Machine Learning

Recently, unmanned aerial vehicles (UAVs) have been receiving significant attention due to the wide range of potential application areas. To support UAV use cases with beyond visual line of sight (BVLOS) and autonomous flights,  cellular networks can provide connectivity points to UAVs and provide remote control and payload communications. However, there are limited datasets to study the coverage of cellular technologies for UAV flights at different altitudes.

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The problem of effective disposal of the trash generated by people has rightfully attracted major interest from various sections of society in recent times. Recently, deep learning solutions have been proposed to design automated mechanisms to segregate waste. However, most datasets used for this purpose are not adequate. In this paper, we introduce a new dataset, TrashBox, containing 17,785 images across seven different classes, including medical and e-waste classes which are not included in any other existing dataset.

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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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Although asking and replying on social media platforms in mixed language is a very common phenomenon these days, there is lack of precise corpora to analyze such code mixed language. Datasets released by various CQA sites are monolingual i.e. only in English language. To perform our task, we needed annotated bilingual dataset which include Question pairs in mashed up language. In view of this scarcity we created a dataset by scraping pairs of questions from distinct social media networks, for-example Yahoo!

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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.

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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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This dataset is for doa estimation when an amplitude-phase error exists.

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PROTEIN STRUCTURE AND SYNTHETIC MULTI-VIEW CLUSTERING DATASETS

Multi-View Clustering (MVC) datasets used in the following paper:

Evolutionary Multi-objective Clustering Over Multiple Conflicting Data Views. Authors: Mario Garza-Fabre, Julia Handl, and Adán José-García. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION. Accepted for publication, November 2022.

This entry contains all 420 datasets used in the paper, including:

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