Web and Big Data

Name: Web and Big Data | 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6-8, 2023, Proceedings, Part I
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7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6-8, 2023, Proceedings, Part I

Xiangyu Song Ruyi Feng Yunliang Chen Jianxin Li Geyong Min(Editor)

Springer (Publisher)

Published on 28. May 2024

XVIII, 515 pages

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978-981-97-2303-4 (ISBN)

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Content

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
1 Introduction
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
References
Locality Sensitive Hashing for Data Placement to Optimize Parallel Subgraph Query Evaluation
1 Introduction
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
6 Related Work
7 Conclusion
References
DUTD: A Deeper Understanding of Trajectory Data for User Identity Linkage
1 Introduction
2 Related Work
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
1 Introduction
2 Related Work
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
6 Conclusion
References
Hierarchically Delegatable and Revocable Access Control for Large-Scale IoT Devices with Tradability Based on Blockchain
1 Introduction
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
References
Distributed Deep Learning for Big Remote Sensing Data Processing on Apache Spark: Geological Remote Sensing Interpretation as a Case Study
1 Introduction
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
References
Graph-Enforced Neural Network for Attributed Graph Clustering
1 Introduction
2 Related Works
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
7 Conclusion
References
MacGAN: A Moment-Actor-Critic Reinforcement Learning-Based Generative Adversarial Network for Molecular Generation
1 Introduction
2 Related Work
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
5 Conclusion
References
Multi-modal Graph Convolutional Network for Knowledge Graph Entity Alignment
1 Introduction
2 Related Work
2.1 Entity Alignment
2.2 Multi-modal Knowledge Graph
3 Methodology
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
References
Subgraph Federated Learning with Global Graph Reconstruction
1 Introduction
2 Related Work
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 Experiment
5.1 Experimental Setups
5.2 Comparison with State-of-the-art Methods (RQ1)
5.3 Ablation Study (RQ2)
5.4 Sensitivity Analysis (RQ3)
6 Conclusion
References
SEGCN: Structural Enhancement Graph Clustering Network
1 Introduction
2 Related Work
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 Experiment
4.1 Benchmark Datasets
4.2 Experimental Setup and Evaluation
4.3 Clustering Results
4.4 Ablation Studies
4.5 Visualization Results
5 Conclusion
References
Designing a Knowledge Graph System for Digital Twin to Assess Urban Flood Risk
1 Introduction
2 Related Work
3 Preliminary
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
6 Conclusion
References
TASML: Two-Stage Adaptive Semi-supervised Meta-learning for Few-Shot Learning
1 Introduction
2 Related Work
2.1 Brain-Inspired Model for Visual Object Recognition
2.2 Meta-learning for Few-Shot Learning
3 Methodology
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
5 Conclusion
References
An Empirical Study of Attention Networks for Semantic Segmentation
1 Introduction
2 Related Work
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
References
Epidemic Source Identification Based on Infection Graph Learning
1 Introduction
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 Experiment
5.1 Datasets and Baselines
5.2 Evaluation Metrics
5.3 Experimental Setting
5.4 Source Identification Performance
5.5 Ablation Study
5.6 Impact of Parameters
5.7 Model Efficiency
6 Conclusion and Future Work
References
Joint Training Graph Neural Network for the Bidding Project Title Short Text Classification
1 Introduction
2 Related Work
2.1 Text Classification
2.2 Short Text Classification
3 Method
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 Experiment
4.1 Datasets
4.2 Data Processing
4.3 Baseline Models
4.4 Experimental Settings
4.5 Results
4.6 Parameter Analysis
5 Conclusion
References
Hierarchical Retrieval of Ancient Chinese Character Images Based on Region Saliency and Skeleton Matching
1 Introduction
2 Related Works
3 Method
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
4 Experiments
4.1 Dataset and Evaluation
4.2 Optimal Number of Channels
4.3 Ablation Study on Each Component
4.4 Fine-Grained Matching Experimental Results
4.5 Performance Comparison
5 Conclusion
References
MHNA: Multi-Hop Neighbors Aware Index for Accelerating Subgraph Matching
1 Introduction
2 Related Work
2.1 Subgraph Matching
2.2 Specialized RDF Systems
3 Preliminaries
4 Multi-Hop Neighbors Aware Index
4.1 Index Schema of MHNA
4.2 Query Processing
5 Experiments
5.1 Experimental Settings
5.2 Experimental Results
6 Conclusion
References
Keywords and Stops Aware Optimal Routes on Road Networks
1 Introduction
2 Related Work
2.1 The Keyword-Aware Routing Problem Under Single Metric
2.2 The Keyword-Aware Routing Problem Under Multiple Metrics
3 Problem Definition
4 Proposed Method for KSOR
4.1 POI Candidate Set Generation
4.2 Valid Routes Generation
4.3 Algorithm for KSOR
5 Experiment Evaluation
5.1 Experimental Setup
5.2 Experimental Results
6 Conclusion
References
The Way to Success: A Multi-level Attentive Embedding Framework for Proposal Teamwork Analysis in Voting-Oriented System
1 Introduction
2 Related Work
3 Preliminaries and Problem Definition
3.1 Dataset Description
3.2 Pre-study on Proposals with Teamwork
3.3 Problem Formulation
4 Technical Solution for MAEF
4.1 An Overview of MAEF
4.2 Individual-Level Embedding Layer
4.3 Team-Level Embedding Learning
4.4 Training Strategy
4.5 Time Complexity and Convergence Analysis
5 Experiments
5.1 Experimental Settings
5.2 Quantitative Evaluations
5.3 Qualitative Discussions
6 Conclusion
References
SCS: A Structural Similarity Measure for Graph Clustering Based on Cycles and Paths
1 Introduction
2 Related Work
3 Structural and Cyclic Similarity
4 Counting Short-Length Cycles
5 Experiments
5.1 Datasets
5.2 Evaluation Quality
5.3 Experimental Results
6 Conclusion
References
Time Series Model Interpretation via Temporal Feature Sampling
1 Introduction
2 Related Work
2.1 Traditional Model Interpretation Methods
2.2 Model Interpretation Methods for Time-Series Data
3 Model Interpretation Methods
3.1 Preliminary Notation
3.2 Introduction to Shapley Values
3.3 TFS: Temporal Feature Sampling
4 Experimental Results and Analysis
4.1 Data Set and Metrics
4.2 Model Setup and Baseline Methods
4.3 Results
5 Conclusion
References
K-PropNet: Knowledge-Enhanced Hybrid Heterogeneous Homogeneous Propagation Network for Recommender System
1 Introduction
2 Related Work
3 K-PropNet
3.1 Overview
3.2 Heterogeneous Propagation
3.3 Homogeneous Propagation
3.4 Forecast
3.5 Loss Function
4 Evaluation
4.1 Experimental Setup
4.2 Results
5 Conclusions
References
Multi-patch Adversarial Attack for Remote Sensing Image Classification
1 Introduction
2 Related Work
2.1 Adversarial Attack on RSI
2.2 Adversarial Patch Attack
3 Proposed Method
3.1 Problem Formulation
3.2 Effective Location Selection Module
3.3 Patch Optimization Module
3.4 Imperceptible MPAA
4 Experiments
4.1 Experimental Setup
4.2 Comparisons
4.3 Ablation Study
4.4 Analysis
5 Conclusions
References
Multi-branch Residual Fusion Network for Imbalanced Visual Regression
1 Introduction
2 Related Works
2.1 Data Level Solutions
2.2 Model Level Solutions
3 The Proposed MBDRFN Method
3.1 Multi-branch Residual Fusion Module
3.2 Dynamic Balance Factors
4 Experiments
4.1 The Details of Two Real-World Datasets
4.2 BaseLines
4.3 Experimental Setting
4.4 Experimental Results
5 Conclusions
References
Learning Temporal Graph Representation via Memory-Aware Autoencoder
1 Introduction
2 Related Work
2.1 Static Graph Representation Learning
2.2 Temporal Graph Representation Learning
3 Preliminaries
4 The Design of MATE
4.1 Node-Wise Memory Unit
4.2 Temporal Representation Learning Module
4.3 Iterative Interactive Process
5 Experiments
5.1 Experimental Setup
5.2 Overall Performance Comparisons
5.3 Hyperparameter Study
5.4 Ablation Study
5.5 Inductive Capability Analysis
6 Conclusion
References
HoME: Homogeneity-Mining-Based Embedding Towards Detecting Illicit Transactions on Bitcoin
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Definition
3.2 Framework Design
3.3 Pure Cluster Search
3.4 Constructing Virtual Vertices
3.5 Two-Phase Walk
3.6 Illicit Transaction Detection
4 Experiments
4.1 Experimental Setup
4.2 Experimental Results
5 Conclusion and Future Work
References
BoundEst: Estimating Join Cardinalities with Tight Upper Bounds
1 Introduction
2 Preliminaries
2.1 Problem Definition
2.2 Related Work
3 Estimating Join Cardinalities with Tight Upper Bounds
3.1 Estimate Join Cardinalities with Upper Bounds
3.2 How Outliers Affect Join Upper Bound Estimation
3.3 Workflow
3.4 Tighten Upper Bounds of Join Cardinalities
4 Experimental Evaluation
4.1 Experimental Setting
4.2 Experimental Results
5 Conclusion
References
A Multi-level Network with Multi-feature Clause Pair Graph for Emotion Cause Pair Extraction
1 Introduction
2 Related Work
2.1 Emotion Cause Extraction
2.2 Emotion-Cause Pair Extraction
3 Methodology
3.1 Task Definition
3.2 An Overview of MLNPG
3.3 Word-Level Network
3.4 Phrase-Level Network
3.5 Multi-feature Clause Pair Graph Network
3.6 Prediction
4 Experiments
4.1 Dataset and Metrics
4.2 Experimental Settings
4.3 Baselines
4.4 Experimental Results
4.5 Ablation Study
5 Conclusion
References
CCBTC: A Blockchain-Based Covert Communication Scheme over Bitcoin Transactions
1 Introduction
2 Related Work
3 Proposed Scheme
3.1 Design Framework
3.2 Message Encoding
3.3 Creating and Sending of Transactions
3.4 Decoding and Extracting
4 Experiments and Analysis
4.1 Experiment Setting
4.2 Experiment Design
4.3 Experiment Implementation
4.4 Experiment Results and Analysis
5 Conclusion
References
Reliability Scheduling Algorithm for Heterogeneous Multi-verified Time Systems
1 Introduction
2 Related Work
3 Model and Problem Statement
3.1 System Model
3.2 Task Model
3.3 Reliability Model
3.4 Problem Statement
4 A Motivation Example
5 The Proposed Algorithm
5.1 Basic Concepts of Backward Scheduling
5.2 Basic Concepts of Forward Scheduling
5.3 Backward-Forward Scheduling Algorithms
6 Experimental Results and Analysis
6.1 Experimental Parameters
6.2 Randomly Generate Application Results
6.3 Real World Application Results
7 Summary
References
Efficient Log Anomaly Detection Based on Dimension Reduction and Attention Aware TCN
1 Introduction
2 Related Work
2.1 Log Representation Learning
2.2 Log Anomaly Detection
3 The Proposed EfficientLog
3.1 Log Representation
3.2 Attention Aware TCN
3.3 Anomaly Detection
4 Experiments
4.1 Datasets
4.2 Baselines
4.3 Implementation and Evaluation Metrics
4.4 Comparison with Baseline in Accurary(RQ1)
4.5 Comparison with Baseline in Efficiency (RQ2)
4.6 Effects of Dimension on EfficientLog (RQ3)
4.7 Ablation Study for Key Components (RQ4)
5 Conclusion
References
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