Advances in Knowledge Discovery and Data Mining

Name: Advances in Knowledge Discovery and Data Mining | 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI
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28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI

De-Nian Yang Xing Xie Vincent S. Tseng Jian Pei Jen-Wei Huang Jerry Chun-Wei Lin(Editor)

Springer (Publisher)

Published on 24. April 2024

XXXIII, 302 pages

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978-981-97-2266-2 (ISBN)

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Content

Intro
General Chairs' Preface
PC Chairs' Preface
Organization
Contents - Part VI
Scientific Data
FR3LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series
1 Introduction
2 Related Work
3 Problem Setup
4 Model Architecture
4.1 Temporal Contextual Consistency
4.2 Non-contrastive Representations Learning
4.3 Deterministic Forecasting
4.4 Probabilistic Forecasting
4.5 End-to-End Training
5 Experiments
5.1 Experimental Results
5.2 Visualization of Latent and Original Series Forecasts
5.3 Further Experimental Setup Details
6 Conclusion
References
Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations
1 Introduction
2 Preliminaries on Numerical Simulations
3 Identification and Analysis of Relevant Metadata
4 Efficient Metadata Capture for Parameter Optimization
4.1 Early Termination of Farming Runs
4.2 PROBE: Probing Specific Parameter Combinations
5 Experimental Evaluation
5.1 Quality of Parameter Optimization Using PROBE
5.2 Efficiency Evaluation
5.3 Reuse of Metadata Acquired Through PROBE
5.4 Generalization to Other Model Problems and Schemes
6 Conclusion and Outlook
References
Material Microstructure Design Using VAE-Regression with a Multimodal Prior
1 Introduction
2 Methodology
3 Related Work
4 Experimental Results
5 Summary and Conclusions
References
A Weighted Cross-Modal Feature Aggregation Network for Rumor Detection
1 Introduction
2 Related Work
2.1 Rumor Detection
2.2 Multimodal Alignment
3 Methodology
3.1 Overview of WCAN
3.2 Feature Extraction
3.3 Weighted Cross-Modal Aggregation Module
3.4 Multimodal Feature Fusion
3.5 Objective Function
4 Experiments
4.1 Datasets
4.2 Baselines
4.3 Ablation Experiment
4.4 Hyper-parameter Analysis
4.5 Visualization on the Representations
4.6 Case Study
5 Conclusions
References
Texts, Web, Social Network
Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers
1 Introduction
2 Related Work
3 Preliminaries
4 Echo Chamber Detection in Signed Networks
5 SEcho Method
5.1 SEcho Metric
5.2 Greedy Optimisation
6 Experiments
7 Conclusion
References
KiProL: A Knowledge-Injected Prompt Learning Framework for Language Generation
1 Introduction
2 Methodology
2.1 Problem Statement
2.2 Knowledge-Injected Prompt Learning Generation
2.3 Training and Inference
3 Experiments
3.1 Datasets
3.2 Settings
3.3 Automatic Evaluation
3.4 Human Annotation
3.5 Ablation Study
3.6 In-Depth Analysis
3.7 Case Study
4 Conclusion
References
GViG: Generative Visual Grounding Using Prompt-Based Language Modeling for Visual Question Answering
1 Introduction
2 Related Work
2.1 Pix2Seq Framework
2.2 Prompt Tuning
3 Methodology
3.1 Prompt Tuning Module
3.2 VG Module
3.3 Conditional Trie-Based Search Algorithm (CTS)
4 Results
4.1 Dataset Description
4.2 Results on WSDM 2023 Toloka VQA Dataset Benchmark
5 Discussion
5.1 Prompt Study
5.2 Interpretable Attention
6 Conclusion
References
Aspect-Based Fake News Detection
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Definition and Model Overview
3.2 Aspect Learning and Extraction
3.3 News Article Classification
4 Experiments
4.1 Datasets
4.2 Experimental Settings
4.3 Evaluation
5 Analysing the Effect of Aspects Across Topics
6 Discussion and Future Work
7 Conclusion
References
DQAC: Detoxifying Query Auto-completion with Adapters
1 Introduction
2 Related Work
3 Methodology
3.1 QDetoxify: Toxicity Classifier for Search Queries
3.2 The DQAC Model
4 Experimental Setup
5 Results and Analyses
6 Conclusions
References
Graph Neural Network Approach to Semantic Type Detection in Tables
1 Introduction
2 Related Works
3 Problem Definition
4 GAIT
4.1 Single-Column Prediction
4.2 Graph-Based Prediction
4.3 Overall Prediction
5 Evaluation
5.1 Evaluation Method
5.2 Results
6 Conclusion
References
TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media
1 Introduction
2 Related Work
3 The Proposed TCGNN Method
3.1 Text-Clustering Graph Construction
3.2 Model Training
4 Experiments
5 Conclusions and Discussion
References
Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Formulation and Motivation
3.2 Standalone ABSA Tasks
3.3 Adaptive Contextual Threshold Masking (ACTM)
3.4 Adaptive Attention Masking (AAM)
3.5 Adaptive Mask Over Masking (AMOM)
3.6 Training Procedure for ATE and ASC
4 Experiments and Results
5 Conclusion
References
An Automated Approach for Generating Conceptual Riddles
1 Introduction
2 Related Work
3 Methodology
3.1 Triples Creator
3.2 Properties Classifier
3.3 Generator
3.4 Validator
4 Evaluation and Results
5 Conclusion and Future Work
References
Time-Series and Streaming Data
DiffFind: Discovering Differential Equations from Time Series
1 Introduction
2 Background and Related Work
2.1 Related Work
2.2 Background - Genetic Algorithms for Architecture Search
3 Proposed Method: DiffFind
4 Experiments
4.1 Q1 - DiffFind is Effective
4.2 Q2 - DiffFind is Explainable
4.3 Q3 - DiffFind is Scalable
5 Conclusions
References
DEAL: Data-Efficient Active Learning for Regression Under Drift
1 Introduction
2 Related Work
3 Problem Statement and Notation
4 Our Method: DEAL
4.1 The Adapted Stream-Based AL Cycle
4.2 Our Drift-Aware Estimation Model
5 Experimental Design
5.1 Baselines
5.2 Evaluation Data
5.3 Evaluation Metrics
6 Evaluation
6.1 Comparison of DEAL Against Baselines
6.2 Impact of the User-Required Error Threshold
7 Conclusion
References
Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting
1 Introduction
2 Related Models
3 Evolving Super Graph Neural Networks
3.1 Preliminary Notations
3.2 Super Graph Construction
4 Diffusion on Evolving Super Graphs
4.1 Predictor
5 Experiments on Large-Scale Datasets
5.1 Forecasting Result and Analysis
5.2 Runtime and Space Usage Analysis
5.3 Ablation Study
6 Conclusion
References
Unlearnable Examples for Time Series
1 Introduction
2 Related Work
2.1 Data Poisoning
2.2 Adversarial Attack
2.3 Unlearnable Examples
3 Error-Minimizing Noise for Time Series
3.1 Objective
3.2 Threat Model
3.3 Challenges
3.4 Problem Formulation
3.5 A Straightforward Baseline Approach
3.6 Controllable Noise on Partial Time Series Samples
4 Experiments
4.1 Experiment Setup
4.2 Against Classification Models
4.3 Against Generative Models
5 Conclusion
References
Learning Disentangled Task-Related Representation for Time Series
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Overview
3.2 Task-Relevant Feature Disentangled
3.3 Task-Adaptive Augmentation Selection
4 Experiments and Discussions
4.1 Datasets and Implementation Details
4.2 Ablation Analysis
4.3 Results on Classification Tasks
4.4 Results on Forecasting Tasks
4.5 Visualization Analysis
5 Conclusion
References
A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification
1 Introduction
2 Related Works
3 Problem Formulation
4 Methodology
4.1 Feature Construction
4.2 Multi-view Representation
4.3 Multi-Encoder-Decoder Transformer (MEDT) Classification
5 Experiments
5.1 Experiments Using Multivariate Time Series Data Benchmarks
5.2 Experiment Using a Real-World Physical Activities Dataset
6 Conclusion
References
Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting
1 Introduction
2 Related Work
3 Preliminaries
3.1 Key Concepts
3.2 Self-representation Learning in Time Series
3.3 Kernel Trick for Modeling Time Series
4 Proposed Method
4.1 Kernel Representation Learning: Modeling Regime Behavior
4.2 Forecasting
5 Experiments
5.1 Data
5.2 Experimental Setup and Evaluation
5.3 Regime Identification
5.4 Benchmark Comparison
5.5 Ablation Study
6 Conclusion
References
Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting
1 Introduction
2 Problem Statement
3 MLPs for Time Series Forecasting
3.1 Nlinear Model
4 Hyperparameters
4.1 Time Series Specific Configuration
4.2 Training Specific Configurations
4.3 TSBench-Metadataset
5 Experimental Setup
6 Results
7 Conclusion
References
Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification
1 Introduction
2 Related Work
2.1 Graph-Based Time Series Classification
2.2 Lower Bound of DTW
3 Problem Formulation
4 Methodology
4.1 Batch Sampling
4.2 LB_Keogh Graph Construction
4.3 Graph Convolution and Classification
4.4 Advantages of Our Model
5 Experimental Evaluation
5.1 Comparing with 1NN-DTW
5.2 Graph Construction Time Comparison with SimTSC
6 Conclusion
References
STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction
1 Introduction
2 Related Work
3 Proposed Model
3.1 Preliminaries and Problem Definition
3.2 Graph Convolution
3.3 Memory-Augmented Attention (MAA) Module
3.4 Memory Updating
4 Experimental Results and Analysis
5 Ablation Study
6 Conclusion
References
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