Neural Information Processing : 30th International Conference, ICONIP 2023, Changsha, China, November 20-23, 2023, Proceedings, Part XIII

• Autore: Luo Biao
• Pubblicazione: Singapore : , : Springer Singapore Pte. Limited, , 2023; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (628 pages)
• Altri autori: ChengLong WuZheng-Guang LiHongyi LiChaojie
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part XIII -- Applications -- Improve Conversational Search with Multi-document Information -- 1 Introduction -- 2 Related Works -- 2.1 Conversational Search -- 2.2 LLM for Information Retrieval -- 3 Method -- 3.1 Preliminaries -- 3.2 Multi-document Conversation Segmentation -- 3.3 LLM-Based Document Summarization -- 3.4 Reranker Based on Passage-Segment-Document Post-training -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Comparison -- 4.3 Experimental Results -- 4.4 Ablation Experiment -- 5 Conclusion -- References -- Recurrent Update Representation Based on Multi-head Attention Mechanism for Joint Entity and Relation Extraction -- 1 Introduction -- 2 Related Work -- 3 Task Formulation -- 4 Methodology -- 4.1 Representation Construction -- 4.2 Multi-head Attention Fusion Layer -- 4.3 Relation Extraction -- 5 Experiments -- 5.1 Experiment Settings -- 5.2 Experimental Results -- 5.3 Number of Heads in Multi-head Attention -- 5.4 Layers of Multi-head Attention Fusion Layers -- 6 Conclusion -- References -- Deep Hashing for Multi-label Image Retrieval with Similarity Matrix Optimization of Hash Centers and Anchor Constraint of Center Pairs -- 1 Introduction -- 2 Related Work -- 3 The Proposed Deep Hashing Framework -- 3.1 Similarity Matrix Optimization of Hash Centers -- 3.2 Loss Function of Multi-label Deep Hashing Model -- 4 Experiments -- 4.1 Baselines and Datasets -- 4.2 Experimental Details and Hyperparameter Settings -- 4.3 Evaluation Criteria -- 4.4 Experimental Comparison Results -- 5 Conclusion -- References -- MDAM: Multi-Dimensional Attention Module for Anomalous Sound Detection -- 1 Introduction -- 2 Related Work -- 2.1 Attention Mechanism -- 2.2 Analysis of Temporal and Frequency Dimension Characteristics -- 3 Method -- 3.1 Multi-Dimensional Attention Module. ; 3.2 Domain Mixture -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison of Different Outlier Detection Algorithms -- 4.3 Pooling Selection -- 4.4 Comparison with Other Attention Modules -- 4.5 Effectiveness of Domain Mixture -- 4.6 Comparison with Other Anomaly Detection Models -- 5 Conclusion -- References -- A Corpus of Quotation Element Annotation for Chinese Novels: Construction, Extraction and Application -- 1 Introduction -- 2 Related Work -- 3 Corpus Construction -- 3.1 Annotation Schema -- 3.2 Corpus Analysis -- 4 Quotation Element Extraction -- 5 Applications of Quotation Elements -- 5.1 Character Recognition Based on Quotation Elements -- 5.2 Gender Classification Based on Quotation Elements -- 6 Conclusion -- References -- Decoupling Style from Contents for Positive Text Reframing -- 1 Introduction -- 2 Related Works -- 2.1 Text Style Transfer -- 2.2 Pre-trained Language Models -- 2.3 Contrastive Learning -- 3 Method -- 3.1 Seq2Seq Text Generation -- 3.2 Decoupling Style from Contents -- 3.3 Preserving Primary Content -- 4 Experiment -- 4.1 Experimental Settings -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Multi-level Feature Enhancement Method for Medical Text Detection -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Overall Network Architecture -- 3.2 Efficient Feature Enhancement Module -- 3.3 Multi-scale Feature Fusion Module -- 3.4 Post-processing -- 3.5 Deformable Convolution and Label Generation -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparisons with Previous Methods -- 5 Conclusion -- References -- Neuron Attribution-Based Attacks Fooling Object Detectors -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Problem Setting -- 3.2 Our Approach -- 4 Experiments -- 4.1 Experimental Setting. ; 4.2 Comparison with Other Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- DKCS: A Dual Knowledge-Enhanced Abstractive Cross-Lingual Summarization Method Based on Graph Attention Networks -- 1 Introduction -- 2 Related Work -- 3 The Framework -- 3.1 Key Clues Extraction -- 3.2 Clue-Focused Graph Encoder -- 3.3 Clue Encoder -- 3.4 Decoder -- 4 Experimental Setup -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experimental Settings -- 5 Results and Analysis -- 5.1 Automatic Evaluation -- 5.2 Ablation Study -- 5.3 Human Judgement -- 5.4 The Impact of Article Length -- 6 Discussion and Conclusion -- References -- A Joint Identification Network for Legal Event Detection -- 1 Introduction -- 2 Model Structure -- 2.1 Encoding Module -- 2.2 Initial Scoring Module -- 2.3 Feature Extraction Module -- 2.4 Aggregation Module -- 3 Model Training -- 3.1 Dataset -- 3.2 Revising the Loss Function by Mask -- 3.3 Adversarial Training -- 4 Experiment -- 4.1 Dataset -- 4.2 Baseline -- 4.3 Evaluation Criteria -- 4.4 Setting -- 4.5 Benchmark Experiment -- 4.6 Ablation Experiment -- 4.7 Benchmark with ChatGPT -- 4.8 Competition Results -- 5 Related Works -- 5.1 Generative-Based Approach -- 5.2 Classification-Based Approach -- 5.3 Graph Neural Network-Based Approach -- 5.4 Span-Based Approach -- 6 Conclusion -- References -- YOLO-D: Dual-Branch Infrared Distant Target Detection Based on Multi-level Weighted Feature Fusion -- 1 Introduction -- 2 Method -- 2.1 Model Architecture -- 2.2 Contour Feature Extraction -- 2.3 Multilevel Feature Fusion -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Experiment Setup -- 3.3 Experimental Data Results -- 3.4 Experimental Visual Results -- 3.5 Ablation and Validation Experiments -- 4 Conclusion -- References -- Graph Convolutional Network Based Feature Constraints Learning for Cross-Domain Adaptive Recommendation. ; 1 Introduction -- 2 Related Work -- 2.1 Cross-Domain Recommendation -- 2.2 Domain Adaptation -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 CDAR-FCL Overview -- 3.3 Graph Embedding Layer -- 3.4 Feature Constraint -- 3.5 Feature Combination -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Effect of Hyper-parameter -- 4.3 Comparison -- 4.4 Visualization -- 4.5 Ablation Study -- 5 Conclusion -- References -- A Hybrid Approach Using Convolution and Transformer for Mongolian Ancient Documents Recognition -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 4 Experimental Results -- 4.1 Dataset and Baseline -- 4.2 Parameter Settings -- 4.3 Results and Analysis -- 5 Conclusion -- References -- Incomplete Multi-view Subspace Clustering Using Non-uniform Hyper-graph for High-Order Information -- 1 Introduction -- 2 Background -- 2.1 Multi-View Subspace Clustering Preliminaries -- 2.2 Tensor Preliminaries -- 2.3 Hypergraph Preliminaries -- 3 The Proposed NUHG-IMSC Method -- 4 Experiment and Results -- 4.1 Data Sets -- 4.2 Methods -- 4.3 Quantified Experimental Results and Variable Visualizations -- 5 Conclusions -- References -- Deep Learning-Empowered Unsupervised Maritime Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Data-Driven Approaches for Maritime Anomaly Detection -- 2.2 Rule-Based Approaches for Maritime Anomaly Detection -- 2.3 Hybrid Approaches for Maritime Anomaly Detection -- 3 Fast GAN-Based Anomalous Vessel Trajectory Detection -- 3.1 Problem Definition -- 3.2 Generation of Vessel Trajectory Images -- 3.3 Unsupervised Learning of Normal Anatomical Variability -- 3.4 Detection of Anomalous Vessel Trajectory -- 4 Experiments -- 4.1 Data Set and Baselines -- 4.2 Experimental Evaluation -- 5 Conclusions -- References -- Hazardous Driving Scenario Identification with Limited Training Samples -- 1 Introduction. ; 2 Related Work -- 2.1 Hazardous Driving Scenario Identification -- 2.2 Motion Profile Based Models -- 3 Data Operation -- 3.1 Motion Profile Generation -- 4 Methodology -- 4.1 Data Augmentation -- 4.2 Model Architecture -- 5 Experiments and Results -- 5.1 Experiment Settings -- 5.2 Effect of Data Augmentation -- 5.3 Comparison with Existing Methods -- 5.4 Impact of Mixed Sample Ratio on Model Performance -- 6 Conclusion and Future Work -- References -- Machine Unlearning with Affine Hyperplane Shifting and Maintaining for Image Classification -- 1 Introduction -- 2 Method -- 2.1 Preliminaries -- 2.2 Affine Hyperplane Unlearning -- 2.3 Affine Hyperplane Maintaining -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Experimental Results -- 3.3 Ablation Study -- 4 Conclusions -- References -- An Interpretable Vulnerability Detection Framework Based on Multi-task Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview of Our Detection Model -- 3.2 Encoder: Features Extraction -- 3.3 Objective Task -- 3.4 Training -- 4 Evaluation and Analysis -- 4.1 Dataset -- 4.2 Experimental Results -- 5 Conclusion -- References -- Co-GAN: A Text-to-Image Synthesis Model with Local and Integral Features -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Local Feature Enhancement Module -- 3.3 Integral Structural Maintenance Module -- 3.4 Loss Function -- 4 Experiments and Discussion -- 4.1 Set up -- 4.2 Experiments and Discussion -- 5 Conclusion -- References -- Graph Contrastive ATtention Network for Rumor Detection -- 1 Introduction -- 2 Preliminary Knowledge -- 3 Graph Attentive Self-supervised Learning Model -- 3.1 Rumor Identification Network -- 3.2 Graph Contrastive Learning Network -- 3.3 Model Fusion -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experimental Results and Analysis -- 4.4 Early Detection. ; 4.5 Ablation Study.
• Titolo autorizzato: Neural Information Processing
• ISBN: 981-9981-78-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910768468503321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science Series