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Machine Learning

Slow moving motions are mostly tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches based on machine and deep learning. Nevertheless, to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow ground motion detections. With this dataset, we propose a new InSAR dataset  for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies.

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Different faults are experienced by a power system, particulary in transmission lines. In this dataset, the IEEE 5-Bus Model was used to different types of transmission line faults.

Indication of the label of the faults come from the time that the fault has been induced in the simulation.

This dataset aims to be utilized for machine learning algorithms, particularly in multi-class classification of the transmission line fault. In this simulation, each fault was induced at each transmission line one instance at a time during a certain period.

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The requirements, their types and priorities are gathered from 43 project teams which will be uselful to automate the phases of requirement engineering i.e. requirements classification and prioritisation. As the publicly available datasets do not contain the complete information (type and priority) about requirements, the dataset is created by collecting the data from 43 BTech project groups. This dataset includes 11 different types of software requirements. The dependency of requirements is also considered while gathering requirements from the project teams.

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We introduce an English Twitter dataset designed for the detection of online drug use, comprising 112,057 tweets accompanied by metadata. This dataset underwent manual annotation by a team of expert annotators consisting of around 30 members, these annotators, possessing diverse multidisciplinary backgrounds and expertise, committed over six months to meticulously label each tweet.

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This dataset contains video-clips of five volunteers developing daily life activities. Each video-clip is recorded with a Far InfraRed (FIR) camera and includes an associated file which contains the three-dimensional and two-dimensional coordinates of the main body joints in each frame of the clip. This way, it is possible to train human pose estimation networks using FIR imagery.

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