Knowledge Science, Engineering and Management

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