1 Define the scattering profile of each type of particle in the atmosphere.

   2.2 Define the relative amount of each type of particle.

   2.2 Define the mean free path.

3. Define other test variables

   3.1 Temperature

   3.2 Refraction index (complex or real)

4. Run the simulations

 

5. With the data obtained, perform data analysis.

Categories:
207 Views

These CSV files contain the wearable sensor data (RIP and IMU )collected from forty subjects during multiple cigarette smoking sessions.

Categories:
314 Views

Pages