Advances in Knowledge Discovery and Data Mining

Name: Advances in Knowledge Discovery and Data Mining | 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part IV
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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part IV

Hisashi Kashima Tsuyoshi Ide Wen-Chih Peng(Editor)

Springer (Publisher)

Published on 29. May 2023

XII, 354 pages

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978-3-031-33383-5 (ISBN)

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Content

Intro
General Chairs' Preface
PC Chairs' Preface
Organization
Contents - Part IV
Scientific Data
Inline Citation Classification Using Peripheral Context and Time-Evolving Augmentation*-12pt
1 Introduction
2 Related Work
3 Methodology
3.1 Cross-Text Attention
3.2 Spatial Fusion
3.3 Time Evolving Augmentation
4 Experiments
4.1 Dataset
4.2 Implementation Details
5 Baselines
6 Analysis
7 Conclusion
References
Social Network Analysis
Post-it: Augmented Reality Based Group Recommendation with Item Replacement
1 Introduction
2 Problem Formulation
3 STAR3
3.1 Interaction- and Preference-Aware Graph Attention Network
3.2 Haptic-Aware Virtual Candidate Item Generator
3.3 Social- and Haptic-Aware Recommender
3.4 Overall Objective
4 Experiments
5 Conclusion
References
Proactive Rumor Control: When Impression Counts
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Influence Model
3.2 Influence Block
3.3 Problem Definition
4 Our Framework
4.1 A Baseline
4.2 Branch-and-Bound Framework
4.3 Computing Upper Bound
4.4 Analysis of Solutions
5 Progressive Branch-and-Bound
6 Experiments
6.1 Experimental Settings
6.2 Effectiveness Test
6.3 Efficiency Test
6.4 Scalability Test
7 Conclusion
References
Spatio-Temporal Data
Generative-Contrastive-Attentive Spatial-Temporal Network for Traffic Data Imputation
1 Introduction
2 Related Work
3 Preliminaries
4 The GCASTN Model
4.1 Generative-Contrastive Self-Supervised Learning
4.2 Data Augmentation via Two-Fold Cross Random Masking
4.3 GCASTN Encoder
4.4 GCASTN Decoder
5 Experiments
5.1 Datasets and Baselines
5.2 Experimental Results
6 Conclusion
References
Road Network Representation Learning with Vehicle Trajectories*-12pt
1 Introduction
2 Problem Definition
3 TrajRNE Approach
3.1 Spatial Flow Convolution
3.2 Structural Road Encoder
3.3 TrajRNE Overview
4 Experimental Evaluation
4.1 Datasets
4.2 Baselines
4.3 Downstream Tasks and Evaluation Metrics
4.4 Experimental Settings
4.5 Performance Results
4.6 Ablation Study
4.7 Parameter Study
5 Related Work
6 Conclusion
References
MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks*-12pt
1 Introduction
2 Problem Statement
3 The MetaCitta Approach
3.1 Spatial Encoder
3.2 Temporal Encoder
3.3 Prediction
3.4 Training Procedure
4 Evaluation Setup
4.1 Datasets
4.2 Baselines
4.3 Experimental Settings
5 Evaluation
5.1 Comparison with Baselines
5.2 Ablation Study
5.3 Training Time Comparison
6 Related Work
7 Conclusion
References
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems
1 Introduction
2 Preliminaries
2.1 Definitions
2.2 Problem Statement
3 Methodology
3.1 Framework Overview
3.2 Embedding Temporal Patterns of the Graph Stream Data
3.3 Generating Dynamic Weighted Attributed Graphs
3.4 Representation Learning for Weighted Attributed Graph
3.5 One-Class Detection with SVDD
4 Experiments
4.1 Experimental Settings
4.2 Experimental Results
5 Related Work
6 Conclusion
References
Texts, Web, Social Media
Words Can Be Confusing: Stereotype Bias Removal in Text Classification at the Word Level
1 Introduction
2 Methodology
2.1 Problem Formulation
2.2 Stereotype Words Detection
2.3 Fusion Model Training
2.4 Unbiased Prediction
3 Experiments
3.1 Settings
3.2 Classification Performance
3.3 Stereotype Word Fairness
3.4 Proportion of Stereotype Words
4 Conclusion
References
Knowledge-Enhanced Hierarchical Transformers for Emotion-Cause Pair Extraction
1 Introduction
2 Related Work
3 Proposed Method
3.1 Overall Architecture
3.2 Commonsense Knowledge Injection
3.3 Knowledge-Enhanced Clause Encoding
3.4 Emotion-Cause Pair Extraction
4 Experiments
4.1 Datasets and Metrics
4.2 Baselines
4.3 Implementation Details
4.4 Comparison with ECPE Methods
5 Conclusion and Future Work
References
PICKD: In-Situ Prompt Tuning for Knowledge-Grounded Dialogue Generation
1 Introduction
2 Related Work
3 Methodology
3.1 Formal Problem Definition
3.2 Contextual Prompting for Knowledge Selection
3.3 BART Fine-Tuning for Response Generation
4 Experimental Setup
4.1 Datasets
4.2 Baseline Methods
4.3 Evaluation Metrics
4.4 Implementation Details
5 Empirical Results
5.1 Automatic Evaluation
5.2 Impact of Prompt Length
5.3 Impact of Knowledge Length
5.4 Manual Evaluation
5.5 Error Analysis
6 Conclusion
References
Fake News Detection Through Temporally Evolving User Interactions
1 Introduction
2 Problem Formulation and Data Structure
3 Proposed Model
3.1 Local Sub-graph Encoding Module
3.2 Global Evolution Capturing Module
3.3 Neural Hawkes Process Module
3.4 Model Training
4 Experiment
4.1 Datasets
4.2 Baseline Methods
4.3 Experiment Setting
4.4 Performance Comparison
4.5 Ablation Study
4.6 Early Detection Performance
4.7 Case Study
5 Related Work
6 Conclusion
References
Improving Machine Translation and Summarization with the Sinkhorn Divergence*-12pt
1 Introduction
2 Related Work
3 Methodology
3.1 Sequence-to-Sequence Model Training
3.2 The Proposed Approach: A Contextual Sinkhorn Divergence
4 Experiments
4.1 Datasets
4.2 Models and Training
4.3 Results and Discussion
5 Conclusion
References
Dual-Detector: An Unsupervised Learning Framework for Chinese Spelling Check
1 Introduction
2 Method
2.1 Overview
2.2 Hybrid Mask Strategy
2.3 Detector Dec-Err
2.4 Candidate Table
2.5 Detector Dec-Eva
2.6 Training
3 Experiments
3.1 Datasets and Settings
3.2 Main Results
3.3 Analysis
4 Conclusion
References
QA-Matcher: Unsupervised Entity Matching Using a Question Answering Model
1 Introduction
2 Preliminaries
2.1 Question Answering
3 Proposed Method
3.1 Idea: Solving Entity Matching as Question Answering
3.2 Problem Setting
3.3 Framework
3.4 Retriever
3.5 Question and Passage Prompts
3.6 QA Classification
3.7 Reclassification
4 Experiments
4.1 Experimental Settings
4.2 Results
4.3 Ablation Study
4.4 Sensitivity Analysis
5 Related Work
6 Conclusion
References
Multi-task Student Teacher Based Unsupervised Domain Adaptation for Address Parsing
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Adaptive Pre-training Using MLM
3.2 Student-Teacher Framework
3.3 Consistency Regularisation Task
3.4 Boundary Detection Task
4 Experiments, Data and Results
4.1 Data
4.2 Experiment Setup
4.3 Baselines
4.4 Results, Ablation Studies, Parameter Study and Case Study
4.5 Training/Inference Time
5 Industrial Usecase
6 Conclusion and Future Work
References
Generative Sentiment Transfer via Adaptive Masking
1 Introduction
2 Problem Definition
3 Methodology
3.1 Framework
3.2 Adaptive Sentiment Token Masking
3.3 Infilling Blanks
4 Experiment
4.1 Experimental Settings
4.2 Quantitative Analysis
4.3 Ablation Study
4.4 Parameter Sensitivity Analysis
5 Conclusion
References
Unsupervised Text Style Transfer Through Differentiable Back Translation and Rewards
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Definition
3.2 Shared Encoding
3.3 Auto-Encoding
3.4 Differentiable Back-Translation
3.5 Reinforcement Learning
3.6 Learning Technique
4 Datasets, Experiments and Results
4.1 Datasets
4.2 Baselines
4.3 Automatic and Human Evaluation
5 Analysis
5.1 Ablation Studies
5.2 Case Study
5.3 Error Analysis
6 Conclusion and Future Works
References
Exploiting Phrase Interrelations in Span-level Neural Approaches for Aspect Sentiment Triplet Extraction*-12pt
1 Introduction
2 Related Works
3 Proposed Method
3.1 Contextual Input Representation
3.2 Span Construction
3.3 Span Filtering
3.4 Triplet Construction
3.5 Model Training
4 Experimental Evaluation
4.1 Experimental Setup
4.2 Results
5 Summary
References
What Boosts Fake News Dissemination on Social Media? A Causal Inference View
1 Introduction
2 Problem Definition
3 Our Framework
3.1 Preliminary
3.2 Causal Feature Representation Learning
3.3 Multimodal Covariates Embedding
4 Experiment
4.1 Evaluation Datasets
4.2 Experiment Setting
4.3 Main Results
4.4 Lexicons Boosting Dissemination
5 Related Work
6 Conclusion
References
Topic-Selective Graph Network for Topic-Focused Summarization
1 Introduction
2 Related Work
2.1 PLM-based Summarization
2.2 Topic-Guided Summarization
2.3 Graph Neural Network
3 Method
3.1 Base Topic-Focused Summarization Model
3.2 Topic-Arc Recognition
3.3 Summarization with Topic-Selective Graph Network
3.4 Training
4 Experiments
4.1 Dataset and Evaluation Metrics
4.2 Experimental Setting
4.3 Main Results
4.4 Ablation Study
4.5 Impact of Topic Node
4.6 Case Study
4.7 Human Evaluation
5 Conclusion
References
Time-Series and Streaming Data
RiskContra: A Contrastive Approach to Forecast Traffic Risks with Multi-Kernel Networks
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Definition
3.2 Spatial-temporal Accident Forecasting
3.3 Contrastive Learning with Mixup
3.4 Loss Function
4 Experiments
4.1 Comparison with the State-of-the-Art
4.2 Ablation Study and Visualization
5 Conclusion
References
Petrel: Personalized Trend Line Estimation with Limited Labels from One Individual
1 Introduction
2 Personalized Trend Line Estimation
2.1 Task Overview
2.2 Challenge of the Task
2.3 Petrel Model - Training
2.4 Petrel Model - Inference
3 Experiments
3.1 Experimental Datasets
3.2 Experimental Scenario
3.3 Baseline Methods
3.4 The Quality of the Estimated Personalized Trends
3.5 Case Study
4 Related Work
4.1 Trend Estimation
4.2 Few-Shot Learning
5 Conclusion
References
A Global View-Guided Autoregressive Residual Network for Irregular Time Series Classification
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Definition
3.2 Structure-Augmented Global Information Extractor
3.3 Global View-Guided Autoregressive Recurrent Neural Network
3.4 Masked Temporal Information Aggregator
3.5 Loss Function
4 Experiments
4.1 Datasets
4.2 Baselines
4.3 Implementation Details
4.4 Main Results
5 Conclusion
References
Quasi-Periodicity Detection via Repetition Invariance of Path Signatures
1 Introduction
2 Related Works
3 Definitions
4 Motivation
5 Theoretical Analysis
6 Algorithmic Design
7 Experiments
8 Discussion and Conclusion
References
Targeted Attacks on Time Series Forecasting*-4pt
1 Introduction
2 Background
3 Proposed Method
3.1 Fast Gradient Sign Method (FGSM) for TSF
3.2 Basic Iterative Method (BIM) for TSF
3.3 nVita (n-Values Time Series Attack)
4 Experimental Setup
5 Results
6 Conclusion
References
cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series
1 Introduction
2 Related Works
3 Proposed Method
3.1 Stochasting Gradient Descent for Evolving Data Streams
3.2 Stochasting Gradient Descent for Streaming Time Series
3.3 Continuous PNN (cPNN)
4 Experimental Evaluation
4.1 Generated Data Streams
4.2 Experimental Setting
5 Results
5.1 Classification Inversion Drift
5.2 Boundary Function Drift
6 Conclusion
References
Dynamic Variable Dependency Encoding and Its Application on Change Point Detection
1 Introduction
2 Related Works
3 Problem Setting and Notations
4 The Proposed DVDE Model
4.1 Module 1: Learning Nonlinear Univariate Features
4.2 Module 2: Learning Short-term Dependency
4.3 Module 3: Learning Actual Dependency
4.4 Module 4: Final Prediction and Application
5 Experiments
5.1 Experiments on Learning Dynamic Dependencies
5.2 Experiments on Change Point Detection
6 Conclusion
References
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Advances in Knowledge Discovery and Data Mining

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