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Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part IV / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part IV / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Autore | Kashima Hisashi |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (360 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
IdeTsuyoshi
PengWen-Chih |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education—Data processing Computer science—Mathematics Computer vision Computer engineering Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Computer Vision Computer Engineering and Networks |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-33383-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part 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. |
| Record Nr. | UNINA-9910728400103321 |
Kashima Hisashi
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part III / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part III / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Autore | Kashima Hisashi |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (419 pages) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
IdeTsuyoshi
PengWen-Chih |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education—Data processing Computer science—Mathematics Computer vision Computer engineering Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Computer Vision Computer Engineering and Networks |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-33380-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Big data -- Toward Explainable Recommendation Via Counterfactual Reasoning -- Online Volume Optimization for Notifications via Long Short-Term Value Modeling -- Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases -- Financial data -- Joint Latent Topic Discovery and Expectation Modeling for Financial Markets -- Let the model make financial senses: a Text2Text generative approach for financial complaint identification -- Information retrieval and search -- Web-scale Semantic Product Search With Large Language Models -- Multi-task learning based Keywords weighted Siamese Model for semantic retrieval -- Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion -- MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval -- Isotropic Representation Can Improve Dense Retrieval -- Knowledge-Enhanced Prototypical Network with Structural Semantics for Few-Shot Relation Classification -- Internet of Things -- MIDFA : Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection -- Medical and biological data -- Vision Transformers for Small Histological Datasets learned through Knowledge Distillation -- Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis -- DKFM: Dual Knowledge-guided Fusion Model for Drug Recommendation -- Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge -- Multimedia and multimodal data -- An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance -- Dynamically-Scaled Deep Canonical Correlation Analysis -- TCR: Short Video Title Generation and Cover Selection with Attention Refinement -- ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval -- Recommender systems -- Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations -- Interest Driven Graph Structure Learning for Session-Based Recommendation -- Multi-behavior Guided Temporal Graph Attention Network for Recommendation -- Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation -- Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities -- Global-Aware External Attention Deep Model for Sequential Recommendation -- Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking -- Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation -- kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval -- Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation -- RLMixer: A Reinforcement Learning Approach For Integrated Ranking With Contrastive User Preference Modeling. |
| Record Nr. | UNINA-9910728399703321 |
|
Kashima Hisashi
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part I / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part I / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Autore | Kashima Hisashi |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (563 pages) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
IdeTsuyoshi
PengWen-Chih |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education—Data processing Computer science—Mathematics Computer vision Computer engineering Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Computer Vision Computer Engineering and Networks |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-33374-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996534464203316 |
|
Kashima Hisashi
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part III / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part III / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Autore | Kashima Hisashi |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (419 pages) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
IdeTsuyoshi
PengWen-Chih |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education—Data processing Computer science—Mathematics Computer vision Computer engineering Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Computer Vision Computer Engineering and Networks |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-33380-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Big data -- Toward Explainable Recommendation Via Counterfactual Reasoning -- Online Volume Optimization for Notifications via Long Short-Term Value Modeling -- Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases -- Financial data -- Joint Latent Topic Discovery and Expectation Modeling for Financial Markets -- Let the model make financial senses: a Text2Text generative approach for financial complaint identification -- Information retrieval and search -- Web-scale Semantic Product Search With Large Language Models -- Multi-task learning based Keywords weighted Siamese Model for semantic retrieval -- Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion -- MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval -- Isotropic Representation Can Improve Dense Retrieval -- Knowledge-Enhanced Prototypical Network with Structural Semantics for Few-Shot Relation Classification -- Internet of Things -- MIDFA : Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection -- Medical and biological data -- Vision Transformers for Small Histological Datasets learned through Knowledge Distillation -- Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis -- DKFM: Dual Knowledge-guided Fusion Model for Drug Recommendation -- Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge -- Multimedia and multimodal data -- An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance -- Dynamically-Scaled Deep Canonical Correlation Analysis -- TCR: Short Video Title Generation and Cover Selection with Attention Refinement -- ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval -- Recommender systems -- Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations -- Interest Driven Graph Structure Learning for Session-Based Recommendation -- Multi-behavior Guided Temporal Graph Attention Network for Recommendation -- Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation -- Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities -- Global-Aware External Attention Deep Model for Sequential Recommendation -- Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking -- Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation -- kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval -- Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation -- RLMixer: A Reinforcement Learning Approach For Integrated Ranking With Contrastive User Preference Modeling. |
| Record Nr. | UNISA-996534464303316 |
|
Kashima Hisashi
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part IV / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part IV / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Autore | Kashima Hisashi |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (360 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
IdeTsuyoshi
PengWen-Chih |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education—Data processing Computer science—Mathematics Computer vision Computer engineering Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Computer Vision Computer Engineering and Networks |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-33383-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part 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. |
| Record Nr. | UNISA-996534463803316 |
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Kashima Hisashi
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part I / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part I / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Autore | Kashima Hisashi |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (563 pages) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
IdeTsuyoshi
PengWen-Chih |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education—Data processing Computer science—Mathematics Computer vision Computer engineering Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Computer Vision Computer Engineering and Networks |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-33374-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910728396003321 |
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Kashima Hisashi
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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