Artificial Intelligence

We test the consumed time of the three steps of the exchange model in order to show that our scheme is feasible.

 

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The training, validation, and test set used for Deep Xi (https://github.com/anicolson/DeepXi). 

 Training set:

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This is the noisy-speech test set used in the original Deep Xi paper: https://doi.org/10.1016/j.specom.2019.06.002. The clean speech and noise used to create the noisy-speech set are also available.

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Noisy-speech set used to test Deep Xi (https://github.com/anicolson/DeepXi). The clean speech and noise used to create the noisy-speech set are also available. The clean-speech recordings are from Librispeech test-clean (http://www.openslr.org/12/).

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The data files contains all the thermal images and error data of the spindle in the experiment.

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About

Dataset described in: 

Daudt, R.C., Le Saux, B., Boulch, A. and Gousseau, Y., 2019. Multitask learning for large-scale semantic change detection. Computer Vision and Image Understanding, 187, p.102783.

 

This dataset contains 291 coregistered image pairs of RGB aerial images from IGS's BD ORTHO database. Pixel-level change and land cover annotations are provided, generated by rasterizing Urban Atlas 2006, Urban Atlas 2012, and Urban Atlas Change 2006-2012 maps. 

 

The dataset is split into five parts:

    - 2006 images 

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10665 Views

This dataset is a collection of brainwave EEG signals from eight subjects. The data is collected in a lab controlled environment under a specific visualization experiment. 

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3554 Views

Master data has played a significant role in improving operational efficiencies and has attracted the attention of many large businesses over the decade. Recent professional searches have also proved a significant growth in the practice and research of managing these master data assets.

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The original dataset SECOM is obtained from the the UC Irvine Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/secom). Then, eachsample is transformed to an image, with each pixel representing a feature. Therefore, image processing mechanisms such as convolutionary neural networks can be utilized for classification.

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