UID:
almahu_9949616579002882
Format:
XXIV, 457 p. 124 illus., 108 illus. in color.
,
online resource.
Edition:
1st ed. 2023.
ISBN:
9783031402838
Series Statement:
Lecture Notes in Artificial Intelligence, 14117
Content:
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. .
Note:
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 -- Reducing The Teacher-Student Gap Via Elastic Student.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031402821
Additional Edition:
Printed edition: ISBN 9783031402845
Language:
English
DOI:
10.1007/978-3-031-40283-8
URL:
https://doi.org/10.1007/978-3-031-40283-8
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