Intro
Preface
Organization
Contents - Part I
Contents - Part II
Big Data Mining and Knowledge Management
Self-attention Based Multimodule Fusion Graph Convolution Network for Traffic Flow Prediction
1 Introduction
2 Spatiotemporal Prediction in Deep Learning
2.1 Time Correlation Research
2.2 Time Correlation Research
3 Prediction Model of Traffic Flow Based on Multi-module Fusion
3.1 Model Frame Diagram
3.2 Space-Time Decoupling
3.3 Spatial Convolution
3.4 Spatial Self-attention
3.5 Temporal Convolution
3.6 Time Self-attention

3.7 Information Fusion and GRU
4 Experimental Analysis
4.1 Dataset
4.2 Analysis of Results
5 Conclusion
References
Data Analyses and Parallel Optimization of the Tropical-Cyclone Coupled Numerical Model
1 Introduction
1.1 A Subsection Sample
2 Model Setup
2.1 Atmospheric Model Setup
2.2 Hydrodynamic Model Setup
2.3 Ocean Wave Model Setup
2.4 HPC Facilities
2.5 Coupled Variables
3 Scaling Experiments
3.1 Parallel Tests Analysis
3.2 SWAN Model Parallel Algorithm Optimization
3.3 Ocean Model Grid Optimization
4 Parallel Test Results

5 Model Results Discussion
6 Conclusion
References
Factorization Machine Based on Bitwise Feature Importance for CTR Prediction
1 Introduction
2 Related Work
3 Our Approach
3.1 Embedding Layer
3.2 Learning
4 Experiments
4.1 Experimental Settings
4.2 Hyperparameter Study
4.3 Ablation Study
4.4 Performance Comparison
5 Conclusion
References
Focusing on the Importance of Features for CTR Prediction
1 Introduction
2 ECABiNet Model
2.1 Sparse Input and Embedding Layer
2.2 Layer Norm
2.3 ECANET Layer
2.4 Feature Cross Layer

2.5 DNN Layer
2.6 Output
3 Experiment
3.1 Experimental Setup
3.2 LayerNorm Effect Comparison
3.3 Comparison of the Effects of Different Attention Modules
3.4 Comparison of the Classic Model
3.5 Study HyperParameter
4 Related Work
5 Conclusions
References
Active Anomaly Detection Technology Based on Ensemble Learning
1 Introduction
2 Problem Statement
3 Proposed Model
3.1 Supervised Ensemble Learning Model
3.2 Human Participation
3.3 Model Self-training
3.4 Experiment
3.5 Conclusion
References

Automatic Generation of Graduation Thesis Comments Based on Multilevel Analysis
1 Introduction
2 Technical Principle
2.1 BERT Model Introduced
2.2 Basic Structure of the BERT Model
2.3 Comparison with Other Algorithms
3 Project Analysis
3.1 Technical Route
3.2 Technical Analysis
4 Project Implementation
4.1 Database Established Modification
4.2 Student Information Input
4.3 Neural Network Training
4.4 Automatically Generate Comments
5 Conclusion
References
A Survey of Malware Classification Methods Based on Data Flow Graph
1 Introduction