Web and Big Data

5th International Joint Conference, APWeb-WAIM 2021, Guangzhou, China, August 23–25, 2021, Proceedings, Part I

AvLeong Hou U,Marc Spaniol

Häftad, Engelska, 2021

Del 12858 i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-08-19
Mått:155 x 235 x 29 mm
Vikt:791 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:498
Förlag:Springer Nature Switzerland AG
ISBN:9783030858957

Utforska kategorier

Innehållsförteckning

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.-