Machine Learning
Supplementary materials of the paper titled
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We obtained 6 million instances to be used as an analysis for modelling CO2 behavior. The Data Logging and sensors nodes acquisition are every 1 second.
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Pedestrian detection has never been an easy task for computer vision and automotive industry. Systems like the advanced driver assistance system (ADAS) highly rely on far infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what is the performance in adverse weather conditions.
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The data set includes three sub-data sets, namely the DAGM2007 data set, the ground crack data set, and the Yibao bottle cap defect data set, which are divided into a training set and a test set, in which the positive and negative samples are unbalanced.
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Computer vision can be used for environment-adaptive control of robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the development and dissemination of image classification algorithms (e.g., convolutional neural networks) to recognize the walking environment. To address these limitations, we developed ExoNet, a large-scale hierarchical dataset of wearable camera images (i.e., egocentric perception) of real-world walking environments.
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A Chinese dataset for table-to-text generation named WIKIBIOCN which inculeds 33,244 biography sentences with related tables from Chinese Wikipedia (July 2018).
The dataset is divided into training set (30,000), verification set (1000) and test set (2,244).
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Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists. This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.
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