Artificial Intelligence
This dataset is a private foot pressure image dataset containing 317 images of high arches (H), 217 images of flat feet (L) and 362 images of normal feet (N).
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- There are six folders corresponding to 6 types of BPPV disorders.
- Each folder has one sample.
Each class is specified by the typical movement of the eye.
+) Lt_Geo_BPPV: eye beats toward the ground, beats stronger to the left side (turn head left).
+) Rt_Geo_BPPV: eye beats toward the ground, beats stronger to the right side (turn head right).
+) Lt_Apo_BPPV: eye beats toward the sky, beats stronger to the left side (turn head right).
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Multi-label event classification label of each sample-document is done with nine bits. The first bit signifies whether an event is present or absent with 1 or 0 respectively. The remaining eight bits signifies presence or absence of (i) covid, (ii) flood, (iii) storm, (iv) heavy rain, (v) cloudburst, (vi) landslide, (vii) earthquake, (viii) Tsunami with 1 or 0. The location and the impact sentence classification labeling are similar.
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A synthetic data for low power (P ≤10 mW) InGaAsP MQW-DFB lasers operating at a wavelength (λ) ranging from 1.53 to 1.57 µm at a case temperature laying between -40 ℃ to 85 ℃ with side mode suppression ratio of more than 35 dB is generated and can be used for laser lifetime prediction using machine learning based approaches.
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The dataset includes processed sequences of optical time domain reflectometry (OTDR) traces incorporating different types of fiber faults namely fiber cut, fiber eavesdropping (fiber tapping), dirty connector and bad splice. The dataset can be used for developping ML-based approaches for optical fiber fault detection, localization, idenification, and characterization.
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Filtered NGSIM and Artificial Datasets used in the paper "A Compositional Paradigm for Read-time systems".
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Data Description:
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This benchmark dataset accompanies an article paper titled ``Learning to Reuse Distractors to support Multiple Choice Question Generation in Education''. It contains a test of 298 educational questions covering multiple subjects & languages and a 77K multilingual pool of distractor vocabulary. The goal is for a given question to propose a list of relevant candidate distractors from the pool of distractors.
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In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA).
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