The data sets correlates with online banana purchase business process, where there are 12 activities in the process. 

business-related data (can both be referenced or modified by business processes) and context data i.e., temperature (can only be referenced by business processes, for example, data from sensors) can be found in these data sets.

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Data validation of the article "A framework for assessing, comparing andpredicting the performance of autonomousRFID-based inventory robots for retail"

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The Cross-sectional Diabetes Risk survey aims to assess the prevalence of diabetes and its risk factors at the same point in time and also provide a "snapshot" of diseases and risk factors simultaneously for individuals belonging to the western region of the Kingdom of Saudi Arabia (KSA).

 

Instructions: 

The survey is available at the following URL:

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https://www.munnamotorgarage.com/fcitrabet/saudi_diabetise_survey_2019-2....

 

The instructions for the use of the dataset and analysis tools are available in our submitted manuscript

(Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-sectional Study) on IEEE access for review.

 

 

 

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A total of 10,003 images of product design submissions of Hardware Product Design Competitionwere collected to form the product design image database, which has 835 samples of awarded class (high aesthetic level), 4,990 samples of qualified class (middle aesthetic level) and 4,178 samples of eliminated class (low aesthetic level). The Product Design Aesthetic Database includes dataset of image features extracted by the optimal method VGG-19.

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Dataset to accompany the manuscript "Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics".

Instructions: 

Dataset to accompany the manuscript "Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics".
(Version 2)

Instructions:
The files contained in this dataset measure the following metrics:

Cache miss ratio: this metric, computed by every requester, is calculated as the ratio between the number of non-satisfied content requests (i.e., interests sent via \ac{D2D} links) and the total number of interest messages sent. The metric is recorded in the following files: CacheMissRatio_Low.csv (low density), CacheMissRatio_Medium.csv (medium density), CacheMissRatio_High.csv (high density).

Content hop count: this metric is computed by every requester and measures the number of hops to reach the content via \ac{D2D} communication links. The metric is recorded in the following files: CacheHitHops_Low.csv (low density), CacheHitHops_Medium.csv (medium density), CacheHitHops_High.csv (high density).

Sent control messages: this metric is computed by every vehicle by counting the total number of sent interest and acknowledgment messages. The metric is recorded in the following files: SentCtrlMsgs_Low.csv (low density), SentCtrlMsgs_Medium.csv (medium density), SentCtrlMsgs_High.csv (high density).

Content downloads: this metric is computed by the centralized controller and counts the number of content downloads from the backend service via RAN for all vehicles during one simulation run. The metric is recorded in the following files: ContentDownloads_Low.csv (low density), ContentDownloads_Medium.csv (medium density), ContentDownloads_High.csv (high density).

Channel busy ratio: this metric is computed by every vehicle and measures the ratio of time the channel is sensed busy over the total active simulation time. The metric is recorded in the following files: CBR_Low.csv (low density), CBR_Medium.csv (medium density), CBR_High.csv (high density).

Every file in the dataset has the following columns: <"Strategy","Clustering","reqProb","Value","sd","se","ci">

- "Strategy" contains the employed seeding algorithm
- "Clustering" contains the employed community detection algorithm
- "reqProb" contains the request probability p
- "Value" contains the mean value of the actual metric that is being measured, which is defined in the file name
- "sd","se","ci" are the standard deviation, standard error of the mean, and confidence interval (95%)

Changes with respect to the previous version:

- We repeated every simulation run using 100 different seeds, instead of 50 as in the previous version of the dataset
- We deleted the "Number of clusters" metric, as it is not being used in the manuscript

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Data set about the thermal response of silica spherical micro-resonators. Contains numerical calculations for different optical properties of the cavity and two different resonator sizes.

 

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The following data set is modelled after the implementers’ test data in 3GPP TS 33.501 “Security architecture and procedures for 5G System” with the same terminology. The data set corresponds to SUCI (Subscription Concealed Identifier) computation in the 5G UE (User Equipment) for IMSI (International Mobile Subscriber Identity) based SUPI (Subscription Permanent Identifier) and ECIES Profile A.

Instructions: 

The following data set is modelled after the implementers’ test data in 3GPP TS 33.501 “Security architecture and procedures for 5G System” with the same terminology. The data set corresponds to SUCI (Subscription Concealed Identifier) computation in the 5G UE (User Equipment) for IMSI (International Mobile Subscriber Identity) based SUPI (Subscription Permanent Identifier) and ECIES Profile A, the IMSI consists of MCC|MNC: '274012'. 

In the 5G system, the globally unique 5G subscription permanent identifier is called SUPI as defined in 3GPP TS 23.501. For privacy reasons, the SUPI from the 5G devices should not be transferred in clear text, and is instead concealed inside the privacy preserving SUCI. Consequently, the SUPI is privacy protected over-the-air of the 5G radio network by using the SUCI. For SUCIs containing IMSI based SUPI, the UE in essence conceals the MSIN (Mobile Subscriber Identification Number) part of the IMSI. On the 5G operator-side, the SIDF (Subscription Identifier De-concealing Function) of the UDM (Unified Data Management) is responsible for de-concealment of the SUCI and resolves the SUPI from the SUCI based on the protection scheme used to generate the SUCI. 

The SUCI protection scheme used in this data set is ECIES Profile A. The size of the scheme-output is a total of 256-bit public key, 64-bit MAC & 40-bit encrypted MSIN. The SUCI scheme-input MSIN is coded as hexadecimal digits using packed BCD coding where the order of digits within an octet is same as the order of MSIN. As the MSINs are odd number of digits, bits 5 to 8 of final octet is coded as ‘1111’.  

# Example Python code to load data into Spark DataFrame

df = spark.read.format("csv").option("inferSchema","true").option("header","true").option("sep",",").load(“5g_suci_using_ecies_profile_a_100k.gz”)

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This dataset is addressed to build time-aware music recommender systems when evolution of user preferences is considered. It was built by processing the data collected by Oscar Celma (https://www.upf.edu/web/mtg/lastfm360k) from last.fm. It consists of more than 80,000 songs listened to by 50 users over a 2-year period, creating a collection of more than 420,000 timestamped plays.

 

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To improve reproductivity of our papar, we would upload experimental data and resources of evaluations.

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Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.

Instructions: 

    

Copyright (C) 2018 Intel Corporation

SPDX-License-Identifier: BSD-3-Clause

 

#########################

Welcome to the Intel WiFi RTT (FTM) 40MHz dataset.

 

The paper and the dataset can be downloaded from:

https://www.researchgate.net/publication/329887019_A_Machine_Learning_Ap...

 

To cite the dataset and code, or for further details, please use:

Nir Dvorecki, Ofer Bar-Shalom, Leor Banin, and Yuval Amizur, "A Machine Learning Approach for Wi-Fi RTT Ranging," ION Technical Meeting ITM/PTTI 2019

 

For questions/comments contact: 

nir.dvorecki@intel.com,

ofer.bar-shalom@intel.com

leor.banin@intel.com

yuval.amizur@intel.com

 

The zip file contains the following files:

1) This README.txt file.

2) LICENSE.txt file.

3) RTT_data.csv - the dataset of FTM transactions

4) Helper Matlab files:

O mainFtmDatasetExample.m - main function to run in order to execute the Matlab example.

O PlotFTMchannel.m - plots the channels of a single FTM transaction.

O PlotFTMpositions.m - plots user and Access Point (AP) positions.

O ReadFtmMeasFile.m - reads the RTT_data.csv file to numeric Matlab matrix.

O SimpleFTMrangeEstimation.m - execute a simple range estimation on the entire dataset.

O Office1_40MHz_VenueFile.mat - contains a map of the office from which the dataset was gathered.

 

#########################

Running the Matlab example:

 

In order to run the Matlab simulation, extract the contents of the zip file and call the mainFtmDatasetExample() function from Matlab.

 

#########################

Contents of the dataset:

 

The RTT_data.csv file contains a header row, followed by 29581 rows of FTM transactions.

The first column of the header row includes an extra "%" in the begining, so that the entire csv file can be easily loaded to Matlab using the command: load('RTT_data.csv')

Indexing the csv columns from 1 (leftmost column) to 467 (rightmost column):

O column 1 - Timestamp of each measurement (sec)

O columns 2 to 4 - Ground truth (GT) position of the client at the time the measurement was taken (meters, in local frame)

O column 5 - Range, as estimated by the devices in real time (meters)

O columns 6 to 8 - Access Point (AP) position (meters, in local frame)

O column 9 - AP index/number, according the convention of the ION ITM 2019 paper

O column 10 - Ground truth range between the AP and client (meters)

O column 11 - Time of Departure (ToD) factor in meters, such that: TrueRange = (ToA_client + ToA_AP)*3e8/2 + ToD_factor (eq. 7 in the ION ITM paper, with "ToA" being tau_0 and the "ToD_factor" lumps up both nu initiator and nu responder)

O columns 12 to 467 - Complex channel estimates. Each channel contains 114 complex numbers denoting the frequency response of the channel at each WiFi tone:

O columns 12 to 125  - Complex channel estimates for first antenna from the client device

O columns 126 to 239 - Complex channel estimates for second antenna from the client device

O columns 240 to 353 - Complex channel estimates for first antenna from the AP device

O columns 354 to 467 - Complex channel estimates for second antenna from the AP device

The tone frequencies are given by: 312.5E3*[-58:-2, 2:58] Hz (e.g. column 12 of the csv contains the channel response at frequency fc-18.125MHz, where fc is the carrier wave frequency).

Note that the 3 tones around the baseband DC (i.e. around the frequency of the carrier wave), as well as the guard tones, are not included.

 

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