This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.

Instructions: 

fGn series used for simulations in the article "Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series".  Available at:

https://arxiv.org/abs/2103.02091.

https://www.techrxiv.org/articles/preprint/Preliminaries_on_the_Accurate....

https://easychair.org/publications/preprint/RQsp.

https://osf.io/3sk7a/.

They should be used in Selfis01b.

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Disclaimer 
DARPA is releasing these files in the public domain to stimulate further research. Their release implies no obligation or desire to support additional work in this space. The data is released as-is. DARPA makes no warranties as to the correctness, accuracy, or usefulness of the released data. In fact, since the data was produced by research prototypes, it is practically guaranteed to be imperfect.
Instructions: 

The data containing red team activities is divided into three sets, each corresponding to the three days of evaluation: 23Sep19, 24Sep19, and 25Sep19. The fourth set (23Sep19-night) contains no threats and contains data from the first night of evaluations, when clients were left running unattended overnight to collect additional baseline data.

During the initial one thousand client test, each mainframe server hosted fifty Windows clients. Half of the clients were taken down from each server for data collection, reducing the number of clients to five hundred, which resulted in a client machine naming continuity gap (e.g. Sys001-Sys025, Sys051-Sys075, …, Sys951-Sys975).

A full description of the contents, including message formats and file structure can be found in the OpTC-data-release.md file attached to this page and included in the root directory of the OpTC.tar.gz.

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SDU-Haier-ND (Shandong University-Haier-Noise Detection) is a sound dataset jointly constructed by Shandong University and Haier, which contains the operating sound of the internal air conditioner collected during the product quality inspection. We collected and marked a batch of quality inspection sounds of air conditioners in real production environments to form this data set, including normal sound samples and abnormal sound samples.

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Instructions: 

This dataset is used for i) analyzing the influence of process information on monitoring signals through signal processing methods; ii) training and testing models of tool monitoring and tool wear prediction especially for cutting conditions with large variations including cutting parameters, material and geometry of cutting tools, and workpiece materials, and also cutting conditions with continuous changes. This data set includes monitoring signals collected from machining process of sidewalls and closed pockets. The sidewall machining belongs to the cutting process with fixed cutting conditions; the closed pocket machining belongs to the cutting process of continuously varying cutting conditions for the reason that the tool path of closed pocket includes line, arc, full cutting and non-full cutting. Although cutting parameters are given fixed in the arc tool path area, the actual cutting parameters (such as feed, cutting width) are constantly changing due to the change of cutting geometry.

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There are hundreds of systems that create aircrafts. During design phases the validation and verification of these systems are done by using the data acquired by the flight test instrumentation system (FTI). Even though, there is no problem with the aircraft systems, if the measurement system is not capable of measuring it well, it results waste of efforts on unnecessary troubleshooting studies. Therefore, when designing an instrumentation system for flight testing purposes, sufficiency of the measurement system has to be proved before installation on the aircraft.

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Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)

 

"Object classification from randomized EEG trials"

 

If you use this code or data, please cite the above paper.

Instructions: 

See the paper "Object classification from randomized EEG trials" on IEEE Xplore.

 

Code for analyzing the dataset is included in the online supplementary materials for the paper.

 

The code from the online supplementary materials is also included here.

 

If you use this code or data, please cite the above paper.

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Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots.

Instructions: 

Extract locally the zip files, read the readme file.

Instructions for dataset usage are included in the open access paper: Franzoni, V., Biondi, G., Milani, A., Emotional sounds of crowds: spectrogram-based analysis using deep learning (2020) Multimedia Tools and Applications, 79 (47-48), pp. 36063-36075. https://doi.org/10.1007/s11042-020-09428-x

File are released under Creative Commons Attribution-ShareAlike 4.0 International License

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221 Views

This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model

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This dataset contains a created ST annotations for all recordings of Abdominal and Direct Fetal ECG Database (ADFECGDB).

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In this dataset we provide the source code we used for the evaluation of the involution delay model. Analog simulations (SPICE) serve as golden reference for the digital predictions. For comparison also the commonly used inertial delay is included.

Due to non-disclosure agreements only the source files are included. The tools and the respective libraries have to be provided by the user.

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