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[null,null,"Titolo",null,"Artificial intelligence . Part II : second CAAI International Conference, CICAI 2022, Beijing, China, August 27-28, 2022, revised selected papers / / Lu Fang [and four others] (editors)"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-20500-6"]
[null,null,"Descrizione fisica",null,"1 online resource (660 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science series ; ; Volume 13605"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- AI Ethics, Privacy, Fairness and Security -- Saliency Map-Based Local White-Box Adversarial Attack Against Deep Neural Networks -- 1 Introduction -- 2 Related Work -- 2.1 Model Interpretability Methods -- 2.2 White-Box Adversarial Attack Methods -- 3 Proposed Approach -- 3.1 Selecting Important Area by Saliency Map -- 3.2 Combining Saliency Map with Single-Step Attack Method -- 3.3 Combining Saliency Map and Iterative Attack Method -- 4 Experiments -- 4.1 Datasets and Networks -- 4.2 Evaluation Indexes -- 4.3 Hyperparameters -- 4.4 Experimental Result -- 5 Conclusion -- References -- Improving Adversarial Attacks with Ensemble-Based Approaches -- 1 Introduction -- 2 Related Works -- 2.1 Optimization-Based Methods -- 2.2 Gradient-Based Methods -- 2.3 Targeted Attacks -- 3 Methodology -- 3.1 Motivation -- 3.2 Ensemble Schemes -- 3.3 Gradient Descent Mechanisms -- 3.4 Optimization Algorithm -- 4 Experimental Results -- 4.1 Experimental Settings -- 4.2 Attacking a Single Model -- 4.3 Attacking an Ensemble of Models -- 5 Conclusion and Future Work -- References -- Applications of Artificial Intelligence -- Browsing Behavioral Intent Prediction on Product Recommendation Pages of E-commerce Platform -- 1 Introduction -- 2 Related Work -- 2.1 Browsing Behavior Analysis -- 2.2 Browsing Behavioral Intent Prediction -- 3 Methodology -- 4 Experiments -- 4.1 Data Collection and Processing -- 4.2 Model Architecture -- 4.3 Interest Analysis Method -- 5 Results and Discussion -- 5.1 Prediction Results -- 5.2 Interest Analysis Results and Discussion -- 6 Conclusion -- References -- An "]
[{"$nulls":4},"Optical Satellite Controller Based on Diffractive Deep Neural Network -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation -- 2.2 Optical Controller Framework -- 2.3 Architecture of the Optical Controller."]
[{"$nulls":4},"2.4 Optimization of the Optical Controller -- 3 Experiments -- 3.1 Dataset Description -- 3.2 Experimental Settings -- 3.3 Experimental Results -- 4 Conclusion -- References -- Incomplete Cigarette Code Recognition via Unified SPA Features and Graph Space Constraints -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Features Extraction Using FPN -- 3.2 Instance Segmentation via Unified SPA Features -- 3.3 Landmark Estimation via Graph Space Constraints -- 3.4 Text Regularization -- 3.5 Loss Function -- 4 Experiments -- 4.1 Evaluation of Instance Segmentation -- 4.2 Performance of Landmark Estimation -- 4.3 End-to-End Performance -- 4.4 Ablation Experiments -- 5 Conclusion -- References -- A Large-Scale Tobacco 3D Bin Packing Model Based on Dual-Task Learning of Group Blocks -- 1 Introduction -- 2 Problem Formulation -- 3 Method -- 3.1 Group Block Generation Algorithm -- 3.2 Dual-Task Learning of Group Blocks -- 4 Experiment -- 5 Conclusions -- References -- ETH-TT: A Novel Approach for Detecting Ethereum Malicious Accounts -- 1 Introduction -- 2 ETH Tracking Tree Method -- 2.1 ETH Tracking Tree and Traceability Rate -- 2.2 Features Extraction -- 2.3 Model Design -- 3 Experiment -- 3.1 Dataset -- 3.2 Experimental Details -- 3.3 Experimental Result -- 3.4 Result Analysis -- 4 Availability of Data and Material -- References -- Multi-objective Meta-return Reinforcement Learning for Sequential Recommendation -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 4 Approach -- 4.1 M2OR-RL Framework -- 4.2 Optimization -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experiment with Multiple Objectives -- 5.3 Experiment with Single Objective -- 6 Conclusion -- A Algorithms -- A.1 Implementation Detail -- A.2 Implementation Details -- References -- Purchase Pattern Based Anti-Fraud Framework in Online E-Commerce Platform Using Graph Neural Network."]
[{"$nulls":4},"1 Introduction -- 2 Related Work -- 2.1 Fraud Detection -- 2.2 Graph Representation Learning -- 3 Proposed Method -- 3.1 Dynamic Purchase Pattern (DPP) -- 3.2 GNN with Similarity and Relation (GSR) -- 4 Experiments -- 4.1 Dataset -- 4.2 Purchase Pattern Visualization -- 4.3 Fraudulent Order Detection Result of GSR -- 5 Conclusions and Future Work -- References -- Physical Logic Enhanced Network for Small-Sample Bi-layer Metallic Tubes Bending Springback Prediction -- 1 Introduction -- 2 Background Knowledge -- 2.1 RDB Processing of BMT -- 2.2 Equivalent Section Theory -- 3 Methodology -- 3.1 Proposed Prediction Architecture -- 3.2 Preliminary Analysis of BMT Equivalence Section -- 3.3 Composition of the Loss Function -- 4 Case Study -- 4.1 Dataset Construction -- 4.2 Precision Analysis of Proposed Method -- 4.3 Effectiveness of PE-NET -- 5 Conclusion -- References -- Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency -- 1 Introduction -- 2 Proposed Method -- 2.1 Saliency-Based Local Region Selection -- 2.2 Quality-Aware Feature Extraction -- 2.3 Quality Prediction -- 3 Experiments -- 3.1 Test Database -- 3.2 The Effect of Visual Quality on IVSS -- 3.3 Performance Comparison with SOTA BIQA Methods -- 4 Conclusion -- References -- Power Grid Bus Cluster Based on Voltage Phasor Trajectory -- 1 Introduction -- 2 Propagation Mechanism of Power Grid Faults -- 3 Trajectory-Based Similarity -- 3.1 Length of Trajectory -- 3.2 Curvature of Trajectory -- 3.3 Topological Distance -- 3.4 Similarity of Trajectories -- 3.5 Power Grid Bus Cluster -- 4 Experiments -- 4.1 Visualize for Each Dimension -- 4.2 Trajectory Similarity Analysis -- 4.3 Trajectory Cluster Analysis -- 5 Conclusion "]
[{"$nulls":4},"-- References -- Following the Lecturer: Hierarchical Knowledge Concepts Prediction for Educational Videos -- 1 Introduction."]
[{"$nulls":4},"2 Related Work -- 3 Preliminaries -- 3.1 Problem Definition -- 3.2 Text-Visual Uniform Section Segmentation -- 4 Spotlight Flow Network -- 4.1 Multimodal Representation Layer -- 4.2 Hierarchical Multi-label Inter-level Constrained Classifier -- 4.3 Training SFNet -- 5 Experiments -- 5.1 Data Description -- 5.2 Baseline Approaches and Experimental Setup -- 5.3 Experimental Results -- 6 Conclusion -- References -- Learning Evidential Cognitive Diagnosis Networks Robust to Response Bias -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Evidential Cognitive Diagnosis Model -- 2.3 Uncertainty-ASSIST (UncASSIST) Dataset -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Main Results on the UncASSIST Dataset -- 3.3 Analysis of the Uncertainty Estimation -- 4 Conclusion -- References -- An Integrated Navigation Method for UAV Autonomous Landing Based on Inertial and Vision Sensors -- 1 Introduction -- 2 Landing Process Analysis and Scheme Design -- 3 Intelligent Identification of Airport Runway Based on Deep Learning Semantic Segmentation -- 3.1 Runway Segmentation Network Design -- 3.2 Runway Edge Feature Extraction -- 4 Modeling of Visual Relative Position/Attitude Measurement Based on the Characteristics of Runway Boundary -- 4.1 Coordinate System and Parameters Definition -- 4.2 Mathematical Modeling of Visual Relative Position/Attitude Measurement -- 5 Information Fusion Model of Inertial/Visual Sensor -- 5.1 System State Equation -- 5.2 System Observation Equation -- 6 Experiment Verification -- 6.1 Experiment Conditions -- 6.2 Experiment Results -- 7 Conclusion -- References -- An Automatic Surface Defect Detection Method with Residual Attention Network -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Overview of Network Framework -- 2.2 Backbone Network -- 2.3 MCF Block -- 3 Experiment Data and Preprocessing -- 3.1 Data Set."]
[{"$nulls":4},"3.2 Image Preprocessing -- 4 Experiment Results and Analysis -- 4.1 Implementation Details -- 4.2 Ablation Study -- 4.3 Performance Comparison -- 5 Conclusion -- References -- Research on Intelligent Decision-Making Irrigation Model of Water and Fertilizer Based on Multi-source Data Input -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Acquisition -- 2.2 Identification of Influencing Factors and Coupling Verification -- 3 Intelligent Decision-Making Irrigation Model of Water and Fertilizer -- 4 Results and Discussion -- 4.1 Comparison of Prediction Performance of Different Models -- 4.2 Analysis of Factors Affecting the Prediction Performance of the Model -- 4.3 Prediction Error Analysis of Water and Fertilizer Irrigation Quantity -- 4.4 Model Stability Analysis -- 5 Conclusion -- References -- Interaction-Aware Temporal Prescription Generation via Message Passing Neural Network -- 1 Introduction -- 2 Related Work -- 2.1 Recurrent Neural Network for Healthcare -- 2.2 Drug Recommendation -- 3 Preliminaries -- 3.1 Dataset -- 3.2 Problem Formulation -- 4 Technical Details -- 4.1 Patient Encoder -- 4.2 Prescription Generator -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Results and Analyses -- 6 Conclusion -- References -- Adversarial and Implicit Modality Imputation with Applications to Depression Early Detection -- 1 Introduction -- 2 Proposed Method -- 2.1 Learning Multi-modal Representations via Auto-encoding -- 2.2 Adversarial and Implicit Modality Imputation (AIMI) -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Comparisons on Robust Multi-modal Depression Diagnosis -- 3.3 Advantages on Modality Imputation and Representation -- 4 Conclusion -- References -- EdgeVR360: Edge-Assisted Multiuser-Oriented Intelligent 360-degree Video Delivery "]
[{"$nulls":4},"Scheme over Wireless Networks -- 1 Introduction -- 2 Related Works -- 2.1 360-degree Video."]
[{"$nulls":4},"2.2 DRL-Based 360-degree Video Delivery."]