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[null,null,"Titolo",null,"Artificial intelligence : second CAAI international conference, CICAI 2022, Beijing, China, August 27-28, 2022, revised selected papers, Part III / / edited by Lu Fang [and four others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2023]"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"3-031-20503-0"]
[null,null,"Descrizione fisica",null,"1 online resource (639 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13606"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computational intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part III -- Intelligent Multilingual Information Processing -- A New Method for Assigning Hesitation Based on an IFS Distance Metric -- 1 Introduction -- 2 Preparatory Knowledge -- 2.1 Intuitionistic Fuzzy Sets -- 2.2 Intuitionistic Fuzzy Set Distance Metric -- 3 Existing Intuitionistic Fuzzy Set Distance Metrics and Their Analysis -- 3.1 Existing Intuitionistic Fuzzy Set Distance Metric -- 3.2 Existing Intuitionistic Fuzzy Set Distance Analysis -- 4 A New Intuitionistic Fuzzy Set Distance Metric -- 5 Example Analysis -- 6 Concluding Remarks -- References -- Adaptive Combination of Filtered-X NLMS and Affine Projection Algorithms for Active Noise Control -- 1 Introduction -- 2 FxNLMS and FxAP Algorithms in ANC System -- 2.1 Framework of ANC -- 2.2 FxNLMS and FxAP Algorithms -- 3 Combined FxNLMS and FxAP Algorithm -- 4 Simulation Results -- 5 Conclusion -- References -- Linguistic Interval-Valued Spherical Fuzzy Sets and Related Properties -- 1 Introduction -- 2 Preliminaries -- 2.1 Spherical Fuzzy Sets -- 2.2 Interval-Valued Spherical Fuzzy Sets -- 2.3 Linguistic Term Sets -- 2.4 Linguistic Spherical Fuzzy Sets -- 3 Linguistic Interval-Valued Spherical Fuzzy Sets -- 3.1 Concepts of LIVSFS and LIVSFN -- 3.2 Basic Operations and Properties of LIVSFS -- 3.3 Basic Operations and Properties of LIVSFN -- 3.4 Comparing Methods and Measurement Formulas of LIVSFNs -- 4 Conclusion -- References -- Knowledge Representation and Reasoning -- A Genetic Algorithm for Causal "]
[{"$nulls":4},"Discovery Based on Structural Causal Model -- 1 Introduction -- 2 Related Work -- 3 Causal Discovery Based on Genetic Algorithm -- 3.1 Data Pre-processing -- 3.2 Causal Relation Pre-discovering -- 3.3 Causal Graph Pruning -- 4 Experiments -- 4.1 Research Questions -- 4.2 Experiment Setting -- 4.3 Results and Analysis -- 5 Conclusion."]
[{"$nulls":4},"References -- Stochastic and Dual Adversarial GAN-Boosted Zero-Shot Knowledge Graph -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Background -- 3.2 Motivation -- 3.3 Framework of SDA -- 4 Experiments -- 4.1 Datasets Description -- 4.2 Baselines and Implementation Details -- 4.3 Result -- 4.4 Ablation Study -- 4.5 Influence of Word Embedding Method -- 5 Conclusions -- References -- Machine Learning -- LS-YOLO: Lightweight SAR Ship Targets Detection Based on Improved YOLOv5 -- 1 Introduction -- 2 Related Work -- 2.1 YOLOv5 Algorithm -- 2.2 CSP Module -- 3 The Proposed Method -- 3.1 LS-YOLO Network Structure -- 4 Experiment and Results -- 4.1 Experimental Configurations and Dataset -- 4.2 Evaluation Indicators -- 4.3 Experimental Results and Analysis -- 5 Conclusions -- References -- Dictionary Learning-Based Reinforcement Learning with Non-convex Sparsity Regularizer -- 1 Introduction -- 2 Related Work -- 3 Dictionary Learning-Based Reinforcement Learning with Non-convex Sparsity Regularizer -- 3.1 Problem Formulation -- 3.2 Optimization and Algorithm -- 4 Experiments and Discussions -- 4.1 Experiment Details -- 4.2 Parameter Selection -- 4.3 Performances Comparison -- 5 Conclusion -- References -- Deep Twin Support Vector Networks -- 1 Introduction -- 2 Related Work -- 3 Deep Twin Support Vector Networks -- 3.1 DTSVN for Binary Classification -- 3.2 Multiclass Deep Twin Support Vector Networks -- 3.3 Algorithm -- 3.4 Comparison with Shallow TSVM and Traditional DNN -- 4 Numerical Experiments -- 4.1 Experiments on Benchmark Datasets -- 4.2 Discussion on Parameter C -- 5 Conclusion -- References -- Region-Based Dense Adversarial Generation for Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Adversarial Examples -- 2.2 Target Adversary Generation -- 2.3 Region-Based Dense Adversary Generation -- 3 RESULT."]
[{"$nulls":4},"3.1 Materials and Configurations -- 3.2 Evaluation Metrics -- 3.3 Results on DRIVE and CELL Datasets -- 3.4 The Adversarial Examples for Data Augmentation -- 4 Conclusion -- References -- Dynamic Clustering Federated Learning for Non-IID Data -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 DCFL Framework -- 4.1 Client Design -- 4.2 Server Design -- 5 Experiments -- 5.1 Experimental Datasets -- 5.2 Experimental Settings -- 5.3 Experimental Results -- 6 Conclusion -- References -- Dynamic Network Embedding by Using Sparse Deep Autoencoder -- 1 Introduction -- 2 Definition and Problem Formulation -- 2.1 Definition -- 2.2 Problem Formulation -- 3 Our Algorithm: SPDNE -- 4 Experimental Results -- 4.1 Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Deep Graph Convolutional Networks Based on Contrastive Learning: Alleviating Over-smoothing Phenomenon -- 1 Introduction -- 2 Related Work -- 2.1 GCNs -- 2.2 Over-smoothing -- 2.3 Graph Contrastive Learning -- 3 Method -- 3.1 Contrast Structure and Data Augmentation -- 3.2 Contrastive Loss -- 3.3 For Node Classification Tasks on GCN -- 4 Experiments -- 4.1 Experimental Details -- 4.2 Pretraining Shallow GCN -- 4.3 Node Classification Results -- 5 Conclusion -- References -- Clustering-based Curriculum Construction for Sample-Balanced Federated Learning -- 1 Introduction -- 2 Related Work -- 2.1 Curriculum Learning -- 2.2 Federated Learning -- 3 Problem Formulation -- 4 Federation Curriculum Learning -- 4.1 Curriculum Generation (CG) "]
[{"$nulls":4},"Module -- 4.2 Curriculum Training (CT) Module -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Performance Comparison -- 5.3 Ablation Study -- 5.4 Case Study -- 6 Conclusion -- References -- A Novel Nonlinear Dictionary Learning Algorithm Based on Nonlinear-KSVD and Nonlinear-MOD -- 1 Introduction -- 2 Model and Formulation."]
[{"$nulls":4},"3 Algorithm -- 3.1 Nonlinear Sparse Coding Based on NL-OMP -- 3.2 Nonlinear Dictionary Update -- 4 Numerical Experiments -- 4.1 Experimental Settings -- 4.2 Evaluation Indicators -- 4.3 Experimental Results -- 5 Conclusions and Discussions -- References -- Tooth Defect Segmentation in 3D Mesh Scans Using Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 3D Shape Segmentation -- 2.2 3D Teeth-related Tasks -- 3 Method -- 3.1 Data Pre-processing -- 3.2 Model Architecture -- 3.3 Loss -- 4 Experiment -- 4.1 Dataset and Experimental Setup -- 4.2 The Overall Performance -- 4.3 Ablation Studies -- 4.4 Visualization -- 5 Conclusion -- References -- Multi-agent Systems -- Crowd-Oriented Behavior Simulation:Reinforcement Learning Framework Embedded with Emotion Model -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Pedestrian Simulation Framework -- 3.2 Decision Realization Based on Emotion Model -- 3.3 Behavior Realization Based on ICM and PPO Algorithm -- 4 Experiments -- 4.1 Introduction to The Experimental System -- 4.2 Behavior Realization Comparative Experiment -- 4.3 A Variety of Emotional Simulation Experiments -- 5 Conclusion -- References -- Deep Skill Chaining with Diversity for Multi-agent Systems* -- 1 Introduction -- 2 Related Works -- 3 MARL with Skill Discovery -- 3.1 How Agents Learn their Policies -- 3.2 Option Framework -- 3.3 Problem Formulation -- 4 Methodology -- 4.1 Option Learning in MAS -- 4.2 Skill Chaining for MARL -- 4.3 Mutual Information for Space Exploration -- 5 Experimental Evaluations -- 5.1 SMAC Environment Setup -- 5.2 Mutual Information Evaluation -- 5.3 Performance Evaluation -- 6 Conclusions -- References -- Natural Language Processing -- Story Generation Based on Multi-granularity Constraints -- 1 Introduction -- 2 Related Works -- 2.1 Generation Framework -- 2.2 Controllable Story Generation."]
[{"$nulls":4},"3 Methodology -- 3.1 Task Definition and Model Overview -- 3.2 Token-Level Constraint -- 3.3 Sentence-Level Constraint -- 4 Experimental Setup -- 4.1 Baselines -- 4.2 Evaluation Metrics -- 5 Results and Discussions -- 5.1 Automatic Evaluation and Human Evaluation -- 5.2 Case Study -- 6 Conclusion -- References -- Chinese Word Sense Embedding with SememeWSD and Synonym Set -- 1 Introduction -- 2 Related Work -- 2.1 Word Embedding -- 2.2 Word Sense Disambiguation and Word Sense Embedding -- 3 Methodology -- 3.1 OpenHowNet and SememeWSD -- 3.2 SWSDS Model -- 4 Experiment -- 4.1 SWSDS Experiment -- 4.2 Effectiveness Evaluation of SWSDS Model -- 5 Conclusion -- References -- Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF -- 1 Introduction -- 2 Related Work -- 2.1 Chinese Medical NER -- 2.2 Nested NER -- 2.3 Pre-trained Model -- 3 Methodology -- 3.1 Adaptive Shared Pre-trained Model -- 3.2 Attentive Conditional Random Fields -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Results and Comparisons -- 4.4 Ablation Studies -- 5 Conclusion -- References -- CycleResume: A Cycle Learning Framework with Hybrid Attention for Fine-Grained Talent-Job Fit -- 1 Introduction -- 2 Framework Design -- 2.1 Problem Formulation -- 2.2 Architecture Overview -- 2.3 Feature Extraction -- 2.4 Fine-Grained Resume Representation Learning -- 2.5 Multi-scale Job-Post Representation Learning -- 2.6 Attention Based Resume-Job "]
[{"$nulls":4},"Fit Prediction -- 2.7 Training Methodology -- 3 Performance Evaluation -- 3.1 Dataset Description -- 3.2 Evaluation Metric -- 3.3 Experiment Setting -- 3.4 Evaluation Result -- 4 Conclusion and Future Work -- References -- Detecting Alzheimer's Disease Based on Acoustic Features Extracted from Pre-trained Models -- 1 Introduction -- 2 Methods."]
[{"$nulls":4},"2.1 Extracting Bottleneck Features and Wav2vec 2.0 Representations from Pre-trained Models."]