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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. | UNISA-996594166503316 |
Yang De-Nian
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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 | ||
| ||
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 | ||
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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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
| 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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin |
| Autore | Yang De-Nian |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (448 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
XieXing
TsengVincent S PeiJian HuangJen-Wei LinJerry Chun-Wei |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education - Data processing Computer science - Mathematics Signal processing Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Signal, Speech and Image Processing Computer Communication Networks |
| 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
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
| 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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin |
| Autore | Yang De-Nian |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (406 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
XieXing
TsengVincent S PeiJian HuangJen-Wei LinJerry Chun-Wei |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education - Data processing Computer science - Mathematics Signal processing Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Signal, Speech and Image Processing Computer Communication Networks |
| 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
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
| 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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin |
| Autore | Yang De-Nian |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (329 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
XieXing
TsengVincent S PeiJian HuangJen-Wei LinJerry Chun-Wei |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education - Data processing Computer science - Mathematics Signal processing Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Signal, Speech and Image Processing Computer Communication Networks |
| 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 |
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Yang De-Nian
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
| 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 / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin |
| Autore | Yang De-Nian |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (380 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
XieXing
TsengVincent S PeiJian HuangJen-Wei LinJerry Chun-Wei |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education - Data processing Computer science - Mathematics Signal processing Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Signal, Speech and Image Processing Computer Communication Networks |
| ISBN | 981-9722-38-1 |
| 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 -- Financial Data -- Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal -- 1 Introduction -- 2 Preliminaries -- 3 Approach -- 3.1 Graph Construction -- 3.2 Transaction Encoding -- 3.3 Neighbor Aggregator -- 3.4 Community Aggregator -- 3.5 Dynamic Adaptor -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Overall Performance -- 4.3 Ablation Study -- 5 Conclusion -- References -- Multi-time Window Ensemble and Maximization of Expected Return for Stock Movement Prediction -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Problem Formulation -- 3.2 Multi-time Window Ensemble Classifier -- 3.3 Base Learner -- 3.4 Proposed Loss Function for Base Learner -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Predictive Performance Comparison -- 4.3 Trading Performance Comparison -- 4.4 Ablation Study -- 4.5 Visualization of Proposed Loss Function -- 5 Conclusion -- References -- MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading -- 1 Introduction -- 2 Problem Formulation -- 3 Methodology -- 3.1 Imitation Learning -- 3.2 Pretrain Module -- 3.3 Multiple Actors -- 3.4 Optimal Transport Regularization -- 4 Experiments -- 4.1 Dataset -- 4.2 Baselines, Evaluation Metrics and Hyperparameters -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Related Work -- 6 Conclusion -- References -- Agent-Based Simulation of Decision-Making Under Uncertainty to Study Financial Precarity -- 1 Introduction -- 2 Background: Modeling Consumption -- 2.1 Capturing Uncertainty -- 3 The Framework: Introducing Real Constraints -- 3.1 Background: Modeling Ruin -- 3.2 Our New Model -- 4 Simulation Study: Precarity -- 4.1 Long Term Precarity -- 4.2 Factors Contributing to Precarity.
5 Simulation Study: Interventions -- 6 Related Work -- 7 Conclusions -- References -- Information Retrieval and Search -- Semantic Completion: Enhancing Image-Text Retrieval with Information Extraction and Compression -- 1 Introduction -- 2 Related Work -- 2.1 Dual-Stream Structure -- 2.2 Single-Stream Structure -- 3 Methodology -- 3.1 Overview -- 3.2 Information Extraction and Compression (IEC) -- 3.3 Training Tasks -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Comparison to Baseline -- 4.3 Ablation Study -- 5 Conclusion -- References -- Fast Edit Distance Prediction for All Pairs of Sequences in Very Large NGS Datasets -- 1 Introduction -- 2 Related Works -- 3 Basic Concepts -- 3.1 Preliminary -- 3.2 Key Ideas -- 3.3 Selecting the Reference Sequences -- 4 Predicting the Edit Distance -- 5 Edit Distance Prediction from Non-matching Sub-sequences -- 5.1 Differences of Distances with Respect to Pairs of Letters -- 5.2 Computational Complexity -- 6 Results and Discussions -- 6.1 Datasets -- 6.2 Selecting K-Mer Length -- 6.3 Comparison of the Proposed Strategies -- 6.4 Edit Distance Prediction -- 6.5 Hierarchical Clustering -- 7 Conclusion -- References -- MixCL: Mixed Contrastive Learning for Relation Extraction -- 1 Introduction -- 2 Mixed Contrastive Learning -- 2.1 Neural Relation Extraction Baseline Model -- 2.2 Entity Centralized Contrastive Learning for Relation Extraction -- 2.3 Mixed Contrastive Learning for Relation Extraction -- 3 Experiments -- 3.1 Datasets and Comparison Models -- 3.2 Main Results -- 4 Analysis -- 4.1 Metric of Quality of Negative Examples -- 4.2 Why Does MixCL Work? -- 5 Related Work -- 5.1 Neural Relation Extraction -- 5.2 Contrastive Learning -- 6 Conclusion -- References -- Decomposing Relational Triple Extraction with Large Language Models for Better Generalization on Unseen Data -- 1 Introduction. 2 Methodology -- 2.1 Problem Formalization -- 2.2 Sub-task 1: Relation Extraction -- 2.3 Sub-task 2: Entity Extraction -- 2.4 Sub-task 3: Triple Filtering -- 3 Experiments -- 3.1 Datasets -- 3.2 Compared Methods and Evaluation Metrics -- 3.3 Overall Performance -- 3.4 Model Generalization Evaluation -- 3.5 Ablation Studies -- 4 Related Work -- 5 Conclusion -- References -- Multi-Query Person Search with Transformers -- 1 Introduction -- 2 Method -- 2.1 Multi-Query Decoder -- 2.2 Detection and Identification Losses -- 3 Experiments -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Comparison to the State-of-the-Art Approaches -- 3.5 Model Analysis -- 4 Conclusion -- References -- BioReX: Biomarker Information Extraction Inspired by Aspect-Based Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Biomarker Extraction (BE) -- 3.2 Result Association (RA) -- 4 Experiments -- 4.1 Experiment Setting -- 4.2 Results and Analysis -- 5 Conclusions -- References -- IR Embedding Fairness Inspection via Contrastive Learning and Human-AI Collaborative Intelligence -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Model Overview -- 3.2 GP Based Contrastive Learning -- 4 Experiments and Deployment -- 4.1 Implementation -- 4.2 Offline Evaluation -- 4.3 Online A/B Testing -- 5 Conclusion -- References -- SemPool: Simple, Robust, and Interpretable KG Pooling for Enhancing Language Models -- 1 Introduction -- 2 Related Work -- 3 Problem Statement and Preliminaries -- 4 Empirical Findings on Robustness -- 5 SemPool: Semantic Graph Pooling -- 5.1 KG Initialization -- 5.2 Pooling -- 5.3 KG Grounding -- 6 Experimental Setting -- 7 Results -- 7.1 Main Results -- 7.2 Ablation Studies and Analysis -- 8 Conclusions -- References -- Medical and Biological Data. Spatial Gene Expression Prediction Using Multi-Neighborhood Network with Reconstructing Attention -- 1 Introduction -- 2 Related Work -- 2.1 Gene Expression Prediction -- 2.2 Vision Transformer (ViT) -- 3 Method -- 3.1 Dual-Scale Attention -- 3.2 Reconstructing Attention -- 3.3 Transformer Block -- 4 Experiment -- 4.1 Datasets -- 4.2 Experiment Setup -- 5 Results and Discussion -- 5.1 Baseline Experiments for Multi-Neighborhood Network -- 5.2 Influence of Number of Neighborhoods -- 5.3 Influence of Attention Mechanisms -- 6 Conclusion -- References -- APFL: Active-Passive Forgery Localization for Medical Images -- 1 Introduction -- 2 Related Work -- 2.1 Active Forgery Localization -- 2.2 Passive Forgery Localization -- 3 Methodology -- 3.1 Active Fuzzy Localization with Reversible Watermarking -- 3.2 Passive Precise Localization with Lightweight KDU-Net -- 4 Experiments -- 4.1 Settings -- 4.2 Comparison Results -- 4.3 Robustness Evaluation -- 4.4 Hyperparameter Evaluation -- 4.5 Ablation Study -- 5 Conclusion -- References -- A Universal Non-parametric Approach for Improved Molecular Sequence Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Compression Methods -- 3.2 Problem Formulation -- 3.3 Our Algorithm -- 3.4 Distance Matrix Symmetry -- 3.5 Kernel Matrix Computation -- 3.6 Experimental Setup -- 3.7 Justification of Employing the Kernel Matrix -- 4 Results and Discussion -- 5 Conclusion -- References -- Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data -- 1 Introduction -- 2 Related Work -- 3 Methodology: NeuroGNN -- 3.1 Node Features Generation -- 3.2 Adjacency Matrix Generation -- 3.3 Prediction Using the Generated NeuroGraph -- 3.4 Pretraining -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 4.3 Ablation Study -- 4.4 Analysis of Graph Embeddings. 4.5 Handling Scarce Training Data -- 5 Conclusion -- References -- A Novel Population Graph Neural Network Based on Functional Connectivity for Mental Disorders Detection -- 1 Introduction -- 2 Methods -- 2.1 Brain Connectomic Graph -- 2.2 Heterogeneous Population Graph -- 3 Dataset and Experimental Evaluation -- 3.1 Dataset and Experimental Setup -- 3.2 Cross-Validation -- 3.3 Ablation Experiment and Parameter Sensitivity Analysis -- 3.4 Interpretability Analysis -- 4 Conclusion -- References -- Weighted Chaos Game Representation for Molecular Sequence Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Vision DL Models -- 4 Experimental Evaluation -- 4.1 Baselines Models: -- 5 Results and Discussion -- 6 Conclusion -- References -- Robust Influence-Based Training Methods for Noisy Brain MRI -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Influence Function on Single Validation Point -- 3.2 Influence Function on Validation Group -- 4 Influence-Based Sample Reweighing -- 4.1 Framework -- 4.2 Calculating Sample Weights -- 5 Influence-Based Sample Perturbation -- 5.1 Framework -- 5.2 Selecting Influential Samples -- 5.3 Adding Influence-Based Perturbation -- 6 Experiments -- 6.1 Evaluation Setup -- 6.2 Proposed Methods and Baselines -- 6.3 Results -- 7 Conclusion -- References -- Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations -- 1 Introduction -- 2 Problem Definition -- 3 The Co-ReaSON Approach -- 3.1 Data Preprocessing -- 3.2 Feature Extraction -- 3.3 Co-ReaSON Predictive Model -- 4 Evaluation Setup -- 5 Evaluation Results -- 6 Related Work -- 7 Conclusion -- References -- A Data-Driven Approach for Building a Cardiovascular Disease Risk Prediction System -- 1 Introduction -- 2 The Proposed AutoML System -- 3 Experiment -- 3.1 The Dataset. 3.2 Evaluation Metrics. |
| Record Nr. | UNINA-9910855370003321 |
|
Yang De-Nian
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 V / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
| 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 V / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin |
| Autore | Yang De-Nian |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (431 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
XieXing
TsengVincent S PeiJian HuangJen-Wei LinJerry Chun-Wei |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Education - Data processing Computer science - Mathematics Signal processing Computer networks Artificial Intelligence Design and Analysis of Algorithms Computers and Education Mathematics of Computing Signal, Speech and Image Processing Computer Communication Networks |
| ISBN | 981-9722-62-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part V -- Multimedia and Multimodal Data -- Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling -- 1 Introduction -- 2 Related Work -- 2.1 Human Activity Recognition (HAR) -- 2.2 Hierarchical Label Modeling -- 3 Problem Formulation -- 4 Our Proposals -- 4.1 Hierarchy-Aware Label Encoding -- 4.2 Activity Data Encoding -- 4.3 Label-Data Joint Embedding Learning -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Study -- 6 Discussions and Conclusion -- References -- Geometrically-Aware Dual Transformer Encoding Visual and Textual Features for Image Captioning -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Features Extractor -- 3.2 Caption Generator -- 3.3 Attention Block -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Experiment Result -- 5 Conclusions -- References -- MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 MHDF Model -- 3.1 Model Overview -- 3.2 Multi-source Heterogeneous Data Amplification -- 3.3 News Textual Feature Fusion -- 3.4 News Visual Feature Fusion -- 3.5 Sentiment Feature Extractor -- 3.6 Feature Integration Classifier -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparison -- 4.4 Ablation Experiments and Validity Verification -- 4.5 Conclusions -- References -- Accurate Semi-supervised Automatic Speech Recognition via Multi-hypotheses-Based Curriculum Learning -- 1 Introduction -- 2 Related Works -- 2.1 Automatic Speech Recognition Methods -- 2.2 Connectionist Temporal Classification (CTC) Loss -- 3 Proposed Method -- 3.1 Multiple Hypotheses for Unlabeled Instances.
3.2 Training ASR Model with Multiple Hypotheses -- 3.3 Curriculum Learning -- 3.4 Theoretical Analysis -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Transcription Performance (Q1) -- 4.3 Speed of Convergence (Q2) -- 4.4 Ablation Study (Q3) -- 5 Conclusions -- References -- MM-PhyQA: Multimodal Physics Question-Answering with Multi-image CoT Prompting -- 1 Introduction -- 2 Related Works -- 2.1 Available Datasets -- 2.2 Large Multimodal Models and Chain-of-Thought -- 3 Novel Dataset -- 3.1 Original Dataset Creation -- 3.2 Data Augmentation Procedure -- 3.3 Chain of Thought Variant -- 3.4 MM-PhyQA Dataset Topics -- 4 Methodology -- 4.1 Multi-image Chain-of-Thought (MI-CoT) -- 5 Experiments -- 5.1 Models -- 6 Results and Discussion -- 6.1 Model Performance -- 6.2 Zero Shot Prompting Vs Supervised Fine-Tuning -- 6.3 Effect of Chain of Thought Prompting -- 6.4 Error Analysis -- 7 Conclusion -- References -- Adversarial Text Purification: A Large Language Model Approach for Defense -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Large Language Models -- 3.2 Adversarial Text Purification -- 4 LLM-Guided Adversarial Text Purification -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Results and Discussion -- 6 Conclusion -- References -- lil'HDoC: An Algorithm for Good Arm Identification Under Small Threshold Gap -- 1 Introduction -- 2 Background -- 2.1 Good Arm Identification -- 3 Problem Setting -- 4 Preliminary -- 5 Algorithm -- 5.1 Correctness of lil'HDoC -- 5.2 First Arms Sampling Complexity -- 5.3 Total Sample Complexity -- 6 Experiment -- 6.1 Dataset -- 6.2 Baseline -- 6.3 Results -- 7 Conclusion -- References -- Recommender Systems -- ScaleViz: Scaling Visualization Recommendation Models on Large Data -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Proposed Solution -- 4.1 Cost Profiling -- 4.2 RL Agent. 5 Evaluations -- 5.1 Experimental Setup -- 5.2 Speed-Up in Visualization Generation -- 5.3 Budget vs. Error Trade-Off -- 5.4 Need for Dataset-Specific Feature Selection -- 5.5 Scalability with Increasing Data Size -- 6 Conclusion -- References -- Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation -- 1 Introduction -- 2 Related Work and Problem Formulation -- 2.1 Problem Formulation -- 3 Methodology -- 3.1 Overview -- 3.2 Statistical Model in CFLS -- 3.3 Optimization -- 3.4 Prediction -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparisons -- 4.4 Influence of Different Cold-Start Levels -- 4.5 Diversity, Interpretability and User Controllability -- 5 Conclusions -- References -- On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Statement -- 4 Variants of a Session-Based Recommender System -- 4.1 Quality Metrics -- 5 Experiments and Evaluation -- 5.1 Selection of Real-World Datasets -- 5.2 Task Definition and Parameter Configuration -- 5.3 Evaluation of Experimental Results -- 6 Conclusion and Future Work -- References -- HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 HMAR -- 2.3 Multi-task Learning -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Evaluation Protocol -- 3.3 Model Performance (RQ1) -- 3.4 Effect of Auxiliary Behaviors and Individual Model Components (RQ2 & -- RQ3) -- 4 Related Work -- 5 Conclusion -- References -- Residual Spatio-Temporal Collaborative Networks for Next POI Recommendation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Problem Formulation -- 3.2 Long-Term Dependence Module -- 3.3 Short-Term Dependence Module -- 3.4 Sample Balancer. 4 Experiments -- 4.1 Experimental Settings -- 4.2 Recommendation Performance -- 4.3 Ablation Study -- 5 Conclusions -- References -- Conditional Denoising Diffusion for Sequential Recommendation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Stepwise Diffuser -- 3.2 Sequence Encoder -- 3.3 Cross-Attentive Conditional Denoising Decoder -- 3.4 Optimization -- 4 Experiments -- 4.1 Plateau of Ranking Prediction -- 4.2 Overall Experiments -- 4.3 Ablation Study -- 4.4 Hyperparameter Sensitivity -- 4.5 Case Study for Stepwise Generation -- 5 Conclusion -- References -- UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering -- 2.2 Explainable and Transparent Recommender Models -- 2.3 The Prototype-Based Collaborative Filtering -- 3 Methodology -- 3.1 User-Item Prototypes Connections Matrix Factorization (UIPC-MF) -- 3.2 Loss Function -- 4 Experiments and Discussion -- 4.1 Evaluation Metrics -- 4.2 Baseline Models -- 4.3 Training Details -- 4.4 Evaluation Results -- 4.5 Explaining UIPC-MF Recommendations -- 4.6 The Impact of L1-Norm in Reduction of Learning Bias -- 5 Conclusion -- References -- Towards Multi-subsession Conversational Recommendation -- 1 Introduction -- 2 Related Works -- 3 MSMCR Scenario -- 3.1 Definition -- 3.2 General Framework -- 4 Methodology -- 4.1 Context-Aware Recommendation -- 4.2 Policy Learning -- 4.3 Model Training -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Overall Performance -- 5.3 Further Experiments -- 6 Conclusion -- References -- False Negative Sample Aware Negative Sampling for Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 Methodology -- 4.1 False Negatives Identification -- 4.2 False Negatives Elimination -- 5 Experiment -- 5.1 Experiment Settings. 5.2 Performance Comparison -- 5.3 Study of EDNS -- 6 Conclusion -- References -- Multi-sourced Integrated Ranking with Exposure Fairness -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Model -- 3.1 Input Layer -- 3.2 Dual RNN Module -- 3.3 Multi-task Module -- 3.4 Model Training -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Baselines -- 4.3 Model Selection -- 4.4 Performance Comparison -- 4.5 Ablation Study -- 4.6 Online A/B Testing -- 5 Conclusion -- References -- Soft Contrastive Learning for Implicit Feedback Recommendations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notations -- 3.2 The SCLRec Framework -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Performance (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Robustness to Interaction Noises (RQ3) -- 5 Conclusion -- References -- Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation -- 1 Introduction -- 2 Problem Formulation -- 3 Approach -- 3.1 Dual-Graph Construction -- 3.2 Dual-Graph Network for Member Preference -- 3.3 Dual-View Fusion for Group Preference -- 3.4 Group Recommendation and Model Training -- 4 Experiments -- 4.1 Experimental Dataset and Setup -- 4.2 Experimental Results and Analysis -- 4.3 Parameter Sensitivity -- 5 Related Works -- 6 Conclusion and Future Work -- References -- TripleS: A Subsidy-Supported Storage for Electricity with Self-financing Management System -- 1 Introduction -- 2 Literature Review -- 2.1 Electricity Subsidy and Operating Reserve -- 2.2 Electricity Management System -- 2.3 Electricity Storage -- 3 Problem Definition and Simulation Environment -- 4 Proposed TripleS -- 5 Experimental Results -- 5.1 Performance Evaluation -- 5.2 Performance Evaluation Under MS Attack -- 5.3 Influence of Self-discharge -- 6 Conclusion -- References -- Spatio-temporal Data. Mask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting. |
| Record Nr. | UNINA-9910851987103321 |
|
Yang De-Nian
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||