11022nam 2200517 450 99650346940331620231201225826.03-031-20503-0(MiAaPQ)EBC7158297(Au-PeEL)EBL7158297(CKB)25732543800041(PPN)268758840(EXLCZ)992573254380004120230418d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial 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]Cham, Switzerland :Springer,[2023]©20231 online resource (639 pages)Lecture Notes in Computer Science ;v.13606Print version: Fang, Lu Artificial Intelligence Cham : Springer,c2023 9783031205026 Includes bibliographical references and index.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 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.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.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) 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.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.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 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.2.1 Extracting Bottleneck Features and Wav2vec 2.0 Representations from Pre-trained Models.Lecture Notes in Computer ScienceArtificial intelligenceComputational intelligenceArtificial intelligence.Computational intelligence.006.3Fang LuMiAaPQMiAaPQMiAaPQBOOK996503469403316Artificial intelligence104454UNISA05090nam 2201465z- 450 991055762580332120210501(CKB)5400000000045157(oapen)https://directory.doabooks.org/handle/20.500.12854/68299(oapen)doab68299(EXLCZ)99540000000004515720202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMolecular Marker Technology for Crop ImprovementBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (302 p.)3-03943-863-8 3-03943-864-6 Since the 1980s, agriculture and plant breeding have changed with the development of molecular marker technology. In recent decades, different types of molecular markers have been used for different purposes: mapping, marker-assisted selection, characterization of genetic resources, etc. These have produced effective genotyping, but the results have been costly and time-consuming due to the small number of markers that could be tested simultaneously. Recent advances in molecular marker technologies such as the development of high-throughput genotyping platforms, genotyping by sequencing, and the release of the genome sequences of major crop plants have opened new possibilities for advancing crop improvement. This Special Issue collects 16 research studies, including the application of molecular markers in 11 crop species, from the generation of linkage maps and diversity studies to the application of marker-assisted selection and genomic prediction.Biology, life sciencesbicsscResearch & information: generalbicsscagaroseagronomic traitsapricotaromaassociation mappingbreedingbrown rice recoverycandidate genescereal cropchromosomecrop breedingdisease resistanceDNA sequencingdroughtdrought stressdurum wheatF2:3 biparental mappingflavonoid biosynthesisfruit astringencyfruit colorationgene prioritizationgene pyramidinggenetic diversitygenetic mapsgenetic relationshipgenome wide association studygenomic predictiongenomic selectiongenomicsgrain qualityGWASGWAS water usehead rice recoveryhigh resolution meltinghigh-density genetic linkage mapintrogression lineISBP markerslandracesleaf rustlinkage disequilibriummaize (Zea mays L.)mapping populationmarker assisted selectionmarker-assisted selectionMarker-assisted selectionmarker-trait associationMASMATHmicrosatellitesmilled rice recoverymilling yield traitsmolecular markersMQTLMTAsmulti-traitnear infra-red spectroscopyparental lineParPMCParPMC2-delpathogen racesPersea americanapersimmonplant breedingpopulation structurePPV resistanceQTLQTL hotspotQTL mappingrice (Oryza sativa L.)root system architectureRubusSDSsedimentation volumeselectionseminal rootsex determinationsimple sequence repeatsimple sequence repeats (SSR)SLAF-seq technologySMRT sequencingSNP markerssorghumstem rustStriga resistance/tolerancesugarcanetetraploid potatoTKWTriticum aestivumtropical maizewheat variabilitywhole genome regressionYRBiology, life sciencesResearch & information: generalSoriano del Castillo José Migueledt2011076Soriano del Castillo José MiguelothBOOK9910557625803321Molecular Marker Technology for Crop Improvement4800220UNINA