Knowledge Science, Engineering and Management

TRAMA

This volume set constitutes the refereed proceedings of the 16th International Conference on Knowledge Science, Engineering and Management, KSEM 2023, which was held in Guangzhou, China, during August 16–18, 2023. The 114 full papers and 30 short papers included in this book were carefully reviewed and selected from 395 submissions. They were organized in topical sections as follows: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management systems; and emerging technologies for knowledge science, engineering and management. 

SOMMARIO

Knowledge Science with Learning and AI.-  Joint Feature Selection and Classifier Parameter Optimization: A Bio-inspired Approach.- Automatic Gaussian Bandwidth Selection for Kernel Principal Component Analysis.- Boosting LightWeight Depth Estimation Via Knowledge Distillation.- Graph Neural Network with Neighborhood Reconnection.- Critical Node Privacy Protection Based on Random Pruning of Critical Trees.- DSEAformer: Forecasting by De-stationary Autocorrelation with Edgebound.- Multitask-based Cluster Transmission for Few-Shot Text Classification.- Hyperplane Knowledge Graph Embedding with Path Neighborhoods and Mapping Properties.- RTAD-TP: Real- Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two- Parameter Estimation.- Multi-Sampling Item Response Ranking Neural Cognitive Diagnosis with Bilinear Feature Interaction.- A Sparse Matrix Optimization Method for Graph Neural Networks Training.- Dual-dimensional Refinement of Knowledge Graph Embedding Representation.- Contextual Information Augmented Few-Shot Relation Extraction.- Dynamic and Static Feature-aware Microservices Decomposition via Graph Neural Networks.- An Enhanced Fitness-distance Balance Slime Mould Algorithm and Its Application in Feature Selection.- Low Redundancy Learning for Unsupervised Multi-view Feature Selection.- Dynamic Feed-Forward LSTM.- Black-box Adversarial Attack on Graph Neural Networks Based on Node Domain Knowledge.- Role and Relationship-Aware Representation Learning for Complex Coupled Dynamic Heterogeneous Networks.- Twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal Knowledge Graph Completion.- Subspace Clustering with Feature Grouping for Categorical Data.- Learning Graph Neural Networks on Feature-Missing Graphs.- Dealing with Over-reliance on Background Graph for Few-shot Knowledge Graph Completion.- Kernel-based feature extraction for time series clustering.- Cluster Robust Inference for embedding-based Knowledge Graph Completion.- Community-enhanced Contrastive Siamese networks for Graph Representation Learning.- Distant Supervision Relation Extraction with Improved PCNN and Multi-level Attention.- Enhancing Adversarial Robustness via Anomaly-aware Adversarial Training.- An Improved Cross-Validated Adversarial Validation Method.- EACCNet: Enhanced Auto-Cross Correlation Network for Few-Shot Classification.- Joint Label-Structure Estimation from Multifaceted Graph Data.- Dual Channel Knowledge Graph Embedding with Ontology Guided Data Augmentation.- Multi-Dimensional Graph Rule Learner.- MixUNet: A Hybrid Retinal Vessels Segmentation Model Combining The Latest CNN and MLPs.- Robust Few-shot Graph Anomaly Detection via Graph Coarsening.- An Evaluation Metric for Prediction Stability with Imprecise Data.- ReducingThe Teacher-Student Gap Via Elastic Student.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783031402821
• Collana: Lecture Notes in Computer Science
• Dimensioni: 235 x 155 mm
• Formato: Brossura
• Illustration Notes: XXIV, 457 p. 124 illus., 108 illus. in color.
• Pagine Arabe: 457
• Pagine Romane: xxiv