Deep Learning -- TeaE: an Efficient Method for Improving the Precision of Teaching Evaluation -- Graph Fusion Multimodal Named Entity Recognition Based on Auxiliary Relation Enhancement -- Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension -- Multi-grained Logical Graph Network for Reasoning-based Machine Reading Comprehension -- Adaptive Prototype Learning with Common and Discriminative Features for Few-shot Relation Extraction -- Fine-grained Knowledge Enhancement for Empathetic Dialogue Generation -- Implicit Sentiment Extraction using Structure Generation with Sentiment Instructor Prompt Template -- SE-Prompt: Exploring Semantic Enhancement with Prompt Tuning for Relation Extraction -- Self-supervised Multi-view Clustering Framework with Graph Filtering and Contrast Fusion -- Semantic Selection and Multi-view Alignment for Image-Text Retrieval -- Voice Conversion with Denoising Diffusion Probabilistic GAN Models -- Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music -- Document-level Relation Extraction with Relational Reasoning and Heterogeneous Graph Neural Networks -- A Chinese Named Entity Recognition Method based on Textual Information Perception Fusion -- Aspect-Based Sentiment Analysis via BERT and Multi-Scale CBAM -- A novel adaptive distribution distance-based feature selection method for video traffic identification -- SVIM: a Skeleton-based View-invariant Method for Online Gesture Recognition -- A Unified Information Diffusion Prediction Model based on Multi-task Learning -- Learning Knowledge Representation with Entity Concept Information -- Domain Adaptive Pre-trained Model for Mushroom Image Classification -- Training Noise Robust Deep Neural Networks with Self-supervised Learning -- Path integration enhanced graph attention network -- Graph Contrastive Learning with HybridNoise Augmentation for Recommendation -- User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation -- Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation -- A Poisoning Attack Based on Variant Generative Adversarial Networks in Recommender Systems -- Label Correlation guided Feature Selection for Multi-label Learning -- Iterative Encode-and-Decode Graph Neural Network -- Community Detection in Temporal Biological Metabolic Networks based on Semi-NMF Method with Node Similarity Fusion -- UKGAT: Uncertain Knowledge Graph Embedding Enriched KGAT for Recommendation -- Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network -- Knowledge Graph Embedding with Relation Rotation and Entity Adjustment by Quaternions -- Towards time-variant-aware Link Prediction in Dynamic Graph through Self-supervised Learning -- Adaptive Heterogeneous graph Contrastive clustering with Multi-Similarity -- Multi-Teacher Local Semantic Distillation from Graph Neural Networks -- AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining -- Rethinking the Evaluation of Deep Neural Network Robustness -- A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training -- TSCMR:Two-Stage Cross-Modal Retrieval -- Effi-Emp: An AI based approach towards positive empathic expressions -- Industry Track Papers -- Research on Image Segmentation Algorithm Based on Level Set. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Predicting learners' performance using MOOC clickstream -- A Fine-grained Verification Method for Blockchain Data Based on Merkle Path Sharding -- A Privacy Preserving Method for Trajectory Data Publishing Based on Geo-indistinguishability -- HA-CMNet: A Driver CTR Model for Vehicle-Cargo Matching in O2O Platform -- A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises -- A method for data exchange and management in the military industry field. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Multi-region Quality Assessment based on Spatial-Temporal Community Detection from Computed Tomography Images.