Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part I / edited by Xiangyu Song, Ruyi Feng, Yunliang Chen, Jianxin Li, Geyong Min.

Format:

Book

Author/Creator:

Song, Xiangyu.

Contributor:

Feng, Ruyi.

Chen, Yunliang.

Li, Jianxin.

Min, Geyong.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 14331

Language:

English

Subjects (All):

Big data.

Data structures (Computer science).

Information theory.

Application software.

Image processing--Digital techniques.

Image processing.

Computer vision.

Data mining.

Big Data.

Data Structures and Information Theory.

Computer and Information Systems Applications.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Data Mining and Knowledge Discovery.

Local Subjects:

Big Data.

Data Structures and Information Theory.

Computer and Information Systems Applications.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (533 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

The 4-volume set LNCS 14331, 14332, 14333, and 14334 constitutes the refereed proceedings of the 7th International Joint Conference, APWeb-WAIM 2023, which took place in Wuhan, China, in October 2023. The total of 138 papers included in the proceedings were carefully reviewed and selected from 434 submissions. They focus on innovative ideas, original research findings, case study results, and experienced insights in the areas of the World Wide Web and big data, covering Web technologies, database systems, information management, software engineering, knowledge graph, recommend system and big data.

Contents:

Intro

Preface

Organization

Contents - Part I

A BERT-Based Semantic Enhanced Model for COVID-19 Fake News Detection

1 Introduction

2 Related Work

2.1 COVID-19 Fake News Collection

2.2 COVID-19 Fake News Detection

2.3 BERT Model

3 Methodology

3.1 Dataset

3.2 Problem Statement

3.3 Text Representation Learning

3.4 Topic Generation

3.5 Classifier Design

4 Experimental Results and Parameter Analysis

4.1 Experimental Results

4.2 Parameter Analysis

5 Conclusion

References

Mining Frequent Geo-Subgraphs in a Knowledge Graph

2 Problem Definition

3 Frequent Geo-Subgraph Mining

4 Optimizations

4.1 Arc Consistency Based Candidate Generation

4.2 Image Vertex Reusage

4.3 Geo-Grid Based Vertex Ordering

5 Experimental Study

5.1 Setup

5.2 Performance Evaluations

6 Related Work

7 Conclusion

Locality Sensitive Hashing for Data Placement to Optimize Parallel Subgraph Query Evaluation

2 Background

2.1 Preliminaries

2.2 Parallel Execution Model

3 Locality Sensitive Hashing for Data Placement

3.1 Vertex Similarity

3.2 Vertex MinHash

4 System Implementation

5 Experiments

5.1 Experimental Setting

5.2 Effect of Our Proposed Techniques

5.3 Comparison with Other Parallel Subgraph Query Systems

5.4 Data Placement Performance

DUTD: A Deeper Understanding of Trajectory Data for User Identity Linkage

3 Preliminary

4 Proposed Model

4.1 Grid Feature Extractor

4.2 Tranformer-Based Encoder

4.3 Matcher

5 Experiment

5.1 Datasets

5.2 Baselines

5.3 Parameter Setting and Evaluation Metrics

5.4 Performance Comparison

5.5 Ablation Study

6 Conclusion

References.

Large-Scale Rank Aggregation from Multiple Data Sources Based D3MOPSO Method

3 Definitions and Problem Formulation

4 Proposed Method

4.1 Strategy on Encoding Scheme and Multi-directional Search

4.2 Particle Swarm Initialization

4.3 Definition of Discrete Position and Velocity

4.4 Discrete Particle Statue Updating

4.5 Framework of the Proposed Algorithm

4.6 Complexity Analysis

5 Experimental Studies

5.1 Comparison Algorithms

5.2 Experimental Settings

5.3 Evaluation Metrics

5.4 The Results

Hierarchically Delegatable and Revocable Access Control for Large-Scale IoT Devices with Tradability Based on Blockchain

2 Building Blocks

2.1 Blockchain and Ethereum

2.2 Digital Signature

2.3 BIP-32 Standard

3 System Assumption and Requirements

3.1 System Entities

3.2 System Assumption

3.3 System Requirements

4 The Proposed Framework

4.1 High-Level Overview

4.2 IoT Device Registration

4.3 Ownership Transfer/Trading of IoT Device

4.4 (Hierarchical) Delegation of Access Control

4.5 Access an IoT Device

4.6 Revocation

5 Experimental Results

6 Security Analysis

7 Conclusions

Distributed Deep Learning for Big Remote Sensing Data Processing on Apache Spark: Geological Remote Sensing Interpretation as a Case Study

2 Related Works

2.1 Distributed Deep Learning's Development Status

2.2 DDL-Based Remote Sensing Data Processing

3 Distributed Deep Learning Frameworks

3.1 MLlib

3.2 SparkTorch and TensorflowOnSpark

3.3 DeepLearning4Java

3.4 BigDL

3.5 Horovod

4 D-AMSDFNet: Distributed Deep Learning-Based AMSDFNet for Geological Remote Sensing Interpretation

4.1 AMSDFNet

4.2 Design of Distributed AMSDFNet

5 Experiments.

5.1 Settings

5.2 Analysis of Experimental Results

6 Conclusions

Graph-Enforced Neural Network for Attributed Graph Clustering

3 Notations and Problem Formulation

4 Degradation Analysis

4.1 Intra-cluster Estrangement

4.2 Attribute Similarity Neglection

4.3 Blurred Cluster Boundaries

5 The Proposed Method

5.1 Multi-task Learning Framework

5.2 High-Order Structural Proximity Enforcement

5.3 Attribute Similarity Enforcement

5.4 Cluster Boundary Enforcement

5.5 Joint Objective Optimization

6 Experiments

6.1 Experiment Settings

6.2 Performance Comparison

6.3 Efficiency Comparison

6.4 Ablation Study

6.5 Hyperparameter Sensitivity Analysis

MacGAN: A Moment-Actor-Critic Reinforcement Learning-Based Generative Adversarial Network for Molecular Generation

3 MacGAN Overview

3.1 GAN

3.2 Autoregressive GAN for SMILES Strings

3.3 Moment Reward

4 Experiment

4.1 Dataset

4.2 Evaluation Measures

4.3 Desired Chemical Properties

4.4 Model Setup

4.5 Experimental Results

Multi-modal Graph Convolutional Network for Knowledge Graph Entity Alignment

2.1 Entity Alignment

2.2 Multi-modal Knowledge Graph

3.1 Definition and Model Overview

3.2 Multi-modal Pre-trained Embedding

3.3 Multi-modal Enhancement Embedding Mechanism

3.4 Objective

4 Experiments

4.1 Datasets

4.2 Experimental Settings

4.3 Baselines

4.4 Main Results

4.5 Ablation Study

4.6 Parameter Analysis

5 Conclusion and Future Work

Subgraph Federated Learning with Global Graph Reconstruction

2.1 Subgraph Federated Learning (SFL).

2.2 Graph Structure Learning (GSL)

2.3 Split Learning

3 Problem Setting

4 Methodology

4.1 Framework Overview

4.2 Local Pre-training

4.3 The Local Graph Learning Module

4.4 The Global Graph Structure Learning Module

4.5 Objective and Training Procedure

5.1 Experimental Setups

5.2 Comparison with State-of-the-art Methods (RQ1)

5.3 Ablation Study (RQ2)

5.4 Sensitivity Analysis (RQ3)

SEGCN: Structural Enhancement Graph Clustering Network

3 Method

3.1 Notations

3.2 Topology Enhancement Module

3.3 Improved Attention-Driven Graph Clustering Network with Global Structure Dynamic Fusion Module

3.4 Optimization Objective Function

4.1 Benchmark Datasets

4.2 Experimental Setup and Evaluation

4.3 Clustering Results

4.4 Ablation Studies

4.5 Visualization Results

Designing a Knowledge Graph System for Digital Twin to Assess Urban Flood Risk

4 The Proposed UrbanFloodKG System

4.1 System Overview

4.2 Data Layer

4.3 Graph Layer

4.4 Algorithm Layer

4.5 Digital Twin Layer

5 Experiment and Discussion

5.1 Dataset and Environment

5.2 Link Prediction Analysis

5.3 Node Classification Analysis

TASML: Two-Stage Adaptive Semi-supervised Meta-learning for Few-Shot Learning

2.1 Brain-Inspired Model for Visual Object Recognition

2.2 Meta-learning for Few-Shot Learning

3.1 Preliminary

3.2 The Two-Stage Semi-supervised Meta-learning Framework

3.3 Unsupervised Visual Representation Learning

3.4 Gradient-Based Meta-learning for Few-Shot Learning

3.5 Global Context-Aware Module

4 Experiments.

4.1 Few-Shot Image Classification

4.2 Ablation Study

4.3 Visualization

An Empirical Study of Attention Networks for Semantic Segmentation

2.1 Enrich Contextual Information Based Methods

2.2 Reduce Computation Complexity Based Methods

3 Experiment

3.1 Datasets

3.2 Implementation Details

4 Analysis

5 Conclusions and Future Works

Epidemic Source Identification Based on Infection Graph Learning

2 Preliminaries

2.1 Problem Description

2.2 Propagation Model

3 Related Work

4 Our Model

4.1 Architecture

4.2 Input Generation

4.3 GCN Layer

4.4 Graph Embedding Layer

4.5 Output Layer

4.6 Loss Function

4.7 Model Complexity

5.1 Datasets and Baselines

5.2 Evaluation Metrics

5.3 Experimental Setting

5.4 Source Identification Performance

5.6 Impact of Parameters

5.7 Model Efficiency

6 Conclusion and Future Work

Joint Training Graph Neural Network for the Bidding Project Title Short Text Classification

2.1 Text Classification

2.2 Short Text Classification

3.1 Extracting Contextual Information

3.2 Graph Structure Construction

3.3 Feature Caching and Replacement

3.4 Graph Convolution Operation

3.5 Classification

4.2 Data Processing

4.3 Baseline Models

4.4 Experimental Settings

4.5 Results

Hierarchical Retrieval of Ancient Chinese Character Images Based on Region Saliency and Skeleton Matching

3.1 Visual Feature Extraction

3.2 Regional Channel Screening

3.3 Saliency Joint Weighting Method.

3.4 Shape Fine Matching Based on Skeleton Context.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789819723034

9819723035

OCLC:

1436830054