Artificial Intelligence
Dataset for the meta-heuristics scheduling algorithm
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A medium-scale synthetic 4D Light Field video dataset for depth (disparity) estimation. From the open-source movie Sintel. The dataset consists of 24 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20–50 frames, and has ground-truth disparity values, so that can be used for training deep learning-based methods. Each scene was rendered with a clean pass after modifying the production file of Sintel with reference to the MPI Sintel dataset.
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This dataset is composed of side channel information (e.g., temperatures, voltages, utilization rates) from computing systems executing benign and malicious code. The intent of the dataset is to allow aritificial intelligence tools to be applied to malware detection using side channel information.
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The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored.
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This dataset contains 1944 data, which are scanned by the HIS-RING PACT system.
the data sampling rate of our system is 40 MSa/s, a 128-elements 2.5MHz full-view ring-shaped transducer with 30mm radius.
continuous updating.....
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Computer vision in animal monitoring has become a research application in stable or confined conditions.
Detecting animals from the top view is challenging due to barn conditions.
In this dataset called ICV-TxLamb, images are proposed for the monitoring of lamb inside a barn.
This set of data is made up of two categories, the first is lamb (classifies the only lamb), the second consists of four states of the posture of lambs, these are: eating, sleeping, lying down, and normal (standing or without activity ).
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AI Ethics Global Document Collection Daniel Schiff, Jason Borenstein, Justin Biddle, & Kelly Laas Documents in the dataset were published between January 2016 through July 2019 This dataset is associated with a (forthcoming) paper in IEEE Transactions on Technology and Society, entitled "AI Ethics in the Public, Private, and NGO Sectors: A Review of a Global Document Collection.
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