Web and Big Data

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

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

Springer (Publisher)

Published on 27. April 2024

XVII, 524 pages

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978-981-97-2387-4 (ISBN)

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Content

Intro
Preface
Organization
Contents - Part III
Adaptive Graph Attention Hashing for Unsupervised Cross-Modal Retrieval via Multimodal Transformers
1 Introduction
2 Related Work
3 Methodology
3.1 Notation and Problem Definition
3.2 Framework Overview
3.3 Objective Function
4 Experiments
4.1 Evaluation Metrics
4.2 Implementation Details
4.3 Comparison Results and Analysis
4.4 Ablation Study
4.5 Parameter Sensitivity Analysis
4.6 Convergence Testing
5 Conclusion
References
Answering Property Path Queries over Federated RDF Systems
1 Introduction
2 Related Work
3 Problem Formulation
4 The Proposed Method
4.1 Query Decomposition and Source Localization
4.2 Thompson-Based MinDFA Construction
4.3 Query Execution Strategy Base on B-DFS
5 Evaluation
5.1 Experimental Environment
5.2 Performance Comparison of Five Property Path Query Symbols
5.3 Performance and Resource Consumption of Different Matching Strategies of MinDFA
5.4 Performance Robustness of Five Property Path Query Symbols
6 Conclusion
References
Distributed Knowledge Graph Query Acceleration Algorithm
1 Introduction
2 Related Works
3 Offline Module for Distributed Construction of Indexes
3.1 MapReduce-Based Data Pre-processing
3.2 Coding-Oriented Construction of Distributed Hierarchical Clustering
4 Online Module for Distributed Parallel Processing of SPARQL Queries
4.1 Splitting and Loading of BitSet-Tree
4.2 Candidate Solution Acquisition
4.3 Shuffle
4.4 Merge Splicing of Candidate Vertices
5 Experiments
5.1 Experimental Settings
5.2 Results and Discussion
6 Conclusions
References
Truth Discovery of Source Dependency Perception in Dynamic Scenarios
1 Introduction
2 Problem Setting
2.1 Notation Definition
2.2 Task Description
3 Preliminary
3.1 Source Dependency Detection Based on Bayesian Model
3.2 Truth Discovery Framework Based on Optimization Model
4 Methodology
4.1 Source Dependency Detection in Dynamic Scenarios
4.2 Dynamic Incremental Model Framework
4.3 Truth Discovery with Source Dependency Perception
5 Experiment
5.1 Experimental Setup
5.2 The Results on Real-World Datasets
5.3 The Results on Synthetic Datasets
6 Related Work
7 Conclusion
References
Truth Discovery Against Disguised Attack Mechanism in Crowdsourcing
1 Introduction
2 Related Work
3 Preliminary
3.1 Disguised Attack Mechanism
3.2 Problem Formulation
3.3 Truth Discovery
4 Methodology
4.1 Behavior-Based Truth Discovery
4.2 Task Assignment Based on WAM
4.3 TD-DA Framework
5 Experiments
5.1 Experiment Setting
5.2 Verification of the Proportion of Malicious Workers
5.3 Experiment on Real-World Datasets
5.4 Experiment on Synthetic Datasets
6 Conclusion
References
Continuous Group Nearest Group Search over Streaming Data
1 Introduction
2 Preliminary
2.1 Related Works
2.2 Problem Definition
3 The Framework KMPT
3.1 The Basic Idea
3.2 The Initialization Algorithm
3.3 The Incremental Maintenance Algorithm
4 The Experiment
4.1 Experiment Settings
4.2 Performance Comparison
5 Conclusion
References
Approximate Continuous Skyline Queries over Memory Limitation-Based Streaming Data
1 Introduction
2 Preliminary
2.1 Related Works
2.2 Problem Definition
3 The Self-adaptive-based Framework -SEAK
3.1 The -CSS Definition
3.2 The Initialization Algorithm
3.3 The Incremental Maintenance Algorithm
3.4 The Partition-Based Optimization Algorithm
4 Performance Evaluation
4.1 Experiment Settings
4.2 Experimental Evaluation
5 Conclusion
References
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection
1 Introduction
2 Related Work
2.1 Attacks on Model Faithfulness
2.2 Defenses Against Backdoor Attack
3 Preliminaries and Attack Formulation
3.1 Federated Learning
3.2 Threat Model
3.3 Choice of Backdoor Triggers
4 Methodology
4.1 Motivation
4.2 Segmenting Local Updates
4.3 Identifying Outliers in Fragments
4.4 Pruning Backdoored Participants
5 Experiments
5.1 Experimental Settings
5.2 Effectiveness of Our Defense
5.3 Comparison with Prior Arts
5.4 Effectiveness on Advanced Attacks
5.5 Ablation Study
6 Conclusion
References
PaTraS: A Path-Preserving Trajectory Simplification Method for Low-Loss Map Matching
1 Introduction
2 Related Work
2.1 Error-Bounded Line Simplification
2.2 Semantic-Preserving Trajectory Simplification
2.3 Analysis of Existing Work
3 Preliminaries
3.1 Basic Definitions
3.2 Methodology Analysis
4 Path-Preserving Trajectory Simplification
4.1 Overview of PaTraS
4.2 Preserving Shortest Paths
4.3 Candidates Pairing
4.4 Similarity Computation
4.5 Pairing Optimization
5 Experiments
5.1 Experimental Setup
5.2 Evaluation Oriented to Map-Matching
5.3 Parameter Sensitivity Study
6 Conclusion
References
Coordinate Descent for k-Means with Differential Privacy
1 Introduction
2 Related Work
3 Preliminary
3.1 k-Means
3.2 Coordinate Descent for k-Means
3.3 A Fast Version of CDKM
3.4 Differential Privacy
4 Proposed Our Method
4.1 Approximate CDKM
4.2 Proposed DP-ACDKM
4.3 Privacy Analysis
5 Experiments
5.1 Privacy-Utility Trade-Off
5.2 Convergence
6 Conclusion
References
DADR: A Denoising Approach for Dense Retrieval Model Training
1 Introduction
2 Related Work
3 Method
3.1 Task Formulation
3.2 Denoising Approach Based on Dynamical Weight
4 Experiment
4.1 Dataset and Metrics
4.2 Experiment Settings
4.3 Experiment Results
5 Conclusions
References
Multi-pair Contrastive Learning Based on Same-Timestamp Data Augmentation for Sequential Recommendation
1 Introduction
2 Related Work
2.1 Self-supervised Learning
2.2 Sequential Recommendation
3 The Proposed Model
3.1 Problem Definition
3.2 Model Framework
3.3 Data Augmentaion
3.4 Masking Operation
3.5 Embedding Layer
3.6 BERT Encoder
3.7 Prediction Layer
3.8 Multi-pair Contrastive Learning
4 Experiments
4.1 Experimental Setup
4.2 Experimental Results
4.3 Hyperparameter Experiments
4.4 Ablation Study
5 Conclusion
References
Enhancing Collaborative Features with Knowledge Graph for Recommendation
1 Introduction
2 Related Work
3 Preliminaries
4 Methodology
4.1 KG Explore Module
4.2 Multi-IMP-GCN
4.3 Model Prediction
4.4 Model Optimization
5 Experiments
5.1 Experimental Settings
5.2 Performance Comparison
5.3 Ablation Studies
5.4 Impacts of Multi-IMP-GCN
6 Conclusion and Future Work
References
PageCNNs: Convolutional Neural Networks for Multi-label Chinese Webpage Classification with Multi-information Fusion
1 Introduction
2 Multi-label Chinese Webpage Classification Models
3 Experimental Results and Discussions
3.1 Multi-label Chinese Webpage Dataset
3.2 Implementation Details and Evaluation Metrics
3.3 Multi-label Chinese Webpage Classification Results
4 Conclusion
References
MFF-Trans: Multi-level Feature Fusion Transformer for Fine-Grained Visual Classification
1 Introduction
2 Related Works
2.1 CNN-Based FGVC Methods
2.2 ViT-Based FGVC Methods
3 Proposed Method
3.1 Vision Transformer Encoder
3.2 Important Token Election Module
3.3 Semantic Connection Enhancing Module
4 Experiments
4.1 DataSets and Implement Details
4.2 Comparisons with Advanced Methods
4.3 Ablation Studies
5 Conclusion
References
Summarizing Doctor's Diagnoses and Suggestions from Medical Dialogues
1 Introduction
2 Related Work
3 Model
3.1 Pointer Generator Network as Backbone
3.2 Input Token Enhancement by Speaker-level Embedding
3.3 Input Token Enhancement by Utterance-level Embedding
4 Experiment
4.1 Dataset
4.2 Baseline Models
4.3 Settings
4.4 Evaluation Metrics
4.5 Automatic Evaluation
4.6 Doctor Evaluation
4.7 Case Study
5 Conclusion
References
HSA: Hyperbolic Self-attention for Sequential Recommendation
1 Introduction
2 Preliminaries and Related Work
2.1 Empirical Analysis of Datasets
2.2 Lorentz Model of Hyperbolic Space
2.3 Self-attention Mechanism for Sequential Recommendation
3 Proposed Approach
3.1 Problem Formulation and Approach Overview
3.2 Item Embeddings in Hyperbolic Space
3.3 Sequence Learning with Self-attention Mechanism
3.4 Prediction Layer
3.5 Model Training
4 Experiments
4.1 Experimental Setup
4.2 Experimental Results
4.3 Performance as a Plugin on Baselines
5 Conclusion
References
CFGCon: A Scheme for Accurately Generating Control Flow Graphs of Smart Contracts
1 Introduction
2 Related Work
3 Preliminaries
4 Proposed Scheme
4.1 Overview of the System Model
4.2 Transform Module
4.3 Division Module
4.4 Connection Module
5 Experiment and Performance Evaluation
5.1 Dataset
5.2 General Test for CFGCon
5.3 Performance Comparision with Existing Approaches
6 Conclusion
References
Hypergraph-Enhanced Self-supervised Heterogeneous Graph Representation Learning
1 Introduction
2 Related Work
2.1 Heterogeneous Graph Embedding
2.2 Hypergraph Embedding
2.3 Self-supervised Learning on Graphs
3 Problem Formulation
4 The Design of HHGR
4.1 Hypergraph Construction
4.2 Hypergraph Encoder
4.3 Cross-View Contrast
4.4 Semantic Positive Samples
5 Experiments
5.1 Experimental Settings
5.2 Node Classification
5.3 Node Clustering
5.4 Visualization
5.5 Ablation Studies
5.6 Analysis of Hyper-parameters
6 Conclusion
References
LAF: A Local Depth Autoregressive Framework for Cardinality Estimation of Multi-attribute Queries
1 Introduction
2 Related Work
3 Problem Description
3.1 Supported Queries
3.2 Density Estimation
4 Local Deep Autoregressive Framework
4.1 Overview
4.2 Encoding Tuples and Dividing Subsets
4.3 Model Architectures
4.4 Model Training with Data and Query
4.5 Estimating Cardinality
5 Experiment
5.1 Experimental Settings
5.2 Performance Comparison
5.3 Attribute Ordering
6 Conclusion
References
MGCN-CT: Multi-type Vehicle Fuel Consumption Prediction Based on Module-GCN and Config-Transfer
1 Introduction
2 Related Work
2.1 Fuel Consumption Prediction
2.2 Time Series Prediction
2.3 Transfer Learning
3 Preliminary
4 Method
4.1 Module Graph with Domain Knowledge
4.2 Module Graph Convolution Network
4.3 Configuration Transfer Module Based on Classifier
4.4 Pre-train and Prediction
5 Experiments
5.1 Experiments Setup
5.2 Fuel Consumption Prediction Performance
5.3 Ablation Studies
5.4 MGCN Module Performance
5.5 CT Module Performance
6 Conclusion
References
Hardware and Software Co-optimization of Convolutional and Self-attention Combined Model Based on FPGA
1 Introduction
2 Background
2.1 Conv+Transformer
2.2 Attention Augmented Convolutional
2.3 Hardware Optimization
3 Algorithm and Hardware Co-optimization
3.1 Quantitative
3.2 Overall Architecture
3.3 Hardware Layer Design
3.4 Softmax Module
3.5 LayerNorm Module
4 Experiments
4.1 Experimental Setup
4.2 Channel Ratio Comparison Experiment
4.3 Hardware Implementation Results
4.4 Multi-platform Comparison
5 Conclusion
References
FBCA: FPGA-Based Balanced Convolutional Attention Module
1 Introduction
2 Related Works
3 Software Design
3.1 Lite Transformer
3.2 Quantization
4 Hardware Design
4.1 The Accelerator Architecture
4.2 Matrix Multiplication Module
4.3 Convolutional Computing Module
4.4 Layer Parallel Optimization
5 Experiment
5.1 Experimental Setup
5.2 Experimental Results
5.3 Multi-platform Comparison
6 Conclusion
References
Multi-level Matching of Natural Language-Based Vehicle Retrieval
1 Introduction
2 Related Work
2.1 Vehicle Retrieval
2.2 Vehicle Re-identification
2.3 Video Retrieval Based on Natural Language
3 Method
3.1 Data Enhancement
3.2 Representation Learning
3.3 Loss Calculation
4 Experiment
4.1 Settings
4.2 Metrics
4.3 Comparison Results
4.4 Ablation Experiment
4.5 Result
5 Conclusion
References
Improving the Consistency of Semantic Parsing in KBQA Through Knowledge Distillation
1 Introduction
2 Method
2.1 Problem Definition
2.2 Teacher Guidance Model
2.3 Student Dynamic Distillation Learning Model
2.4 Dynamic Weight Assignment Model
3 Experiments and Result Analysis
3.1 Experimental Settings
3.2 Experimental Results
4 Related Work
5 Conclusion and Future Work
References
DYGL: A Unified Benchmark and Library for Dynamic Graph
1 Introduction
2 The Framework Design
2.1 Dataset Structures
2.2 Data Normalization Initiative
2.3 Comprehensive Models
2.4 Fast Initialization and Unified Process
3 Experiments with Library
3.1 Compared Methods
3.2 Datasets
3.3 Experimental Setting
3.4 Results and Discussion
3.5 Results
4 Conclusion
References
TrieKV: Managing Values After KV Separation to Optimize Scan Performance in LSM-Tree
1 Introduction
2 Background and Motivation
3 TrieKV Design
3.1 Architectural Overview
3.2 DyTrieIndex
3.3 pbCompaction
3.4 Main Procedure
4 Evaluation
4.1 Experimental Setup
4.2 Range Query Performance
4.3 Point Query Performance
4.4 Write Performance
4.5 Evaluate TrieKV Under YCSB Default Workloads
5 Related Work
6 Conclusion
References
Bit Splicing Frequent Itemset Mining Algorithm Based on Dynamic Grouping
1 Introduction
2 Related Work
3 Proposed Method
3.1 Preprocessing
3.2 Dynamic Grouping
3.3 Build MPL
3.4 Mining Based on MPL
4 Pruning Strategy
4.1 Consecutive Supersets Pruning
4.2 Grouping Pruning
5 Experiment
5.1 Test Environment and Dataset
5.2 Time Evaluation
5.3 Memory Evaluation
5.4 Result Analysis
6 Conclusion
References
Entity Resolution Based on Pre-trained Language Models with Two Attentions
1 Introduction
2 Related Work
3 Pre-trained Model with Two Attentions
3.1 Problem Definition
3.2 Framework of IGaBERT Model
3.3 Interactive Attention
3.4 Comparison
3.5 Global Attention
3.6 Gate and Binary Classifier
4 Experimental Results
4.1 Parameters and Settings
4.2 Data Augmentation
4.3 Datasets
4.4 Comparison with Existing Models
4.5 Ablation Study
4.6 Case Study
5 Conclusion
References
A High-Performance Hybrid Index Framework Supporting Inserts for Static Learned Indexes
1 Introduction
2 Motivation
2.1 Dynamic PGM-Index
2.2 The Problem
2.3 The Reason
3 Hybrid Index Framework
3.1 Overview
3.2 The Dynamic Layer for Inserts
3.3 The Static Layer for Lookups
3.4 Self-tuning Algorithm
4 Performance Evaluation
4.1 Experimental Settings
4.2 Performance Evaluation Under Read-Write Workloads
4.3 Lookup Performance Under Read-Write Workloads
4.4 Insert Performance Under Read-Write Workloads
4.5 Learning Overhead of Hybrid Index Framework
5 Related Work
6 Conclusion
References
A Study on Historical Behaviour Enabled Insider Threat Prediction
1 Introduction
2 Related Works
3 Methodology
3.1 Problem Setting
3.2 Framework
3.3 ML and DL Models
4 Experiments
4.1 Dataset and Data Processing
4.2 ML and DL Performance Comparison
4.3 The Impact of Historical Window Length
5 Conclusion
References
PV-PATE: An Improved PATE for Deep Learning with Differential Privacy in Trusted Industrial Data Matrix
1 Introduction
2 Related Work
2.1 DP Mechanisms for DL
2.2 DP-SGD vs PATE
3 Background and Notation
3.1 Differential Privacy
3.2 Rényi Differential Privacy
3.3 PATE and PV-PATE
4 Personalized Voting for PATE in TDM
4.1 Teacher Credibility
4.2 Adaptive Voting Mechanism
4.3 Model Sharing Mechanism
4.4 Privacy Analysis
5 Experiments
5.1 Datasets and Models
5.2 Aggregation Mechanism with Teacher Credibility
5.3 Student Training with Adaptive Voting Mechanism
5.4 Student Training with Model Sharing Mechanism
6 Conclusion
References
LayerBF: A Space Allocation Policy for Bloom Filter in LSM-Tree
1 Introduction
2 Background and Motivation
2.1 LSM-Tree
2.2 Bloom Filter
2.3 Motivation
3 Design
3.1 Main Idea
3.2 Minimize Average False Positive Rate
3.3 LayerBF Allocation Strategy
4 Evalution
4.1 Experimental Setup
4.2 Average False Positive Rate
4.3 Overall Performance Evaluation
4.4 YCSB Benchmarks
5 Related Work
6 Conclusion
References
HTStore: A High-Performance Mixed Index Based Key-Value Store for Update-Intensive Workloads
1 Introduction
2 Background and Motivation
2.1 Log-Structured Merge-Tree (LSM-Tree)
2.2 Non-volatile Memory (NVM)
2.3 Motivation
3 HTStore Design
3.1 Mixed Index Design
3.2 Eliminating Redundant Data
3.3 Accelerating Point Query
4 Performance Evaluation
5 Related Work
6 Conclusions and Future Work
References
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Web and Big Data

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