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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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.
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.
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.
The dataset image is the time-domain waveforms of the 7 fault states of the rolling bearing vibration signal, which provide research data for subsequent data processing and feature extraction.
The support dataset for paper "Prediction for loosening life of bolted joints using IMUs with dimensionality reduction"
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.
130 videos are available, captured in Patras, Greece, displaying drivers in real cars, moving under nighttime conditions where drowsiness detection is more important.The participating drivers are: 11 males and 10 females with different features (hair color, beard, glasses, etc). The videos are split in 2 categories:
content-based dataset that composes of 12 features for eight common types of files (JPG, PNG, HTML, TXT, MP4, M4A, MOV, and MP3) to be suitable for file type identification (FTI). These features were extracted from pool of file fragment of size 512 byte each from all the prementioned eight types. This dataset is developed in such a way that can be used for supervised and unsupervised ML model. It provides the ability to classifying and clustering the above-mentioned type into two levels.
