11566nam 2200529 450 99646452690331620231110220346.0981-16-5943-5(CKB)4100000012025725(MiAaPQ)EBC6724623(Au-PeEL)EBL6724623(OCoLC)1268471650(PPN)258052988(EXLCZ)99410000001202572520220616d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierData sciencePart II 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021, Taiyuan, China, September 17-20, 2021, proceedings /Jianchao Zeng [and four others]Singapore :Springer,[2021]©20211 online resource (532 pages)Communications in Computer and Information Science ;v.1452981-16-5942-7 Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Social Media and Recommendation Systems -- Natural Language Inference Using Evidence from Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Model Architecture -- 3.1 Clue Extraction -- 3.2 Evidence and Sentence Encoding -- 3.3 Evidence Integration -- 3.4 Final Classifier -- 4 Experiments -- 4.1 Datasets and Knowledge Resources -- 4.2 Effectiveness of Different KGs and Clue Encoders -- 4.3 Experiments on MedNLI -- 4.4 Experiments on SciTail -- 4.5 Implementation Details -- 5 Conclusion and Future Works -- References -- Construction of Multimodal Chinese Tourism Knowledge Graph -- 1 Introduction -- 2 Related Work -- 3 MCTKG Construction -- 3.1 Semi-structured Knowledge Extraction -- 3.2 Entity Expansion -- 3.3 Ontology Construction -- 3.4 Attribute Normalization -- 3.5 Automatic Generation Algorithm of Tourist Routes -- 3.6 MCTKG Sharing Platform -- 4 Experiments -- 4.1 Attribute Normalization Evaluation -- 4.2 Route Evaluation -- 5 Conclusion -- References -- Study on Binary Code Evolution with Concrete Semantic Analysis -- 1 Introduction -- 2 Approach Overview -- 2.1 Motivation Example -- 2.2 Problem Statement -- 2.3 Frame of Overview -- 3 Methodology -- 3.1 Comparing Similarity -- 3.2 Locating Difference -- 3.3 Analyzing Semantic Patterns -- 3.4 Identifying Evolution -- 4 Evaluation -- 4.1 Locating Difference -- 4.2 What Semantics Are Evolved -- 5 Related Work -- 6 Conclusion -- References -- Research on COVID-19 Internet Derived Public Opinions Prediction Based on the Event Evolution Graph -- 1 Introduction -- 2 The Method and Steps of Building the Event Evolution Graph -- 2.1 Data Acquisition and Preprocessing -- 2.2 Causal Judgment and Event Extraction -- 2.3 Text Vectorization and Event Clustering.3 An Empirical Study of COVID-19 Network Public Opinion Event Evolution Graph -- 3.1 COVID-19 Event Data Acquisition and Preprocessing -- 3.2 COVID-19 Event Data Acquisition and Preprocessing -- 3.3 The Construction of Event Evolution Graph -- 4 Analysis and Discussion -- 5 Conclusion -- References -- A Text Correlation Algorithm for Stock Market News Event Extraction -- 1 Introduction -- 2 Related Work -- 2.1 Extraction of Stock Market News Information -- 2.2 Text Correlation Algorithm -- 3 Our Proposed Method -- 3.1 Word Vector Model -- 3.2 Structured Event and Event Vector -- 3.3 Text Correlation Algorithm Based on Matching and Clustering -- 4 Experiments -- 4.1 Data Collection and Preprocessing -- 4.2 Relevance Between Words -- 4.3 Correlation Between Event Triples -- 4.4 Relevance Between Texts -- 4.5 Correlation Between Text Sets -- 4.6 Interpretable Information of Correlation Calculation -- 5 Conclusion -- References -- Citation Recommendation Based on Community Merging and Time Effect -- 1 Introduction -- 2 Problem Statement -- 3 Community Merging -- 4 CMTE-PathSim Model -- 4.1 Meta Path-Based Feature Space -- 4.2 Citation Probability -- 4.3 Time Effect -- 4.4 Citation Recommendation -- 5 Experiments -- 5.1 Dataset and Methods Setup -- 5.2 Verifying Time Effect Factor -- 5.3 Comparison with Benchmark Algorithm -- 6 Conclusions and Future Work -- References -- Sentiment Analysis for MOOC Course Reviews -- 1 Introduction -- 2 Related Work -- 3 MOOC Text Comment Sentiment Analysis Model -- 3.1 ALBERT Pre-trained Language Model -- 3.2 BiGRU Neural Network Model -- 3.3 Capsule Network -- 3.4 Attention Mechanism -- 3.5 Fully Connected Layer -- 4 Experimental Analysis -- 4.1 Introduction to the Data Set -- 4.2 Evaluation Standard -- 4.3 Experimental Comparison Model -- 4.4 Analysis of Experimental Results -- 5 Conclusion -- References.Analyzing Interpretability Semantically via CNN Visualization -- 1 Introduction -- 2 Related Work -- 3 Interpretation by Filters Screening and Semantic Labeling -- 3.1 Data Preparation -- 3.2 Screening Convolution Features -- 3.3 Deconvolutional Visualization -- 3.4 Semantic Annotation of Filters -- 4 Experiments -- 4.1 Visualization of Screened Filters -- 4.2 The Coincidence Degree of Interpretable Regions and Expert Regions -- 4.3 Semantic Consistency of Filters -- 5 Conclusion -- References -- Probing Filters to Interpret CNN Semantic Configurations by Occlusion -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Dataset and Semantic Configurations -- 3.2 Probe Filters to Recognize Specified Visual Semantic Concepts by Occluding -- 3.3 Visualization for Evaluation -- 4 Experiment -- 4.1 Dataset and Experimental Setup -- 4.2 Annotation Task -- 4.3 Experiment Result -- 5 Conclusion -- References -- Targeted BERT Pre-training and Fine-Tuning Approach for Entity Relation Extraction -- 1 Introduction -- 2 Related Work -- 2.1 Methods with Distant Supervision -- 2.2 Methods with Pre-trained Language Model -- 3 Methodology -- 3.1 Separated Pipeline Model Architecture -- 3.2 Verb-mLM Task for Relation Classification Model -- 3.3 Entity-MLM Task for Entity-Pair Annotation Model -- 3.4 Inter-layer Sharing Attention Mechanism -- 4 Experiments -- 4.1 Datasets -- 4.2 Experiment on Entity Relation Classification -- 4.3 Experiment on Entity-Pair Annotation -- 4.4 Results Analysis -- 5 Conclusion -- References -- Data Security and Privacy -- Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud -- 1 Introduction -- 2 Background -- 3 SECaaS -- 3.1 User's Perception Module -- 3.2 Decision Module -- 3.3 Multi-compiler -- 3.4 Cyberspace Mimic Environments -- 4 Experiments -- 4.1 Security Experiments -- 4.2 Performance Evaluation.5 Related Works -- 5.1 Multi-compiler -- 5.2 MVEE -- 6 Conclusion and Future Works -- References -- Searchable Encryption System for Big Data Storage -- 1 Introduction -- 2 Functional Interaction Model and System Architecture -- 2.1 Functional Interaction Model -- 2.2 System Architecture -- 3 Main Cryptographic Algorithm -- 3.1 Key Management Algorithm -- 3.2 File Classification Encryption/Decryption Algorithm -- 3.3 Symmetric Searchable Encryption Algorithm -- 3.4 Dynamic Update of Cipher Index -- 4 Deployment and System Test -- 4.1 Deployment -- 4.2 System Test -- 4.3 Advantage Analysis -- 5 Conclusion -- References -- Research on Plaintext Resilient Encrypted Communication Based on Generative Adversarial Network -- 1 Introduction -- 2 Adversarial Neural Cryptography -- 2.1 GANs Principle -- 2.2 Adversarial Neural Cryptography Model -- 2.3 Function Design -- 3 Plaintext Leakage in the Original Model -- 3.1 Neural Network Design in the Original Model -- 3.2 Leakage Experiments Under the Original Model -- 4 Model Optimization -- 4.1 Increase Decryption Speed -- 4.2 Decrease the Correct Rate of Attacker's Cracking -- 5 Conclusions -- References -- An Improved LeNet-5 Model Based on Encrypted Data -- 1 Introduction -- 2 Preliminaries -- 2.1 Homomorphic Encryption -- 2.2 Convolutional Neural Network -- 2.3 LeNet-5 Model -- 3 Improved LeNet-5 Model in Ciphertext Domain -- 3.1 Mapping from the Plaintext Domain to the Ciphertext Domain -- 3.2 Improved LeNet-5 Model -- 4 Model Analysis -- 4.1 Feasibility Analysis -- 4.2 Efficiency Analysis -- 5 Conclusions -- References -- Design and Implementation of Intrusion Detection System Based on Neural Network -- 1 Introduction -- 2 Related Technologies -- 2.1 Intrusion Detection System -- 2.2 BP Neural Network -- 3 Design of Intrusion Detection System Based on Neural Network -- 3.1 System Framework.3.2 Event Collector -- 3.3 Event Analyzer -- 4 Intrusion Detection Experiment Based on Neural Network -- 4.1 Experimental Environment -- 4.2 Experimental Results and Analysis -- 5 Conclusions -- References -- IoT Security Situational Awareness Based on Q-Learning and Bayesian Game -- 1 Introduction -- 2 Related Works -- 3 Q-Learning Algorithm -- 3.1 Markov Decision Process -- 3.2 Q-Learning Method -- 4 IoT Security Situational Awareness Model Based on Q-Learning and Bayesian Game -- 4.1 Characteristics of IoT Security -- 4.2 Model Selection and Definition -- 4.3 Selection of the Optimal Defense Strategy -- 4.4 IoT Security Situational Assessment -- 4.5 Model Algorithm -- 5 Experimental Simulation -- 5.1 Experimental Setup -- 5.2 Analysis of Results -- 6 Conclusion -- References -- Protecting Web Application Code and Sensitive Data with Symmetric and Identity-Based Cryptosystems -- 1 Introduction -- 2 Symmetric and Public Key Cryptographic Algorithms -- 2.1 Symmetric Cryptographic Algorithms -- 2.2 Public-Key Cryptographic Algorithms -- 3 Web Security Scheme for Protecting Sensitive Data and Program Code -- 3.1 The Idea and General Architecture -- 3.2 The Security Scheme -- 3.3 Experimental Results and Performance -- 3.4 Security Analysis -- 4 Conclusion -- References -- IoT Honeypot Scanning and Detection System Based on Authorization Mechanism -- 1 Introduction -- 2 Related Work -- 2.1 Network Scanning and Detection -- 2.2 IoT Honeypot -- 3 IoT Honeypot Scanning and Detection System Based on Authorization Mechanism -- 3.1 Authorization System -- 3.2 Honeypot System -- 4 Experiments -- 5 Conclusion -- References -- Privacy Protection Model for Blockchain Data Sharing Based on zk-SNARK -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 zk-SNARK -- 3.2 CP-ABE -- 3.3 Blockchain Data Sharing Model.4 Privacy Protection Model for Blockchain Data Sharing."The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; ​social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo." -- Publisher's description.Communications in Computer and Information Science Data miningCongressesBig dataCongressesData miningBig data006.312Zeng JianchaoMiAaPQMiAaPQMiAaPQBOOK996464526903316Data Science1562261UNISA