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[null,"1.","Record Nr.",null,"UNISALENTO991000406059707536"]
[null,null,"Autore",null,"Ospitali, Giancarlo"]
[null,null,"Titolo",null,"Lo Stato italiano e il suo ordinamento giuridico : con note esemplificative, quesiti ed esercitazioni / Giancarlo Ospitali"]
[null,null,"Pubbl/distr/stampa",null,"Padova : CEDAM, 1975"]
[null,null,"Edizione",null,"[3. ed. ampliata dalla legislazione stradale]"]
[null,null,"Descrizione fisica",null,"XIII, 812 p. ; 23 cm."]
[null,null,"Disciplina",null,"342.45"]
[null,null,"Soggetti",null,"Italia Ordinamento giuridico"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Alleg. tabella di aggiornamento ai decreti fiscali"]
[null,"2.","Record Nr.",null,"UNINA9910580148103321"]
[null,null,"Titolo",null,"Artificial Intelligence and Security : 8th International Conference, ICAIS 2022, Qinghai, China, July 15–20, 2022, Proceedings, Part II / / 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,"3-031-06788-6"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (701 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 13339"]
[null,null,"Disciplina",null,"006.3"]
[{"$nulls":4},"005.8"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Data protection"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Data and Information Security"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Artificial Intelligence -- Remface: Study on Mini-sized Mobilenetv2 and Retinaface -- 1 Introduction -- 2 Related Works -- 2.1 Background Researches -- 2.2 Description of RetinaFace Model -- 2.3 Loss Function -- 2.4 Anchor Box -- 2.5 MobileNet -- 3 Related Works -- 3.1 Theory of the MobileNetV2 -- 3.2 Mini-MV2 -- 3.3 Improved Fusion of Mini-MV2 with RetinaFace -- 4 Related Works -- 5 Conclusion -- References -- Research on Application of Principal Component Analysis in 3d Video Dimension Reduction -- 1 Introduction -- 2 Commonly Used Video Image Dimensionality Reduction Method -- 2.1 Classification of Dimensionality Reduction Methods -- 2.2 The Effect of Dimension Reduction -- 2.3 Purpose of Dimension Reduction -- 2.4 Application Scope of Principal Component Analysis -- 2.5 Principal Component Analysis Method -- 2.6 Assumptions for PCA Application -- 3 Work Flow and Principle of Principal Component Analysis -- 3.1 Principal Component Analysis Workflow -- 3.2 Principle of Principal Component Analysis -- 4 "]
[{"$nulls":4},"Application Effect of Principal Component Analysis in 3d Video Image Dimensionality Reduction -- 5 Conclusion -- References -- Single Platform Passive Location Algorithm Using Position Information and Azimuth -- 1 Introduction -- 2 Target Location Calculation Method -- 2.1 Storage of Sensor Incoming Longitude, Latitude and Azimuth Data Using Sliding Time Windows -- 2.2 Datum Point Establishment -- 2.3 The Solution of r and -- 2.4 Using Linear Regression to Obtain the Anti-noise R -- 2.5 Determine if R Has Converged -- 2.6 Use R and Azimuth to Derive the Target Position -- 3 Simulation Experiment -- 4 Conclusion -- References -- DCAL: A New Method for Defending Against Adversarial Examples -- 1 Introduction -- 2 Relate Work -- 2.1 Adversarial Examples -- 2.2 Adversarial Attack Algorithm."]
[{"$nulls":4},"2.3 Existing Methods for Defenses -- 3 Method -- 3.1 Overview -- 3.2 Objective Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Defense Evaluation -- 5 Conclusion -- References -- Anomaly Detection Model for Key Places Based on Improved YOLOv5 -- 1 Introduction -- 2 Related Work -- 3 Anomaly Detection Models and Improvements -- 3.1 Network Structure of YOLOv5 -- 3.2 YOLOv5 Algorithm Improvements -- 3.3 Improving the FPN Structure -- 4 Experimental Procedure and Results -- 4.1 Experimental Preparation -- 4.2 Simulation and Experimental Results -- 5 Conclusion -- 5.1 Internet-Related Terrorist Content Remains a Concern and is Being Addressed -- 5.2 Existing Problems and Future Prospects -- References -- Non-contact Heart Rate Detection Based on Fusion Method of Visible Images and Infrared Images -- 1 Introduction -- 2 Related Work -- 2.1 Related Work About Non-contact Heart Rate Detection -- 2.2 Related Work About Fusion of Infrared Images and Visible Images -- 3 Heart Rate Detection Algorithm -- 4 Image Fusion Algorithm -- 4.1 A Subsection Sample -- 4.2 Fusion Method in the Fusion Layer -- 4.3 Loss Function -- 5 First Section -- 5.1 Experimental Equipment -- 5.2 Experimental Process and Results -- 6 Conclusion -- References -- Intelligent Data Analysis System Based on Edge Computing -- 1 Introduction -- 1.1 Background -- 1.2 Related Work -- 2 Solution: Intelligent Data Analysis System Based on Edge Computing -- 2.1 System Architecture -- 2.2 Edge AI -- 3 Experiments and Analysis -- 3.1 Data Sets and Requirements -- 3.2 Experimental Result -- 3.3 Conclusions -- References -- Portfolio Optimization Based on Quantum HHL Algorithm -- 1 Introduction -- 2 Algorithm Description -- 3 Models and Solution -- 3.1 Model -- 3.2 Problem Setting -- 4 Conclusion -- References."]
[{"$nulls":4},"Infrared Ship Video Target Tracking Based on Cross-Connection and Spatial Transformer Network -- 1 Introduction -- 2 Related Work -- 3 IR Ship Video Target Tracking Method Based on Cross-Connection and Spatial Transformer Network -- 3.1 Feature Extraction Module Based on Cross-Layer Connection -- 3.2 Feature Extraction Module Based on Spatial Transformer Network -- 4 Experiments -- 4.1 Data Set Introduction -- 4.2 Comparison Results with Other Methods -- 4.3 The Influence of Different Cross-Layer Connections -- 4.4 The Influence of Spatially Transform Network -- 5 Conclusion -- References -- Whale Optimization Algorithm Based on Artificial Fish Swarm Algorithm -- 1 Introduction -- 2 Overview of Traditional Whale Algorithm and Artificial Fish Swarm Algorithm -- 2.1 Overview of Traditional Whale Algorithm -- 2.2 Overview of Artificial Fish School Algorithm -- 3 The New Whale Optimization Algorithm -- 3.1 Model Design of Optimization Algorithm -- 3.2 The Specific Optimization Process of the Algorithm -- 4 Application of NWOA in the Scenario of Scenic Route Planning -- 4.1 Construction of Application Scenarios -- 4.2 Specific Application of NWOA -- 5 Experimental Simulation and Result Analysis -- 5.1 Experimental Parameter Settings -- 5.2 "]
[{"$nulls":4},"Benchmark Function -- 5.3 Experimental Results and Analysis -- 6 Conclusions -- References -- An Underwater Image Color Correction Algorithm Based on Underwater Scene Prior and Residual Network -- 1 Introduction -- 2 Underwater Image Imaging Model -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Model Detail -- 3.3 Synthetic Dataset -- 4 Experimental Results and Analysis -- 4.1 Synthetic Underwater Image Evaluation Results -- 4.2 Real Underwater Image Evaluation Results -- 5 Conclusion -- References -- An Improved TextRank-Based Method for Chinese Text Summarization -- 1 Introduction -- 2 Related Works."]
[{"$nulls":4},"2.1 Text Segmentation Based on N-Gram -- 2.2 Text Summarization Based on TextRank -- 3 Chinese Text Summarization Using Improved TextRank -- 3.1 Basic Principle -- 3.2 Chinese Word Segmentation Optimization -- 3.3 A Subsection Sample -- 4 Experimental Results and Analysis -- 4.1 Experimental Preparation -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Lhasa Dialect Recognition of Different Phonemes Based on TDNN Method -- 1 Introduction -- 2 Lhasa Tibetan Phones -- 2.1 Linguistic Unit -- 2.2 Different Phoneme Sets -- 3 Combining Vowels and Consonants (VC) -- 3.1 Corpus Text Design -- 3.2 Experimental Data -- 3.3 GMM-HMM Setup -- 3.4 DNN-HMM Setup -- 3.5 TDNN-HMM Setup -- 4 Experimental Results -- 5 Conclusion -- References -- A Power Grid Equipment Fault Prediction Model Based on Faster RCNN and Video Streaming -- 1 Introduction -- 2 Preliminaries -- 2.1 Faster RCNN Structure -- 2.2 Feature Extraction -- 2.3 Region Proposal Network (RPN) -- 2.4 Classification Detection -- 3 A Power Grid Equipment Fault Prediction Model Based on Faster RCNN -- 3.1 The Power Grid Equipment Fault Prediction Model -- 3.2 Dataset Processing -- 3.3 Feature Extraction -- 3.4 Region Proposal Network (RPN) -- 4 Experiments and Results -- 4.1 Experiment Platform and Dataset -- 4.2 Result Analysis -- 5 Conclusion -- References -- A Wireless Transmission Model of Power Grid Equipment State Based on Compressed Sensing -- 1 Introduction -- 2 Preliminaries -- 2.1 Sparse Decomposition -- 2.2 Reconstruction Algorithm -- 3 A Wireless Transmission Model of Power Grid Equipment State Based on Compressed Sensing -- 3.1 The Wireless Transmission Model of Power Grid Equipment State -- 3.2 Dataset Processing -- 3.3 Sparse Decomposition -- 3.4 Measurement Matrix -- 3.5 Online Monitoring -- 4 Simulation Experiment and Result Analysis."]
[{"$nulls":4},"4.1 Experiment Platform and Power Grid Equipment Dataset -- 4.2 Performance Comparison -- 4.3 Reconstruction Effect -- 5 Conclusion -- References -- Multi-sequence Satellite Image Cloud Removal Based on Dual Residual Network -- 1 Introduction -- 1.1 Traditional Method -- 1.2 Traditional Method -- 2 Method -- 2.1 Residual Connection -- 2.2 Dual Residual Block -- 2.3 Sub-pixel Unsampling -- 3 Experiment and Evaluation -- 3.1 Sentinel-2 Cloudy Dataset -- 3.2 Data Enhencement -- 3.3 Residual Connection -- 3.4 Implemetation Details -- 3.5 Result -- 4 Conclusion -- References -- Research on Rumor Detection Based on RoBERTa-BiGRU Model -- 1 Introduction -- 2 Related Work -- 3 Microblog Text Rumors Detection Model -- 3.1 Concrete Model -- 3.2 Text Processing Layer -- 3.3 Ro BERTa Text Pre-training Layer -- 3.4 BiGRU Layer -- 3.5 Output Layer -- 4 Simulation Experiment Environment -- 4.1 Simulation Experiment Equipment -- 4.2 Data Set -- 4.3 Experiment Parameter -- 4.4 Contrast Experiment -- 5 Conclusion -- References -- A Novel Pruning Method Based on Correlation Applied in Full-Connection Layer Neurons -- 1 Introduction -- 2 Related Work -- 3 A Pruning Method Based on Neuron Correlation -- 3.1 Neuronal Importance Order -- 3.2 "]
[{"$nulls":4},"Pruning Criterion -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison Experiment -- 5 Conclusion -- References -- Big Data -- Research and Application of Carrier User Viscosity Evaluation Method Based on CNN Algorithm Model -- 1 Overview -- 2 Current Situation and Problems of Existing User Viscosity Evaluation Methods -- 3 Method and Design -- 4 The Design of Technical Methods -- 4.1 Summary of Technical Methods -- 4.2 Sample Data -- 4.3 Algorithm Model Construction and Principle -- 5 Application of Technical Methods -- 5.1 The Data Source -- 5.2 Model Index Design -- 5.3 Data Sample Processing and Segmentation."]
[{"$nulls":4},"5.4 Model Evaluation Index."]
[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."]