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Once the three courses for the three learning scenarios - C# OOP programming, Sphero Edu visual programming and VEDILS authoring tool - were taught, the three student groups were asked to indicate using a scale between one and four - to avoid the selection of neutral options - their perception of the clarity and the interest of the exposition (CL and IT indicators), as well as the time spent studying the course contents (ST indicator).

140 views
  • Other
  • Last Updated On: 
    Tue, 11/20/2018 - 14:40

    For a detailed describtion of this dataset see accompanying publication "Stand-alone Heartbeat Detection in Multidimensional Mechanocardiograms" by Kaisti M., et al. IEEE Sensors 2018, 10.1109/JSEN.2018.2874706. This datasets consists of 29 mechanocardiogram recordings with ECG reference from healthy subjects in supine position. All data were recorded with sensors attached to the sternum using double-sided tape. Mechanocardigrams incude 3-axis accelorometer signals (seismocardiograms) and 3-axis gyroscope signals (gyrocardiograms).

    459 views
  • Health
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    The data set contains 360 instances of images of transparent PET preforms with a constant actual color. The measured color is varring because of illumination conditions. The measured color variation factor is described as color value correction coeficient.

    48 views
  • Sensors
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    Wearable Inertial Measurement Units (IMU) measuring acceleration, earth magnetic field and gyroscopic measurements can be considered for capturing human skeletal postures in real time. This dataset contains IMU readings (accelerometer, magnetometer and gyroscope) for common shoulder exercises: extension- flexion and abduction-adduction and simultaneously measures VICON readings and Kinect readings.

    1055 views
  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    The data format is described as follows:

    Event: {‘acc’: array([[x_axis], [y_axis], [z_axis], ‘gyr’,array([x_axis], [y_axis], [z_axis], ‘label’: No ]

    No =1 means acceleration.

    No =2 means normal driving.

    No =3 means collision.

    No =4 means left turn.

    No =5 means right turn.

     

    1222 views
  • Sensors
  • Last Updated On: 
    Mon, 04/01/2019 - 09:40

    This dataset is for validate and evaluate English-Bangla machine translation systems.

    1046 views
  • Computational Intelligence
  • Last Updated On: 
    Thu, 08/02/2018 - 10:06

    This dataset contains 70,861 English-Bangla sentence pairs and more than 0.8 million tokens in each side.

    1547 views
  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    This paper describes a set of 300 pseudo-random task graphs which can be used for evaluating Mobile Cloud, Fog and Edge computing systems. The pseudo-random task graphs are based upon graphs that have previously appeared in IEEE papers. The graphs are described in Matlab code, which is easy to read, edit and execute. Each task has an amount of computational work to perform, expressed in Mega-cycles per second. Each edge has an amount of data to transfer between tasks, expressed in Kilobits or Kilobytes of data.

    266 views
  • Communications
  • Last Updated On: 
    Mon, 08/06/2018 - 16:22

    For the development and evaluation of organ localization methods, we build a set of annotations of organ bounding boxes based on the MICCAI Liver Tumor Segmentation (LiTS) challenge dataset. Bounding boxes of 11 body organs are included:  heart (53/28), left lung (52/21), right lung (52/21), liver (131/70), spleen (131/70), pancreas (131/70), left kidney (129/70), right kidney (131/69), bladder (109/67), left femoral head (109/66) and right femoral head (105/66). The number in the parentheses indicates the number of the organs annotated in training and testing sets.

    1467 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Mon, 04/15/2019 - 03:09

    This dataset includes  the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014. There are 4 columns in the file, which represent viewer ID, the current channel number, the next channel number, the date of the month, respectively. The first column, the ID code of a viewer, ranks in descent with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior. 

     

     

     

     

    291 views
  • Communications
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

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