Advanced Data Mining and Applications
Description
The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
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Knowledge Graph .- Improving Distance Based Knowledge Graph Embedding via Contrastive Learning.- Knowledge Graph Completion with Information Adaptation and Refinement.- Duet Representation Learning with Entity Multi-Attribute Information in Knowledge Graphs.- TKGAT: Temporal Knowledge Graph Representation Learning Using Attention Network.- Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion.- Two Birds With One Stone: An Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition.- HEM: an improved parametric link prediction algorithm based on hybrid network evolution mechanism.- Joint embedding of local structures and evolutionary patterns for temporal link prediction.- Multimedia .- DQN-based Stitching Algorithm for Unmanned Aerial Vehicle Images.- HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for image classification.- DetOH: An Anchor-free Object Detector with Only Heatmaps.- LAANet: An Efficient Automatic Modulation Recognition Model Based on LSTM-Autoencoder and Attention Mechanism.- A Compact Phoneme-To-Audio Aligner for Singing Voice.- Song-to-Video Translation: Writing a Video from Song Lyrics Based on Multimodal Pre-Training.- Medical Image Analysis .- Breast Cancer Histopathology Image Classification Using Frequency Attention Convolution Network.- Wavelet-SVDD: Anomaly Detection and Segmentation with Frequency Domain Attention.- Anatomical-Functional Fusion Network for Lesion Segmentation using Dual-view CEUS.- SpMVNet: Spatial Multi-view Network for Head and Neck Organs at Risk Segmentation.- SNN-BS: A Clinical Terminology Standardization Method using Siamese Networks with Batch Sampling Strategy.- Natural Language .- Read then Respond: Multi-granularity Grounding Prediction for Knowledge-Grounded Dialogue Generation.- SUMO-PEGASUS: A Two-Stage Model for Long Text Summarization.- An Extractive Automatic Summarization Method for Chinese Long Text.- A Likelihood Probability-based Online Summarization Ranking Model.- Spatial Commonsense Reasoning for Machine Reading Comprehension.- Multimodal learning for Automatic Summarization: A Survey.- Deep Knowledge Tracing with Concept Trees.- Privacy and Security .- Cryptography-Inspired Federated Learning for Generative Adversarial Networks and Meta Learning.- A Hessian-Based Federated Learning Approach to tackle Statistical Heterogeneity.- Analyzing the Convergence of Federated Learning with Biased Client Participation.- Privacy Lost in online Education: Analysis of Web Tracking Evolution.- DANAA: Towards transferable attacks with double adversarial neuron attribution.- CRNN-SA: A Network Intrusion Detection Method Based on Deep Learning.- Applications .- Conspiracy SpoofingOrders Detection with Transformer-based Deep Graph Learning.- A Novel Explainable Rumor Detection Model with Fusing Objective Information.- VLS: A Reinforcement Learning-based Value Lookahead Strategy for Multi-product Order Fulfillment.- Deep Reinforcement Learning for Stock Trading with Behavioral Finance Strategy.- Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students.- How does ChatGPT Affect Fake News Detection Systems?.- CNGT: Co-attention Networks with Graph Transformer for Fact Verification.- Multi-Modal .- Supervised Discriminative Discrete Hashing for Cross-modal Retrieval.- A Knowledge-enhanced Inferential Network for Cross-modality Multi-hop VQA.- Multi-Head Similarity Feature Representation and Filtration for Image-Text Matching.- Multimodal Conditional VAE for Zero-shot Real-world Event Discovery.- MAMRP: Multi-modal Data Aware Movie Rating Prediction.- MFMGC: A Multi-Modal Data Fusion Model for Movie Genre Classification.- TED-CS: Textual Enhanced Sensitive Video Detection with Common Sense Knowledge.
