This two-volume set, LNCS 12858 and 12859, constitutes the thoroughly refereed proceedings of the 5th International Joint Conference, APWeb-WAIM 2021, held in Guangzhou, China, in August 2021.
The 44 full papers presented together with 24 short papers, and 6 demonstration papers were carefully reviewed and selected from 184 submissions. The papers are organized around the following topics: Graph Mining; Data Mining; Data Management; Topic Model and Language Model Learning; Text Analysis; Text Classification; Machine Learning; Knowledge Graph; Emerging Data Processing Techniques; Information Extraction and Retrieval; Recommender System; Spatial and Spatio-Temporal Databases; and Demo.
Inhaltsverzeichnis
Graph Mining. - Co-Authorship Prediction Based on Temporal Graph Attention. - Degree-specific Topology Learning for Graph Convolutional Network. - Simplifying Graph Convolutional Networks as Matrix Factorization. - RASP: Graph Alignment through Spectral Signatures. - FANE: A Fusion-based Attributed Network Embedding Framework. - Data Mining. - What Have We Learned from Open Review? . - Unsafe Driving Behavior Prediction for Electric Vehicles. - Resource Trading with Hierarchical Game for Computing-Power Network Market. - Analyze and Evaluate Database-Backed Web Applications with WTool. - Semi-supervised Variational Multi-view Anomaly Detection. - A Graph Attention Network Model for GMV Forecast on Online Shopping Festival. - Suicide Ideation Detection on Social Media during COVID-19 via Adversarial and Multi-task Learning. - Data Management. - An Efficient Bucket Logging for Persistent Memory. - Data Poisoning Attacks on Crowdsourcing Learning. - Dynamic Environment Simulation for Database PerformanceEvaluation. - LinKV: an RDMA-enabled KVS for High Performance and Strict Consistency under Skew. - Cheetah: An Adaptive User-space Cache for Non-volatile Main Memory File Systems. - Topic Model and Language Model Learning. - Chinese Word Embedding Learning with Limited Data. - Sparse Biterm Topic Model for Short Texts. - EMBERT: A Pre-trained Language Model for Chinese Medical Text Mining. - Self-Supervised Learning for Semantic Sentence Matching with Dense Transformer Inference Network. - An Explainable Evaluation of Unsupervised Transfer Learning for Parallel Sentences Mining. - Text Analysis. - Leveraging Syntactic Dependency and Lexical Similarity for Neural Relation Extraction. - A Novel Capsule Aggregation Framework for Natural Language Inference. - Learning Modality-Invariant Features by Cross-Modality Adversarial Network for Visual Question Answering. - Difficulty-controllable Visual Question Generation. - Incorporating Typological Features into Language Selection for Multilingual Neural Machine Translation. - Removing Input Confounder for Translation Quality Estimation via a Causal Motivated Method. - Text Classification. - Learning Refined Features for Open-World Text Classification. - Emotion Classification of Text Based on BERT and Broad Learning System. - Improving Document-level Sentiment Classification with User-Product Gated Network. - Integrating RoBERTa Fine-Tuning and User Writing Styles for Authorship Attribution of Short Texts. - Dependency Graph Convolution and POS Tagging Transferring for Aspect-based Sentiment Classification. - Machine Learning. - DTWSSE: Data Augmentation with a Siamese Encoder for Time Series. - PT-LSTM: Extending LSTM for Efficient processing Time Attributes in Time Series Prediction. - Loss Attenuation for Time Series Prediction Respecting Categories of Values. - PFL-MoE: Personalized Federated Learning Based on Mixture of Experts. - A New Density Clustering Method using Mutual Nearest Neighbor. -
