12644nam 22008055 450 991061927930332120230810180136.03-031-14903-310.1007/978-3-031-14903-0(MiAaPQ)EBC7119395(Au-PeEL)EBL7119395(CKB)25176339500041(DE-He213)978-3-031-14903-0(PPN)26585637X(EXLCZ)992517633950004120220817d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIntelligence Science IV 5th IFIP TC 12 International Conference, ICIS 2022, Xi'an, China, October 28–31, 2022, Proceedings /edited by Zhongzhi Shi, Yaochu Jin, Xiangrong Zhang1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (480 pages)IFIP Advances in Information and Communication Technology,1868-422X ;659Print version: Shi, Zhongzhi Intelligence Science IV Cham : Springer International Publishing AG,c2022 9783031149023 Includes bibliographical references and index.Intro -- Preface -- Organization -- Abstracts of Keynote and Invited Talks -- Tactile Situations: A Basis for Manual Intelligence and Learning -- Brain-like Perception and Cognition: Challenges and Thinking -- Dealing with Concept Drifts in Data Streams -- A Novel Bionic Imaging and Its Intelligent Processing -- Skill Learning in Dynamic Scene for Robot Operations -- Emerging Artificial Intelligence Technologies in Healthcare -- Memory Cognition -- Contents -- Brain Cognition -- Mouse-Brain Topology Improved Evolutionary Neural Network for Efficient Reinforcement Learning -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 The Allen Mouse Brain Atlas -- 3.2 The Clustered Hierarchical Circuits -- 3.3 The Neuron Model -- 3.4 Coping the Biological Circuits to Artificial Ones -- 3.5 The Network Learning -- 4 Experiments -- 4.1 The Clustered Brain Regions -- 4.2 The Network Topology from Biological Mouse Brain -- 4.3 Results with Circuit-46 and Random Networks -- 4.4 Result Comparison with Different Algorithms -- 5 Discussion -- References -- DNM-SNN: Spiking Neural Network Based on Dual Network Model -- 1 Introduction -- 2 Methods -- 2.1 Traditional SNN Supervised Learning Algorithm Framework and Its Limitations -- 2.2 Proposed Dual-Model Spike Network Supervised Learning Algorithm -- 2.3 Proposed Multi-channel Mix Module Prediction Method -- 2.4 The Chosen Network Model -- 2.5 Selection of Spiking Neurons -- 3 Experimental Results -- 3.1 Single- and Dual-Model Resnet11 Performance on the CIFAR-10 Dataset -- 3.2 Related Work Comparison -- 4 Conclusion -- References -- A Memetic Algorithm Based on Adaptive Simulated Annealing for Community Detection -- 1 Introduction -- 2 Background -- 2.1 Modularity -- 2.2 Normalized Mutual Information -- 3 Description of MA-ASA -- 3.1 Segmented Label Propagation -- 3.2 Selection and Crossover Operation.3.3 Mutation Operation -- 3.4 Improved Simulated Annealing -- 3.5 Framework of MA-ASA -- 4 Experiments and Analysis -- 4.1 Experimental Settings -- 4.2 Experimental Results and Analysis -- 5 Conclusion -- References -- The Model of an Explanation of Self and Self-awareness Based on Need Evolution -- 1 Background and Significance -- 2 The Nature and Needs of Life -- 2.1 The Nature and Representation of the Self -- 2.2 The Primary Needs and Principle of Life -- 3 Evolution and Representation of the Needs of Life -- 3.1 Needs Representation and Original Self-evolution in Single-Celled and Complex Organisms -- 3.2 Representation Needs and Self-awareness of Human -- 4 Self-model Based on the Evolution of Needs -- 4.1 Iterative Model of Needs Evolution -- 4.2 Evolutionary Model of the Self -- 5 Dicussion and Conclusion -- References -- Spiking Neuron Network Based on VTEAM Memristor and MOSFET-LIF Neuron -- 1 Introduction -- 2 Proposed Method -- 2.1 Leaky Integrate-and-Fire Model -- 2.2 Design of LIF Circuit -- 2.3 Correspondence Between Network and Circuit -- 2.4 Processing of the DVS128 Gesture Dataset -- 2.5 Network Formulation -- 3 Performance Analysis and Discussion -- 4 Conclusion -- References -- Machine Learning -- A Deception Jamming Discrimination Method Based on Semi-supervised Learning with Generative Adversarial Networks -- 1 Introduction -- 2 Signal Model -- 2.1 The Construction of a Multistatic Radar System Model -- 2.2 Generation of Echo Data -- 3 The Discrimination Network Based on SGAN -- 4 Simulation -- 4.1 Simulation Analysis -- 4.2 Simulation Results with Different PRI -- 4.3 The Comparison of Different Discrimination Methods -- 5 Conclusion -- References -- Fast Node Selection of Networked Radar Based on Transfer Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Radar Node Selection -- 2.2 Reinforcement Learning.2.3 Transfer Learning -- 3 Methodology -- 3.1 Revisiting of Monte Carlo Tree -- 3.2 The Lower Bound of Cramero (CLRB) -- 3.3 Selection Flow -- 3.4 Variable-Number Node Search -- 3.5 Transfer Reinforcement Learning -- 4 Experiments and Analysis -- 5 Conclusion -- References -- Weakly Supervised Liver Tumor Segmentation Based on Anchor Box and Adversarial Complementary Learning -- 1 Introduction -- 2 Approach -- 2.1 Anchor Boxes Generation -- 2.2 Adversarial Complementary Learning -- 2.3 Application -- 2.4 Pseudo Mask Generation -- 3 Experiments -- 3.1 Datasets and Evaluated Metric -- 3.2 Classification Network and Hyperparameter Settings -- 3.3 Segmentation Network and Test Results -- 4 Conclusions -- References -- Weakly Supervised Whole Cardiac Segmentation via Attentional CNN -- 1 Introduction -- 2 Method -- 2.1 Pseudo Masks -- 2.2 Deep U-Net Network -- 2.3 Improved Weighted Cross-Entropy Loss -- 3 Experimental and Results -- 3.1 Datasets and Implementation Details -- 3.2 Patch Selection -- 3.3 Experimental Results -- 3.4 Ablation Experiments -- 3.5 Generality Experiments -- 4 Conclusion -- References -- Noisy Label Learning in Deep Learning -- 1 Introduction -- 2 Preliminary Knowledge -- 2.1 Noisy Labels in Deep Learning -- 2.2 Noisy Label Dataset and Noisy Label Types -- 2.3 Analysis the Problems in Noisy Label Learning -- 3 Existing Methods of Noisy Label Learning -- 3.1 Full-Equal-Using Method -- 3.2 Clean-Based Method -- 3.3 Full-Differ-Using Method -- 4 Problems in Existing Methods -- 4.1 Difference Between Synthetic Dataset and the Actual Dataset -- 4.2 Problems with Existing Methods -- 4.3 Possible Solutions -- 5 Conclusion -- References -- Accelerating Deep Convolutional Neural Network Inference Based on OpenCL -- 1 Introduction -- 2 Related Work -- 3 Design, Implementation and Optimization of CNN on OpenCL.3.1 Parallel Strategy for Convolution Layer -- 3.2 Parallel Strategy for Other Layers -- 3.3 Kernel Fusion and Increasing Global Task -- 4 Experiment and Evaluations -- 4.1 Experimental Environment -- 4.2 Performance Comparison of Depthwise Convolution Operations -- 4.3 Comparison of Parallel DCNN Inference Performance -- 4.4 Performance Comparison of Different Hardware Environments -- 5 Conclusions -- References -- A Simple Approach to the Multiple Source Identification of Information Diffusion -- 1 Introduction -- 2 Related Works and Motivations -- 2.1 Related Methods -- 2.2 Motivations -- 3 Preliminaries and Problem Formulation -- 3.1 Susceptible-Infected (SI) Model -- 3.2 Problem Formulation -- 4 KST Method -- 4.1 Analysis -- 4.2 KST Method -- 5 KST-Improved Method -- 6 Evaluation -- 6.1 Experiments Settings -- 6.2 Accuracy of Identifying Sources -- 7 Conclusion -- References -- Data Intelligence -- A Directed Search Many Objective Optimization Algorithm Embodied with Kernel Clustering Strategy -- 1 Introduction -- 2 The Proposed Method -- 2.1 Directed Search Sampling and Guiding Solutions -- 2.2 Environmental Selection -- 3 Experimental Results and Analysis -- 4 Conclusion -- References -- A Two-Branch Neural Network Based on Superpixel Segmentation and Auxiliary Samples -- 1 Introduction -- 2 Proposed Method -- 2.1 Selection of Auxiliary Samples -- 2.2 The Structure of TBN-SPAS -- 3 Implementation Process of TBN-MERS -- 4 Experiment and Analysis -- 4.1 Experimental Settings -- 4.2 The Role of Auxiliary Branch -- 4.3 Comparison with Existing Methods -- 5 Conclusions -- References -- Augmentation Based Synthetic Sampling and Ensemble Techniques for Imbalanced Data Classification -- 1 Introduction -- 2 Augmentation Based Synthetic Sampling Method -- 2.1 Data Augmentation (DA) -- 2.2 Notations -- 2.3 Proposed Method.3 Experiment Settings and Result Analysis -- 3.1 Datasets -- 3.2 Evaluation Metric -- 3.3 Experimental Results -- 4 Integration of Augmentation Based Synthetic Sampling Method and Ensemble Techniques -- 5 Conclusion -- References -- Language Cognition -- BA-GAN: Bidirectional Attention Generation Adversarial Network for Text-to-Image Synthesis -- 1 Introduction -- 2 Related Work -- 3 Our Model -- 3.1 Text Encoder and Image Encoder -- 3.2 Multi-stage Generative Adversarial Networks -- 4 Experiments -- 5 Conclusion -- References -- Personalized Recommendation Using Extreme Individual Guided and Adaptive Strategies -- 1 Introduction -- 2 Background -- 2.1 Definition of Recommendation Problem -- 2.2 Multi-objective Optimization Problem -- 2.3 Probs -- 3 Proposed Algorithm -- 3.1 Framework of MOEA-EIMA -- 3.2 Individual Encoding and Initialization -- 3.3 The Two Objectives -- 3.4 Genetic Operators -- 4 Experiments and Analysis -- 4.1 Experiment Settings -- 4.2 Experimental Results -- 5 Conclusions -- References -- Improved Transformer-Based Implicit Latent GAN with Multi-headed Self-attention for Unconditional Text Generation -- 1 Introduction -- 1.1 Generative Adversarial Network (GAN) for Unconditional Text Generation -- 1.2 Research Objective and Content -- 2 Related Works -- 3 Model Architecture -- 3.1 Overall Framework -- 3.2 Multi-headed Self Attention Based Generator -- 3.3 Training Details -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Microsoft COCO: Common Objects in Context -- 4.3 Ablation Experiment -- 5 Conclusion and Future Work -- References -- Learning a Typhoon Bayesian Network Structure from Natural Language Reports -- 1 Introduction -- 2 Related Works -- 3 The Framework of Learning Typhoon Bayesian Network Structures -- 3.1 State Extraction Model -- 3.2 Standardize State Information -- 3.3 Causal Relationship Extraction.3.4 Generate Typhoon Bayesian Network.This book constitutes the refereed proceedings of the 5th International Conference on Intelligence Science, ICIS 2022, held in Xi'an, China, in August 2022. The 41 full and 5 short papers presented in this book were carefully reviewed and selected from 85 submissions. They were organized in topical sections as follows: Brain cognition; machine learning; data intelligence; language cognition; remote sensing images; perceptual intelligence; wireless sensor; and medical artificial intelligence.IFIP Advances in Information and Communication Technology,1868-422X ;659Artificial intelligenceComputer visionApplication softwarePattern recognition systemsEducationData processingArtificial IntelligenceComputer VisionComputer and Information Systems ApplicationsAutomated Pattern RecognitionComputers and EducationComputer and Information Systems ApplicationsArtificial intelligence.Computer vision.Application software.Pattern recognition systems.EducationData processing.Artificial Intelligence.Computer Vision.Computer and Information Systems Applications.Automated Pattern Recognition.Computers and Education.Computer and Information Systems Applications.929.605006.3Shi ZhongzhiJin YaochuZhang XiangrongMiAaPQMiAaPQMiAaPQBOOK9910619279303321Intelligence Science IV3058343UNINA