Machine Learning
Dataset generated with Unreal Engine 4 and Nvidia NDDS. Contains 1500 images of each object: Forklift, pallet, shipping container, barrel, human, paper box, crate, and fence. These 1500 images are split into 500 images from each environment: HDRI and distractors, HDRI with no distractors, and a randomized environment with distractors.
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This work aims to identify anomalous patterns that could be associated with performance degradation and failures in datacenter nodes, such as Virtual Machines or Virtual Machines clusters. The early detection of anomalies can enable early remediation measures, such as Virtual Machines migration and resource reallocation before losses occur. One way to detect anomalous patterns in datacenter nodes is using monitoring data from the nodes, such as CPU and memory utilization.
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Normal
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21
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EN-IN
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This dataset has 2 different RFID manufacturer tags (2 of each) with different EPC content. Signal Strength data of all 4 tags were taken in the frequency spectrum in the US UHF (900-910 MHz) range. There are 100 data readings per tag, so 400 files total.
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Contains 12 datasets for feature selection dimensionality reduction and machine learning.
The dimension of the selected datasets ranges from 10 to 1000, and the number of instances ranges from 200 to 3000.
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FARLEAD2 receives a test scenario from the developer, and verifies a related functional behavior by witnessing the test scenario in the Application Under Test, on a real mobile device. The 'results.zip' file contains 204 Comma-Separated Values (CSV) files and a Perl script 'createtable.pl' that generates Table 2 in the manuscript. Each CSV file contains the results of ten runs of a witness generator for a test scenario under a given level of information. The experimental test scenarios are located in the 'scenarios.zip' file.
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The support dataset for paper "Prediction for loosening life of bolted joints using IMUs with dimensionality reduction"
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Anthropometric studies focusing on facial metrics and their proportions form an important research area devoted to observations of the appearance of the human skull. Many different applications include the use of craniometry for maxillofacial reconstruction and surgery. The paper and the associated dataset explores the possibility of using selected craniometric points and associated metric to observe spatial changes during the maxillofacial surgery treatment. The experimental dataset includes observations of 27 individuals.
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