INTELLIGENT DATA AUGMENTATION FOR SUPERVISED ANOMALY DETECTION ASSOCIATED WITH A CYBER-PHYSICAL SYSTEMA Cyber-Physical System ("CPS") may have monitoring nodes that generate a series of current monitoring node values representing current operation of the CPS. A normal space data source may store, for...
Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce ... G Scafarto,M Ciortan,S Tihon,... 被引量: 0发表: 2023年 Co...
Data augmentation for deep graph learning: A survey, KDD 2022 [Paper] Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods, Briefings in Bioinformatics 2022 [Paper] [Code] Amlb: an automl benchmark, arXiv 2022 [Paper] A benchmar...
Featuretools An open source framework for automated feature engineering written in python Optimus Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. Albumentations А fast and framework agnostic image augmentation library that implements a diverse set ...
Contrastive Attributed Network Anomaly Detection with Data Augmentation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining; Springer: Cham, Switzerland, 2022; pp. 444–457. [Google Scholar] Hoeltgebaum, H.; Adams, N.; Fernandes, C. Estimation, Forecasting, and Anomaly Detection ...
Data Augmentation and Deep Learning. “Image Data Augmentation techniques” discusses each image augmentation technique in detail along with experimental results. “Design considerations for image Data Augmentation” discusses additional characteristics of augmentation such as test-time augmentation and the ...
CNN classification for anomaly intrusion detection trained based on the extended system call sequence Full size image GCNN intrusion detection model based on predicted sequence augmentation method There is a limitation in the convolutional intrusion detection model described above. That is, the predicted ...
select article Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks Research articleOpen access Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks Wasim Khan, Afsaruddin Mohd, Mohammad...
and phase spectrums for data augmentation in time series anomaly detection using a convolutional neural network (CNN). In order to create effective models, researchers, in certain circumstances, turn to hybrid techniques that combine ML and DL methodologies. All of these techniques, in particular, ...
FLAG: Adversarial Data Augmentation for Graph Neural Networks, 📝arXiv'2020, Code Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning, 📝arXiv'2020 Watermarking Graph Neural Networks by Random Graphs, 📝arXiv'2020 Training Robust Graph Neural Network by Applying...