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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 III
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 III
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (448 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-59-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part III -- Interpretability and Explainability -- Neural Additive and Basis Models with Feature Selection and Interactions -- 1 Introduction -- 2 Generalized Additive Models (GAMs) -- 2.1 Neural Additive Model (NAM) -- 2.2 Neural Basis Model (NBM) -- 3 NAM and NBM with Feature Selection -- 3.1 Motivation -- 3.2 Model Architecture -- 3.3 Implementation Remark -- 4 Discussion of Model Complexities -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Baselines -- 5.3 Results -- 6 Conclusion -- References -- Random Mask Perturbation Based Explainable Method of Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Explainable Method -- 4.1 Node Importance Based on Fidelity -- 4.2 Explanation Sparsity -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Quantitative Experiments -- 5.3 Ablation Study -- 5.4 Use Case -- 6 Conclusion -- References -- RouteExplainer: An Explanation Framework for Vehicle Routing Problem -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework: RouteExplainer -- 3.1 Many-to-Many Edge Classifier -- 3.2 Counterfactual Explanation for VRP -- 4 Experiments -- 4.1 Quantitative Evaluation of the Edge Classifier -- 4.2 Qualitative Evaluation of Generated Explanations -- 5 Conclusion and Future Work -- References -- On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values -- 1 Introduction -- 2 Related Work -- 3 Outlier Detection Ensembles -- 4 The bagged Shapley Values -- 5 Theoretical Guarantees for the Approximation -- 6 Experiments -- 6.1 Quality of the Approximation -- 6.2 Effectiveness -- 6.3 Scalability -- 7 Conclusions -- References -- Interpreting Pretrained Language Models via Concept Bottlenecks -- 1 Introduction -- 2 Related Work -- 2.1 Interpreting Pretrained Language Models.
2.2 Learning from Noisy Labels -- 3 Enable Concept Bottlenecks for PLMs -- 3.1 Problem Setup -- 4 C3M: A General Framework for Learning CBE-PLMs -- 4.1 ChatGPT-Guided Concept Augmentation -- 4.2 Learning from Noisy Concept Labels -- 5 Experiments -- 6 Conclusion -- A Definitions of Training Strategies -- B Details of the Manual Concept Annotation for the IMDB Dataset -- C Implementation Detail -- D Parameters and Notations -- E Statistics of Data Splits -- F Statistics of Concepts in Transformed Datasets -- G More Results on Explainable Predictions -- H A Case Study on Test-Time Intervention -- I Examples of Querying ChatGPT -- References -- Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Jacobian Saliency Map (JSM) -- 3.2 Jacobian-Augmented Loss Function (JAL) -- 4 Experiments -- 4.1 Dataset -- 4.2 Preprocessing -- 4.3 Multimodal Classification -- 4.4 Performance Evaluation -- 5 Conclusion -- References -- Towards Nonparametric Topological Layers in Neural Networks -- 1 Introduction -- 1.1 Background -- 1.2 Motivation and Challenges -- 1.3 Contributions -- 2 Preliminaries and Related Work -- 2.1 Basics of Topology -- 2.2 Topological Neural Network -- 2.3 Functional Spaces for Machine Learning -- 3 Methodology -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Implementation -- 4.3 Overall Performance -- 4.4 Learning Rate -- 4.5 Temporal-Spatial Correlation -- 5 Conclusion -- References -- Online, Streaming, Distributed Algorithms -- Streaming Fair k-Center Clustering over Massive Dataset with Performance Guarantee -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Related Work -- 1.3 Our Contribution -- 2 A Two-Pass Algorithm with Approximation Ratio 3 -- 2.1 The -Independent Center Set -- 2.2 The Two-Pass Streaming Algorithm.
3 The Streaming Algorithm with an Approximation Ratio 7 -- 3.1 The Streaming Algorithm for Constructing 1 and 2 -- 3.2 Post-streaming Construction of Center Set C from 12 -- 4 Experimental Results -- 4.1 Experimental Setting -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Projection-Free Bandit Convex Optimization over Strongly Convex Sets -- 1 Introduction -- 2 Related Work -- 2.1 Projection-Free OCO Algorithms -- 2.2 Bandit Convex Optimization -- 3 Main Results -- 3.1 Preliminaries -- 3.2 Our Proposed Algorithm -- 3.3 Theoretical Guarantees -- 4 Experiments -- 4.1 Problem Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Adaptive Prediction Interval for Data Stream Regression -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Adaptive Prediction Interval(AdaPI) -- 5 Experiments and Results -- 5.1 Comparison to Interval Forecast -- 5.2 Comparison Between MVE and AdaPI -- 6 Conclusions -- References -- Probabilistic Guarantees of Stochastic Recursive Gradient in Non-convex Finite Sum Problems -- 1 Introduction -- 1.1 Related Works -- 1.2 Our Contributions -- 1.3 Notation -- 2 Prob-SARAH Algorithm -- 3 Theoretical Results -- 3.1 Technical Assumptions -- 3.2 Main Results on Complexity -- 3.3 Proof Sketch -- 4 Numerical Experiments -- 4.1 Logistic Regression with Non-convex Regularization -- 4.2 Two-Layer Neural Network -- 5 Conclusion -- References -- Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Model Overview -- 3.2 Client Model Clustering -- 3.3 Dynamic Weighted Graph Construction -- 3.4 Knowledge Propagation and Aggregation -- 3.5 Precise Personalized Model Distribution -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 4.4 Case Study -- 4.5 Hyperparameter Study.
5 Conclusion -- References -- Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Threat Model -- 3.2 FMs Empowered Backdoor Attacks to HFL -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Experimental Results -- 4.3 Homogeneous Setting Evaluation -- 4.4 Case Study: Attack Effectiveness v.s. Public Data Utilization Ratio -- 4.5 Hyper-Parameter Study: ASR v.s. Poisoning Ratio -- 5 Conclusion -- References -- Combating Quality Distortion in Federated Learning with Collaborative Data Selection -- 1 Introduction -- 2 Related Works -- 3 Proposal -- 3.1 Preliminaries -- 3.2 Design Principle -- 3.3 Collaborative Sample Selection (CSS) -- 4 Evaluation -- 4.1 Datasets and Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Probabilistic Models and Statistical Inference -- Neural Marked Hawkes Process for Limit Order Book Modeling -- 1 Introduction -- 2 Background -- 3 Neural Marked Hawkes Process -- 4 Related Work -- 5 Experiments -- 6 Conclusion -- References -- How Large Corpora Sizes Influence the Distribution of Low Frequency Text n-grams -- 1 Introduction -- 2 Background and Related Work -- 3 The Model -- 4 Results -- 4.1 The Corpora Collection -- 4.2 The Range of k Values for W(k,C -- L,n) Prediction -- 4.3 The Assessment Criteria and Parameter Estimation -- 4.4 Comparison with Other Models -- 4.5 Obtained Results -- 4.6 The Predictions with Growing Corpus Size -- 5 Conclusions -- References -- Meta-Reinforcement Learning Algorithm Based on Reward and Dynamic Inference -- 1 Introduction -- 2 Background -- 2.1 Meta-Reinforcement Learning -- 2.2 Context-Based Meta-Reinforcement Learning -- 2.3 Parametric Task Distributions -- 3 Problem Statement -- 4 Method -- 4.1 Reward and Dynamics Inference.
4.2 Meta-Reinforcement Learning Algorithm Based on Reward and Dynamics Inference Encoders -- 5 Experiment -- 5.1 Common MuJoCo Environments -- 5.2 Cartesian Product Combinations of Tasks with Different Goals and Dynamics -- 6 Discussion -- References -- Security and Privacy -- SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree -- 1 Introduction -- 2 Preliminaries -- 2.1 Gradient Boosting Decision Tree -- 2.2 Paillier Homomorphic Encryption -- 2.3 SecureBoost -- 2.4 Performance Bottlenecks Analysis for SecureBoost -- 3 Proposed SecureBoost+ Framework -- 3.1 Ciphertext Operation Optimization -- 3.2 Training Mechanism Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Ciphertext Operation Optimization Evaluation -- 4.3 Training Mechanism Optimization Evaluation -- 5 Conclusion -- References -- Construct a Secure CNN Against Gradient Inversion Attack -- 1 Introduction -- 2 Preliminary -- 2.1 Federated Learning -- 2.2 Gradient Inversion Attack -- 2.3 Recursive Gradient Attack on Privacy (R-GAP) -- 3 Secure Convolutional Neural Networks -- 4 Experiment -- 4.1 Quantitative Results -- 4.2 Quantitative Results -- 5 Related Work -- 6 Limitation and Conclusion -- References -- Backdoor Attack Against One-Class Sequential Anomaly Detection Models -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Deep One-Class Sequential Anomaly Detection -- 3.2 Mutual Information Maximization -- 4 Methodology -- 4.1 Threat Model -- 4.2 The Proposed Attack -- 4.3 Post-deployment Attack -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Conclusions -- References -- Semi-supervised and Unsupervised Learning -- DALLMi: Domain Adaption for LLM-Based Multi-label Classifier -- 1 Introduction -- 2 Language Model and Domain Adaptation -- 3 DALLMi -- 4 Experiments -- 5 Conclusion -- References.
Contrastive Learning for Unsupervised Sentence Embedding with False Negative Calibration.
Record Nr. UNINA-9910851994303321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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 IV
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 IV
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (380 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-38-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910855370003321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
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
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (406 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-42-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNINA-9910851982903321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
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
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (329 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-66-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNISA-996594166503316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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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 III
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 III
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (448 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-59-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part III -- Interpretability and Explainability -- Neural Additive and Basis Models with Feature Selection and Interactions -- 1 Introduction -- 2 Generalized Additive Models (GAMs) -- 2.1 Neural Additive Model (NAM) -- 2.2 Neural Basis Model (NBM) -- 3 NAM and NBM with Feature Selection -- 3.1 Motivation -- 3.2 Model Architecture -- 3.3 Implementation Remark -- 4 Discussion of Model Complexities -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Baselines -- 5.3 Results -- 6 Conclusion -- References -- Random Mask Perturbation Based Explainable Method of Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Explainable Method -- 4.1 Node Importance Based on Fidelity -- 4.2 Explanation Sparsity -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Quantitative Experiments -- 5.3 Ablation Study -- 5.4 Use Case -- 6 Conclusion -- References -- RouteExplainer: An Explanation Framework for Vehicle Routing Problem -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework: RouteExplainer -- 3.1 Many-to-Many Edge Classifier -- 3.2 Counterfactual Explanation for VRP -- 4 Experiments -- 4.1 Quantitative Evaluation of the Edge Classifier -- 4.2 Qualitative Evaluation of Generated Explanations -- 5 Conclusion and Future Work -- References -- On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values -- 1 Introduction -- 2 Related Work -- 3 Outlier Detection Ensembles -- 4 The bagged Shapley Values -- 5 Theoretical Guarantees for the Approximation -- 6 Experiments -- 6.1 Quality of the Approximation -- 6.2 Effectiveness -- 6.3 Scalability -- 7 Conclusions -- References -- Interpreting Pretrained Language Models via Concept Bottlenecks -- 1 Introduction -- 2 Related Work -- 2.1 Interpreting Pretrained Language Models.
2.2 Learning from Noisy Labels -- 3 Enable Concept Bottlenecks for PLMs -- 3.1 Problem Setup -- 4 C3M: A General Framework for Learning CBE-PLMs -- 4.1 ChatGPT-Guided Concept Augmentation -- 4.2 Learning from Noisy Concept Labels -- 5 Experiments -- 6 Conclusion -- A Definitions of Training Strategies -- B Details of the Manual Concept Annotation for the IMDB Dataset -- C Implementation Detail -- D Parameters and Notations -- E Statistics of Data Splits -- F Statistics of Concepts in Transformed Datasets -- G More Results on Explainable Predictions -- H A Case Study on Test-Time Intervention -- I Examples of Querying ChatGPT -- References -- Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Jacobian Saliency Map (JSM) -- 3.2 Jacobian-Augmented Loss Function (JAL) -- 4 Experiments -- 4.1 Dataset -- 4.2 Preprocessing -- 4.3 Multimodal Classification -- 4.4 Performance Evaluation -- 5 Conclusion -- References -- Towards Nonparametric Topological Layers in Neural Networks -- 1 Introduction -- 1.1 Background -- 1.2 Motivation and Challenges -- 1.3 Contributions -- 2 Preliminaries and Related Work -- 2.1 Basics of Topology -- 2.2 Topological Neural Network -- 2.3 Functional Spaces for Machine Learning -- 3 Methodology -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Implementation -- 4.3 Overall Performance -- 4.4 Learning Rate -- 4.5 Temporal-Spatial Correlation -- 5 Conclusion -- References -- Online, Streaming, Distributed Algorithms -- Streaming Fair k-Center Clustering over Massive Dataset with Performance Guarantee -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Related Work -- 1.3 Our Contribution -- 2 A Two-Pass Algorithm with Approximation Ratio 3 -- 2.1 The -Independent Center Set -- 2.2 The Two-Pass Streaming Algorithm.
3 The Streaming Algorithm with an Approximation Ratio 7 -- 3.1 The Streaming Algorithm for Constructing 1 and 2 -- 3.2 Post-streaming Construction of Center Set C from 12 -- 4 Experimental Results -- 4.1 Experimental Setting -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Projection-Free Bandit Convex Optimization over Strongly Convex Sets -- 1 Introduction -- 2 Related Work -- 2.1 Projection-Free OCO Algorithms -- 2.2 Bandit Convex Optimization -- 3 Main Results -- 3.1 Preliminaries -- 3.2 Our Proposed Algorithm -- 3.3 Theoretical Guarantees -- 4 Experiments -- 4.1 Problem Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Adaptive Prediction Interval for Data Stream Regression -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Adaptive Prediction Interval(AdaPI) -- 5 Experiments and Results -- 5.1 Comparison to Interval Forecast -- 5.2 Comparison Between MVE and AdaPI -- 6 Conclusions -- References -- Probabilistic Guarantees of Stochastic Recursive Gradient in Non-convex Finite Sum Problems -- 1 Introduction -- 1.1 Related Works -- 1.2 Our Contributions -- 1.3 Notation -- 2 Prob-SARAH Algorithm -- 3 Theoretical Results -- 3.1 Technical Assumptions -- 3.2 Main Results on Complexity -- 3.3 Proof Sketch -- 4 Numerical Experiments -- 4.1 Logistic Regression with Non-convex Regularization -- 4.2 Two-Layer Neural Network -- 5 Conclusion -- References -- Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Model Overview -- 3.2 Client Model Clustering -- 3.3 Dynamic Weighted Graph Construction -- 3.4 Knowledge Propagation and Aggregation -- 3.5 Precise Personalized Model Distribution -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 4.4 Case Study -- 4.5 Hyperparameter Study.
5 Conclusion -- References -- Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Threat Model -- 3.2 FMs Empowered Backdoor Attacks to HFL -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Experimental Results -- 4.3 Homogeneous Setting Evaluation -- 4.4 Case Study: Attack Effectiveness v.s. Public Data Utilization Ratio -- 4.5 Hyper-Parameter Study: ASR v.s. Poisoning Ratio -- 5 Conclusion -- References -- Combating Quality Distortion in Federated Learning with Collaborative Data Selection -- 1 Introduction -- 2 Related Works -- 3 Proposal -- 3.1 Preliminaries -- 3.2 Design Principle -- 3.3 Collaborative Sample Selection (CSS) -- 4 Evaluation -- 4.1 Datasets and Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Probabilistic Models and Statistical Inference -- Neural Marked Hawkes Process for Limit Order Book Modeling -- 1 Introduction -- 2 Background -- 3 Neural Marked Hawkes Process -- 4 Related Work -- 5 Experiments -- 6 Conclusion -- References -- How Large Corpora Sizes Influence the Distribution of Low Frequency Text n-grams -- 1 Introduction -- 2 Background and Related Work -- 3 The Model -- 4 Results -- 4.1 The Corpora Collection -- 4.2 The Range of k Values for W(k,C -- L,n) Prediction -- 4.3 The Assessment Criteria and Parameter Estimation -- 4.4 Comparison with Other Models -- 4.5 Obtained Results -- 4.6 The Predictions with Growing Corpus Size -- 5 Conclusions -- References -- Meta-Reinforcement Learning Algorithm Based on Reward and Dynamic Inference -- 1 Introduction -- 2 Background -- 2.1 Meta-Reinforcement Learning -- 2.2 Context-Based Meta-Reinforcement Learning -- 2.3 Parametric Task Distributions -- 3 Problem Statement -- 4 Method -- 4.1 Reward and Dynamics Inference.
4.2 Meta-Reinforcement Learning Algorithm Based on Reward and Dynamics Inference Encoders -- 5 Experiment -- 5.1 Common MuJoCo Environments -- 5.2 Cartesian Product Combinations of Tasks with Different Goals and Dynamics -- 6 Discussion -- References -- Security and Privacy -- SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree -- 1 Introduction -- 2 Preliminaries -- 2.1 Gradient Boosting Decision Tree -- 2.2 Paillier Homomorphic Encryption -- 2.3 SecureBoost -- 2.4 Performance Bottlenecks Analysis for SecureBoost -- 3 Proposed SecureBoost+ Framework -- 3.1 Ciphertext Operation Optimization -- 3.2 Training Mechanism Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Ciphertext Operation Optimization Evaluation -- 4.3 Training Mechanism Optimization Evaluation -- 5 Conclusion -- References -- Construct a Secure CNN Against Gradient Inversion Attack -- 1 Introduction -- 2 Preliminary -- 2.1 Federated Learning -- 2.2 Gradient Inversion Attack -- 2.3 Recursive Gradient Attack on Privacy (R-GAP) -- 3 Secure Convolutional Neural Networks -- 4 Experiment -- 4.1 Quantitative Results -- 4.2 Quantitative Results -- 5 Related Work -- 6 Limitation and Conclusion -- References -- Backdoor Attack Against One-Class Sequential Anomaly Detection Models -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Deep One-Class Sequential Anomaly Detection -- 3.2 Mutual Information Maximization -- 4 Methodology -- 4.1 Threat Model -- 4.2 The Proposed Attack -- 4.3 Post-deployment Attack -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Conclusions -- References -- Semi-supervised and Unsupervised Learning -- DALLMi: Domain Adaption for LLM-Based Multi-label Classifier -- 1 Introduction -- 2 Language Model and Domain Adaptation -- 3 DALLMi -- 4 Experiments -- 5 Conclusion -- References.
Contrastive Learning for Unsupervised Sentence Embedding with False Negative Calibration.
Record Nr. UNISA-996594167903316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
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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
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
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (329 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-66-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910851993803321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
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Lo trovi qui: Univ. Federico II
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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
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
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (406 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-42-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNISA-996594167803316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
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 IV
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 IV
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (380 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-38-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996601563903316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui