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[null,null,"Titolo",null,"Artificial Intelligence and Security : 8th International Conference, ICAIS 2022, Qinghai, China, July 15–20, 2022, Proceedings, Part I / / edited by Xingming Sun, Xiaorui Zhang, Zhihua Xia, Elisa Bertino"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"3-031-06794-0"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (734 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 13338"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Artificial Intelligence -- DDG-Based Optimization Metrics for Defect Prediction -- 1 Introduction -- 2 Related Work -- 3 Compilation Optimization Metrics -- 4 Dataset and Experimental Setup -- 4.1 Dataset -- 4.2 Metrics Models -- 4.3 Performance Metrics -- 4.4 Experimental Methodology -- 4.5 Results and Analysis -- 5 Conclusion -- References -- Link Prediction Based on Sampled Single Vertices -- 1 Introduction -- 2 Local Random Walk and Evaluation Index -- 2.1 LRW -- 2.2 AUC -- 2.3 Precision -- 2.4 Ranking Score -- 3 The Idea of Sampling -- 4 Methods -- 5 Experimental Results and Analysis -- 6 Conclusion -- References: -- Operation Configuration Optimization of Power Gas Energy Hub System Considering NOx Emission -- 1 Introduction -- 2 Energy Hub -- 3 Particle Swarm Optimization (PSO) -- 4 Objective Function -- 4.1 Emission Cost -- 4.2 System Operation Cost -- 4.3 Objective Function -- 5 Constraint Condition -- 5.1 Power System Constraints -- 5.2 Energy Hub Constraints -- 5.3 Natural Gas System Constraints -- 5.4 Energy Storage Equipment Constraints -- 6 Example Analysis -- 6.1 Example Description -- 6.2 Simulation Results -- 6.3 Comparative Analysis of Numerical Examples -- References -- Fire Detection "]
[{"$nulls":4},"Approach Based on Vision Transformer -- 1 Introduction -- 2 Related Work -- 2.1 CNNs Based Fire Detection -- 2.2 Vision Transformer -- 2.3 Linear Embedding -- 2.4 Vision Transformer Encoder -- 2.5 Multi-head Self-Attention Layer (MSA) -- 3 Proposed Method -- 3.1 Swin Transformer Block -- 3.2 Shifted-Window Based Self-Attention -- 3.3 Shifted Window Partitioning in Successive Blocks -- 4 Experimental Results and Discussion -- 4.1 Dataset -- 4.2 Data Augmentation -- 4.3 Implementation Details -- 4.4 Compared with CNN-Based Fire Detection Methods."]
[{"$nulls":4},"5 Conclusion -- References -- Construction and Application of Air Pollutant Concentration Prediction Model Based on Multivariate -- 1 Introduction -- 2 Related Work -- 2.1 Forecast Based on Grey Model -- 2.2 Forecast Based on Bayesian Network Model -- 3 Related Work -- 3.1 Grey Prediction Model Based on Fractional Order -- 3.2 Forecasting Model Based on Bayesian Network -- 4 Experimental Result and Analysis -- 4.1 Dataset -- 4.2 Evaluation Index -- 4.3 The Experimental Results and Analysis -- 4.4 Experimental Analysis Summary -- 5 Conclusion -- References -- Research on Management Optimization of College Student Apartment Based on MCR Model -- 1 Preface -- 2 Study Area and Research Method -- 2.1 Brief Introduction of the Study Area -- 2.2 Methods of Study -- 3 Research Results -- 3.1 MCR Maps for Four Different Types of Sources -- 3.2 Comprehensive MCR Chart for Four Grades -- 4 Discussion -- 5 Conclusion -- References -- Sub-base Station Power Optimization Based on QoS and Interference Temperature Constraints for Multi-user Input and Output -- 1 Introduction -- 1.1 Related Work -- 1.2 Related Work -- 1.3 Organization of the Paper -- 2 System and Network Model -- 2.1 System Model -- 2.2 Mathematical Modeling -- 3 Problem Solution Method -- 3.1 Mathematical Modeling -- 3.2 Maximum Throughput Optimization Based on QoS and Interference Temperature Constraints -- 4 Simulation Result -- 5 Conclusions -- References -- Resource Allocation for D2D Communication Underlaying Cellular Network -- 1 Introduction -- 1.1 Related Works -- 1.2 Contributions -- 1.3 Organization of the Paper -- 2 System Model -- 3 Space Matching Based Power Allocation Algorithm -- 3.1 Problem Analysis -- 3.2 Resource Allocation -- 4 Simulation Result -- 5 Conclusion -- References -- Application Research of Safety Helmet Detection Based on Low Computing Power Platform Using YOLO v5."]
[{"$nulls":4},"1 Introduction -- 2 Algorithm Analysis -- 2.1 YOLO -- 2.2 YOLO v5 -- 3 Algorithm Improvement -- 3.1 SENet -- 3.2 Generalized Focal Loss -- 4 Result -- 4.1 Experimental Environment -- 4.2 Model Training -- 4.3 Result Analysis -- 5 Conclusion -- References -- Application Research of MES in Intelligent Manufacturing Training Factory -- 1 Introduction -- 2 Requirement Analysis -- 2.1 Various Types of Equipment and Data from Complex Sources -- 2.2 Complex Structure and Different Foundations of Training Objects -- 2.3 Different Training Contents and Durations and Large Temporary Changes -- 3 MES Construction Goals -- 3.1 Efficient and Automatic Scheduling, and Automatic Matching with Long-Term and Short-Term Training Projects -- 3.2 Automatic Supervision of Site Production Information, Automatic Warning of Delayed Orders and Active Declaration Acceptance of Exception -- 3.3 Elimination of Information Islands to Reach System Integration -- 4 Detailed Design of Production Management Information System Based on MES -- 4.1 Application of Artificial Fish Swarm Algorithm -- 4.2 MES Functional Architecture Design -- 4.3 MES Functional Module Design -- 5 Conclusion -- References -- Design of Temperature Monitoring System Using Distributed Intelligent CAN Bus Networks -- 1 Introduction -- 2 "]
[{"$nulls":4},"Hierarchical Structure and Representation of CAN Bus -- 2.1 Hierarchical Structure of CAN Bus -- 2.2 Bit Value Representation of CAN Bus -- 3 Hardware Design of Temperature Monitoring System -- 3.1 Working Principle of CAN Controller SJA1000 -- 3.2 Calculation of Baud Rate of CAN Bus Communication -- 3.3 CAN Bus Driver -- 3.4 Modu Hardware Design -- 3.5 Multiple Nodes Communication with CAN Bus -- 4 Software Design of Temperature Monitoring System -- 5 Conclusion -- References -- Distant Supervised Relation Extraction Based on Sentence-Level Attention with Relation Alignment."]
[{"$nulls":4},"1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Method Description -- 3.2 Word Embedding Layer -- 3.3 Position Embedding Layer -- 3.4 Sentence Coding Layer -- 3.5 Bag of Sentences Coding -- 3.6 Objective Function and Optimization -- 4 Experiments -- 4.1 Datasets -- 4.2 Hyperparameter Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- Dual Attention Mechanisms Based Auto-Encoder for Video Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning Based Anomaly Detection -- 2.2 Attention Mechanisms -- 3 Method -- 3.1 Dual Attention Mechanisms Based Auto-Encoder -- 3.2 Channel Attention and Spatial Attention -- 3.3 Anomaly Detection -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Experimental Results -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- Radar Fusion Monocular Depth Estimation Based on Dual Attention -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Radar Data -- 3.2 Deep Ordinal Regression Network -- 3.3 Attention Mechanism -- 3.4 Dual Attention Fusion Module -- 3.5 Loss Function -- 3.6 Implementation Details -- 4 Experiment -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Comparison of Different Dual Attention Model -- 4.4 Comparison of Different Attention Timing -- 4.5 Comparison of Different Weather Condition -- 5 Conclusion -- References -- Application of Artificial Intelligence in Financial Risk Management -- 1 Introduction -- 2 Application Status of AI in Financial Risk Management -- 2.1 Inevitable Trend of AI Intervention in Financial Risk Prevention and Control System -- 2.2 Based on Data-Driven Mode -- 2.3 Successful Application Cases of AI in Financial Risk Management -- 3 Potential Risks of AI Application in Financial Risk Management -- 3.1 Technical Risks -- 3.2 Information Security Risks -- 3.3 Risks at the Regulatory Level."]
[{"$nulls":4},"3.4 Legal and Regulatory Risks and Institutional Risks -- 4 Countermeasures and Suggestions on the Rational Use of AI in Financial Risk Management -- 4.1 Train High-Tech Talents and Improve the Level of Information Technology -- 4.2 Consolidate Information Data Security of AI -- 4.3 Build a New \"See-Through\" Intelligent Regulatory System -- 4.4 Build a Relatively Perfect System of Laws and Regulations to Standardize AI Development -- 5 Application Requirements of AI in Future Financial Risks -- 5.1 Optimize Intelligent Model Algorithm and Improve the Supervision Ability of Financial Institutions -- 5.2 Strengthen Intelligent Data Analyses and Maintain the Fairness and Order of Financial Markets -- 5.3 Develop Intelligent Forecasting Models to Monitor Systemic Risks in Financial System -- 6 Conclusion -- References -- Research on Commercial Sector Electricity Load Model Based on Exponential Smoothing Method -- 1 Introduction -- 1.1 Research Background -- 1.2 Current Status of Time Series Research -- 1.3 Research Exponential Smoothing -- 2 Exponential Smoothing Algorithm -- 2.1 Exponential Smoothing -- 2.2 Exponential Smoothing Model -- 2.3 Evaluation Criteria for Model Accuracy -- 3 Establish an Exponential Smoothing Model -- 3.1 Three Times Exponential Smoothing Method -- 3.2 Construction of Model -- 3.3 Forecast Electricity Consumption -- 4 Exponential Smoothing Method "]
[{"$nulls":4},"Applied to Electricity Consumption Forecasting -- 4.1 Implementation of the Method and Data Selection -- 4.2 Data Preprocessing -- 4.3 Experimental Design -- 4.4 Forecast of Electricity Load -- 4.5 Algorithm Analysis -- 5 Summary and Outlook -- References -- Research on Dynamic Monitoring of Train Running Part Using Integrated Detection System -- 1 Introduction -- 2 System Overview -- 3 Detection and Analysis of Breakdown on Running Parts of Train."]
[{"$nulls":4},"3.1 Fault Detection of Traction Motor."]
[null,null,null,"Sommario/riassunto","This three-volume set LNCS 13338-13340 constitutes the thoroughly refereed proceedings of the 8th International Conference on Artificial Intelligence and Security, ICAIS 2022, which was held in Qinghai, China, in July 2022. The total of 166 papers included in the 3 volumes were carefully reviewed and selected from 1124 submissions. The papers present research, development, and applications in the fields of artificial intelligence and information security."]