*.csv
This dataset contains the data of three different positions of persons. The main focus of this dataset is on three positions those are Sit, Stand and Sleep. This dataset is collected by using a 3-axis accelerometer sensor value using the Inertial Measurement Unit (IMU) (MPU-9250) Sensor. We collected this data by positioning this instrument on the arm of the person.
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262 Views
This dataset is gathered by using Inertial Measurement Unit Sensor (IMU) (MPU-9250) positioned on the seat of vehicle (Van). This dataset represents the real time sensory data collected with the help of vehicle i.e. School Van on a road at different places in Punjab. The objective of this dataset is to provide an accurate data for plain road and a road with pits.
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471 Views
This dataset curbs real time sensory data collected through different vehicles such as Cycle, Car, Bike and Bus on the humpty-dumpty road. This dataset is collected by using Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the seats of vehicle. Through some vehicles (Cycle and Bike) are not having place to keep sensor, but it was designed to handle all the hurdles of road having potholes. The dataset aims to tell the exact accuracy of pothole and plane road. This dataset can be used in future for government to allocate budget to repair the rough road.
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762 Views
This dataset contains job and their skills extracted from the job adverisments.
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2406 Views
Experiments about satisfaction from ridesharing, from mturk
The first two experiemtns asked which explanations are likely to increase user satisfaction
The third experiment ask for satisfaction (1-7) given a scenario and some explanations. It's divided to three:
- pbe: explanations are all known info
- random: explanatiitons are random subset of knwon info
- axis: smart choosing of subset of the known info
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121 Views
The Baseline set described in the IEEE article (https://ieeexplore.ieee.org/document/10077565) as Baseline_set contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects; the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.
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1036 Views
This dataset is used to illustrate an application of the "klm-based profiling and preventing security attack (klm-PPSA)" system. The klm-PPSA system is developed to profile, detect, and then prevent known and/or unknown security attacks before a user access a cloud. This dataset was created based on “a.patrik” user logical attempts scenarios when accessing his cloud resources and/or services. You will find attached the CSV file associated with the resulted dataset. The dataset contains 460 records of 13 attributes (independent and dependent variables).
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358 Views
Deployment of serverless functions depends on various factors. This dataset presents deployment time of a Python serverless function with various deployment package size, deployed on 6 regions of AWS and 6 regions of IBM. Deployment scripts are executed from Innsbruck, Austria.
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380 Views
Effects of the "spawn start" of the Monte Carlo serverless function that simulates Pi.
The functions are orchestrated as a workflow and executed with the xAFCL enactment engine (https://doi.org/10.1109/TSC.2021.3128137) on three regions (US, EU, Asia) of three cloud providers AWS Lambda, Google Cloud Functions, and IBM Cloud Functions.
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132 Views
This is a dataset that contains 50,000 transactions and 13 features/columns, the data set is used to perform market basket analysis in association rule mining.
A random function was used during the generation and the function generates random numbers of 0's and 1's for all 13 features of each transaction.
The probability of generating a 1 is twice as high as 0, this way there will be a strong or almost-strong association between the features.
1 means an item was purchased by the user and 0 means the item was not purchased.
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2651 Views