Machine Learning
Training data of Human Motion Recognition via Wearable plastic Fiber Sensing System
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The uncompressed dataset consists in a folder which contains:
- A XML file per author (Twitter user) with 200 tweets. The name of the XML file correspond to the unique author id.
- A truth.txt file with the list of authors and the ground truth.
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This dataset was created by following these steps. First, online reviews of HMD VR devices are collected and refined. Second, variables are deduced from previous studies, and then appropriate keyword candidates for the deduced variables are selected. Topic modeling is conducted to examine whether the deduced variables sufficiently represent all the reviews, and other variables are added if necessary.
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Mizo or Lushai language is the official language of Mizoram, a state in the north-eastern part of India. It is an under-resourced language that falls under the Tibeto-Burman language family and is highly tonal in nature.
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Mizo or Lushai language is the official language of Mizoram, a state in the north-eastern part of India. It is an under-resourced language that falls under the Tibeto-Burman language family and is highly tonal in nature.
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Abstract—A radar signal multiparameter-based deinterleaving method is proposed in this work. Semantic information formed by the coupling of the pulse repetition interval (PRI),
radio frequency (RF), pulse width (PW), and pulse amplitude (PA) of a radar signal is used to deinterleave radar signals. A bidirectional gated recurrent unit (BGRU) is employed, and
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This dataset is created by an experimental setup of a DC-PV -Battery-based grid-connected distributed generation system. This dataset is split into four parts such as irradiance, and temperature, which were measured by a meteorological station, and lastly, PV output current and voltage acquired by an inverter. Furthermore, we can have a chance to obtain the output PV power by multiplying current and voltage. The dataset has 288 elements for one day as a time series since the station obtains the data within five minutes. However, the whole dataset has three days of data with 864 elements.
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AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.
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