Parallel Architectures, Algorithms and Programming: 10th International Symposium, PAAP 2019, Guangzhou, China, December 12–14, 2019, Revised Selected ... in Computer and Information Science)

Preface\nOrganization\nContents\nArchitectures\nOn a Coexisting Scheme for Multiple Flows in Multi-radio Multi-channel Wireless Mesh Networks\n	1 Introduction\n	2 Related Work\n	3 Multi-flow Coexistence\n		3.1 Model for Coexisting Links\n		3.2 The Link Coexistence Algorithm\n	4 Performance Evaluation\n	5 Conclusion\n	References\nNon-linear K-Barrier Coverage in Mobile Sensor Network\n	1 Introduction\n	2 Related Work\n	3 Network Model\n	4 Forming One Sub-barrier\n		4.1 Flattening Algorithm\n		4.2 The Algorithm of Forming One Sub-barrier\n	5 Forming K-Barrier Algorithm\n	6 Simulation Results\n	7 Conclusion\n	References\nInterrupt Responsive Spinlock Mechanism Based on MCS for Multi-core RTOS\n	1 Introduction\n	2 Related Work\n	3 IRS Mechanism\n		3.1 Real-Time Analysis\n		3.2 Interrupt Responsive Spinlock\n	4 Implementation of MCS-IRS\n		4.1 State Transitions of MCS-IRS\n		4.2 State Transitions in Interrupt Handling\n	5 Experiment Analysis\n		5.1 Design of Experiments\n		5.2 Worst-Case Interrupt Disable Time\n		5.3 Worst-Case Interrupt Response Latency\n	6 Conclusion\n	References\nA Novel Speedup Evaluation for Multicore Architecture Based Topology of On-Chip Memory\n	1 Introduction and Motivation\n	2 Related Work\n	3 Principle of ETOM Evaluation\n		3.1 The Limitations and Assumptions\n		3.2 Computing the Speedups\n	4 An Application Instantiation of ETOM\n		4.1 Enlightenments from ETOM\n		4.2 The Preferred Multi-core Architecture, TriBA\n		4.3 The ETOM Speedup of TriBA\n	5 Conclusion\n	References\nImproving the Performance of Collective Communication for the On-Chip Network\n	1 Introduction\n	2 Related Works\n	3 Hierarchy Self Similar Cubic Network\n		3.1 Self Similar Cubic Network\n		3.2 Hierarchy Self Similar Cubic on-Chip Network\n	4 HSSC Transmission Mechanisms\n		4.1 High-Performance Transmission Mechanisms\n		4.2 Packet Analysis and Decision Mechanism\n	5 Experiment Results\n		5.1 Experimental Environment\n		5.2 The Performance of HSSC On-Chip Network\n	6 Conclusions\n	References\nA Survey of Multicast Communication in Optical Network-on-Chip (ONoC)\n	1 Introduction\n	2 Architecture Design\n		2.1 All-Optical ONoC\n		2.2 Hybrid Electrical-Optical ONoC\n		2.3 3D ONoC\n	3 Networking Design\n		3.1 Design for a Single Multicast\n		3.2 Design for Multiple Multicasts\n	4 Design Challenges and Future Work\n		4.1 Design Challenges\n		4.2 Future Directions\n	5 Conclusion\n	References\nVirtual Network Embedding Based on Core and Coritivity of Graph\n	1 Introduction\n	2 Model and Problem Description\n		2.1 Substrate Network and Virtual Network Request\n		2.2 Description of the VNE Problem\n	3 Core and Coritivity of Graph\n	4 Algorithm\n		4.1 The Main Algorithm\n		4.2 Calculation of the Continuous Branch Number\n		4.3 Optimization of BCVNE Algorithm\n	5 Evaluation\n		5.1 Evaluation Setting\n		5.2 Compared Algorithms\n		5.3 Evaluation Results\n	6 Conclusion\n	References\nNon-time-Sharing Full-Duplex SWIPT Relay System with Energy Access Point\n	1 Introduction\n	2 System Model\n	3 Problem Description\n	4 Contrast System\n		4.1 Half-Duplex Relay System with SWIPT (HD-SWIPT)\n		4.2 Traditional Full-Duplex Relay System Without SWIPT (FD-no-SWIPT)\n	5 Simulation Results\n	6 Conclusion\n	References\nRecent Developments in Content Delivery Network: A Survey\n	1 Introduction\n	2 Cache Strategy\n		2.1 Centrality-Measures Based Algorithm\n		2.2 Cooperative Caching\n		2.3 Mobile Edge Caching\n	3 The Cost of CDN\n		3.1 The Energy Consumption Cost of CDN\n		3.2 The Delivery Cost and Storage Cost of CDN\n	4 Conclusion\n	References\nHigh Performance Systems\nWeighted Mean Deviation Similarity Index for Objective Omnidirectional Video Quality Assessment\n	1 Introduction\n	2 Related Work\n		2.1 Video Quality Assessment on Omnidirectional Video\n		2.2 SSIM and MDSI on 2D Image\n	3 Proposed Method\n		3.1 Weighted Mean Deviation Similarity Index\n		3.2 Investigated Temporal Sampling on Objective Omnidirectional VQA\n	4 Experimental Results\n		4.1 Omnidirectional Video Quality Assessment Database\n		4.2 Performance Evaluation\n		4.3 Influence of Temporal Sampling on Objective Omnidirectional VQA\n	5 Conclusion\n	References\nTire X-ray Image Defects Detection Based on Adaptive Thresholding Method\n	1 Introduction\n	2 Related Work\n		2.1 Tire X-ray Image Segmentation\n		2.2 Adaptive Threshold Binarization\n		2.3 Morphology Open/Close Operations\n		2.4 Image Thinning Algorithm\n	3 Proposed Algorithms and Implementation Details\n		3.1 Column Based Adaptive Threshold Binarization Algorithm\n		3.2 Texture Feature Based Segmentation Algorithm\n		3.3 A Thin Line Detection Algorithm\n		3.4 Implementation Details\n	4 Experiments\n		4.1 Data\n		4.2 Experimental Details\n		4.3 Test Results and Analysis\n	5 Conclusion\n	References\nHalftone Image Reconstruction Based on SLIC Superpixel Algorithm\n	1 Introduction\n	2 Inverse Halftonging Algorithm\n	3 Image Segmentation Based on SLIC\n	4 Affinity Propagation Algorithm\n	5 Image Reconstruction\n	6 Conclusions\n	References\nStudy on the Method of Extracting Diabetes History from Unstructured Chinese Electronic Medical Record\n	1 Background\n	2 Data Description and Problem Definition\n		2.1 Data Description\n		2.2 Problem Definition\n	3 Methods and Steps\n		3.1 Establishment of Customized Corpus\n		3.2 Analysis of Annotation Results and Discovery of Medical History Description Rules\n		3.3 Medical History Information Extraction Based on Rule Base\n		3.4 Negation Detection\n	4 Result Analysis and Verification\n	References\nDeep Residual Optimization for Stereoscopic Image Color Correction\n	1 Introduction\n	2 Related Work\n	3 Proposed Method\n		3.1 Result Initialization\n		3.2 Optimization Network\n		3.3 Loss Function\n	4 Experiment\n	5 Conclusion\n	References\nOld Man Fall Detection Based on Surveillance Video Object Tracking\n	1 Introduction\n	2 Object Detection and Object Tracking Algorithm\n		2.1 Object Detection\n		2.2 Object Tracking\n	3 Fall Detection Method Design\n		3.1 Data Set\n		3.2 Training\n		3.3 Object Tracking\n	4 Experiment\n		4.1 Fall Detection\n		4.2 Object Tracking\n		4.3 Fall Video Detection\n		4.4 Evaluation of Experimental Results\n	5 Conclusion\n	References\nElectric Bicycle Violation Automatic Detection in Unconstrained Scenarios\n	1 Introduction\n	2 Related Technologies\n	3 Violation Detection Process\n	4 Experiment and Result Analysis\n		4.1 Dataset\n		4.2 Training\n		4.3 Detection\n		4.4 Recognition of License Plate Number\n	5 Conclusion\n	References\nBuilding a Lightweight Container-Based Experimental Platform for HPC Education\n	1 Introduction\n	2 Major Techniques\n		2.1 Docker\n		2.2 Guacamole\n	3 System Design\n	4 System Implementation\n	5 System Deployment and Evaluation\n	6 Conclusion\n	References\nAutomatic Generation and Assessment of Student Assignments for Parallel Programming Learning\n	1 Introduction\n	2 System Design\n		2.1 User Manager Module\n		2.2 Student Module\n		2.3 Teacher Module\n		2.4 Automatic Assessment Module\n	3 System Implementation\n		3.1 Design of Question Database\n		3.2 Question Generation and Assessment\n	4 System Deployment and Evaluation\n	5 Conclusion\n	References\nHSM2: A Hybrid and Scalable Metadata Management Method in Distributed File Systems\n	1 Introduction\n	2 Background and Motivation\n		2.1 Metadata Management Methods\n		2.2 Namespace Locality vs. Load Balance\n	3 HSM2\n		3.1 Metadata Partitioning\n		3.2 Metadata Indexing\n		3.3 Load Balance\n		3.4 Downsides\n	4 Evaluation\n		4.1 Experiment Setup\n		4.2 Experimental Results\n	5 Conclusion\n	References\nAlgorithms\nHeuristic Load Scheduling Algorithm for Stateful Cloud BPM Engine\n	1 Introduction\n	2 Related Work\n	3 Problem Statement\n	4 Heuristic Cloud BPMS Engine Load Scheduling Algorithm\n		4.1 Load Scheduler\n		4.2 Simple Busyness Prediction\n		4.3 Formal Definition of Target Problem and Constraints\n		4.4 Best Fit Decreasing Based on Busyness Metrics Heuristic Algorithm\n	5 Experimental Design and Results\n	6 Conclusions and Outlook\n	References\nAn Improved Heuristic-Dynamic Programming Algorithm for Rectangular Cutting Problem\n	1 Introduction\n	2 Problem Description\n	3 Algorithm Description\n		3.1 Basic Definition\n		3.2 Discretization Sets\n		3.3 Dynamic Programming\n		3.4 Algorithm Complexity\n	4 Calculation Results and Analysis\n		4.1 International Samples\n		4.2 Randomly Generated Sample\n	5 Conclusion\n	References\nConstrained Optimization via Quantum Genetic Algorithm for Task Scheduling Problem\n	1 Introduction\n	2 Related Work\n	3 Problem Formulation\n		3.1 Task Model\n		3.2 Heterogeneous Multiprocessor System\n	4 Proposed Method\n		4.1 Encoding\n		4.2 Quantization\n		4.3 Task Sequence\n		4.4 Fitness\n		4.5 Adaptive Penalty Method Quantum Genetic Algorithm\n	5 Experiment and Discussion\n		5.1 Simulation and Analyse\n	6 Conclusion\n	References\nA Method of Business Process Bottleneck Detection\n	1 Introduction\n	2 Related Work\n		2.1 Bottleneck Analysis\n		2.2 Theory of Constrain\n	3 Definition and Detection of Bottleneck\n		3.1 Definitions\n		3.2 Bottleneck Detection in Business Processes\n		3.3 Bottleneck Relief Methods\n	4 Experiment and Result Analysis\n		4.1 Bottleneck Detection\n		4.2 Application in Multi-process Environment\n	5 Conclusion\n	References\nOptimized Layout of the Soil Moisture Sensor in Tea Plantations Based on Improved Dijkstra Algorithm\n	1 Introduction\n	2 Dijkstra Algorithm and Sensor Optimization Layout\n		2.1 Basic Dijkstra Algorithm\n		2.2 Improving the Dijkstra Algorithm\n	3 Ant Colony Optimization\n		3.1 Overview of Ant Colony Optimization\n		3.2 Basic Principles of Ant Colony Optimization\n		3.3 Ant Colony Algorithm Applied to Tea Plantation\n	4 Sensor Optimization Layout Tests\n		4.1 Source of Test Data\n		4.2 Test Results\n		4.3 Analysis and Evaluation of Test Results\n	5 Summary\n	References\nEfficient Algorithms for Constrained Clustering with Side Information\n	1 Introduction\n		1.1 Related Work\n		1.2 Our Results\n		1.3 Organization\n	2 Constrained k-means Clustering Algorithm with Incidental Information\n	3 Evaluation Approaches\n	4 Experimental Results\n		4.1 Experimental Dataset and Processing\n		4.2 Experimental Specific Results\n	5 Conclusion\n	References\nMulti-objective Scheduling of Logistics UAVs Based on Simulated Annealing\n	1 Introduction\n	2 Related Works and Contributions\n	3 Problem Description and Model\n		3.1 Problem Description\n	4 Proposed Algorithm\n		4.1 Encoding Method\n		4.2 Initialization of the Solution\n		4.3 Local Search Algorithm Framework\n		4.4 Simulated Annealing Algorithm Framework\n	5 Simulation Experiments\n		5.1 Parameter Calibration\n		5.2 Robustness Analysis\n	6 Conclusions\n	References\nIFME: Influence Function Based Model Explanation for Black Box Decision Systems\n	1 Introduction\n	2 Related Work\n		2.1 Influence Function\n		2.2 Prediction Explanation\n		2.3 Model Explanation\n	3 Problem Formulation and Preliminaries\n		3.1 Model Explanation Problem\n		3.2 Influence Function\n		3.3 LIME\n	4 Local Interpretable Prediction Explanation\n		4.1 Finding the Key Training Points\n		4.2 Influence Function Based Prediction Explanation\n	5 Model Explanation\n	6 Experiments and Results\n		6.1 Experiment Setup\n		6.2 Faithfulness of Explanations\n	7 Conclusion and Future Work\n	References\nGPU-Accelerated Parallel Aligning Long Reads with High Error Rate Using Enhanced Sparse Suffix Array\n	1 Introduction\n	2 Method\n		2.1 Index Construction\n		2.2 CPU-GPU Parallel Alignment Algorithm\n	3 Experiment\n		3.1 Experimental Environment and Data\n		3.2 Experimental Results\n	4 Conclusion\n	References\nAn Improved Algorithm for Building Suffix Array in External Memory\n	1 Introduction\n	2 Preliminaries\n	3 Reduction Phase of DSA-IS\n	4 Details of DSA-IS+\n		4.1 Method A\n		4.2 Method B\n	5 Experiments\n		5.1 Performance Evaluation\n		5.2 Discussion\n	6 Conclusion\n	References\nIn-Place Suffix Sorting on a Multicore Computer with Better Design\n	1 Introduction\n	2 Preliminaries\n	3 LMS-substring Naming\n		3.1 Prior Art: pSACAK\n		3.2 New Proposal: pSACAK+\n		3.3 Step 1 for  Z1\n		3.4 Step 2 for  X1\n	4 Experiments\n		4.1 Time and Space\n		4.2 Time Ratio\n	5 Conclusion\n	References\nSecurity and Privacy\nAny Privacy Risk if Nobody’s Personal Information Being Collected?\n	1 Introduction\n	2 Related Work\n	3 Problem Description\n		3.1 A Common Scenario of Privacy Risk\n		3.2 Problem Definition\n		3.3 Evaluation Metric\n	4 Learning Scheme\n		4.1 Basic Idea\n		4.2 Knowledge Module\n		4.3 Learning Module\n		4.4 Availability of UTLS\n	5 Experiment and Evaluation\n		5.1 Data Description\n		5.2 UTLS Process\n		5.3 Experiment Evaluation\n	6 Conclusions\n	References\nBlockchain-Based Access Control Schemes for Medical Image Analysis System\n	1 Introduction\n	2 System Model\n		2.1 Deep Learning for Data Cleaning and Classification\n	3 Proposed Scheme\n		3.1 Data Cleaning\n		3.2 Classification\n		3.3 Security Analysis\n	4 Experiments and Comparisons\n		4.1 Running Time Based Comparisons in CL-PKE and CL-PKS\n		4.2 Experiments of DR Classification\n	5 Conclusion\n	References\nResearch on DDoS Abnormal Traffic Detection Under SDN Network\n	1 Introduction\n		1.1 Research Background and Significance\n		1.2 Research Status\n	2 Technical Background\n		2.1 SDN Architecture\n		2.2 Openflow Protocol\n		2.3 SVM Algorithm\n	3 Specific Analysis\n		3.1 Experimental Environment Description\n		3.2 Experimental Topology Description\n		3.3 Topology Construction of Mininet\n		3.4 Analysis of Ryu Controller Application Implementation\n		3.5 Network Traffic Simulation Collection\n		3.6 Traffic Characteristics\n		3.7 Model Detection\n		3.8 Analysis of Experimental Results and Comparison of Efficiency\n	4 Conclusions\n	References\nAn Efficient Group Signature Based Digital Currency System\n	1 Introduction\n		1.1 Related Works\n		1.2 Contributions\n	2 Preliminaries\n		2.1 Bilinear Map\n		2.2 q-SDH Problem\n		2.3 Decision Linear Problem\n		2.4 Group Signature\n	3 The System\n		3.1 System I\n		3.2 System II\n		3.3 RingCT Technique Integration\n		3.4 Revocation\n	4 Performance\n	5 Conclusion\n	References\nDetecting Anomalies in Cluster System Using Hybrid Deep Learning Model\n	1 Introduction\n	2 Related Work\n		2.1 Log Processing Approaches\n		2.2 Anomaly Detection Algorithm\n	3 The Hybrid Model\n		3.1 Log Processing\n		3.2 Overview of the Hybrid Model\n		3.3 The Structure of the Hybrid Model\n	4 Experiments\n		4.1 Baseline Methods\n		4.2 Dataset and Experiment Setup\n		4.3 Metrics\n		4.4 Experiment Results\n		4.5 Model Analysis\n	5 Conclusion\n	References\nCustoms-Based Blockchain Solution for Exportation Protection\n	1 Introduction\n	2 Related Work\n	3 The Proposed Blockchain-Based System\n		3.1 Overview\n		3.2 Architecture and Processes\n		3.3 Banks and Car Rental Companies\n		3.4 Police Department and RTA\n		3.5 Dubai Customs\n	4 Proof-of-Concept (POC)\n		4.1 Assets\n		4.2 Transactions\n		4.3 Smart Contracts\n	5 Conclusion and Future Works\n	References\nCustoms-Based Distributed Risk Assessment Method\n	1 Introduction\n	2 The Proposed Method\n		2.1 Overview\n		2.2 Transactions Representation Step\n		2.3 The Local-Density Outlier Factor (LOF)\n		2.4 The Classification Step\n	3 Evaluation\n	4 Related Work\n	5 Conclusion\n	References\nPufferfish Privacy Mechanism Based on Multi-dimensional Markov Chain Model for Correlated Categorical Data Sequences\n	1 Introduction\n	2 Related Work\n	3 Preliminaries\n		3.1 Pufferfish Privacy Mechanism\n		3.2 Multi-dimensional Markov Chain Models\n		3.3 Additional Notion\n	4 A Mechanism for 2-Dimensional Correlated Data\n		4.1 Problem Statement\n		4.2 Our Mechanism\n	5 Experiments\n	6 Conclusion\n	References\nLattice Based Multi-replica Remote Data Integrity Checking for Data Storage on Cloud\n	1 Introduction\n	2 Related Work\n	3 System and Security Models\n		3.1 System Model\n		3.2 Security Model\n	4 Preliminaries\n		4.1 Some Definition About Lattice\n		4.2 Discrete Gaussian on Lattices\n		4.3 Trapdoors for Lattices and Sample Basis Algorithm\n		4.4 SIS Hard Problem\n	5 Our Construction\n	6 Security Analysis\n		6.1 Correctness\n		6.2 Soundness\n		6.3 Privacy Preserving\n	7 Performance Analysis\n		7.1 Computation Analysis\n		7.2 Communication Analysis\n	8 Conclusion\n	References\nSecure Multi-Party Computation on Blockchain: An Overview\n	1 Introduction\n	2 Secure Multi-Party Computation\n		2.1 Security Model\n		2.2 Related Work\n	3 Blockchain Technology\n		3.1 Blockchain Architecture\n	4 SMPC on Blockchain\n		4.1 Overview\n		4.2 State-of-the-Art Researches\n	5 Conclusion\n	References\nA Survey of Privacy-Preserving Techniques on Trajectory Data\n	1 Introduction\n	2 Key Issues in Trajectory Privacy Protection\n		2.1 Basic Concept\n		2.2 Types of Trajectory Privacy Protection\n		2.3 The Classification and Measurement Standards of Trajectory Privacy Preserving Techniques\n		2.4 The Architecture of Trajectory Privacy Protection\n	3 Generalization Method\n		3.1 km–Anonymity Trajectory Data Privacy Preserving Methods\n		3.2 Privacy Preserving Method Based on Prefix Tree\n		3.3 A Method of Trajectory Privacy Protection Based on Semantic Anonymous agent\n		3.4 Dynamic Trajectory Privacy-Preserving Publishing\n		3.5 Grid Based l–Diversity Principle\n		3.6 A Trajectory Privacy Preserving Method Based on Time Obfuscation\n	4 Suppression Method\n		4.1 Trajectory Privacy Preserving Method Based on Disturbance\n		4.2 Trajectory Privacy Preserving Method Based on Trajectory Frequency Suppression\n	5 Pseudo Data Method\n	6 Privacy Protection Effectiveness Metric and Comparative Analysis\n		6.1 Privacy Protection Effectiveness Metric\n		6.2 Comparative Analysis\n	7 Future Research Directions\n		7.1 The Optimization of Privacy Location and the Reasonable Construction of Anonymization Box\n		7.2 Protect the Trajectory Information in Continuous Location Queries\n		7.3 Research on Privacy Preserving Technology in Big Data Environment\n		7.4 Other Issues that Need to Be Addressed\n	8 Conclusions\n	References\nBig Data Processing and Deep Learning\nMinimizing Off-Chip Memory Access for Deep Convolutional Neural Network Training\n	1 Introduction\n	2 Data Dependence of BN Layers and Memory Access Characteristics\n		2.1 Data Dependence of BN Layer\n		2.2 Memory Access Characteristics\n	3 BN Reconstruction and Multi-layer Fusion Computing\n		3.1 Multi-layer Fusion Computing\n		3.2 Data Volume Model and Operation Volume Model\n	4 Evaluation\n		4.1 Quantitative Evaluation on BRC and BRCB\n		4.2 BRC and BRCB’s Optimization on Memory Access\n		4.3 BRC and BRCB’s Optimization Effects on Accelerators Efficiency\n	5 Related Works\n		5.1 Promoting on-Chip Data Reuse\n		5.2 Intermediate Results Compression\n	6 Summary\n	References\nResultant Gradient Flow Method for Multiple Objective Programming Based on Efficient Computing\n	1 Introduction\n	2 Resultant Gradient Flow Method\n		2.1 Flow-Based Method\n		2.2 Resultant Gradient Descent Method for Multiple Objective Programming\n		2.3 Multiple Objective Programming\n	3 A Case Study and Its Profit Modeling\n		3.1 Maximum Overall Profit\n		3.2 Modeling of Maximization Overall Profit\n	4 Experimental Study\n	5 Conclusion\n	References\nBarrage Video Recommendation Method Based on Convolutional Recursive Neural Network\n	1 Introduction\n	2 Related Works\n		2.1 Traditional Video Recommendation\n		2.2 Video Recommendation Based on Convolutional Neural Network\n	3 Problem Formulation\n	4 Video Recommendation Structure\n		4.1 Barrage Data Preprocessing\n		4.2 RCNN Model\n	5 Experimental Results\n	6 Conclusion\n	References\nA Pipelining Strategy for Accelerating Convolutional Networks on ARM Processors\n	1 Introduction\n	2 Related Work\n	3 Convolution and Pipelines on ARM CPUs\n		3.1 Convolution\n		3.2 Instruction Pipeline on ARMv7 CPUs\n	4 Methods\n		4.1 Pipelining of SIMD Instructions\n		4.2 Optimizing 33 Convolution\n	5 Experimental Results\n		5.1 Experimental Settings\n		5.2 Latency of 33 Convolution\n	6 Conclusion\n	References\nPrediction Model of Suspect Number Based on Deep Learning\n	1 Introduction\n	2 Related Works\n	3 Data Preprocessing\n		3.1 Time Data Processing\n		3.2 Location Data Processing\n		3.3 Method Data Processing\n		3.4 Loss Amount Data Processing\n		3.5 Weather Data Processing\n	4 Model Building\n		4.1 Feature Selection\n		4.2 Text Data Model Construction\n		4.3 Model Construction\n	5 Experimental Results and Analysis\n		5.1 The Experimental Data\n		5.2 DNN Parameter Setting\n		5.3 Experimental Results and Analysis\n	6 Conclusion\n	References\nA Sequence-to-Sequence Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation\n	1 Introduction\n	2 The Transformer Model\n		2.1 Embedding Layer\n		2.2 Dilated Convolution\n		2.3 Convolutional Blocks\n		2.4 Residual Connection\n		2.5 The Decoder\n		2.6 Inference Layer\n	3 Training\n	4 Experiments\n		4.1 Datasets\n		4.2 Hyper-parameters\n		4.3 Experiment Environment and Evaluation Criteria\n		4.4 The Performance Evaluation of the CWS\n		4.5 The Ability in Processing Long Sentences\n	5 Conclusion\n	References\nAuthor Index