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[null,null,"Titolo",null,"Data science . Part I : 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022, Chengdu, China, August 19-22, 2022 : proceedings / / Yang Wang [and five others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"981-19-5194-2"]
[null,null,"Descrizione fisica",null,"1 online resource (455 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science"]
[null,null,"Disciplina",null,"005.7"]
[null,null,"Soggetti",null,"Big data"]
[{"$nulls":4},"Data mining"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Hyperparameter Study -- 4.3 Ablation Study -- 4.4 Performance Comparison -- 5 Conclusion -- References -- Focusing on the Importance of Features for CTR Prediction -- 1 Introduction -- 2 ECABiNet Model -- 2.1 Sparse Input and Embedding Layer -- 2.2 Layer Norm -- 2.3 ECANET Layer -- 2.4 Feature Cross Layer -- 2.5 DNN Layer -- 2.6 Output -- 3 Experiment -- 3.1 Experimental Setup."]
[{"$nulls":4},"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 -- 2 Data Flow Graph -- 2.1 Basic Concepts of the Data Flow Graph -- 2.2 Data Flow Graph Corresponding to Common APIs -- 2.3 Extension of Data Flow Graphs -- 3 Malware Classification Based on Data Flow Graph -- 3.1 User-Defined Data Flow Graph Feature-based Malware Classification -- 3.2 Data Flow Graph Similarity-Based Malware Classification -- 3.3 Graph Neural Network-Based Malware Classification -- 4 Discussion -- 5 Conclusion -- References -- Anomaly Detection of Multivariate Time Series Based on Metric Learning -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Model -- 3.1 Preprocessing -- 3.2 Encoder for High-Dimensional Time Series Data -- 3.3 Attentional Center Learning -- 3.4 Loss Function -- 3.5 Semisupervised Learning -- 4 Experiments -- 4.1 Dataset -- 4.2 Setup -- 4.3 Result."]
[{"$nulls":4},"5 Conclusion -- References -- Social Network Analysis of Coauthor Networks in Inclusive Finance in China -- 1 Introduction and Motivation -- 2 Data Collection and Preprocessing -- 3 Results -- 3.1 General Characteristics of the Coauthor Network -- 3.2 Ego Characteristics of the Coauthor Network -- 3.3 The Evolution of Cohesive Subgroups in the Coauthor Network -- 4 Conclusions -- References -- Multirelationship Aware Personalized Recommendation Model -- 1 Introduction -- 2 Preliminary Preparation -- 2.1 Problem Definition -- 2.2 Data Preprocessing -- 2.3 User Relationship Graphs -- 3 Modeling and Training -- 3.1 MrAPR Model -- 3.2 Model Training -- 4 Experiment -- 4.1 Dataset -- 4.2 Baselines and Evaluation Metrics -- 4.3 Parameter Settings -- 4.4 Ablation Experiments -- 5 Conclusion -- References -- Machine Learning for Data Science -- Preliminary Study on Adapting ProtoPNet to Few-Shot Learning Using MAML -- 1 Introduction -- 2 Related Work -- 2.1 Few-Shot Learning -- 2.2 Interpretability -- 3 Proposed Methods -- 3.1 Adapting ProtoPNet to MAML -- 3.2 Evaluating Models -- 4 Experiments -- 4.1 Datasets -- 4.2 Experiment 1: Omniglot Few-Shot Classification -- 4.3 Experiment 2: MiniImagenet Few-Shot Classification -- 4.4 Experiment 3: Interpretability Analysis on Omngilot -- 4.5 Experiment 4: Preliminary Interpretability Analysis on MiniImagenet -- 5 Conclusion and Future Work -- References -- A Preliminary Study of Interpreting CNNs Using Soft Decision Trees -- 1 Introduction -- 2 Related Work -- 3 Proposed "]
[{"$nulls":4},"Methods -- 3.1 Model Foundations -- 3.2 Using Normal/Soft Decision Trees to Interpret CNNs -- 3.3 Evaluating Interpretability -- 4 Experiments -- 4.1 Dataset and Experimental Setup -- 4.2 Experiment 1: Classification Performance -- 4.3 Experiment 2: Visualization of Normal/Soft Decision Trees' Top Features."]
[{"$nulls":4},"4.4 Experiment 3: Interpretability Performance -- 4.5 Experiment 4: Scores of Human Experts on Tag Clarity -- 5 Conclusion and Future Work -- References -- Deep Reinforcement Learning with Fuse Adaptive Weighted Demonstration Data -- 1 Introduction -- 2 Related Work -- 2.1 Deep Reinforcement Learning -- 2.2 Multiagent Reinforcement Learning -- 3 Methods -- 4 Experimental Results and Analysis -- 4.1 Experimental Environment and Data -- 4.2 Results and Analysis -- 5 Discussion -- References -- DRIB: Interpreting DNN with Dynamic Reasoning and Information Bottleneck -- 1 Introduction -- 2 Related Works -- 2.1 Explain the Existing Deep Learning Models -- 2.2 Construction of Interpretable Deep Learning Models -- 3 Method -- 3.1 Dynamic Reasoning Decision Module -- 3.2 Information Bottleneck Verification Module -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Interpretability of Calculation in Dynamic Reasoning Decision -- 4.3 Explainability of Attribution in the Information Bottleneck -- 4.4 Visualization of Understandability -- 5 Conclusion -- References -- Multimedia Data Management and Analysis -- Advanced Generative Adversarial Network for Image Superresolution -- 1 Introduction -- 2 Related Work -- 3 GAN and SRGAN -- 4 Proposed Method -- 4.1 Generator Network Structure -- 4.2 Discriminator Network Structure -- 4.3 Loss Function -- 5 Experiment Results and Analysis -- 5.1 Implementation Details -- 5.2 Datasets and Evaluation Metrics -- 5.3 Experimental Results and Analysis -- 5.4 Ablation Study -- 6 Conclusions -- References -- Real-World Superresolution by Using Deep Degradation Learning -- 1 Introduction -- 2 Related Work -- 2.1 Real-World Superresolution -- 2.2 Contrastive Learning -- 3 PurPosed Method -- 3.1 Overview of the Unsupervised Framework -- 3.2 Degradation Model -- 3.3 Reconstruction Model -- 4 Experiments -- 4.1 Training Data."]
[{"$nulls":4},"4.2 Training Details -- 4.3 Training Details -- 5 Conclusion -- References -- Probability Loop Closure Detection with Fisher Kernel Framework for Visual SLAM -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Fisher Vector Generation -- 3.2 Probability Visual Vocabulary -- 3.3 Loop Closure Detection -- 4 Results and Discussion -- 4.1 Dataset and Preprocessing -- 4.2 Evaluation Metrics -- 4.3 2D Motion -- 4.4 3D Motion -- 4.5 Bidirectional Loops -- 4.6 Ablation Study -- 5 Conclusions -- References -- A Complex Background Image Registration Method Based on the Optical Flow Field Algorithm -- 1 Introduction -- 2 The Proposed Method -- 3 Evaluation Functions -- 4 Experimental Results -- 5 Conclusion -- References -- Collaborative Learning Method for Natural Image Captioning -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 P2PM: Pix2Pix Inverting Module -- 3.2 NLGM: Natural Language Generation Module -- 3.3 Collaborative Learning Framework -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Main Results -- 5 Conclusion -- References -- Visual Analysis of the National Characteristics of the COVID-19 Vaccine Based on Knowledge Graph -- 1 Introduction -- 2 Related Studies -- 3 Construction of the COVID-19 Vaccine Knowledge Graph -- 3.1 Data Acquisition -- 3.2 Entity Extraction to Construct a Relational Model -- 3.3 Knowledge Graph Establishment -- 4 Visual Analysis of the National Characteristics of the COVID-19 Vaccine -- 5 Conclusions and Recommendations -- References -- Speech Recognition for Parkinson's Disease Based on Improved Genetic "]
[{"$nulls":4},"Algorithm and Data Enhancement Technology -- 1 Introduction -- 2 The Proposed Methods -- 2.1 Method -- 3 Speech Recognition and Diagnosis -- 3.1 Data Preprocessing -- 3.2 Improved GA-SVM Model -- 3.3 Speech Recognition Algorithm -- 4 Experiment and Evaluation -- 4.1 Experiment Setup."]
[{"$nulls":4},"4.2 Comparison and Analysis of Results."]