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Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part III
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part III
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (448 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-59-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part III -- Interpretability and Explainability -- Neural Additive and Basis Models with Feature Selection and Interactions -- 1 Introduction -- 2 Generalized Additive Models (GAMs) -- 2.1 Neural Additive Model (NAM) -- 2.2 Neural Basis Model (NBM) -- 3 NAM and NBM with Feature Selection -- 3.1 Motivation -- 3.2 Model Architecture -- 3.3 Implementation Remark -- 4 Discussion of Model Complexities -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Baselines -- 5.3 Results -- 6 Conclusion -- References -- Random Mask Perturbation Based Explainable Method of Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Explainable Method -- 4.1 Node Importance Based on Fidelity -- 4.2 Explanation Sparsity -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Quantitative Experiments -- 5.3 Ablation Study -- 5.4 Use Case -- 6 Conclusion -- References -- RouteExplainer: An Explanation Framework for Vehicle Routing Problem -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework: RouteExplainer -- 3.1 Many-to-Many Edge Classifier -- 3.2 Counterfactual Explanation for VRP -- 4 Experiments -- 4.1 Quantitative Evaluation of the Edge Classifier -- 4.2 Qualitative Evaluation of Generated Explanations -- 5 Conclusion and Future Work -- References -- On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values -- 1 Introduction -- 2 Related Work -- 3 Outlier Detection Ensembles -- 4 The bagged Shapley Values -- 5 Theoretical Guarantees for the Approximation -- 6 Experiments -- 6.1 Quality of the Approximation -- 6.2 Effectiveness -- 6.3 Scalability -- 7 Conclusions -- References -- Interpreting Pretrained Language Models via Concept Bottlenecks -- 1 Introduction -- 2 Related Work -- 2.1 Interpreting Pretrained Language Models.
2.2 Learning from Noisy Labels -- 3 Enable Concept Bottlenecks for PLMs -- 3.1 Problem Setup -- 4 C3M: A General Framework for Learning CBE-PLMs -- 4.1 ChatGPT-Guided Concept Augmentation -- 4.2 Learning from Noisy Concept Labels -- 5 Experiments -- 6 Conclusion -- A Definitions of Training Strategies -- B Details of the Manual Concept Annotation for the IMDB Dataset -- C Implementation Detail -- D Parameters and Notations -- E Statistics of Data Splits -- F Statistics of Concepts in Transformed Datasets -- G More Results on Explainable Predictions -- H A Case Study on Test-Time Intervention -- I Examples of Querying ChatGPT -- References -- Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Jacobian Saliency Map (JSM) -- 3.2 Jacobian-Augmented Loss Function (JAL) -- 4 Experiments -- 4.1 Dataset -- 4.2 Preprocessing -- 4.3 Multimodal Classification -- 4.4 Performance Evaluation -- 5 Conclusion -- References -- Towards Nonparametric Topological Layers in Neural Networks -- 1 Introduction -- 1.1 Background -- 1.2 Motivation and Challenges -- 1.3 Contributions -- 2 Preliminaries and Related Work -- 2.1 Basics of Topology -- 2.2 Topological Neural Network -- 2.3 Functional Spaces for Machine Learning -- 3 Methodology -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Implementation -- 4.3 Overall Performance -- 4.4 Learning Rate -- 4.5 Temporal-Spatial Correlation -- 5 Conclusion -- References -- Online, Streaming, Distributed Algorithms -- Streaming Fair k-Center Clustering over Massive Dataset with Performance Guarantee -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Related Work -- 1.3 Our Contribution -- 2 A Two-Pass Algorithm with Approximation Ratio 3 -- 2.1 The -Independent Center Set -- 2.2 The Two-Pass Streaming Algorithm.
3 The Streaming Algorithm with an Approximation Ratio 7 -- 3.1 The Streaming Algorithm for Constructing 1 and 2 -- 3.2 Post-streaming Construction of Center Set C from 12 -- 4 Experimental Results -- 4.1 Experimental Setting -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Projection-Free Bandit Convex Optimization over Strongly Convex Sets -- 1 Introduction -- 2 Related Work -- 2.1 Projection-Free OCO Algorithms -- 2.2 Bandit Convex Optimization -- 3 Main Results -- 3.1 Preliminaries -- 3.2 Our Proposed Algorithm -- 3.3 Theoretical Guarantees -- 4 Experiments -- 4.1 Problem Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Adaptive Prediction Interval for Data Stream Regression -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Adaptive Prediction Interval(AdaPI) -- 5 Experiments and Results -- 5.1 Comparison to Interval Forecast -- 5.2 Comparison Between MVE and AdaPI -- 6 Conclusions -- References -- Probabilistic Guarantees of Stochastic Recursive Gradient in Non-convex Finite Sum Problems -- 1 Introduction -- 1.1 Related Works -- 1.2 Our Contributions -- 1.3 Notation -- 2 Prob-SARAH Algorithm -- 3 Theoretical Results -- 3.1 Technical Assumptions -- 3.2 Main Results on Complexity -- 3.3 Proof Sketch -- 4 Numerical Experiments -- 4.1 Logistic Regression with Non-convex Regularization -- 4.2 Two-Layer Neural Network -- 5 Conclusion -- References -- Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Model Overview -- 3.2 Client Model Clustering -- 3.3 Dynamic Weighted Graph Construction -- 3.4 Knowledge Propagation and Aggregation -- 3.5 Precise Personalized Model Distribution -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 4.4 Case Study -- 4.5 Hyperparameter Study.
5 Conclusion -- References -- Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Threat Model -- 3.2 FMs Empowered Backdoor Attacks to HFL -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Experimental Results -- 4.3 Homogeneous Setting Evaluation -- 4.4 Case Study: Attack Effectiveness v.s. Public Data Utilization Ratio -- 4.5 Hyper-Parameter Study: ASR v.s. Poisoning Ratio -- 5 Conclusion -- References -- Combating Quality Distortion in Federated Learning with Collaborative Data Selection -- 1 Introduction -- 2 Related Works -- 3 Proposal -- 3.1 Preliminaries -- 3.2 Design Principle -- 3.3 Collaborative Sample Selection (CSS) -- 4 Evaluation -- 4.1 Datasets and Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Probabilistic Models and Statistical Inference -- Neural Marked Hawkes Process for Limit Order Book Modeling -- 1 Introduction -- 2 Background -- 3 Neural Marked Hawkes Process -- 4 Related Work -- 5 Experiments -- 6 Conclusion -- References -- How Large Corpora Sizes Influence the Distribution of Low Frequency Text n-grams -- 1 Introduction -- 2 Background and Related Work -- 3 The Model -- 4 Results -- 4.1 The Corpora Collection -- 4.2 The Range of k Values for W(k,C -- L,n) Prediction -- 4.3 The Assessment Criteria and Parameter Estimation -- 4.4 Comparison with Other Models -- 4.5 Obtained Results -- 4.6 The Predictions with Growing Corpus Size -- 5 Conclusions -- References -- Meta-Reinforcement Learning Algorithm Based on Reward and Dynamic Inference -- 1 Introduction -- 2 Background -- 2.1 Meta-Reinforcement Learning -- 2.2 Context-Based Meta-Reinforcement Learning -- 2.3 Parametric Task Distributions -- 3 Problem Statement -- 4 Method -- 4.1 Reward and Dynamics Inference.
4.2 Meta-Reinforcement Learning Algorithm Based on Reward and Dynamics Inference Encoders -- 5 Experiment -- 5.1 Common MuJoCo Environments -- 5.2 Cartesian Product Combinations of Tasks with Different Goals and Dynamics -- 6 Discussion -- References -- Security and Privacy -- SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree -- 1 Introduction -- 2 Preliminaries -- 2.1 Gradient Boosting Decision Tree -- 2.2 Paillier Homomorphic Encryption -- 2.3 SecureBoost -- 2.4 Performance Bottlenecks Analysis for SecureBoost -- 3 Proposed SecureBoost+ Framework -- 3.1 Ciphertext Operation Optimization -- 3.2 Training Mechanism Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Ciphertext Operation Optimization Evaluation -- 4.3 Training Mechanism Optimization Evaluation -- 5 Conclusion -- References -- Construct a Secure CNN Against Gradient Inversion Attack -- 1 Introduction -- 2 Preliminary -- 2.1 Federated Learning -- 2.2 Gradient Inversion Attack -- 2.3 Recursive Gradient Attack on Privacy (R-GAP) -- 3 Secure Convolutional Neural Networks -- 4 Experiment -- 4.1 Quantitative Results -- 4.2 Quantitative Results -- 5 Related Work -- 6 Limitation and Conclusion -- References -- Backdoor Attack Against One-Class Sequential Anomaly Detection Models -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Deep One-Class Sequential Anomaly Detection -- 3.2 Mutual Information Maximization -- 4 Methodology -- 4.1 Threat Model -- 4.2 The Proposed Attack -- 4.3 Post-deployment Attack -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Conclusions -- References -- Semi-supervised and Unsupervised Learning -- DALLMi: Domain Adaption for LLM-Based Multi-label Classifier -- 1 Introduction -- 2 Language Model and Domain Adaptation -- 3 DALLMi -- 4 Experiments -- 5 Conclusion -- References.
Contrastive Learning for Unsupervised Sentence Embedding with False Negative Calibration.
Record Nr. UNINA-9910851994303321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part IV
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part IV
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (380 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-38-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910855370003321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part I
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part I
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (406 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-42-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models.
2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation -- 3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References -- SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection -- 1 Introduction -- 2 Proposed Model for Multi-view Anomaly Detection -- 2.1 The SeeM Model and Its Inference -- 2.2 Complexity Analysis -- 2.3 Anomaly Score -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Multi-view Anomaly Detection Performance -- 3.3 Latent Dimension Analysis -- 3.4 Non-linear Projections -- 3.5 A Use Case with Real-World Multi-view Data -- 4 Related Work -- 5 Conclusion -- References -- Classification -- QWalkVec: Node Embedding by Quantum Walk -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Quantum Walks on Graphs -- 3 Related Works -- 3.1 Problems -- 4 Proposed Method: QWalkVec -- 4.1 Algorithm -- 5 Evaluations.
5.1 Experimental Settings and Dataset -- 5.2 Overall Results -- 6 Conclusion -- References -- Human-Driven Active Verification for Efficient and Trustworthy Graph Classification -- 1 Introduction -- 2 Related Work -- 2.1 Human-in-the-loop Machine Learning -- 2.2 Deep Learning for Case-Based Reasoning -- 2.3 Interpretable Graph Neural Networks -- 3 Methodology -- 3.1 Problem Formulation and Framework Overview -- 3.2 Human-Compatible Representation Learning -- 3.3 Interpretable Predictor -- 3.4 Prediction Explanation -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementations and Configurations -- 4.3 Predictive Performance Comparison -- 4.4 Benefits of Human-AI Interactions -- 4.5 User Perception of Prediction Explanations -- 4.6 Is Instance-Level Feedback Helpful in Any Cases? -- 5 Discussions of Fairness and Ethical Issues -- 6 Conclusion and Future Work -- References -- SASBO: Sparse Attack via Stochastic Binary Optimization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Sparse Adversarial Attack via Stochastic Binary Optimization -- 4 Experiments and Results -- 4.1 Non-targeted Attack -- 4.2 Targeted Attack -- 4.3 Visualization -- 5 Conclusion -- References -- LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Overall Framework -- 3.3 Utterance Encoder -- 3.4 Malevolence Shift Detection -- 3.5 Hierarchy-Aware Label Encoder -- 3.6 Malevolence Detection in Dialogues -- 3.7 Multi-task Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Compared Baselines -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Analysis of Malevolence Shift Detection -- 4.7 Case Study -- 4.8 Analysis of LLMs -- 5 Conclusion -- References.
Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features -- 1 Introduction -- 2 Preliminary -- 2.1 Knowledge Graph -- 2.2 Knowledge Graph Completion -- 2.3 Dynamic Graph Attention Variant GATv2 -- 3 Methodology -- 3.1 Structural Local Contexts Aggregation -- 3.2 High-Order Connected Contexts Aggregation -- 3.3 Decoder -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusions -- References -- Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations -- 1 Introduction -- 2 Related Works -- 2.1 Multi-label Recognition with Full Annotations -- 2.2 Multi-label Recognition with Limited Annotations -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Ambiguity-Aware Instance Weighting -- 3.3 Total Training Loss -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Ablation Studies -- 4.4 Model Analysis -- 5 Conclusion -- References -- Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification -- 1 Introduction -- 1.1 Node Classification -- 1.2 Graph Neural Network (GNN) -- 1.3 Chaotic Neural Oscillator (CNO) -- 2 Methodology -- 3 Experiment -- 3.1 Datasets -- 3.2 Settings and Baselines -- 3.3 Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Adversarial Learning of Group and Individual Fair Representations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Problem Statement -- 4.2 Model -- 4.3 Theoretical Properties of Loss Functions -- 4.4 Optimization with Focal Loss -- 5 Experiments and Analysis -- 6 Conclusion -- References -- Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage -- 1 Introduction -- 2 Methodology -- 2.1 Data Partitioning -- 2.2 Classification and Evaluation -- 3 Experimental Study -- 3.1 Datasets -- 3.2 Results.
4 Discussion and Recommendations -- 5 Conclusions and Future Work -- References -- Clustering -- Clustering-Friendly Representation Learning for Enhancing Salient Features -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 The Framework of cIDFD -- 3.2 Loss for Background Feature Extraction -- 3.3 Loss for Target Feature Extraction -- 3.4 Two-Stage Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison with Conventional Methods -- 4.3 Representation Distribution -- 4.4 Similarity Distribution -- 5 Conclusion -- References -- ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Contribution -- 2 Related Work -- 3 Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning (ImMC-CSFL) -- 3.1 Deep Feature Extraction Module -- 3.2 Common Information Learning Module -- 3.3 Specific Information Learning Module -- 3.4 Deep Multi-view Clustering Based on Common-Specific Feature Learning -- 4 Experiment -- 4.1 Experimental Datasets and Evaluation Criteria -- 4.2 Methods of Comparison -- 4.3 Experimental Results -- 5 Summary -- References -- Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes -- 1 Introduction -- 2 Multivariate Beta Mixture Model -- 2.1 Multivariate Beta Distribution -- 2.2 MBMM Density Function and Generative Process -- 2.3 Parameter Learning for the MBMM -- 2.4 The Similarity Score Between Data Points -- 3 Experiments -- 3.1 Comparisons on the Synthetic Datasets -- 3.2 Comparison on the Real Datasets -- 3.3 Distance Between Data Points -- 4 Related Work -- 5 Discussion -- References -- AutoClues: Exploring Clustering Pipelines via AutoML and Diversification -- 1 Introduction -- 2 Related Works -- 3 AutoClues -- 3.1 Formalization -- 3.2 Implementation.
4 Benchmark Generation and Empirical Evaluation.
Record Nr. UNINA-9910851982903321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (329 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-66-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part VI -- Scientific Data -- FR3LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 4 Model Architecture -- 4.1 Temporal Contextual Consistency -- 4.2 Non-contrastive Representations Learning -- 4.3 Deterministic Forecasting -- 4.4 Probabilistic Forecasting -- 4.5 End-to-End Training -- 5 Experiments -- 5.1 Experimental Results -- 5.2 Visualization of Latent and Original Series Forecasts -- 5.3 Further Experimental Setup Details -- 6 Conclusion -- References -- Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations -- 1 Introduction -- 2 Preliminaries on Numerical Simulations -- 3 Identification and Analysis of Relevant Metadata -- 4 Efficient Metadata Capture for Parameter Optimization -- 4.1 Early Termination of Farming Runs -- 4.2 PROBE: Probing Specific Parameter Combinations -- 5 Experimental Evaluation -- 5.1 Quality of Parameter Optimization Using PROBE -- 5.2 Efficiency Evaluation -- 5.3 Reuse of Metadata Acquired Through PROBE -- 5.4 Generalization to Other Model Problems and Schemes -- 6 Conclusion and Outlook -- References -- Material Microstructure Design Using VAE-Regression with a Multimodal Prior -- 1 Introduction -- 2 Methodology -- 3 Related Work -- 4 Experimental Results -- 5 Summary and Conclusions -- References -- A Weighted Cross-Modal Feature Aggregation Network for Rumor Detection -- 1 Introduction -- 2 Related Work -- 2.1 Rumor Detection -- 2.2 Multimodal Alignment -- 3 Methodology -- 3.1 Overview of WCAN -- 3.2 Feature Extraction -- 3.3 Weighted Cross-Modal Aggregation Module -- 3.4 Multimodal Feature Fusion -- 3.5 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Ablation Experiment.
4.4 Hyper-parameter Analysis -- 4.5 Visualization on the Representations -- 4.6 Case Study -- 5 Conclusions -- References -- Texts, Web, Social Network -- Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Echo Chamber Detection in Signed Networks -- 5 SEcho Method -- 5.1 SEcho Metric -- 5.2 Greedy Optimisation -- 6 Experiments -- 7 Conclusion -- References -- KiProL: A Knowledge-Injected Prompt Learning Framework for Language Generation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Statement -- 2.2 Knowledge-Injected Prompt Learning Generation -- 2.3 Training and Inference -- 3 Experiments -- 3.1 Datasets -- 3.2 Settings -- 3.3 Automatic Evaluation -- 3.4 Human Annotation -- 3.5 Ablation Study -- 3.6 In-Depth Analysis -- 3.7 Case Study -- 4 Conclusion -- References -- GViG: Generative Visual Grounding Using Prompt-Based Language Modeling for Visual Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Pix2Seq Framework -- 2.2 Prompt Tuning -- 3 Methodology -- 3.1 Prompt Tuning Module -- 3.2 VG Module -- 3.3 Conditional Trie-Based Search Algorithm (CTS) -- 4 Results -- 4.1 Dataset Description -- 4.2 Results on WSDM 2023 Toloka VQA Dataset Benchmark -- 5 Discussion -- 5.1 Prompt Study -- 5.2 Interpretable Attention -- 6 Conclusion -- References -- Aspect-Based Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition and Model Overview -- 3.2 Aspect Learning and Extraction -- 3.3 News Article Classification -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Evaluation -- 5 Analysing the Effect of Aspects Across Topics -- 6 Discussion and Future Work -- 7 Conclusion -- References -- DQAC: Detoxifying Query Auto-completion with Adapters -- 1 Introduction -- 2 Related Work -- 3 Methodology.
3.1 QDetoxify: Toxicity Classifier for Search Queries -- 3.2 The DQAC Model -- 4 Experimental Setup -- 5 Results and Analyses -- 6 Conclusions -- References -- Graph Neural Network Approach to Semantic Type Detection in Tables -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 GAIT -- 4.1 Single-Column Prediction -- 4.2 Graph-Based Prediction -- 4.3 Overall Prediction -- 5 Evaluation -- 5.1 Evaluation Method -- 5.2 Results -- 6 Conclusion -- References -- TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media -- 1 Introduction -- 2 Related Work -- 3 The Proposed TCGNN Method -- 3.1 Text-Clustering Graph Construction -- 3.2 Model Training -- 4 Experiments -- 5 Conclusions and Discussion -- References -- Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation and Motivation -- 3.2 Standalone ABSA Tasks -- 3.3 Adaptive Contextual Threshold Masking (ACTM) -- 3.4 Adaptive Attention Masking (AAM) -- 3.5 Adaptive Mask Over Masking (AMOM) -- 3.6 Training Procedure for ATE and ASC -- 4 Experiments and Results -- 5 Conclusion -- References -- An Automated Approach for Generating Conceptual Riddles -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Triples Creator -- 3.2 Properties Classifier -- 3.3 Generator -- 3.4 Validator -- 4 Evaluation and Results -- 5 Conclusion and Future Work -- References -- Time-Series and Streaming Data -- DiffFind: Discovering Differential Equations from Time Series -- 1 Introduction -- 2 Background and Related Work -- 2.1 Related Work -- 2.2 Background - Genetic Algorithms for Architecture Search -- 3 Proposed Method: DiffFind -- 4 Experiments -- 4.1 Q1 - DiffFind is Effective -- 4.2 Q2 - DiffFind is Explainable -- 4.3 Q3 - DiffFind is Scalable -- 5 Conclusions -- References.
DEAL: Data-Efficient Active Learning for Regression Under Drift -- 1 Introduction -- 2 Related Work -- 3 Problem Statement and Notation -- 4 Our Method: DEAL -- 4.1 The Adapted Stream-Based AL Cycle -- 4.2 Our Drift-Aware Estimation Model -- 5 Experimental Design -- 5.1 Baselines -- 5.2 Evaluation Data -- 5.3 Evaluation Metrics -- 6 Evaluation -- 6.1 Comparison of DEAL Against Baselines -- 6.2 Impact of the User-Required Error Threshold -- 7 Conclusion -- References -- Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting -- 1 Introduction -- 2 Related Models -- 3 Evolving Super Graph Neural Networks -- 3.1 Preliminary Notations -- 3.2 Super Graph Construction -- 4 Diffusion on Evolving Super Graphs -- 4.1 Predictor -- 5 Experiments on Large-Scale Datasets -- 5.1 Forecasting Result and Analysis -- 5.2 Runtime and Space Usage Analysis -- 5.3 Ablation Study -- 6 Conclusion -- References -- Unlearnable Examples for Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Data Poisoning -- 2.2 Adversarial Attack -- 2.3 Unlearnable Examples -- 3 Error-Minimizing Noise for Time Series -- 3.1 Objective -- 3.2 Threat Model -- 3.3 Challenges -- 3.4 Problem Formulation -- 3.5 A Straightforward Baseline Approach -- 3.6 Controllable Noise on Partial Time Series Samples -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Against Classification Models -- 4.3 Against Generative Models -- 5 Conclusion -- References -- Learning Disentangled Task-Related Representation for Time Series -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Overview -- 3.2 Task-Relevant Feature Disentangled -- 3.3 Task-Adaptive Augmentation Selection -- 4 Experiments and Discussions -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Analysis -- 4.3 Results on Classification Tasks -- 4.4 Results on Forecasting Tasks -- 4.5 Visualization Analysis.
5 Conclusion -- References -- A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Methodology -- 4.1 Feature Construction -- 4.2 Multi-view Representation -- 4.3 Multi-Encoder-Decoder Transformer (MEDT) Classification -- 5 Experiments -- 5.1 Experiments Using Multivariate Time Series Data Benchmarks -- 5.2 Experiment Using a Real-World Physical Activities Dataset -- 6 Conclusion -- References -- Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Key Concepts -- 3.2 Self-representation Learning in Time Series -- 3.3 Kernel Trick for Modeling Time Series -- 4 Proposed Method -- 4.1 Kernel Representation Learning: Modeling Regime Behavior -- 4.2 Forecasting -- 5 Experiments -- 5.1 Data -- 5.2 Experimental Setup and Evaluation -- 5.3 Regime Identification -- 5.4 Benchmark Comparison -- 5.5 Ablation Study -- 6 Conclusion -- References -- Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting -- 1 Introduction -- 2 Problem Statement -- 3 MLPs for Time Series Forecasting -- 3.1 Nlinear Model -- 4 Hyperparameters -- 4.1 Time Series Specific Configuration -- 4.2 Training Specific Configurations -- 4.3 TSBench-Metadataset -- 5 Experimental Setup -- 6 Results -- 7 Conclusion -- References -- Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification -- 1 Introduction -- 2 Related Work -- 2.1 Graph-Based Time Series Classification -- 2.2 Lower Bound of DTW -- 3 Problem Formulation -- 4 Methodology -- 4.1 Batch Sampling -- 4.2 LB_Keogh Graph Construction -- 4.3 Graph Convolution and Classification -- 4.4 Advantages of Our Model -- 5 Experimental Evaluation.
5.1 Comparing with 1NN-DTW.
Record Nr. UNISA-996594166503316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part III
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part III
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (448 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-59-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part III -- Interpretability and Explainability -- Neural Additive and Basis Models with Feature Selection and Interactions -- 1 Introduction -- 2 Generalized Additive Models (GAMs) -- 2.1 Neural Additive Model (NAM) -- 2.2 Neural Basis Model (NBM) -- 3 NAM and NBM with Feature Selection -- 3.1 Motivation -- 3.2 Model Architecture -- 3.3 Implementation Remark -- 4 Discussion of Model Complexities -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Baselines -- 5.3 Results -- 6 Conclusion -- References -- Random Mask Perturbation Based Explainable Method of Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Explainable Method -- 4.1 Node Importance Based on Fidelity -- 4.2 Explanation Sparsity -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Quantitative Experiments -- 5.3 Ablation Study -- 5.4 Use Case -- 6 Conclusion -- References -- RouteExplainer: An Explanation Framework for Vehicle Routing Problem -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework: RouteExplainer -- 3.1 Many-to-Many Edge Classifier -- 3.2 Counterfactual Explanation for VRP -- 4 Experiments -- 4.1 Quantitative Evaluation of the Edge Classifier -- 4.2 Qualitative Evaluation of Generated Explanations -- 5 Conclusion and Future Work -- References -- On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values -- 1 Introduction -- 2 Related Work -- 3 Outlier Detection Ensembles -- 4 The bagged Shapley Values -- 5 Theoretical Guarantees for the Approximation -- 6 Experiments -- 6.1 Quality of the Approximation -- 6.2 Effectiveness -- 6.3 Scalability -- 7 Conclusions -- References -- Interpreting Pretrained Language Models via Concept Bottlenecks -- 1 Introduction -- 2 Related Work -- 2.1 Interpreting Pretrained Language Models.
2.2 Learning from Noisy Labels -- 3 Enable Concept Bottlenecks for PLMs -- 3.1 Problem Setup -- 4 C3M: A General Framework for Learning CBE-PLMs -- 4.1 ChatGPT-Guided Concept Augmentation -- 4.2 Learning from Noisy Concept Labels -- 5 Experiments -- 6 Conclusion -- A Definitions of Training Strategies -- B Details of the Manual Concept Annotation for the IMDB Dataset -- C Implementation Detail -- D Parameters and Notations -- E Statistics of Data Splits -- F Statistics of Concepts in Transformed Datasets -- G More Results on Explainable Predictions -- H A Case Study on Test-Time Intervention -- I Examples of Querying ChatGPT -- References -- Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Jacobian Saliency Map (JSM) -- 3.2 Jacobian-Augmented Loss Function (JAL) -- 4 Experiments -- 4.1 Dataset -- 4.2 Preprocessing -- 4.3 Multimodal Classification -- 4.4 Performance Evaluation -- 5 Conclusion -- References -- Towards Nonparametric Topological Layers in Neural Networks -- 1 Introduction -- 1.1 Background -- 1.2 Motivation and Challenges -- 1.3 Contributions -- 2 Preliminaries and Related Work -- 2.1 Basics of Topology -- 2.2 Topological Neural Network -- 2.3 Functional Spaces for Machine Learning -- 3 Methodology -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Implementation -- 4.3 Overall Performance -- 4.4 Learning Rate -- 4.5 Temporal-Spatial Correlation -- 5 Conclusion -- References -- Online, Streaming, Distributed Algorithms -- Streaming Fair k-Center Clustering over Massive Dataset with Performance Guarantee -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Related Work -- 1.3 Our Contribution -- 2 A Two-Pass Algorithm with Approximation Ratio 3 -- 2.1 The -Independent Center Set -- 2.2 The Two-Pass Streaming Algorithm.
3 The Streaming Algorithm with an Approximation Ratio 7 -- 3.1 The Streaming Algorithm for Constructing 1 and 2 -- 3.2 Post-streaming Construction of Center Set C from 12 -- 4 Experimental Results -- 4.1 Experimental Setting -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Projection-Free Bandit Convex Optimization over Strongly Convex Sets -- 1 Introduction -- 2 Related Work -- 2.1 Projection-Free OCO Algorithms -- 2.2 Bandit Convex Optimization -- 3 Main Results -- 3.1 Preliminaries -- 3.2 Our Proposed Algorithm -- 3.3 Theoretical Guarantees -- 4 Experiments -- 4.1 Problem Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Adaptive Prediction Interval for Data Stream Regression -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Adaptive Prediction Interval(AdaPI) -- 5 Experiments and Results -- 5.1 Comparison to Interval Forecast -- 5.2 Comparison Between MVE and AdaPI -- 6 Conclusions -- References -- Probabilistic Guarantees of Stochastic Recursive Gradient in Non-convex Finite Sum Problems -- 1 Introduction -- 1.1 Related Works -- 1.2 Our Contributions -- 1.3 Notation -- 2 Prob-SARAH Algorithm -- 3 Theoretical Results -- 3.1 Technical Assumptions -- 3.2 Main Results on Complexity -- 3.3 Proof Sketch -- 4 Numerical Experiments -- 4.1 Logistic Regression with Non-convex Regularization -- 4.2 Two-Layer Neural Network -- 5 Conclusion -- References -- Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Model Overview -- 3.2 Client Model Clustering -- 3.3 Dynamic Weighted Graph Construction -- 3.4 Knowledge Propagation and Aggregation -- 3.5 Precise Personalized Model Distribution -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 4.4 Case Study -- 4.5 Hyperparameter Study.
5 Conclusion -- References -- Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Threat Model -- 3.2 FMs Empowered Backdoor Attacks to HFL -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Experimental Results -- 4.3 Homogeneous Setting Evaluation -- 4.4 Case Study: Attack Effectiveness v.s. Public Data Utilization Ratio -- 4.5 Hyper-Parameter Study: ASR v.s. Poisoning Ratio -- 5 Conclusion -- References -- Combating Quality Distortion in Federated Learning with Collaborative Data Selection -- 1 Introduction -- 2 Related Works -- 3 Proposal -- 3.1 Preliminaries -- 3.2 Design Principle -- 3.3 Collaborative Sample Selection (CSS) -- 4 Evaluation -- 4.1 Datasets and Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Probabilistic Models and Statistical Inference -- Neural Marked Hawkes Process for Limit Order Book Modeling -- 1 Introduction -- 2 Background -- 3 Neural Marked Hawkes Process -- 4 Related Work -- 5 Experiments -- 6 Conclusion -- References -- How Large Corpora Sizes Influence the Distribution of Low Frequency Text n-grams -- 1 Introduction -- 2 Background and Related Work -- 3 The Model -- 4 Results -- 4.1 The Corpora Collection -- 4.2 The Range of k Values for W(k,C -- L,n) Prediction -- 4.3 The Assessment Criteria and Parameter Estimation -- 4.4 Comparison with Other Models -- 4.5 Obtained Results -- 4.6 The Predictions with Growing Corpus Size -- 5 Conclusions -- References -- Meta-Reinforcement Learning Algorithm Based on Reward and Dynamic Inference -- 1 Introduction -- 2 Background -- 2.1 Meta-Reinforcement Learning -- 2.2 Context-Based Meta-Reinforcement Learning -- 2.3 Parametric Task Distributions -- 3 Problem Statement -- 4 Method -- 4.1 Reward and Dynamics Inference.
4.2 Meta-Reinforcement Learning Algorithm Based on Reward and Dynamics Inference Encoders -- 5 Experiment -- 5.1 Common MuJoCo Environments -- 5.2 Cartesian Product Combinations of Tasks with Different Goals and Dynamics -- 6 Discussion -- References -- Security and Privacy -- SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree -- 1 Introduction -- 2 Preliminaries -- 2.1 Gradient Boosting Decision Tree -- 2.2 Paillier Homomorphic Encryption -- 2.3 SecureBoost -- 2.4 Performance Bottlenecks Analysis for SecureBoost -- 3 Proposed SecureBoost+ Framework -- 3.1 Ciphertext Operation Optimization -- 3.2 Training Mechanism Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Ciphertext Operation Optimization Evaluation -- 4.3 Training Mechanism Optimization Evaluation -- 5 Conclusion -- References -- Construct a Secure CNN Against Gradient Inversion Attack -- 1 Introduction -- 2 Preliminary -- 2.1 Federated Learning -- 2.2 Gradient Inversion Attack -- 2.3 Recursive Gradient Attack on Privacy (R-GAP) -- 3 Secure Convolutional Neural Networks -- 4 Experiment -- 4.1 Quantitative Results -- 4.2 Quantitative Results -- 5 Related Work -- 6 Limitation and Conclusion -- References -- Backdoor Attack Against One-Class Sequential Anomaly Detection Models -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Deep One-Class Sequential Anomaly Detection -- 3.2 Mutual Information Maximization -- 4 Methodology -- 4.1 Threat Model -- 4.2 The Proposed Attack -- 4.3 Post-deployment Attack -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Conclusions -- References -- Semi-supervised and Unsupervised Learning -- DALLMi: Domain Adaption for LLM-Based Multi-label Classifier -- 1 Introduction -- 2 Language Model and Domain Adaptation -- 3 DALLMi -- 4 Experiments -- 5 Conclusion -- References.
Contrastive Learning for Unsupervised Sentence Embedding with False Negative Calibration.
Record Nr. UNISA-996594167903316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
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Lo trovi qui: Univ. di Salerno
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Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (329 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-66-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part VI -- Scientific Data -- FR3LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 4 Model Architecture -- 4.1 Temporal Contextual Consistency -- 4.2 Non-contrastive Representations Learning -- 4.3 Deterministic Forecasting -- 4.4 Probabilistic Forecasting -- 4.5 End-to-End Training -- 5 Experiments -- 5.1 Experimental Results -- 5.2 Visualization of Latent and Original Series Forecasts -- 5.3 Further Experimental Setup Details -- 6 Conclusion -- References -- Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations -- 1 Introduction -- 2 Preliminaries on Numerical Simulations -- 3 Identification and Analysis of Relevant Metadata -- 4 Efficient Metadata Capture for Parameter Optimization -- 4.1 Early Termination of Farming Runs -- 4.2 PROBE: Probing Specific Parameter Combinations -- 5 Experimental Evaluation -- 5.1 Quality of Parameter Optimization Using PROBE -- 5.2 Efficiency Evaluation -- 5.3 Reuse of Metadata Acquired Through PROBE -- 5.4 Generalization to Other Model Problems and Schemes -- 6 Conclusion and Outlook -- References -- Material Microstructure Design Using VAE-Regression with a Multimodal Prior -- 1 Introduction -- 2 Methodology -- 3 Related Work -- 4 Experimental Results -- 5 Summary and Conclusions -- References -- A Weighted Cross-Modal Feature Aggregation Network for Rumor Detection -- 1 Introduction -- 2 Related Work -- 2.1 Rumor Detection -- 2.2 Multimodal Alignment -- 3 Methodology -- 3.1 Overview of WCAN -- 3.2 Feature Extraction -- 3.3 Weighted Cross-Modal Aggregation Module -- 3.4 Multimodal Feature Fusion -- 3.5 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Ablation Experiment.
4.4 Hyper-parameter Analysis -- 4.5 Visualization on the Representations -- 4.6 Case Study -- 5 Conclusions -- References -- Texts, Web, Social Network -- Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Echo Chamber Detection in Signed Networks -- 5 SEcho Method -- 5.1 SEcho Metric -- 5.2 Greedy Optimisation -- 6 Experiments -- 7 Conclusion -- References -- KiProL: A Knowledge-Injected Prompt Learning Framework for Language Generation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Statement -- 2.2 Knowledge-Injected Prompt Learning Generation -- 2.3 Training and Inference -- 3 Experiments -- 3.1 Datasets -- 3.2 Settings -- 3.3 Automatic Evaluation -- 3.4 Human Annotation -- 3.5 Ablation Study -- 3.6 In-Depth Analysis -- 3.7 Case Study -- 4 Conclusion -- References -- GViG: Generative Visual Grounding Using Prompt-Based Language Modeling for Visual Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Pix2Seq Framework -- 2.2 Prompt Tuning -- 3 Methodology -- 3.1 Prompt Tuning Module -- 3.2 VG Module -- 3.3 Conditional Trie-Based Search Algorithm (CTS) -- 4 Results -- 4.1 Dataset Description -- 4.2 Results on WSDM 2023 Toloka VQA Dataset Benchmark -- 5 Discussion -- 5.1 Prompt Study -- 5.2 Interpretable Attention -- 6 Conclusion -- References -- Aspect-Based Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition and Model Overview -- 3.2 Aspect Learning and Extraction -- 3.3 News Article Classification -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Evaluation -- 5 Analysing the Effect of Aspects Across Topics -- 6 Discussion and Future Work -- 7 Conclusion -- References -- DQAC: Detoxifying Query Auto-completion with Adapters -- 1 Introduction -- 2 Related Work -- 3 Methodology.
3.1 QDetoxify: Toxicity Classifier for Search Queries -- 3.2 The DQAC Model -- 4 Experimental Setup -- 5 Results and Analyses -- 6 Conclusions -- References -- Graph Neural Network Approach to Semantic Type Detection in Tables -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 GAIT -- 4.1 Single-Column Prediction -- 4.2 Graph-Based Prediction -- 4.3 Overall Prediction -- 5 Evaluation -- 5.1 Evaluation Method -- 5.2 Results -- 6 Conclusion -- References -- TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media -- 1 Introduction -- 2 Related Work -- 3 The Proposed TCGNN Method -- 3.1 Text-Clustering Graph Construction -- 3.2 Model Training -- 4 Experiments -- 5 Conclusions and Discussion -- References -- Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation and Motivation -- 3.2 Standalone ABSA Tasks -- 3.3 Adaptive Contextual Threshold Masking (ACTM) -- 3.4 Adaptive Attention Masking (AAM) -- 3.5 Adaptive Mask Over Masking (AMOM) -- 3.6 Training Procedure for ATE and ASC -- 4 Experiments and Results -- 5 Conclusion -- References -- An Automated Approach for Generating Conceptual Riddles -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Triples Creator -- 3.2 Properties Classifier -- 3.3 Generator -- 3.4 Validator -- 4 Evaluation and Results -- 5 Conclusion and Future Work -- References -- Time-Series and Streaming Data -- DiffFind: Discovering Differential Equations from Time Series -- 1 Introduction -- 2 Background and Related Work -- 2.1 Related Work -- 2.2 Background - Genetic Algorithms for Architecture Search -- 3 Proposed Method: DiffFind -- 4 Experiments -- 4.1 Q1 - DiffFind is Effective -- 4.2 Q2 - DiffFind is Explainable -- 4.3 Q3 - DiffFind is Scalable -- 5 Conclusions -- References.
DEAL: Data-Efficient Active Learning for Regression Under Drift -- 1 Introduction -- 2 Related Work -- 3 Problem Statement and Notation -- 4 Our Method: DEAL -- 4.1 The Adapted Stream-Based AL Cycle -- 4.2 Our Drift-Aware Estimation Model -- 5 Experimental Design -- 5.1 Baselines -- 5.2 Evaluation Data -- 5.3 Evaluation Metrics -- 6 Evaluation -- 6.1 Comparison of DEAL Against Baselines -- 6.2 Impact of the User-Required Error Threshold -- 7 Conclusion -- References -- Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting -- 1 Introduction -- 2 Related Models -- 3 Evolving Super Graph Neural Networks -- 3.1 Preliminary Notations -- 3.2 Super Graph Construction -- 4 Diffusion on Evolving Super Graphs -- 4.1 Predictor -- 5 Experiments on Large-Scale Datasets -- 5.1 Forecasting Result and Analysis -- 5.2 Runtime and Space Usage Analysis -- 5.3 Ablation Study -- 6 Conclusion -- References -- Unlearnable Examples for Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Data Poisoning -- 2.2 Adversarial Attack -- 2.3 Unlearnable Examples -- 3 Error-Minimizing Noise for Time Series -- 3.1 Objective -- 3.2 Threat Model -- 3.3 Challenges -- 3.4 Problem Formulation -- 3.5 A Straightforward Baseline Approach -- 3.6 Controllable Noise on Partial Time Series Samples -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Against Classification Models -- 4.3 Against Generative Models -- 5 Conclusion -- References -- Learning Disentangled Task-Related Representation for Time Series -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Overview -- 3.2 Task-Relevant Feature Disentangled -- 3.3 Task-Adaptive Augmentation Selection -- 4 Experiments and Discussions -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Analysis -- 4.3 Results on Classification Tasks -- 4.4 Results on Forecasting Tasks -- 4.5 Visualization Analysis.
5 Conclusion -- References -- A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Methodology -- 4.1 Feature Construction -- 4.2 Multi-view Representation -- 4.3 Multi-Encoder-Decoder Transformer (MEDT) Classification -- 5 Experiments -- 5.1 Experiments Using Multivariate Time Series Data Benchmarks -- 5.2 Experiment Using a Real-World Physical Activities Dataset -- 6 Conclusion -- References -- Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Key Concepts -- 3.2 Self-representation Learning in Time Series -- 3.3 Kernel Trick for Modeling Time Series -- 4 Proposed Method -- 4.1 Kernel Representation Learning: Modeling Regime Behavior -- 4.2 Forecasting -- 5 Experiments -- 5.1 Data -- 5.2 Experimental Setup and Evaluation -- 5.3 Regime Identification -- 5.4 Benchmark Comparison -- 5.5 Ablation Study -- 6 Conclusion -- References -- Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting -- 1 Introduction -- 2 Problem Statement -- 3 MLPs for Time Series Forecasting -- 3.1 Nlinear Model -- 4 Hyperparameters -- 4.1 Time Series Specific Configuration -- 4.2 Training Specific Configurations -- 4.3 TSBench-Metadataset -- 5 Experimental Setup -- 6 Results -- 7 Conclusion -- References -- Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification -- 1 Introduction -- 2 Related Work -- 2.1 Graph-Based Time Series Classification -- 2.2 Lower Bound of DTW -- 3 Problem Formulation -- 4 Methodology -- 4.1 Batch Sampling -- 4.2 LB_Keogh Graph Construction -- 4.3 Graph Convolution and Classification -- 4.4 Advantages of Our Model -- 5 Experimental Evaluation.
5.1 Comparing with 1NN-DTW.
Record Nr. UNINA-9910851993803321
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part I
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part I
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (406 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-42-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models.
2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation -- 3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References -- SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection -- 1 Introduction -- 2 Proposed Model for Multi-view Anomaly Detection -- 2.1 The SeeM Model and Its Inference -- 2.2 Complexity Analysis -- 2.3 Anomaly Score -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Multi-view Anomaly Detection Performance -- 3.3 Latent Dimension Analysis -- 3.4 Non-linear Projections -- 3.5 A Use Case with Real-World Multi-view Data -- 4 Related Work -- 5 Conclusion -- References -- Classification -- QWalkVec: Node Embedding by Quantum Walk -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Quantum Walks on Graphs -- 3 Related Works -- 3.1 Problems -- 4 Proposed Method: QWalkVec -- 4.1 Algorithm -- 5 Evaluations.
5.1 Experimental Settings and Dataset -- 5.2 Overall Results -- 6 Conclusion -- References -- Human-Driven Active Verification for Efficient and Trustworthy Graph Classification -- 1 Introduction -- 2 Related Work -- 2.1 Human-in-the-loop Machine Learning -- 2.2 Deep Learning for Case-Based Reasoning -- 2.3 Interpretable Graph Neural Networks -- 3 Methodology -- 3.1 Problem Formulation and Framework Overview -- 3.2 Human-Compatible Representation Learning -- 3.3 Interpretable Predictor -- 3.4 Prediction Explanation -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementations and Configurations -- 4.3 Predictive Performance Comparison -- 4.4 Benefits of Human-AI Interactions -- 4.5 User Perception of Prediction Explanations -- 4.6 Is Instance-Level Feedback Helpful in Any Cases? -- 5 Discussions of Fairness and Ethical Issues -- 6 Conclusion and Future Work -- References -- SASBO: Sparse Attack via Stochastic Binary Optimization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Sparse Adversarial Attack via Stochastic Binary Optimization -- 4 Experiments and Results -- 4.1 Non-targeted Attack -- 4.2 Targeted Attack -- 4.3 Visualization -- 5 Conclusion -- References -- LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Overall Framework -- 3.3 Utterance Encoder -- 3.4 Malevolence Shift Detection -- 3.5 Hierarchy-Aware Label Encoder -- 3.6 Malevolence Detection in Dialogues -- 3.7 Multi-task Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Compared Baselines -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Analysis of Malevolence Shift Detection -- 4.7 Case Study -- 4.8 Analysis of LLMs -- 5 Conclusion -- References.
Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features -- 1 Introduction -- 2 Preliminary -- 2.1 Knowledge Graph -- 2.2 Knowledge Graph Completion -- 2.3 Dynamic Graph Attention Variant GATv2 -- 3 Methodology -- 3.1 Structural Local Contexts Aggregation -- 3.2 High-Order Connected Contexts Aggregation -- 3.3 Decoder -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusions -- References -- Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations -- 1 Introduction -- 2 Related Works -- 2.1 Multi-label Recognition with Full Annotations -- 2.2 Multi-label Recognition with Limited Annotations -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Ambiguity-Aware Instance Weighting -- 3.3 Total Training Loss -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Ablation Studies -- 4.4 Model Analysis -- 5 Conclusion -- References -- Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification -- 1 Introduction -- 1.1 Node Classification -- 1.2 Graph Neural Network (GNN) -- 1.3 Chaotic Neural Oscillator (CNO) -- 2 Methodology -- 3 Experiment -- 3.1 Datasets -- 3.2 Settings and Baselines -- 3.3 Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Adversarial Learning of Group and Individual Fair Representations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Problem Statement -- 4.2 Model -- 4.3 Theoretical Properties of Loss Functions -- 4.4 Optimization with Focal Loss -- 5 Experiments and Analysis -- 6 Conclusion -- References -- Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage -- 1 Introduction -- 2 Methodology -- 2.1 Data Partitioning -- 2.2 Classification and Evaluation -- 3 Experimental Study -- 3.1 Datasets -- 3.2 Results.
4 Discussion and Recommendations -- 5 Conclusions and Future Work -- References -- Clustering -- Clustering-Friendly Representation Learning for Enhancing Salient Features -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 The Framework of cIDFD -- 3.2 Loss for Background Feature Extraction -- 3.3 Loss for Target Feature Extraction -- 3.4 Two-Stage Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison with Conventional Methods -- 4.3 Representation Distribution -- 4.4 Similarity Distribution -- 5 Conclusion -- References -- ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Contribution -- 2 Related Work -- 3 Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning (ImMC-CSFL) -- 3.1 Deep Feature Extraction Module -- 3.2 Common Information Learning Module -- 3.3 Specific Information Learning Module -- 3.4 Deep Multi-view Clustering Based on Common-Specific Feature Learning -- 4 Experiment -- 4.1 Experimental Datasets and Evaluation Criteria -- 4.2 Methods of Comparison -- 4.3 Experimental Results -- 5 Summary -- References -- Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes -- 1 Introduction -- 2 Multivariate Beta Mixture Model -- 2.1 Multivariate Beta Distribution -- 2.2 MBMM Density Function and Generative Process -- 2.3 Parameter Learning for the MBMM -- 2.4 The Similarity Score Between Data Points -- 3 Experiments -- 3.1 Comparisons on the Synthetic Datasets -- 3.2 Comparison on the Real Datasets -- 3.3 Distance Between Data Points -- 4 Related Work -- 5 Discussion -- References -- AutoClues: Exploring Clustering Pipelines via AutoML and Diversification -- 1 Introduction -- 2 Related Works -- 3 AutoClues -- 3.1 Formalization -- 3.2 Implementation.
4 Benchmark Generation and Empirical Evaluation.
Record Nr. UNISA-996594167803316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part IV
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part IV
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (380 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-38-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996601563903316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Big Data Analysis and Deep Learning Applications [[electronic resource] ] : Proceedings of the First International Conference on Big Data Analysis and Deep Learning / / edited by Thi Thi Zin, Jerry Chun-Wei Lin
Big Data Analysis and Deep Learning Applications [[electronic resource] ] : Proceedings of the First International Conference on Big Data Analysis and Deep Learning / / edited by Thi Thi Zin, Jerry Chun-Wei Lin
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XIV, 386 p. 190 illus.)
Disciplina 005
Collana Advances in Intelligent Systems and Computing
Soggetto topico Computational intelligence
Optical data processing
Data mining
Artificial intelligence
Computational Intelligence
Image Processing and Computer Vision
Data Mining and Knowledge Discovery
Artificial Intelligence
ISBN 981-13-0869-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Big data analysis -- Machine learning and applications -- Monitoring system by using image processing -- Conventional neural networks and its applications -- Information and communication -- Industrial information systems and applications.
Record Nr. UNINA-9910484759203321
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019
Materiale a stampa
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Data-driven mining, learning and analytics for secured smart cities : trends and advances / / Chinmay Chakraborty, Jerry Chun-Wei Lin, Mamoun Alazab, editors
Data-driven mining, learning and analytics for secured smart cities : trends and advances / / Chinmay Chakraborty, Jerry Chun-Wei Lin, Mamoun Alazab, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (390 pages)
Disciplina 307.760285
Collana Advanced Sciences and Technologies for Security Applications
Soggetto topico Smart cities
ISBN 3-030-72139-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editors -- Analytics of Multiple-Threshold Model for High Average-Utilization Patterns in Smart City Environments -- 1 Introduction -- 2 Review of Related Works -- 2.1 High Utility Itemset Mining (HUIM) -- 2.2 High Average-Utility Itemset Mining -- 2.3 Multi-threshold Pattern Mining Works -- 3 Background of HAUIM and Problem Statement -- 4 Designed Model and Pruning Stratrgies -- 4.1 Developed Closure Property -- 4.2 Proposed Multi-HAUIM Model -- 4.3 Designed Strategy 1 -- 4.4 Designed Strategy 2 -- 5 Experimental Evaluation -- 5.1 Runtime Evaluation -- 5.2 Evaluation of Candidate Size -- 5.3 Evaluation of the Used Memory -- 5.4 Evaluation of Scalability -- 6 Conclusion and Future Work -- References -- Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities -- 1 Introduction -- 1.1 Smart City Applications -- 1.2 Technologies Used in Smart Cities and Integrated Technology in the Smart City-Edge/Cloud -- 1.3 Security Loophole in Smart Cities -- 1.4 AI/ML Based Counter Measures -- 1.5 Open Issues, Challenges and Recommendation -- 1.6 Conclusion and Future Scope -- References -- Smart Cities Ecosystem in the Modern Digital Age: An Introduction -- 1 Introduction -- 2 Smart Cities Concepts -- 3 Smart Cities Applications -- 4 Importance of Big Data for Smart Cities -- 5 Blockchain for Smart Cities -- 6 Machine Learning for Smart Cities -- 7 Discussion -- 7.1 Challenges on the Implementation of Smart City -- 8 Trends and Future Directions -- 9 Conclusions -- References -- A Reliable Cloud Assisted IoT Application in Smart Cities -- 1 Introduction -- 2 Literature Survey -- 3 Previous Work -- 4 Proposed Architecture -- 5 Analysis of the Contribution -- 6 Future Work -- 7 Conclusion -- References -- Lightweight Security Protocols for Securing IoT Devices in Smart Cities.
1 Introduction to Smart City Initiatives -- 2 Case Study: Smart Singapore -- 3 Smart City Backbone: Internet-of-Things (IoT) -- 4 The Requirement of a Lightweight Security Solution -- 5 Lightweight Block Ciphers -- 6 Lightweight Stream Ciphers and Hash Functions -- 7 Opportunities and Challenges -- 8 Conclusion and Future Scope -- References -- Blockchain Integrated Framework for Resolving Privacy Issues in Smart City -- 1 Introduction -- 2 Overview of Blockchain -- 2.1 Types of Blockchain -- 2.2 Working Steps of Blockchain -- 2.3 Protocols -- 3 Smart City: An Overview -- 4 Security and Privacy Issues in IoT -- 5 Blockchain Usage in Smart City -- 5.1 Applications of Blockchain -- 5.2 Problem Domains in Blockchain -- 6 Proposed Architecture -- 7 Challenges and Future Research Directions -- 8 Conclusion -- References -- Field Programmable Gate Array (FPGA) Based IoT for Smart City Applications -- 1 Introduction -- 2 Artificial Intelligence (AI) and Internet of Things (IoT) for Smart Cities -- 3 FPGA for Deep Learning -- 3.1 AI and Deep Learning Applications on FPGAs -- 4 What Exactly is Field Programmable Gate Array (FPGA)? -- 4.1 Benefits of FPGAs -- 4.2 FPGAs and Artificial Intelligence -- 5 FPGA Based IoT Architecture and Applications for Secured Smart Cities -- 5.1 FPGA Based IoT for Smart Homes -- 5.2 FPGA Based IoT for Data Encryption, Storage, and Security -- 5.3 FPGA Based IoT for Safety and Surveillance Applications -- 6 FPGA Based IoT Architecture and Applications for Healthcare Analytics -- 6.1 Advantages of Programmable Logic -- 6.2 Medical Applications for Programmable Logic -- 7 IoT Architecture and Its Applications for Urban Planning Based on FPGA -- 7.1 FPGA Based IoT for 5G and Beyond -- 7.2 FPGA Based IoT for Energy Management -- 8 Further Applications of FPGA Based IoT for Smart Cities.
8.1 FPGA Based Neuroscience and Its IoT Applications -- 8.2 FPGA Implementation of Automatic Monitoring Systems for Industrial Applications -- 8.3 Reconfigurable Embedded Web Services Based on FPGA -- 8.4 Smart Sensor Based on SoCs for Incorporation in Industrial Internet of Things -- 8.5 FPGA Based Health Monitoring System -- 9 Futuristic Applications and Challenges of FPGA Based IoT for Smart Cities -- 10 Conclusion -- References -- Modified Transaction Against Double-Spending Attack Using Blockchain to Secure Smart Cities -- 1 Introduction -- 1.1 Work Contribution -- 2 Proof of Work Classes -- 2.1 Challenge-Response -- 2.2 Solution-Verification -- 3 Distribution and Cryptographic Attacks -- 3.1 Characteristics of Uniform Distribution -- 3.2 Cryptographic Attacks -- 4 Blockchain Overview -- 4.1 Bitcoin -- 4.2 Public Ledger -- 4.3 BlockChain Mechanism -- 4.4 Consensus Algorithm -- 4.5 PoW (Proof of Work) -- 4.6 PoS (Proof of Stake) -- 5 Basic Blockchain Design -- 6 Modes of Operation -- 6.1 Electronic Code Book (ECB) -- 6.2 Cipher Block Chaining (CBC) -- 6.3 Cipher Feedback (CFB) -- 6.4 Output Feedback (OFB) -- 6.5 Counter (CTR) -- 7 Modified Blockchain Design -- 8 Performance Analysis -- 9 Conclusion -- References -- Smart City Ecosystem Opportunities: Perspectives and Challenges -- 1 Introduction -- 2 Smart City Layers -- 3 Smart City Value Creators -- 4 Related Works -- 5 Role of Big Data in Smart City -- 5.1 Big Data Layers in Smart City Ecosystem -- 5.2 Issues in Smart City Big Data -- 6 Role of Internet of Things (IOT) in Smart City Ecosystem -- 6.1 IOT Open Issues in Smart City -- 6.2 Communication Vulnerabilities -- 6.3 Physical Security Issues and Remedies in IOT -- 7 Role of Artificial Intelligence (AI) in Smart City Ecosystem -- 7.1 Applications of Artificial Intelligence (AI) in Smart City Ecosystem.
7.2 Application of Artificial Intelligence for Smart Citizens or Individuals -- 7.3 Artificial Intelligence (AI) Challenges in Building the Smart City -- 8 Role of Crowdsourcing in Smart Cities -- 9 Conclusion -- References -- Data-Driven Generative Design Integrated with Hybrid Additive Subtractive Manufacturing (HASM) for Smart Cities -- 1 Introduction -- 2 Generative Design Approach -- 3 Generative Design Applications -- 4 Hybrid Additive Subtractive Manufacturing and Generative Design for Smart Cities -- 5 Generative Design Integrated with Hybrid Additive Subtractive Manufacturing -- 6 Case Study: Generate Design of a Chassis for a Drone -- 7 Conclusion and Future Scope -- References -- End-to-End Learning for Autonomous Driving in Secured Smart Cities -- 1 Introduction -- 2 Background and Related Works -- 2.1 End-To-End Learning Paradigm -- 2.2 Modular Pipeline Paradigm -- 2.3 Adversarial Attacks and Defenses -- 2.4 Building upon and Contrasting with Related Works -- 3 Proposed Model: Temporal Conditional Imitation Learning (TCIL) -- 4 Experiment and Results -- 4.1 Dataset -- 4.2 Training -- 4.3 Evaluation of System Performance -- 4.4 Comparison with the State-Of-Art -- 4.5 Ongoing Work: Evaluation of Defense Against Adversarial Attacks -- 5 Conclusion and Future Research Direction -- 6 Future Research Directions -- 6.1 Improving Dataset and Learning Method -- 6.2 Improving Defense Against Adversarial Attacks -- References -- Smart City Technologies for Next Generation Healthcare -- 1 Introduction -- 2 Smart City-An Overview -- 2.1 Smart People -- 2.2 Smart Infrastructure -- 2.3 Smart Economy -- 2.4 Smart Mobility -- 2.5 Smart Environment -- 2.6 Smart Healthcare -- 2.7 Smart Education -- 2.8 Smart Governance -- 3 Layers of Smart City Ecosystem -- 4 Smart City Ecosystem- Layer-Wise Protocols.
5 Next Generation Healthcare and Internet of Healthcare Things (IoHT) -- 5.1 Device Connectivity -- 5.2 Data Processing -- 5.3 Cloud Computing -- 5.4 Edge Computing -- 5.5 Security and Privacy of Healthcare Data -- 6 Integration of Smart Healthcare with Other Smart City Components -- 6.1 Infrastructural Collaboration -- 6.2 Smart Education -- 6.3 Medical Waste Management -- 6.4 Anytime, Anywhere Services -- 7 Open Issues, Challenges and Recommendations -- 8 Conclusion -- References -- An Investigation on Personalized Point-of-Interest Recommender System for Location-Based Social Networks in Smart Cities -- 1 Introduction -- 2 POI Based Recommendation Systems Based on Topographical Features -- 2.1 Mining Topographical Impact for Collaborative POI Recommendation -- 2.2 Exploring Geographical Inclinations for POI Recommendation -- 2.3 Integrating Matrix Factorization with Joint Geographical Modeling (GeoMF) Method for POI Recommender System -- 2.4 A Ranking Based Geographical Factorization (Rank-GeoMF) Approach for POI Recommender System -- 2.5 Integration of Geographical Impact with POI Recommender Systems -- 2.6 General Topographical Probabilistic Based Factor Approach for Point of Interest Recommendation -- 2.7 Exploiting Geographical Neighborhood Characteristics for POI Recommender System -- 3 POI Based Recommendation Systems Based on Temporal Features -- 3.1 Time-Aware POI Recommendation -- 3.2 A Probabilistic Framework to Exploit Correlation of Temporal Impact in a Time-Aware Locale Recommender System -- 4 POI Based Recommendation Systems Based on User Behavior -- 4.1 Exploiting Sequential Influence for Location Recommendation (LORE) -- 4.2 Joint Modeling Behavior Based on Check in Approach -- 4.3 Exploiting User Check-in Data for Location Recommendation in LSBN.
4.4 Extraction of User Check-in Behavior with Random Walk for Urban POI Recommender Systems.
Record Nr. UNISA-996464510103316
Cham, Switzerland : , : Springer, , [2021]
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