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 I

• Autore: Yang De-Nian
• Pubblicazione: Singapore : , : Springer Singapore Pte. Limited, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (406 pages)
• Altri autori: XieXing TsengVincent S PeiJian HuangJen-Wei LinJerry Chun-Wei
• Nota di contenuto: Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models. ; 2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation -- 3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References -- SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection -- 1 Introduction -- 2 Proposed Model for Multi-view Anomaly Detection -- 2.1 The SeeM Model and Its Inference -- 2.2 Complexity Analysis -- 2.3 Anomaly Score -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Multi-view Anomaly Detection Performance -- 3.3 Latent Dimension Analysis -- 3.4 Non-linear Projections -- 3.5 A Use Case with Real-World Multi-view Data -- 4 Related Work -- 5 Conclusion -- References -- Classification -- QWalkVec: Node Embedding by Quantum Walk -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Quantum Walks on Graphs -- 3 Related Works -- 3.1 Problems -- 4 Proposed Method: QWalkVec -- 4.1 Algorithm -- 5 Evaluations. ; 5.1 Experimental Settings and Dataset -- 5.2 Overall Results -- 6 Conclusion -- References -- Human-Driven Active Verification for Efficient and Trustworthy Graph Classification -- 1 Introduction -- 2 Related Work -- 2.1 Human-in-the-loop Machine Learning -- 2.2 Deep Learning for Case-Based Reasoning -- 2.3 Interpretable Graph Neural Networks -- 3 Methodology -- 3.1 Problem Formulation and Framework Overview -- 3.2 Human-Compatible Representation Learning -- 3.3 Interpretable Predictor -- 3.4 Prediction Explanation -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementations and Configurations -- 4.3 Predictive Performance Comparison -- 4.4 Benefits of Human-AI Interactions -- 4.5 User Perception of Prediction Explanations -- 4.6 Is Instance-Level Feedback Helpful in Any Cases? -- 5 Discussions of Fairness and Ethical Issues -- 6 Conclusion and Future Work -- References -- SASBO: Sparse Attack via Stochastic Binary Optimization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Sparse Adversarial Attack via Stochastic Binary Optimization -- 4 Experiments and Results -- 4.1 Non-targeted Attack -- 4.2 Targeted Attack -- 4.3 Visualization -- 5 Conclusion -- References -- LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Overall Framework -- 3.3 Utterance Encoder -- 3.4 Malevolence Shift Detection -- 3.5 Hierarchy-Aware Label Encoder -- 3.6 Malevolence Detection in Dialogues -- 3.7 Multi-task Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Compared Baselines -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Analysis of Malevolence Shift Detection -- 4.7 Case Study -- 4.8 Analysis of LLMs -- 5 Conclusion -- References. ; Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features -- 1 Introduction -- 2 Preliminary -- 2.1 Knowledge Graph -- 2.2 Knowledge Graph Completion -- 2.3 Dynamic Graph Attention Variant GATv2 -- 3 Methodology -- 3.1 Structural Local Contexts Aggregation -- 3.2 High-Order Connected Contexts Aggregation -- 3.3 Decoder -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusions -- References -- Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations -- 1 Introduction -- 2 Related Works -- 2.1 Multi-label Recognition with Full Annotations -- 2.2 Multi-label Recognition with Limited Annotations -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Ambiguity-Aware Instance Weighting -- 3.3 Total Training Loss -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Ablation Studies -- 4.4 Model Analysis -- 5 Conclusion -- References -- Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification -- 1 Introduction -- 1.1 Node Classification -- 1.2 Graph Neural Network (GNN) -- 1.3 Chaotic Neural Oscillator (CNO) -- 2 Methodology -- 3 Experiment -- 3.1 Datasets -- 3.2 Settings and Baselines -- 3.3 Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Adversarial Learning of Group and Individual Fair Representations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Problem Statement -- 4.2 Model -- 4.3 Theoretical Properties of Loss Functions -- 4.4 Optimization with Focal Loss -- 5 Experiments and Analysis -- 6 Conclusion -- References -- Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage -- 1 Introduction -- 2 Methodology -- 2.1 Data Partitioning -- 2.2 Classification and Evaluation -- 3 Experimental Study -- 3.1 Datasets -- 3.2 Results. ; 4 Discussion and Recommendations -- 5 Conclusions and Future Work -- References -- Clustering -- Clustering-Friendly Representation Learning for Enhancing Salient Features -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 The Framework of cIDFD -- 3.2 Loss for Background Feature Extraction -- 3.3 Loss for Target Feature Extraction -- 3.4 Two-Stage Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison with Conventional Methods -- 4.3 Representation Distribution -- 4.4 Similarity Distribution -- 5 Conclusion -- References -- ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Contribution -- 2 Related Work -- 3 Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning (ImMC-CSFL) -- 3.1 Deep Feature Extraction Module -- 3.2 Common Information Learning Module -- 3.3 Specific Information Learning Module -- 3.4 Deep Multi-view Clustering Based on Common-Specific Feature Learning -- 4 Experiment -- 4.1 Experimental Datasets and Evaluation Criteria -- 4.2 Methods of Comparison -- 4.3 Experimental Results -- 5 Summary -- References -- Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes -- 1 Introduction -- 2 Multivariate Beta Mixture Model -- 2.1 Multivariate Beta Distribution -- 2.2 MBMM Density Function and Generative Process -- 2.3 Parameter Learning for the MBMM -- 2.4 The Similarity Score Between Data Points -- 3 Experiments -- 3.1 Comparisons on the Synthetic Datasets -- 3.2 Comparison on the Real Datasets -- 3.3 Distance Between Data Points -- 4 Related Work -- 5 Discussion -- References -- AutoClues: Exploring Clustering Pipelines via AutoML and Diversification -- 1 Introduction -- 2 Related Works -- 3 AutoClues -- 3.1 Formalization -- 3.2 Implementation. ; 4 Benchmark Generation and Empirical Evaluation.
• Titolo autorizzato: Advances in Knowledge Discovery and Data Mining
• ISBN: 981-9722-42-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996594167803316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science Series