This dataset was used in the article "Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR and Catharino RR (2020) Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front. Bioeng. Biotechnol. 8:6. doi: 10.3389/fbioe.2020.00006", open access available at: https://doi.org/10.3389/fbioe.2020.00006.

Instructions: 

WGMSML-Data folder contains the mass spectra input data for the Matlab scripts which are in WGMSML-MATLAB-SourceCode folder. WGMSML-ExecutionLogsAndPlots contains logs and plots generated by the execution of the Matlab code over the input data. Main scripts are enumerated in the order of execution.

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This dataset was created for research on blockchain anomaly and fraud detection. And donated to IEEE data port online community.

https://github.com/epicprojects/blockchain-anomaly-detection

 

Files: 

bitcoin_hacks_2010_2013.csv: Contains known hashes of bitcoin theft/malicious transactions from 2010-2013

malicious_tx_in.csv: Contains hashes of input transactions flowing into malicious transactions.

Instructions: 

The dataset contains transaction hashes of all bitcoin Heists, Thefts, Hacks, Scams, and Losses from 2010-2014. These datasets are constructed from the information bitcoin forum (https://bitcointalk.org/index.php?topic=576337.0) and Blockchain.com

 References:

  1. https://arxiv.org/abs/1611.03942

  2. https://arxiv.org/abs/1611.03941

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Information:

 This dataset was created for research on blockchain anomaly and fraud detection. And donated to IEEE data port online community.

Research experiments for this dataset can be found at https://github.com/epicprojects/blockchain-anomaly-detection

 

 

Instructions: 

 

*This dataset is created by parsing raw bitcoin .BLK files. Using this dataset one can create a directed acyclic graph (DAG) of bitcoin transaction network as mentioned in references.

 

DIMENSIONS:

  • tx_hash_from: Input transaction hash
  • tx_hash_to: Output transaction hash
  • datetime: Represents the date and time of the transaction
  • amount_bitcoins: The amount of bitcoins transferred.

 

 

REFERENCES:

  1. https://arxiv.org/abs/1611.03942
  2. https://arxiv.org/abs/1611.03941
  3. https://arxiv.org/abs/1107.4524
  4. http://anonymity-in-bitcoin.blogspot.com/2011/09/code-datasets-and-spsn1...
  5. http://snap.stanford.edu/class/cs224w-2013/projects2013/cs224w-030-final...

 

 

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1261 Views

Information:

This dataset was created for research on blockchain anomaly and fraud detection. And donated to IEEE data port online community. 

https://github.com/epicprojects/blockchain-anomaly-detection

 

 

 

Instructions: 

A directed-acyclic graph is created from the bitcoin transaction data and metadata is extracted to create this dataset. 

 

DIMENSIONS:

  • tx_hash: Hash of the bitcoin transaction.
  • indegree: Number of transactions that are inputs of tx_hash
  • outdegree: Number of transactions that are outputs of tx_hash.
  • in_btc: Number of bitcoins on each incoming edge to tx_hash.
  • out_btc: Number of bitcoins on each outgoing edge from tx_hash.
  • total_btc: Net number of bitcoins flowing in and out from tx_hash.
  • mean_in_btc: Average number of bitcoins flowing in for tx_hash.
  • mean_out_btc: Average number of bitcoins flowing out for tx_hash.
  • in-malicious: Will be 1 if the tx_hash is an input of a malicious transaction.
  • out-malicious: Will be 1 if the tx_hash is an output of a malicious transaction.
  • is-malicious: Will be 1 if the tx_hash is a malicious transaction.
  • out_and_tx_malicious: Will be 1 if the tx_hash is a malicious transaction or an output of a malicious transaction.
  • all_malicious: Will be 1 if the tx_hash is a malicious transaction or an output of a malicious transaction or input of a malicious transaction.

 

REFERENCES:

  1. https://arxiv.org/abs/1611.03942
  2. https://arxiv.org/abs/1611.03941
  3. https://arxiv.org/abs/1107.4524
  4. http://anonymity-in-bitcoin.blogspot.com/2011/09/code-datasets-and-spsn1...
  5. http://snap.stanford.edu/class/cs224w-2013/projects2013/cs224w-030-final...

 

 

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Normal
0

false
false
false

EN-US
X-NONE
AR-SA

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The orchid flower dataset was selected from the northern part of Thailand. The dataset contains Thai native orchid flowers, and each class contains at least 20 samples. The orchid dataset including 52 species and the visual characteristics of the flower are varying in terms of shape, color, texture, size, and the other parts of the orchid plant like a leaf, inflorescence, roots, and surroundings. All images are taken from many devices such as a digital camera, a mobile phone, and other equipment. The orchids dataset contains 3,559 images from 52 categories.

Instructions: 

Download links:

Test - https://drive.google.com/open?id=1AGYAHLJFS4qiLyNLznHDKtWZx0d4RCK1

Train - https://drive.google.com/open?id=1AHwLH3-P8eWAXgXMs-FU2Ni6b2YMO5yY

 

This dataset is only for research purposes.

 

Please remember cited correctly the paper: "Orchids Classification Using Spatial Transformer Network with Adaptive Scaling"

 

BibTeX:

 

@inproceedings{sarachai2019orchids,

  title={Orchids Classification Using Spatial Transformer Network with Adaptive Scaling},

  author={Sarachai, Watcharin and Bootkrajang, Jakramate and Chaijaruwanich, Jeerayut and Somhom, Samerkae},

  booktitle={International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2019},

  pages={1--10},

  DOI={978-3-030-33607-3_1},

  year={2019},

  organization={Springer International Publishing}

}

 

 

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819 Views

We provide a large benchmark dataset consisting of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; and 1.7 million data-points for swipes. Data was collected between April 2017 and June 2017 after the required IRB approval. Data from 117 participants, in a session lasting between 2 to 2.5 hours each, performing multiple activities such as: typing (free and fixed text), gait (walking, upstairs and downstairs) and swiping activities while using desktop, phone and tablet is shared.

Instructions: 

Detailed description of all data files is provided in the *BBMAS_README.pdf* file along with the dataset. 

 

 

Please cite:

[1] Amith K. Belman and Vir V. Phoha. 2020. Discriminative Power of Typing Features on Desktops, Tablets, and Phones for User Identification. ACM Trans. Priv. Secur. Volume 23,Issue 1, Article 4 (February 2020), 36 pages. DOI:https://doi.org/10.1145/3377404

[2]Amith K. Belman, Li Wang, S. S. Iyengar, Pawel Sniatala, Robert Wright, Robert Dora, Jacob Baldwin, Zhanpeng Jin and Vir V. Phoha, "Insights from BB-MAS -- A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone", arXiv:1912.02736 , 2019. 

[3] Amith K. Belman, Li Wang, Sundaraja S. Iyengar, Pawel Sniatala, Robert Wright, Robert Dora, Jacob Baldwin, Zhanpeng Jin, Vir V. Phoha, "SU-AIS BB-MAS (Syracuse University and Assured Information Security - Behavioral Biometrics Multi-device and multi-Activity data from Same users) Dataset ", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/rpaz-0h66

 

 

 

 

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6496 Views

Overview

Instructions: 
  • Drift types (A): gradual, incremental, recurring and sudden
  • Drift perspectives (B): time and trace
  • Noise percentage (C): 0, 5, 10, 15, 20
  • Number of cases in the stream (D): 100, 500, 1000
  • Change patterns (E): baseline, cb, cd, cf, cp, IOR, IRO, lp, OIR, pl, pm, re, RIO, ROI, rp, sw

 

The file name follows the pattern [A]_[B]_noise[C]_[D]_[E]

An identical version of this dataset in the MXML format is available at: http://www.uel.br/grupo-pesquisa/remid/?page_id=145

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438 Views

Hepatitis B virus (HBV) causes chronic infection in 250 million people worldwide. Chronic HBV carriers have a high risk of fibrosis, cirrhosis, and hepatocellular carcinoma. Drug resistance often develops during long-term antiviral treatment using HBV polymerase/reverse transcriptase inhibitors, making anti-HBV therapies as well as drug development more complicated. In this paper, a machine learning method based on large scale of gene expression profiles was used for anti-HBV drug screening.

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Traditional Static Timing Analysis (STA) assumes only single input switches at a time with the side input held at non-controlling value. This introduces unnecessary pessimism or optimism which may cause degradation of performance or chip failure.  Modeling Multi-Input Switching (MIS) requires a good amount of simulations hence we provide a dataset comprising of SPICE simulations done on 2 input NAND and NOR gate.

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