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This dataset was collected from an industrial control system running the Modbus protocol. It is used to train a deep adversarial learning model. This model is used to generate fuzzing data in the same format as the real one. The data is a sequence of hexadecimal numbers. The followed generated data is produced by the already trained model.
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Co-cultures are a traditional method for studying the cellular properties of cell to cell interactions among different cell types. How network properties in these multicellular synthetic systems vary from monocultures are of particular interest. Understanding the changes in the functional output of these in vitro spiking neural networks can provide new insights into in vivo systems and how to develop biological system models that better reflect physiological conditions - something of paramount importance to the progress of synthetic biology.
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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).
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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).
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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.
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This dataset is for validate and evaluate English-Bangla machine translation systems.
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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.
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