Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021.The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows:
Part I: Applications of knowledge discovery and data mining of specialized data;
Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics;
Part III: Representation learning and embedding, and learning from data.

List of contents

Applications of Knowledge Discovery.- Fuzzy World:A Tool Training Agent from Concept Cognitive to Logic Inference.- Collaborative Reinforcement Learning Framework to Model Evolution of Cooperation in Sequential Social Dilemmas.- SIGTRAN: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks.- VOA*: Fast Angle-Based Outlier Detection Over High-Dimensional Data Streams.- Learning Probabilistic Latent Structure for Outlier Detection from Multi-View Data.- GLAD-PAW: Graph-based Log Anomaly Detection by Position Aware Weighted Graph Attention Network.- CubeFlow: Money Laundering Detection with Coupled Tensors.- Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection.- Unsupervised Domain Adaptation for 3D Medical Image with High Efficiency.- A Hierarchical Structure-Aware Embedding Method for Predicting Phenotype-Gene Associations.- Autonomous Vehicle Path Prediction using Conditional Variational Autoencoder Networks.- Heterogeneous Graph Attention Network for Small and Medium-sized Enterprises Bankruptcy Prediction.- Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.- Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-Training.- Attack Is the Best Defense: A Multi-Mode Poisoning PUF against Machine Learning Attacks.- Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets.- TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions.- Lifelong Learning based Disease Diagnosis on Clinical Notes.- GrabQC: Graph based Query Contextualization for automated ICD coding.- Deep Gaussian Mixture Model on Multiple Interpretable Features of Fetal Heart Rate for Pregnancy Wellness.- Adverse Drug Events Detection, Extraction and Normalization from Online Comments of Chinese Patent Medicines.- Adaptive Graph Co-Attention Networks for Traffic Forecasting.- Dual-Stage Bayesian Sequence to Sequence Embeddings for Energy Demand Forecasting.- AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life.- Data Mining of Specialized Data.- Analyzing Topic Transitions in Text-based Social Cascades using Dual-Network Hawkes Process.- HiPaR: Hierarchical Pattern-Aided Regression.- Improved Topology Extraction using Discriminative Parameter Mining of Logs.- Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion.- A k-MCST based Algorithm for Discovering Core-Periphery Structures in Graphs.- Detecting Sequentially Novel Classes with Stable Generalization Ability.- Learning-based Dynamic Graph Stream Sketch.- Discovering Dense Correlated Subgraphs in Dynamic Networks.- Fake News Detection with Heterogenous Deep Graph Convolutional Network.- Incrementally Finding the  Vertices Absent from the Maximum Independent Sets.- Neighbours and Kinsmen: HatefulUsers Detection with Graph Neural Network.- Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs.- A Meta-path based Graph Convolutional Network with Multi-Scale Semantic Extractions for Heterogeneous Event Classification.- Noise-Enhanced Unsupervised Link Prediction.- Weak Supervision Network Embedding for Constrained Graph Learning.- RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment               .- Graph Attention Networks with Positional Embeddings.- Unified Robust Training for Graph Neural Networks against Label Noise.- Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs.- A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention.- Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads. -Reliably Calibrated Isotonic Regression.- Multiple Instance Learning for Unilateral Data.- An Online Learning Algorithm for Non-Stationary Imbalanced Data by Extra-Charging Minority Class.- Locally Linear Support Vector Machines for Imbalanced Data Classification. - Low-Dimensional Representation Learning from Imbalanced Data Streams.- PhotoStylist: Altering the Style of Photos based on the Connotations of Texts.- Gazetteer-Guided Keyphrase Generation from Research Papers.- Minits-AllOcc: An Efficient Algorithm for Mining Timed Sequential Patterns.- T^3N: Harnessing Text and Temporal Tree Network for Rumor Detection on Twitter.- AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection.- SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction.- Content matters: A GNN-based Model Combined with Text Semantics for Social Network Cascade Prediction.- TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting.- Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-hailing Demand Forecasting.- A Proximity Forest for Multivariate Time Series Classification.- C²-Guard: A Cross-Correlation Gaining Framework for Urban Air Quality Prediction.- Simultaneous multiple POI population patternanalysis system with HDP mixture regression.- Interpretable Feature Construction for Time Series Extrinsic Regression.- SEPC: Improving Joint Extraction of Entities and Relations by Strengthening Entity Pairs Connection.

Product details

• Assisted by: R. K. Agrawal (Editor), R.K. Agrawal (Editor), Tanmoy Chakraborty (Editor), Hon Cheng (Editor), Hong Cheng (Editor), Kamal Karlapalem (Editor), Naren Ramakrishnan (Editor), Naren Ramakrishnan et al (Editor), P. Krishna Reddy (Editor), Jaideep Srivastava (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 29.07.2021
• EAN: 9783030757618
• ISBN: 978-3-0-3075761-8
• No. of pages: 834
• Dimensions: 155 mm x 45 mm x 235 mm
• Illustrations: XXXV, 834 p. 30 illus.
• Series: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT