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Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review
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Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model
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Causal Inference Techniques for Microservice Performance Diagnosis: Evaluation and Guiding Recommendations
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TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
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InterpretableSAD: Interpretable Anomaly Detection in Sequential Log Data
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CloudRanger: Root Cause Identification for Cloud Native Systems
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inferredcites →https://doi.org/10.1109/cloudintelligence52565.2021.00015openalex-snapshot
MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems
inferredcites →https://doi.org/10.1109/cns.2019.8802833openalex-snapshot
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
inferredcites →https://doi.org/10.1109/dsa51864.2020.00047openalex-snapshot
Fault Diagnosis for Open Source Software Based on Dynamic Tracking
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Robust Anomaly Detection on Unreliable Data
inferredcites →https://doi.org/10.1109/globalsip.2013.6736861openalex-snapshot
Stochastic gradient descent with differentially private updates
inferredcites →https://doi.org/10.1109/hpcc-smartcity-dss50907.2020.00055openalex-snapshot
Context-Aware Learning for Anomaly Detection with Imbalanced Log Data
inferredcites →https://doi.org/10.1109/icc45041.2023.10279563openalex-snapshot
DeepEAD: Explainable Anomaly Detection from System Logs
inferredcites →https://doi.org/10.1109/icde.2003.1260802openalex-snapshot
LOCI: fast outlier detection using the local correlation integral
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CrowdOTA: An Online Task Assignment System in Crowdsourcing
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CrowdRL: An End-to-End Reinforcement Learning Framework for Data Labelling
inferredcites →https://doi.org/10.1109/icdm.2008.151openalex-snapshot
Comparative Evaluation of Anomaly Detection Techniques for Sequence Data
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Multivariate Time-Series Anomaly Detection via Graph Attention Network
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Review on wrapper feature selection approaches
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Cluster-based majority under-sampling approaches for class imbalance learning
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A Trustworthy Network Fault Diagnosis Approach
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Decentralized Cloud Storage Using Blockchain
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MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems
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TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems
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Semi-Supervised Log-Based Anomaly Detection via Probabilistic Label Estimation
inferredcites →https://doi.org/10.1109/icws.2019.00022openalex-snapshot
MS-Rank: Multi-Metric and Self-Adaptive Root Cause Diagnosis for Microservice Applications
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Root-Cause Metric Location for Microservice Systems via Log Anomaly Detection
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Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005
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Privacy-Preserving Outlier Detection with High Efficiency over Distributed Datasets
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Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks
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SwissLog: Robust and Unified Deep Learning Based Log Anomaly Detection for Diverse Faults
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LogSayer: Log Pattern-driven Cloud Component Anomaly Diagnosis with Machine Learning
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Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach
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Equifax and the Latest Round of Identity Theft Roulette
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Factual and Counterfactual Explanations for Black Box Decision Making
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A Secure Federated Transfer Learning Framework
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MicroRCA: Root Cause Localization of Performance Issues in Microservices
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Trustworthy AI in the Age of Pervasive Computing and Big Data
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Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI
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Towards an Interpretable Autoencoder: A Decision-Tree-Based Autoencoder and its Application in Anomaly Detection
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Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
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Semi-Supervised Multivariate Statistical Network Monitoring for Learning Security Threats
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Federated Learning With Differential Privacy: Algorithms and Performance Analysis
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Log-Based Anomaly Detection With Robust Feature Extraction and Online Learning
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Secure Data Storage and Recovery in Industrial Blockchain Network Environments
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Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures
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A Generative Model of Urban Activities from Cellular Data
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MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
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Anomaly Detection via Online Oversampling Principal Component Analysis
inferredcites →https://doi.org/10.1109/tkde.2015.2503753openalex-snapshot
Robust Ensemble Clustering Using Probability Trajectories
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Vertical Federated Learning: Concepts, Advances, and Challenges
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Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks With Data Privacy
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Adversarial Examples: Attacks and Defenses for Deep Learning
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Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection
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HitAnomaly: Hierarchical Transformers for Anomaly Detection in System Log
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Two-dimensional pca: a new approach to appearance-based face representation and recognition
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Combining multiple clusterings using evidence accumulation
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Multiset Feature Learning for Highly Imbalanced Data Classification
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Adversarial Attacks on Time Series
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Anomaly Detection Through a Bayesian Support Vector Machine
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Multisensor Fault Diagnosis Modeling Based on the Evidence Theory
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CauseInfer: Automated End-to-End Performance Diagnosis with Hierarchical Causality Graph in Cloud Environment
inferredcites →https://doi.org/10.1109/tsc.2018.2853167openalex-snapshot
Blockchain for Large-Scale Internet of Things Data Storage and Protection
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Self-Adaptive Root Cause Diagnosis for Large-Scale Microservice Architecture
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Task Failure Prediction in Cloud Data Centers Using Deep Learning
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An Experiment with the Edited Nearest-Neighbor Rule
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Exploratory Undersampling for Class-Imbalance Learning
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EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection Algorithms with Multidimensional Data
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Self-Supervised Anomaly Detection from Distributed Traces
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Performance Evaluation of Anomaly Detection in Imbalanced System Log Data
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XAI—Explainable artificial intelligence
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Feature bagging for outlier detection
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LDA-based document models for ad-hoc retrieval
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Proceedings of the 24th international conference on Machine learning
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Importance weighted active learning
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Performance Anomaly Detection and Bottleneck Identification
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Log clustering based problem identification for online service systems
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Deep Learning with Differential Privacy
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Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks
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Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
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Feedback-Guided Anomaly Discovery via Online Optimization
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A Survey of Methods for Explaining Black Box Models
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Interactive Anomaly Detection on Attributed Networks
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Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
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Time-Series Anomaly Detection Service at Microsoft
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Outlier detection using isolation forest and local outlier factor
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Robust log-based anomaly detection on unstable log data
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AutoMAP: Diagnose Your Microservice-based Web Applications Automatically
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Efficient algorithms for mining outliers from large data sets
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Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding
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HALO: Hierarchy-aware Fault Localization for Cloud Systems
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A Survey on Bias and Fairness in Machine Learning
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A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems
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Trustworthy Artificial Intelligence: A Review
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Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey
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Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation
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An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT
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Trustworthy AI: A Computational Perspective
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Trustworthy AI: From Principles to Practices
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Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better
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A Unified Approach to Interpreting Model Predictions
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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