Machine Learning
Microarchitectural attacks have become more threatening the society than before with the increasing diversity of attacks such as Spectre and Meltdown. Vendor patches cannot keep up with the pace of the new threats, which makes the dynamic anomaly detection tools more evident than before. Unfortunately, hardware performance counters (HPCs) utilized in previous works lead to high performance overhead and detection of a few microarchitectural attacks due to the small number of counters that can be profiled concurrently.
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The datasets were originally crawled from the Meetup.com by authors of this work[1]. It consists of two part:
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This dataset is created for rope crossings type classification. Crossings were divided into upper and lower types. Data were collected under four different backgrounds.
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This dataset was created for Deformable Linear Objects(DLOs) segmentation and crossings classification under complex background.
RGB images, overlap maps, gradient maps are included for segmentation task. The quantity of DLOs range from one to three.
Upper and lower type of crossings are defined for classification task.
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The dataset contains short video clips of four shoulder exercises.
- Arm flexion and extension
- Arm abduction and adduction
- Arm lateral and medial rotation
- Arm circumduction
The videos are labeled as either correct or incorrect.
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<p>This multilingual Twitter dataset spans over 2 years from October 2019 to the end of 2021, including 3 months before the outbreak of the COVID-19 pandemic.</p>
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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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