Machine Learning
Five well-known Border Gateway Anomalies (BGP) anomalies:
WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, occurred in May 2017, May 2005, January 2003, September 2001, and July 2001, respectively.
The Reseaux IP Europeens (RIPE) BGP update messages are publicly available from the Network Coordination Centre (NCC) and contain:
WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, and regular data: https://www.ripe.net/analyse/.
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Since there is no image-based personality dataset, we used the ChaLearn dataset for creating a new dataset that met the characteristics we required for this work, i.e., selfie images where only one person appears and his face is visible, labeled with the person's apparent personality in the photo.
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the measurement data simulated data of Hd-TCP and its comparisons' performance on the real high-speed railways scenario
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These datasets are used to detect Intrusions in Controller Area Network (CAN) bus. Intrusions are detected using various Machine Learning and Deep Learning algorithms.
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monitoring, processing and prediction data
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ArPC is an Arabic paraphrase identification corpus. It consists of 1331 sentence pairs along with their binary score that indicates weather the pairs are paraphrase or not. The corpus has been manually annotated by three Arabic native speakers.
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754 Views
The PMMW real-time imager, SAIR-U, is developed by Microwave Laboratory of Beihang University, China.It could be (or has been) used in non-contact, non-cooperative (i.e. no need for a fixed posture) security, especially in the environment of large passenger flow. This is the dataset used in the experiment in paper"Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on YOLOv3 Algorithm"
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The PMMW real-time imager, SAIR-U, is developed by Microwave Laboratory of Beihang University, China.It could be (or has been) used in non-contact, non-cooperative (i.e. no need for a fixed posture) security, especially in the environment of large passenger flow. This is the dataset used in the experiment in paper"Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on YOLOv3 Algorithm"
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The dataset corresponds to the variables that affect the process of passing the sheet between rodsizer and the spooner section for a paper machine.
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556 Views
Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.
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