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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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Kashima Hisashi  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
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
Kashima Hisashi  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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 II / / 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 II / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (562 pages)
Disciplina 006.312
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 3-031-33377-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part II -- Graphs and Networks -- Improving Knowledge Graph Entity Alignment with Graph Augmentation -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 4 Methodology -- 4.1 Entity-Relation Encoder -- 4.2 Model Training with Graph Augmentation -- 4.3 Alignment Inference -- 5 Experimental Setup -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Discussion and Conclusion -- References -- MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Translation-Based Approaches -- 2.2 Matrix Factorization-Based Approaches -- 2.3 Neural Network-Based Approaches -- 3 Methodology -- 3.1 Problem Formulation and Notations -- 3.2 Overall Architecture Design -- 3.3 Model Architecture -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Protocol and Metric -- 4.3 Hyperparameters and Baselines -- 4.4 Results and Discussion -- 4.5 Analysis -- 5 Conclusion and Future Work -- References -- GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation -- 1 Introduction -- 2 Related Works -- 2.1 Temporal Dynamics Modeling on Graph-Structured Data -- 2.2 Representation Learning on Graphs with Edge Features -- 3 Proposed Methods -- 3.1 Problem Formulation -- 3.2 Overview of GTEA -- 3.3 Learning Edge Embeddings for Interaction Sequences -- 3.4 Representation Learning with Temporal Edge Aggregation -- 3.5 Model Training for Different Graph-Related Tasks -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results of Overall Performance -- 4.3 Experiments Analyses -- 5 Conclusions -- References -- You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network -- 1 Introduction.
2 Related Works -- 3 Problem Formulation -- 4 Methodology -- 4.1 Global Topology Structure Extraction -- 4.2 Graph Structure Memory Augmented Representation Learning -- 4.3 Objective Function of GSM-GNN -- 5 Experiments -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Performance on Node Classification -- 5.4 Flexibility of GSM-GNN for Various GNNs -- 5.5 Ablation Study -- 6 Conclusion -- References -- UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Statement -- 2.2 Motivations and Challenges -- 2.3 Related Work -- 3 Approach -- 3.1 Overview -- 3.2 1-Hop Attention Module with Attention Baseline Mechanism -- 3.3 Confidence Score Prediction and Training Objective -- 3.4 Pseudo-neighbor Augmented Graph Attention Network -- 4 Experiment -- 4.1 Settings -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 4.4 Deterministic Settings -- 5 Conclusion and Future Work -- References -- Mining Frequent Sequential Subgraph Evolutions in Dynamic Attributed Graphs -- 1 Introduction -- 2 Related Work -- 3 Notations -- 3.1 Dynamic Attributed Graph -- 3.2 A New Pattern Domain -- 3.3 Interesting Measures and Constraints -- 4 Mining Frequent Sequential Subgraph Evolutions -- 4.1 Extraction of Subgraph Candidates -- 4.2 Generation of Size-1 Patterns by Graph Addition -- 4.3 Extension of Patterns -- 5 Experiments -- 6 Conclusion -- References -- CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories -- 1 Introduction -- 2 Problem Definition -- 3 The CondTraj-GAN Framework -- 3.1 Training -- 3.2 Trajectory Inference -- 4 Evaluation Setup -- 4.1 Dataset -- 4.2 Model Setups -- 4.3 Evaluation Metrics -- 4.4 Baselines -- 5 Evaluation -- 5.1 Trajectory Generation Performance -- 5.2 Ablation Study -- 6 Related Work -- 7 Conclusion and Future Work -- References.
A Graph Contrastive Learning Framework with Adaptive Augmentation and Encoding for Unaligned Views -- 1 Introduction -- 2 Related Work -- 2.1 Graph Contrastive Learning -- 2.2 Adversarial Training -- 3 Method -- 3.1 Preliminaries -- 3.2 Adaptive Augmentation -- 3.3 Encoding Methods for Homophilic and Heterophilic Graphs -- 3.4 G-EMD-based Contrastive Loss -- 3.5 Adversarial Training on GCAUV -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Performance on Node Classification -- 4.3 Ablation Studies -- 5 Conclusion -- References -- MPool: Motif-Based Graph Pooling -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminaries and Problem Formulation -- 3.2 Motif Based Graph Pooling Models -- 3.3 Readout Function and Output Layer -- 4 Experiment -- 4.1 Overall Evaluation -- 5 Conclusion -- References -- Anti-Money Laundering in Cryptocurrency via Multi-Relational Graph Neural Network -- 1 Introduction -- 2 Methodology -- 2.1 Graph Construction -- 2.2 Representation Embedding -- 2.3 Inter-relation Aggregation -- 2.4 Adaptive Neighbor Sampler -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Demystifying Mixing Behavior -- 3.3 Performance Comparison -- 3.4 Ablation Study -- 3.5 Adaptive Sampler Analysis -- 4 Conclusion -- References -- Interpretability and Explainability -- CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Counterfactual Explanation -- 3.2 Normalizing Flow -- 4 Methodology -- 4.1 General Architecture of CeFlow -- 4.2 Normalizing Flows for Categorical Features -- 4.3 Conditional Flow Gaussian Mixture Model for Tabular Data -- 4.4 Counterfactual Generation Step -- 5 Experiments -- 6 Conclusion -- References.
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out -- 1 Introduction -- 2 Related Work -- 3 Data Collection -- 3.1 Study 1: Pre-event Control Period -- 3.2 Study 2: Post-event New User Period -- 4 Feedback Effect on Engagement -- 4.1 Covariates and Outcome Variables -- 4.2 Observational Causal Methods -- 4.3 Time to Next Engagement -- 4.4 Number of Active Days -- 5 Language Convergence in New User Cohort -- 5.1 New and Existing User Cohort Definition -- 5.2 New User's Self-Selection: Drop-out or Adaption -- 6 Discussions -- References -- Toward Interpretable Machine Learning: Constructing Polynomial Models Based on Feature Interaction Trees -- 1 Introduction -- 2 Related Work -- 2.1 SHAP and Pair-Wise Interaction Values -- 2.2 Polynomial Model and EBM -- 3 Methodology -- 3.1 Black-box Model Creation -- 3.2 Global SHAP Interaction Value Score Calculation -- 3.3 Tree-building Process -- 4 Experiments -- 4.1 Model Performance -- 4.2 Evaluating Interpretability -- 4.3 Usability Study -- 5 Conclusion -- References -- Kernel Methods -- BioSequence2Vec: Efficient Embedding Generation for Biological Sequences -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 BioSequence2Vec Representation -- 4 Experimental Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References -- Matrices and Tensors -- Relations Between Adjacency and Modularity Graph Partitioning -- 1 Introduction -- 2 Preliminaries -- 3 Dominant Eigenvectors of Modularity and Adjacency Matrices -- 4 Normalized Adjacency and Modularity Clustering -- 5 Experiments -- 5.1 Synthetic Data Sets -- 5.2 PenDigit Data Sets from MNIST Database -- 6 Conclusion -- References -- Model Selection and Evaluation -- Bayesian Optimization over Mixed Type Inputs with Encoding Methods -- 1 Introduction -- 2 Related Work.
2.1 BO for Categorical and Continuous Inputs -- 2.2 Encoding Methods -- 3 Background -- 3.1 Problem Statement -- 3.2 Bayesian Optimization -- 4 The Proposed Framework -- 4.1 Target Mean Encoding BO -- 4.2 Aggregate Ordinal Encoding BO -- 5 Experiments -- 5.1 Baseline Method and Evaluation Measures -- 5.2 Performance and Computation Time -- 6 Conclusion -- References -- Online and Streaming Algorithms -- Using Flexible Memories to Reduce Catastrophic Forgetting -- 1 Introduction -- 2 Related Work -- 3 The Continual Learning Problem -- 4 The Stability Wrapper (SW) for Replay Buffer Replacements -- 5 Experimental Results -- 6 Conclusion -- References -- Fair Healthcare Rationing to Maximize Dynamic Utilities -- 1 Introduction -- 1.1 Our Models -- 1.2 Our Contributions -- 2 Algorithms for Model 1 -- 2.1 Online Algorithm for Model 1 -- 2.2 Charging Scheme -- 2.3 Tight Example for the Online Algorithm -- 3 Online Algorithm for Model 2 -- 3.1 Outline of the Charging Scheme -- 4 Strategy-Proofness of the Online Algorithm -- 5 Experimental Evaluation -- 5.1 Methodology -- 5.2 Datasets -- 5.3 Results and Discussions -- 6 Conclusion -- References -- A Multi-player MAB Approach for Distributed Selection Problems -- 1 Introduction -- 2 Related Work -- 3 Platform Model and Problem Formulation -- 4 The Offline Optimization Problem -- 5 Online Learning Algorithm -- 6 Experiment -- 7 Conclusion -- References -- A Thompson Sampling Approach to Unifying Causal Inference and Bandit Learning -- 1 Introduction -- 2 Model -- 2.1 The Bandit Learning Model -- 2.2 The Data Model -- 2.3 Problem Formulation -- 3 Limitations of Naively Applying Thompson Sampling -- 3.1 VirTS: Naively Applying Thompson Sampling -- 3.2 Limitations of VirTS -- 4 VirTS-DF: Improving VirTS via Offline Data Filtering -- 5 Experiments on Real-world Data -- 5.1 Experimental Settings.
5.2 Experiment Results.
Record Nr. UNINA-9910728394703321
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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 II / / 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 II / / edited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (562 pages)
Disciplina 006.312
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 3-031-33377-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part II -- Graphs and Networks -- Improving Knowledge Graph Entity Alignment with Graph Augmentation -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 4 Methodology -- 4.1 Entity-Relation Encoder -- 4.2 Model Training with Graph Augmentation -- 4.3 Alignment Inference -- 5 Experimental Setup -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Discussion and Conclusion -- References -- MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Translation-Based Approaches -- 2.2 Matrix Factorization-Based Approaches -- 2.3 Neural Network-Based Approaches -- 3 Methodology -- 3.1 Problem Formulation and Notations -- 3.2 Overall Architecture Design -- 3.3 Model Architecture -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Protocol and Metric -- 4.3 Hyperparameters and Baselines -- 4.4 Results and Discussion -- 4.5 Analysis -- 5 Conclusion and Future Work -- References -- GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation -- 1 Introduction -- 2 Related Works -- 2.1 Temporal Dynamics Modeling on Graph-Structured Data -- 2.2 Representation Learning on Graphs with Edge Features -- 3 Proposed Methods -- 3.1 Problem Formulation -- 3.2 Overview of GTEA -- 3.3 Learning Edge Embeddings for Interaction Sequences -- 3.4 Representation Learning with Temporal Edge Aggregation -- 3.5 Model Training for Different Graph-Related Tasks -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results of Overall Performance -- 4.3 Experiments Analyses -- 5 Conclusions -- References -- You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network -- 1 Introduction.
2 Related Works -- 3 Problem Formulation -- 4 Methodology -- 4.1 Global Topology Structure Extraction -- 4.2 Graph Structure Memory Augmented Representation Learning -- 4.3 Objective Function of GSM-GNN -- 5 Experiments -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Performance on Node Classification -- 5.4 Flexibility of GSM-GNN for Various GNNs -- 5.5 Ablation Study -- 6 Conclusion -- References -- UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Statement -- 2.2 Motivations and Challenges -- 2.3 Related Work -- 3 Approach -- 3.1 Overview -- 3.2 1-Hop Attention Module with Attention Baseline Mechanism -- 3.3 Confidence Score Prediction and Training Objective -- 3.4 Pseudo-neighbor Augmented Graph Attention Network -- 4 Experiment -- 4.1 Settings -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 4.4 Deterministic Settings -- 5 Conclusion and Future Work -- References -- Mining Frequent Sequential Subgraph Evolutions in Dynamic Attributed Graphs -- 1 Introduction -- 2 Related Work -- 3 Notations -- 3.1 Dynamic Attributed Graph -- 3.2 A New Pattern Domain -- 3.3 Interesting Measures and Constraints -- 4 Mining Frequent Sequential Subgraph Evolutions -- 4.1 Extraction of Subgraph Candidates -- 4.2 Generation of Size-1 Patterns by Graph Addition -- 4.3 Extension of Patterns -- 5 Experiments -- 6 Conclusion -- References -- CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories -- 1 Introduction -- 2 Problem Definition -- 3 The CondTraj-GAN Framework -- 3.1 Training -- 3.2 Trajectory Inference -- 4 Evaluation Setup -- 4.1 Dataset -- 4.2 Model Setups -- 4.3 Evaluation Metrics -- 4.4 Baselines -- 5 Evaluation -- 5.1 Trajectory Generation Performance -- 5.2 Ablation Study -- 6 Related Work -- 7 Conclusion and Future Work -- References.
A Graph Contrastive Learning Framework with Adaptive Augmentation and Encoding for Unaligned Views -- 1 Introduction -- 2 Related Work -- 2.1 Graph Contrastive Learning -- 2.2 Adversarial Training -- 3 Method -- 3.1 Preliminaries -- 3.2 Adaptive Augmentation -- 3.3 Encoding Methods for Homophilic and Heterophilic Graphs -- 3.4 G-EMD-based Contrastive Loss -- 3.5 Adversarial Training on GCAUV -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Performance on Node Classification -- 4.3 Ablation Studies -- 5 Conclusion -- References -- MPool: Motif-Based Graph Pooling -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminaries and Problem Formulation -- 3.2 Motif Based Graph Pooling Models -- 3.3 Readout Function and Output Layer -- 4 Experiment -- 4.1 Overall Evaluation -- 5 Conclusion -- References -- Anti-Money Laundering in Cryptocurrency via Multi-Relational Graph Neural Network -- 1 Introduction -- 2 Methodology -- 2.1 Graph Construction -- 2.2 Representation Embedding -- 2.3 Inter-relation Aggregation -- 2.4 Adaptive Neighbor Sampler -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Demystifying Mixing Behavior -- 3.3 Performance Comparison -- 3.4 Ablation Study -- 3.5 Adaptive Sampler Analysis -- 4 Conclusion -- References -- Interpretability and Explainability -- CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Counterfactual Explanation -- 3.2 Normalizing Flow -- 4 Methodology -- 4.1 General Architecture of CeFlow -- 4.2 Normalizing Flows for Categorical Features -- 4.3 Conditional Flow Gaussian Mixture Model for Tabular Data -- 4.4 Counterfactual Generation Step -- 5 Experiments -- 6 Conclusion -- References.
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out -- 1 Introduction -- 2 Related Work -- 3 Data Collection -- 3.1 Study 1: Pre-event Control Period -- 3.2 Study 2: Post-event New User Period -- 4 Feedback Effect on Engagement -- 4.1 Covariates and Outcome Variables -- 4.2 Observational Causal Methods -- 4.3 Time to Next Engagement -- 4.4 Number of Active Days -- 5 Language Convergence in New User Cohort -- 5.1 New and Existing User Cohort Definition -- 5.2 New User's Self-Selection: Drop-out or Adaption -- 6 Discussions -- References -- Toward Interpretable Machine Learning: Constructing Polynomial Models Based on Feature Interaction Trees -- 1 Introduction -- 2 Related Work -- 2.1 SHAP and Pair-Wise Interaction Values -- 2.2 Polynomial Model and EBM -- 3 Methodology -- 3.1 Black-box Model Creation -- 3.2 Global SHAP Interaction Value Score Calculation -- 3.3 Tree-building Process -- 4 Experiments -- 4.1 Model Performance -- 4.2 Evaluating Interpretability -- 4.3 Usability Study -- 5 Conclusion -- References -- Kernel Methods -- BioSequence2Vec: Efficient Embedding Generation for Biological Sequences -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 BioSequence2Vec Representation -- 4 Experimental Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References -- Matrices and Tensors -- Relations Between Adjacency and Modularity Graph Partitioning -- 1 Introduction -- 2 Preliminaries -- 3 Dominant Eigenvectors of Modularity and Adjacency Matrices -- 4 Normalized Adjacency and Modularity Clustering -- 5 Experiments -- 5.1 Synthetic Data Sets -- 5.2 PenDigit Data Sets from MNIST Database -- 6 Conclusion -- References -- Model Selection and Evaluation -- Bayesian Optimization over Mixed Type Inputs with Encoding Methods -- 1 Introduction -- 2 Related Work.
2.1 BO for Categorical and Continuous Inputs -- 2.2 Encoding Methods -- 3 Background -- 3.1 Problem Statement -- 3.2 Bayesian Optimization -- 4 The Proposed Framework -- 4.1 Target Mean Encoding BO -- 4.2 Aggregate Ordinal Encoding BO -- 5 Experiments -- 5.1 Baseline Method and Evaluation Measures -- 5.2 Performance and Computation Time -- 6 Conclusion -- References -- Online and Streaming Algorithms -- Using Flexible Memories to Reduce Catastrophic Forgetting -- 1 Introduction -- 2 Related Work -- 3 The Continual Learning Problem -- 4 The Stability Wrapper (SW) for Replay Buffer Replacements -- 5 Experimental Results -- 6 Conclusion -- References -- Fair Healthcare Rationing to Maximize Dynamic Utilities -- 1 Introduction -- 1.1 Our Models -- 1.2 Our Contributions -- 2 Algorithms for Model 1 -- 2.1 Online Algorithm for Model 1 -- 2.2 Charging Scheme -- 2.3 Tight Example for the Online Algorithm -- 3 Online Algorithm for Model 2 -- 3.1 Outline of the Charging Scheme -- 4 Strategy-Proofness of the Online Algorithm -- 5 Experimental Evaluation -- 5.1 Methodology -- 5.2 Datasets -- 5.3 Results and Discussions -- 6 Conclusion -- References -- A Multi-player MAB Approach for Distributed Selection Problems -- 1 Introduction -- 2 Related Work -- 3 Platform Model and Problem Formulation -- 4 The Offline Optimization Problem -- 5 Online Learning Algorithm -- 6 Experiment -- 7 Conclusion -- References -- A Thompson Sampling Approach to Unifying Causal Inference and Bandit Learning -- 1 Introduction -- 2 Model -- 2.1 The Bandit Learning Model -- 2.2 The Data Model -- 2.3 Problem Formulation -- 3 Limitations of Naively Applying Thompson Sampling -- 3.1 VirTS: Naively Applying Thompson Sampling -- 3.2 Limitations of VirTS -- 4 VirTS-DF: Improving VirTS via Offline Data Filtering -- 5 Experiments on Real-world Data -- 5.1 Experimental Settings.
5.2 Experiment Results.
Record Nr. UNISA-996534464603316
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Algorithmic Game Theory [[electronic resource] ] : 16th International Symposium, SAGT 2023, Egham, UK, September 4–7, 2023, Proceedings / / edited by Argyrios Deligkas, Aris Filos-Ratsikas
Algorithmic Game Theory [[electronic resource] ] : 16th International Symposium, SAGT 2023, Egham, UK, September 4–7, 2023, Proceedings / / edited by Argyrios Deligkas, Aris Filos-Ratsikas
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (435 pages)
Disciplina 511.8
Collana Lecture Notes in Computer Science
Soggetto topico Computer simulation
Data structures (Computer science)
Information theory
Application software
Artificial intelligence
Algorithms
Computer networks
Computer Modelling
Data Structures and Information Theory
Computer and Information Systems Applications
Artificial Intelligence
Design and Analysis of Algorithms
Computer Communication Networks
ISBN 3-031-43254-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Computational Aspects and Efficiency in Games -- Computational Social Choice -- Fair Division -- Matching and Mechanism Design.
Record Nr. UNINA-9910743692203321
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Algorithmic Game Theory [[electronic resource] ] : 16th International Symposium, SAGT 2023, Egham, UK, September 4–7, 2023, Proceedings / / edited by Argyrios Deligkas, Aris Filos-Ratsikas
Algorithmic Game Theory [[electronic resource] ] : 16th International Symposium, SAGT 2023, Egham, UK, September 4–7, 2023, Proceedings / / edited by Argyrios Deligkas, Aris Filos-Ratsikas
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (435 pages)
Disciplina 511.8
Collana Lecture Notes in Computer Science
Soggetto topico Computer simulation
Data structures (Computer science)
Information theory
Application software
Artificial intelligence
Algorithms
Computer networks
Computer Modelling
Data Structures and Information Theory
Computer and Information Systems Applications
Artificial Intelligence
Design and Analysis of Algorithms
Computer Communication Networks
ISBN 3-031-43254-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Computational Aspects and Efficiency in Games -- Computational Social Choice -- Fair Division -- Matching and Mechanism Design.
Record Nr. UNISA-996550561803316
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui