Artificial Intelligence
A synthetic laser reliability dataset generated using generative adversarial networks (GANs) is provided. The data includes normalized current measurements estimated at the following times: 2, 20, 40, 60, 80, 100, 150, 500, 1000, and 1500 hours. The data can be used to train machine learning models to solve different predictive maintenance tasks such as prediction of performance degradation, remainng useful prediction, and so on.
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A monitoring data, which includes several OTDR traces incorporating various types of fiber events (e.g. reflective, non-reflective, merged events) induced along an optical fiber link, is provided. Different fiber faults such as fiber cut, and fiber bend are modeled using optical components such as connectors and variable optical attenuators (VOAs). The data can be used to train machine learning models for solving fiber fault diagnosis problems.
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The dataset contains labeled sentences. The sentences having information related to (1) infections, (2) suffering from pneumonea, (3) deaths, and (4) health updates from government/WHO, are labeled with 1 and the rest are labeled with 0. Source of all the news articles: https://www.thehindu.com/archive/
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The monitored data is obtained using the optical time domain reflectometry (OTDR) principle, which is commonly used for troubleshooting fiber optic cables or links. The data set contains raw OTDR traces that include one or two reflective events caused by the placement of one or two reflectors and/or an open physical contact (PC) at the end of the monitored optical fiber link.
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Building segmentation image data set
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These datasets contain the database of the local television channels in the greater manila area (GMA) in the Philippines as of 2020. There are 8 databases corresponding to 8 eight channels namely Channel4.xlsx, Channel5.xlsx, Channel7.xlsx, Channel9.xlsx, Channel11.xlsx,
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Touch-screens are the basic and convenient human-computer interface. They are extensively used in digital musical applications, where a complex action-perception loop is involved. Therefore, it is crucial to establish a rich vibrotacticle feedback to improve the quality of the user's interaction. This paper explores the capacity of Generative Adversarial Networks (GANs) to generate time-reversed signals that can achieve localized vibrotactile feedback on a rigid surface.
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A converstional stance detection dataset.
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