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[null,null,"Titolo",null,"Future Data and Security Engineering : 8th International Conference, FDSE 2021, Virtual Event, November 24–26, 2021, Proceedings / / edited by Tran Khanh Dang, Josef Küng, Tai M. Chung, Makoto Takizawa"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-91387-2"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (428 pages)"]
[null,null,"Collana",null,"Information Systems and Applications, incl. Internet/Web, and HCI, , 2946-1642 ; ; 13076"]
[null,null,"Disciplina",null,"005.8"]
[null,null,"Soggetti",null,"Application software"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Telecommunication"]
[{"$nulls":4},"Signal processing"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Communications Engineering, Networks"]
[{"$nulls":4},"Signal, Speech and Image Processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Invited Keynotes -- Federated Learning: Issues in Medical Application -- 1 Introduction -- 2 Federated Learning in Medical Applications -- 2.1 Physical Disorder Predictions -- 2.2 Mental Disorder Predictions -- 2.3 Other Medical Applications -- 3 Research Issues -- 3.1 Heterogeneity Issues -- 3.2 Client Management Issues -- 3.3 Traceability and Accountability Issues -- 3.4 Privacy and Security Issues -- 4 Modular Framework Under Development -- 5 Conclusion and Future Works -- References -- Time in Data Models -- 1 Introduction -- 2 Representing Time -- 3 Snapshots vs. Historical Data Models -- 4 Temporal Databases -- 5 Temporal Multiversion Data Models -- 6 Temporal Integrity Constraints -- 7 Conclusions -- References -- Big Data Analytics and Distributed Systems -- Distributed Scalable Association Rule Mining over Covid-19 Data -- 1 Introduction -- 2 Scrutiny of Related Work -- 3 Preliminaries -- 3.1 Apriori -- 3.2 FP-Growth -- 3.3 k-Nearest Neighbours -- 3.4 "]
[{"$nulls":4},"Apache Spark -- 4 Methodology -- 4.1 Hardware and Software Configuration -- 4.2 Dataset Description -- 4.3 Data Pre-processing -- 5 Results and Discussion -- 5.1 Core Utilization -- 5.2 Node Utilization -- 5.3 Number of Transactions -- 6 Conclusion -- References -- Threshold Benefit for Groups Influence in Online Social Networks -- 1 Introduction -- 2 Related Work -- 3 Information Diffusion Models -- 3.1 Independent Cascade Model -- 3.2 Problem Definition -- 4 Proposed Algorithm -- 4.1 Estimation of i -- 4.2 Main Algorithm -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experiment Results -- 6 Conclusion -- References -- Application Based Cigarette Detection on Social Media Platforms Using Machine Learning Algorithms -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 MMDetection -- 3.3 Mask R-CNN."]
[{"$nulls":4},"3.4 Cascade Mask R-CNN -- 3.5 HTC -- 4 Performance Evaluation -- 4.1 Mask R-CNN -- 4.2 Cascade Mask R-CNN -- 4.3 HTC -- 5 Conclusions -- References -- Efficient Brain Hemorrhage Detection on 3D CT Scans with Deep Neural Network -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Architecture of U-Net Deep Neural Network -- 3.2 Overfitting and Underfitting -- 3.3 Loss Function -- 3.4 Xavier Initialization -- 3.5 Measure of the Average Precision -- 4 Proposed Method -- 4.1 The Training Phase -- 4.2 The Testing Phase -- 5 Experimental Results -- 5.1 Description of Dataset and Environment -- 5.2 Scenario Descriptions -- 5.3 Scenario Evaluation -- 6 Conclusion -- References -- Advances in Machine Learning for Big Data Analytics -- Multi-class Bagged Proximal Support Vector Machines for the ImageNet Challenging Problem -- 1 Introduction -- 2 Support Vector Machines -- 3 Proximal Support Vector Machines -- 4 Multi-class Bagged Proximal Support Vector Machines -- 4.1 Multi-class Proximal Support Vector Machines -- 4.2 Parallel Bagged Proximal Support Vector Machines for Large-Scale Multi-class -- 5 Experimental Results -- 5.1 Software Programs -- 5.2 ILSVRC 2010 Dataset -- 5.3 Tuning Parameter -- 5.4 Classification Results -- 6 Conclusion and Future Works -- References -- Selective Combination and Management of Distributed Machine Learning Models -- 1 Introduction -- 2 Related Research and Approaches -- 2.1 Related Research -- 2.2 Approach -- 3 Combining Feature Models -- 3.1 Distributed Feature Models -- 3.2 Sequential Combining Method -- 3.3 Adaptive Selection Method -- 3.4 Similarity Model Retrieved Method -- 3.5 Comparison of Performance by Combining -- 3.6 Combining Policy -- 4 Feature Model Management -- 4.1 Management Policy -- 4.2 Similarity Retrieval of Feature Models -- 4.3 R-Trees in a Distributed Environment -- 4.4 Evaluation."]
[{"$nulls":4},"5 Issues and Summary -- References -- Improving ADABoost Algorithm with Weighted SVM for Imbalanced Data Classification -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 3.1 Initialize Adaptive ADABoost Weights -- 3.2 Positive Label Sensitive Confidence Weights of the Membership Classifier -- 3.3 Im.ADABoost.W-SVM Algorithm -- 4 Experiments -- 5 Conclusion -- References -- Feature Learning and Data Generative Models for Facial Expression Recognition -- 1 Introduction -- 2 Related Works -- 2.1 Expression Recognition on Face Images -- 2.2 Generative Adversarial Network Model -- 3 FER2013 Data Set Analysis -- 3.1 Overview of FER2013 Data Set -- 3.2 Statistics of Data Set FER2013 -- 3.3 Challenges on the FER2013 Data Set -- 4 The Proposed Approach -- 4.1 Problem Statement -- 4.2 Develop the Data Generation Model -- 4.3 Design Identity Models -- 5 Experimental Results -- 5.1 Model Training Process -- 5.2 Accuracy Evaluation -- 6 Conclusion -- References -- Industry 4.0 and Smart City: Data Analytics and Security -- Authorization Strategies and "]
[{"$nulls":4},"Classification of Access Control Models -- 1 Introduction -- 2 Authorization Strategies -- 2.1 Discretionary Strategy (DAC) -- 2.2 Mandatory Strategy (MAC) -- 2.3 Hybrid Strategy -- 3 Access Control Models -- 3.1 Access Control by Explicit Object-Subject Assignment (OSA) -- 3.2 Access Control by Model-Specific Rules (MsR) -- 3.3 Access Control by Roles -- 3.4 Access Control by Content -- 3.5 Access Control by Context -- 4 Comparative Studies -- 5 Analysis -- 6 Conclusion -- References -- Motorbike Counting in Heavily Crowded Scenes -- 1 Introduction -- 2 Related Work -- 3 Crowd Counting Methods -- 3.1 Bag-of-Visual-Words -- 3.2 Lempitsky-Zisserman's Method -- 3.3 Qing Wen et al.'s Method -- 3.4 Donatello Conte et al.'s Method -- 3.5 Deep Convolutional Neural Network for Object Counting."]
[{"$nulls":4},"3.6 Context-Aware Crowd Counting (CAN) ch12liu2019context -- 4 Experiments and Discussion -- 4.1 The Dataset -- 4.2 Evaluation Method -- 4.3 Implementation Details -- 4.4 Experimental Results -- 5 Conclusion and Future Work -- References -- Pesticide Label Detection Using Bounding Prediction-Based Deep Convolutional Networks -- 1 Introduction and Motivation -- 2 Technical Background -- 2.1 Deep Learning and Convolutional Neural Networks (CNNs) -- 2.2 You Only Look Once (YOLO) -- 2.3 Single Shot Detection (SSD) -- 3 Experiments -- 3.1 Datasets -- 3.2 Models' Architecture Configuration -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Mobile-App Deployment -- 5 Conclusion -- References -- Intelligent Urban Transportation System to Control Road Traffic with Air Pollution Orientation -- 1 Introduction -- 2 Related Work -- 3 IUTAR Concepts and Solutions -- 3.1 System Context Overview -- 3.2 GAINS Vehicles Categories and Emission Factors -- 3.3 Modeling Optimal Traffic Light Cycle -- 4 Application Results -- 4.1 Training Dataset with Image Pre-processing -- 4.2 Test Run Results -- 4.3 IUTAR Dashboard -- 5 Conclusion and Future Works -- References -- A Data Union Method Using Hierarchical Clustering and Set Unionability -- 1 Introduction -- 2 Background and Related Works -- 2.1 Related Works -- 2.2 Dataset Similarity Measurement -- 3 Proposed Method -- 3.1 Schema Step -- 3.2 Clustering Step -- 3.3 Union Step -- 4 Experiment and Evaluation -- 5 Conclusion -- References -- Blockchain and IoT Applications -- A Consensus-Based Load-Balancing Algorithm for Sharded Blockchains -- 1 Introduction -- 2 Problem Definition -- 2.1 Types of Blockchains -- 2.2 Sharding Strategies -- 2.3 The Load-Balancing Problem for Sharded Account-Based Blockchains -- 3 Algorithms -- 3.1 Diffusion Algorithm -- 3.2 Centralized Algorithms -- 4 Experimentation."]
[{"$nulls":4},"4.1 Sharding Simulations -- 4.2 Numerical Results -- 4.3 Discussion -- 5 Conclusion -- References -- Neighboring Information Exploitation for Anomaly Detection in Intelligent IoT -- 1 Introduction -- 2 Propose Scheme -- 2.1 The Overall Process -- 2.2 Data Processing Model -- 3 Performance Evaluation -- 3.1 Experimental Dataset and Evaluation Method -- 3.2 Experimental Results -- 4 Conclusion -- References -- Feature Representation of AutoEncoders for Unsupervised IoT Malware Detection -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 AutoEncoder -- 3.2 Principle Component Analysis -- 3.3 Self-Organizing Maps -- 4 Hybrid AEs and SOMs for IoT Malware Detection -- 4.1 Latent Representation of AEs -- 4.2 SOM-Based Clustering Algorithm -- 5 Experiments -- 5.1 Datasets -- 5.2 Parameters Settings -- 5.3 Evaluation Metrics -- 6 Results and Discussion -- 6.1 IoT Data Analysis -- 6.2 Unknown/New IoT Attack Detection -- 6.3 Transfer Learning -- 7 Conclusions and Future Work -- References -- Machine Learning and Artificial Intelligence for Security and Privacy -- Potential Threat of Face Swapping to eKYC with Face Registration and Augmented Solution with Deepfake Detection -- 1 Introduction -- 2 "]
[{"$nulls":4},"Related Work -- 2.1 Know Your Customer -- 2.2 Deepfake Generation and Detection -- 3 Deepfake Attack on eKYC Systems -- 3.1 Regular Registration Process for eKYC -- 3.2 Attack Registration Process with Deepfake -- 4 Deepfake Detection to Protect eKYC Systems -- 4.1 Deepfake Detection -- 4.2 Enhanced eKYC with Deepfake Detection -- 5 Experiments -- 6 Conclusion -- References -- Spliced Image Forgery Detection Based on the Combination of Image Pre-processing and Inception V3 -- 1 Introduction -- 2 Literature Review -- 3 Problem Statement -- 4 Proposed Method -- 4.1 The Architecture of Inception V3 -- 4.2 Spliced Image Forgery Detection -- 5 Simulation Results."]
[{"$nulls":4},"6 Conclusion."]
[null,null,null,"Sommario/riassunto","This book constitutes the proceedings of the 8th International Conference on Future Data and Security Engineering, FDSE 2021, which was supposed to be held in Ho Chi Minh City, Vietnam, in November 2021, but the conference was held virtually due to the COVID-19 pandemic. The 24 full papers presented together with 2 invited keynotes were carefully reviewed and selected from 168 submissions. The selected papers are organized into the following topical headings: Big Data Analytics and Distributed Systems; Advances in Machine Learning for Big Data Analytics; Industry 4.0 and Smart City: Data Analytics and Security; Blockchain and IoT Applications; Machine Learning and Artificial Intelligence for Security and Privacy; Emerging Data Management Systems and Applications."]