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[null,null,"Titolo",null,"Database and Expert Systems Applications : 32nd International Conference, DEXA 2021, Virtual Event, September 27–30, 2021, Proceedings, Part II / / edited by Christine Strauss, Gabriele Kotsis, A Min Tjoa, Ismail Khalil"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-86475-8"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (377 pages)"]
[null,null,"Collana",null,"Information Systems and Applications, incl. Internet/Web, and HCI, , 2946-1642 ; ; 12924"]
[null,null,"Disciplina",null,"005.74"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Database management"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Database Management"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Abstracts of Keynote Talks -- Privacy in the Era of Big Data, Machine Learning, IoT, and 5G -- Don't Handicap AI without Explicit Knowledge -- Extreme-Scale Model-Based Time Series Management with ModelarDB -- Big Minds Sharing their Vision on the Future of AI (Panel) -- Contents - Part II -- Contents - Part I -- Authenticity, Privacy, Security and Trust -- Less is More: Feature Choosing under Privacy-Preservation for Efficient Web Spam Detection -- 1 Introduction -- 2 The PPGAFS Approach -- 2.1 Preselecting Privacy-Preserving Features -- 2.2 Generating Minimum Feature Subset Based on the Improved GA -- 3 Spam Detection and Verification Experiment Analysis -- 3.1 Web Spam Detection Procedure -- 3.2 Dataset and Evaluation Measures -- 3.3 Experiment Design and Result Analysis -- 4 Conclusion -- References -- Construction of Differentially Private Summaries Over Fully Homomorphic Encryption -- 1 Introduction -- 2 Preliminaries -- 2.1 "]
[{"$nulls":4},"Homomorphic Encryption -- 2.2 Differential Privacy -- 3 Related Work -- 3.1 Combination of Homomorphic Encryption and Differential Privacy -- 3.2 Range Queries Under Differential Privacy -- 4 Proposed Method -- 4.1 Overview -- 4.2 Adoption of Differential Privacy over Fully Homomorphic Encryption -- 4.3 Security Analysis -- 5 Experimental Evaluation -- 5.1 Experimental Setup -- 5.2 DP-Summary Construction Time -- 5.3 Accuracy of DP-Summary -- 6 Conclusion -- References -- SafecareOnto: A Cyber-Physical Security Ontology for Healthcare Systems -- 1 Introduction -- 2 Safecare Ontology -- 3 Knowledge Acquisition -- 4 Formalization and Implementation -- 4.1 Concepts Identification -- 4.2 Relationships Identification -- 4.3 Axioms Definition -- 4.4 Implementation -- 5 Safecare Use Cases -- 6 Related Work -- 7 Conclusion -- References."]
[{"$nulls":4},"Repurpose Image Identification for Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework -- 3.1 Event Type Classifier -- 3.2 Image Repurpose Detector -- 4 Experimental Evaluation -- 4.1 Experimental Datasets -- 4.2 Experiments on Event Type Classification -- 4.3 Comparative Study -- 4.4 Variants of RECAST -- 4.5 Case Study -- 5 Conclusion -- References -- Data and Information Processing -- An Urgency-Aware and Revenue-Based Itemset Placement Framework for Retail Stores -- 1 Introduction -- 2 Proposed Framework of the Problem -- 3 URIP: Urgency-Aware Itemset Placement Scheme -- 4 Performance Evaluation -- 5 Conclusion -- References -- NV-QALSH: An NVM-Optimized Implementation of Query-Aware Locality-Sensitive Hashing -- 1 Introduction -- 2 Preliminaries -- 2.1 The c-ANN Search Problem -- 2.2 The QALSH Method -- 2.3 Non-Volatile Memory -- 2.4 LB-Tree and LB-QALSH -- 3 Optimization Designs -- 3.1 Three-Level Storage Architecture -- 3.2 Leaf Node Optimization -- 3.3 Collision Counting Granularity Optimization -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Datasets and Queries -- 4.3 Evaluation Metrics -- 4.4 Benchmark Methods -- 4.5 Results and Analysis -- 5 Conclusion -- References -- NCRedis: An NVM-Optimized Redis with Memory Caching -- 1 Introduction -- 2 Implementation of NCRedis -- 2.1 Architecture of NCRedis -- 2.2 Log-Free Designs of LFSlab -- 2.3 Handling Persistent Memory Leak by LFSlab -- 2.4 Log-Free Designs of NCRedis -- 3 Evaluation -- 3.1 Experimental Setup -- 3.2 Memtier Benchmark Test -- 4 Conclusions -- References -- A Highly Modular Architecture for Canned Pattern Selection Problem -- 1 Introduction -- 2 System Architecture -- 2.1 Graph Similarity Module -- 2.2 Graph Clustering Module -- 2.3 Graph Connection Module -- 2.4 Pattern Mining Module -- 3 Conclusions -- References -- AutoEncoder for Neuroimage."]
[{"$nulls":4},"1 Introduction -- 2 The Proposed Approach -- 2.1 Variational AutoEncoder Based Regression -- 2.2 Supervised Linear Autoencoder -- 2.3 Implementation Details -- 3 Experiments -- 4 Conclusion -- References -- Knowledge Discovery -- Towards New Model for Handling Inconsistency Issues in DL-Lite Knowledge Bases -- 1 Introduction -- 2 Related Works -- 3 DL-Lite Ontology and Management of Inconsistencies: An Overview -- 4 Most-Possible Repair Proposed Approach -- 4.1 Most-Possible Repair Algorithm -- 4.2 Experimental Study and Results Analysis -- 5 Conclusion and Prospects -- References -- ContextWalk: Embedding Networks with Context Information Extracted from News Articles -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Challenges -- 4 Algorithm -- 4.1 Context Embedding -- 4.2 ContextWalk -- 4.3 Complexity -- 5 Experiments -- 5.1 Compare Clusterings -- 5.2 Network and Embedding Distances -- 6 Discussion -- References -- FIP-SHA - Finding Individual Profiles Through SHared Accounts -- 1 Introduction "]
[{"$nulls":4},"-- 2 Background -- 3 Related Work -- 4 FIP-SHA -- 4.1 Session Representation -- 5 Experimental Evaluation Setup and Metrics -- 6 Results -- 6.1 Cut Off Sessions -- 6.2 Clustering -- 6.3 Analysis of (Weighted) User Separation -- 6.4 Discussion -- 7 Final Considerations -- References -- A Tag-Based Transformer Community Question Answering Learning-to-Rank Model in the Home Improvement Domain -- 1 Introduction -- 2 Related Work -- 3 Task Definition -- 4 Our Approach -- 4.1 Transformer Models -- 4.2 Input and Tag Representation -- 4.3 CQA Pair Matching Model -- 4.4 Model Optimisation -- 4.5 Candidate Answers Ranking -- 5 Dataset Building and Validation -- 5.1 Subjective CQA -- 5.2 Gold Standard Definition -- 6 Evaluation -- 6.1 Experiment Setup -- 6.2 Rank-Aware Evaluation Metrics -- 6.3 Results -- 7 Conclusion -- References."]
[{"$nulls":4},"An Autonomous Crowdsourcing System -- 1 Introduction -- 2 Related Work -- 3 Crowdsourcing Task -- 3.1 Workflow -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Machine Learning -- The Effect of IoT Data Completeness and Correctness on Explainable Machine Learning Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Observation, Analysis and Validation -- 5 Conclusion -- References -- Analysis of Behavioral Facilitation Tweets for Large-Scale Natural Disasters Dataset Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Extraction of Behavioral Facilitation Tweets -- 3.1 A Classifier Based on LSTM -- 3.2 A Classifier Based on BiLSTM -- 3.3 A Classifier Based on BERT -- 4 Experiment 1: Comparison of Models for Classification Accuracy -- 4.1 Data -- 4.2 Method -- 4.3 Result -- 5 Experiment 2: Analysis Characteristics of BF-Tweets in a Large-Scale Disaster Situation -- 5.1 Experimental Conditions -- 5.2 Results -- 5.3 Discussion -- 6 Conclusion -- References -- Using Cross Lingual Learning for Detecting Hate Speech in Portuguese -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Evaluation and Results -- 5 Final Remarks -- References -- MMEnsemble: Imbalanced Classification Framework Using Metric Learning and Multi-sampling Ratio Ensemble -- 1 Introduction -- 2 Related Work: Resampling Approaches -- 2.1 Oversampling -- 2.2 Undersampling -- 3 MMEnsemble -- 3.1 Base Ensemble Classifier - MLEnsemble -- 3.2 Ensemble Using Asset-Based Weighting -- 4 Experimental Evaluation -- 4.1 Settings -- 4.2 Results -- 4.3 Lessons Learned -- 5 Conclusion -- References -- Evaluate the Contribution of Multiple Participants in Federated Learning -- 1 Introduction -- 2 Method -- 2.1 Shapley Value for Models -- 2.2 Invalid Shapley Value -- 2.3 Method -- 2.4 Properties -- 3 Experiment."]
[{"$nulls":4},"3.1 Utility Function -- 3.2 Noisy Labels -- 4 Conclusion -- References -- DFL-Net: Effective Object Detection via Distinguishable Feature Learning -- 1 Introduction -- 2 Related Work -- 3 Design of DFL-Net -- 3.1 High-Level Idea of DFL-Net -- 3.2 Full-Scale Fusion -- 3.3 Attention Guided Feature Refinement -- 4 Performance Evaluation -- 4.1 Settings -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusion and Future Work -- References -- Transfer Learning for Larger, Broader, and Deeper Neural-Network Quantum States -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Quantum Many-Body Systems -- 3.2 Deep Neural-Network Quantum States -- 4 Methodology -- 5 Performance Evaluation -- 5.1 Broader Networks -- 5.2 Deeper Networks -- 6 Conclusion -- References -- LGTM: A Fast and Accurate kNN Search Algorithm in High-Dimensional Spaces -- 1 Introduction -- 2 Theoretical Motivation -- 2.1 Preliminary -- 2.2 Theoretical Foundation -- 3 LGTM: From Theory to Practice -- 3.1 Pre-processing -- 3.2 Online (Query) Processing -- 4 Experiment -- 4.1 Comparison with AKNNG -- 4.2 Comparison with State-of-the-art "]
[{"$nulls":4},"Algorithms -- 5 Conclusion -- References -- TSX-Means: An Optimal K Search Approach for Time Series Clustering -- 1 Introduction -- 2 Notations and Definitions -- 3 TSX-Means: A New Method for Time Series Clustering -- 3.1 Principle of the Method -- 3.2 TSX-Means Algorithm -- 4 Experimental Results -- 5 Conclusion and Perspectives -- References -- A Globally Optimal Label Selection Method via Genetic Algorithm for Multi-label Classification -- 1 Introduction -- 2 Preliminaries -- 3 The Proposed Method -- 3.1 Uninformative Label Reduction via EBMD -- 3.2 Most Informative Label Selection via GA -- 3.3 Label Selection Algorithm Combining EBMD and GA -- 4 Experiments -- 4.1 Basic Experimental Settings -- 4.2 Experimental Results and Analysis -- 5 Conclusions."]
[{"$nulls":4},"References."]
[null,null,null,"Sommario/riassunto","This two-volume set, LNCS 12923 and 12924, constitutes the thoroughly refereed proceedings of the 5th International Conference on Database and Expert Systems Applications, DEXA 2021. Due to COVID-19 pandemic, the conference was held virtually. The 37 full papers presented together with 31 short papers in these volumes were carefully reviewed and selected from a total of 149 submissions. The papers are organized around the following topics: big data; data analysis and data modeling; data mining; databases and data management; information retrieval; prediction and decision support."]