LEADER 10879nam 2200541 450 001 996464484703316 005 20211020193109.0 010 $a3-030-73197-9 035 $a(CKB)4100000011881110 035 $a(MiAaPQ)EBC6536810 035 $a(Au-PeEL)EBL6536810 035 $a(OCoLC)1245665671 035 $a(PPN)255289405 035 $a(EXLCZ)994100000011881110 100 $a20211020d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDatabase systems for advanced applications $e26th international conference, DASFAA 2021, Taipei, Taiwan, April 11-14, 2021 : proceedings Part II /$fChristian S. Jensen [and seven others] editors 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (817 pages) 225 1 $aLecture Notes in Computer Science ;$v12682 311 $a3-030-73196-0 327 $aIntro -- Preface -- Organization -- Contents - Part II -- Text and Unstructured Data -- Multi-label Classification of Long Text Based on Key-Sentences Extraction -- 1 Introduction -- 2 Related Work -- 2.1 Multi-Label Learning -- 2.2 Multi-Task Learning -- 3 Model -- 3.1 Task Definition -- 3.2 Sentence Encoder -- 3.3 Key-Sentences Extraction with Semi-supervised Learning -- 3.4 Multi-label Prediction Based Multi-label Attention -- 3.5 Optimization -- 4 Experiments -- 4.1 Data -- 4.2 Baseline Models and Evaluation Metrics -- 4.3 Experimental Settings -- 4.4 Results and Analysis -- 4.5 Ablation Test -- 4.6 Case Study -- 5 Conclusion -- References -- Automated Context-Aware Phrase Mining from Text Corpora -- 1 Introduction -- 2 Methodology -- 2.1 Problem Definition -- 2.2 Overview -- 2.3 Data Process -- 2.4 Topic-Aware Phrase Recognition Network (TPRNet) -- 2.5 Instance Selection Network (ISNet) -- 2.6 Training Details -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Experimental Results -- 3.3 Impact of Topic Information -- 3.4 Effectiveness of Selection Policy -- 4 Related Work -- 5 Conclusion -- References -- Keyword-Aware Encoder for Abstractive Text Summarization -- 1 Introduction -- 2 Related Work -- 3 Model Description -- 3.1 Keywords Extraction -- 3.2 Dependency-Based Keyword Sequence -- 3.3 Keyword-Aware Encoder -- 3.4 Summary Decoder -- 3.5 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Evaluation Metric -- 4.4 Implementation Details -- 4.5 Evaluation -- 5 Discussion -- 5.1 Visualization of Gates and Attention Weights -- 5.2 Influence of Keyword Extraction Ratio -- 5.3 Analysis of Content Selection Methods -- 5.4 Case Study -- 6 Conclusion -- References -- Neural Adversarial Review Summarization with Hierarchical Personalized Attention -- 1 Introduction -- 2 Related Work -- 3 Proposed Method. 327 $a3.1 Problem Formulation -- 3.2 Review Encoder -- 3.3 Abstractive Summary Generation -- 4 Experimental Setup -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 5 Result and Discussion -- 5.1 Performance Evaluation -- 5.2 Ablation Study -- 5.3 Case Study -- 5.4 Visualization of Attention -- 6 Conclusion -- References -- Generating Contextually Coherent Responses by Learning Structured Vectorized Semantics -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Model Overview -- 3.2 Hierarchical Centralized Encoder -- 3.3 Inference Network -- 3.4 Decoder with Calibration Mechanism -- 3.5 Loss Function -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Automatic Metric-Based Evaluation -- 4.3 Manual Evaluation -- 4.4 Further Analysis of Our Method -- 4.5 Case Study -- 5 Conclusion -- References -- Latent Graph Recurrent Network for Document Ranking -- 1 Introduction -- 2 Related Work -- 2.1 Interaction Based Neural Ranking Models -- 2.2 Pretrained Neural Language Models for IR -- 2.3 Graph Neural Network -- 3 Method -- 3.1 Formalization -- 3.2 Architecture -- 3.3 Loss Function -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Effectiveness Analysis -- 4.3 Ablation Study for Masking Strategy -- 4.4 Ablation Study for Distance Learning Task -- 4.5 Query Length Analysis -- 5 Conclusion -- References -- Discriminative Feature Adaptation via Conditional Mean Discrepancy for Cross-Domain Text Classification -- 1 Introduction -- 2 Preliminary -- 2.1 Kernels and Hilbert Space Embedding -- 2.2 Hilbert Space Embedding of Conditional Distributions -- 3 Proposed Model -- 3.1 Conditional Mean Discrepancy -- 3.2 Aligned Adaptation Networks with Adversarial Learning -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 4.3 Analysis -- 5 Related Work -- 6 Conclusion -- References. 327 $aDiscovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network -- 1 Introduction -- 2 Related Work -- 3 The CAPSAR Model -- 3.1 Model Overview -- 3.2 Sequence Encoder -- 3.3 Location Proximity with Given Aspect -- 3.4 Capsule Layers with Sharing-Weight Routing -- 3.5 Model Training with Aspect Reconstruction -- 3.6 Combining CAPSAR with BERT -- 4 Experiments -- 4.1 Datasets -- 4.2 Compared Methods -- 4.3 Experimental Settings -- 4.4 Results on Standard ATSA -- 4.5 Results on Aspect Term Detection -- 5 Conclusion -- References -- Discriminant Mutual Information for Text Feature Selection -- 1 Introduction -- 2 Related Work -- 2.1 Text Representation -- 2.2 Mutual Information -- 2.3 mRMR -- 3 Discriminant Mutual Information -- 3.1 Text Preprocessing -- 3.2 Discriminant Mutual Information -- 4 Experiments and Analysis -- 4.1 Datasets -- 4.2 Classifiers and Evaluation Measure -- 4.3 Experimental Results -- 5 Conclusion -- References -- CAT-BERT: A Context-Aware Transferable BERT Model for Multi-turn Machine Reading Comprehension -- 1 Introduction -- 2 Related Work -- 2.1 Machine Reading Comprehension -- 2.2 Transfer Learning -- 3 The CAT-BERT Model -- 3.1 Task Description and Overall Framework -- 3.2 Context-Aware BERT Encoding -- 3.3 Transfer Learning with Task-Specific Attention -- 3.4 Dynamic Training Policy -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Overall Results -- 4.4 Comparison of Transfer Policies -- 4.5 The Benefit from the Attention Mechanism -- 4.6 Error Analysis -- 5 Conclusion and Future Work -- References -- Unpaired Multimodal Neural Machine Translation via Reinforcement Learning -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Overview -- 3.3 Reward Computation -- 3.4 Objective Function -- 3.5 Training Details -- 4 Experiments -- 4.1 Datasets. 327 $a4.2 Baseline Methods -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Impact of Hyper-parameter -- 4.6 Case Study -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Multimodal Named Entity Recognition with Image Attributes and Image Knowledge -- 1 Introduction -- 2 Related Work -- 2.1 Traditional NER with Text only -- 2.2 MNER with Image and Text -- 2.3 Other Multimodal Tasks -- 3 Our Proposed Model -- 3.1 Problem Formulation -- 3.2 Introducting Image Attributes and Knowledge -- 3.3 Feature Extraction -- 3.4 Modality Fusion -- 3.5 Conditional Random Fields -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Baselines -- 4.4 Results and Discussion -- 4.5 Bad Case Analysis -- 5 Conclusions -- References -- Multi-task Neural Shared Structure Search: A Study Based on Text Mining -- 1 Introduction -- 2 Our Approach -- 2.1 Multi-task Shared Structure Encoding (SSE) -- 2.2 Shared Structure and Auxiliary Task Search -- 2.3 Variant of Vanilla NAS Approach -- 2.4 m-Sparse Search Approach for Neural-Based Multi-task Model (m-S4MT) -- 2.5 Task-Wise Greedy Generation Search Approach for Neural-Based Multi-task Model (TGG-S3MT) -- 3 Experiments -- 3.1 Datasets -- 3.2 Experimental Settings -- 3.3 Q1: Are SSE and m-S4MT Effective? -- 3.4 Q2: Is TGG-S3MT Effective? -- 3.5 Q3: Which Search Approach Is More Efficient? -- 4 Related Work -- 4.1 Multi-task Methods in Text Mining -- 4.2 Network Architecture Search for Multi-task Models -- 4.3 Peer Review Prediction -- 5 Conclusion -- References -- A Semi-structured Data Classification Model with Integrating Tag Sequence and Ngram -- 1 Introduction -- 2 Related Works -- 3 TSGram Feature -- 3.1 Basic Definitions -- 3.2 Constructing a TSGram Feature Space -- 4 TSGram-Based Classifier -- 4.1 TSGrams Class Model -- 4.2 Classifying Documents Using the TSGrams Class Model. 327 $a5 Experimental Study -- 5.1 Experimental Setting -- 5.2 Effects of the Length and Numbers of TSGrams -- 5.3 Effects of TSGram Feature Selection Parameter and Feature Combination -- 5.4 Classification Results -- 6 Conclusions -- References -- Inferring Deterministic Regular Expression with Unorder and Counting -- 1 Introduction -- 2 Preliminaries -- 2.1 Regular Expression with Unorder and Counting -- 2.2 SORE, SOREUC, SOA and Unorder Unit -- 3 Finite Automaton with Unorder and Counting (FAUC) -- 3.1 Unorder Markers, Counters and Update Instructions -- 3.2 Finite Automata with Unorder and Counting -- 4 Inference of SOREUCs -- 4.1 Computing Unorder Units -- 4.2 Constructing FAUC -- 4.3 Running FAUC -- 4.4 Generating SOREUC -- 5 Experiments -- 5.1 Expressiveness of SOREUCs -- 5.2 Conciseness, Generalization Ability and Time Performance -- 6 Conclusion -- References -- MACROBERT: Maximizing Certified Region of BERT to Adversarial Word Substitutions -- 1 Introduction -- 2 Methods -- 2.1 Certified Region -- 2.2 Perturbation Distribution Based on Multi-Hop Neighbors -- 2.3 Robust Training by Maximizing Certified Region -- 3 Experiment -- 3.1 Experimental Data and Baselines -- 3.2 Results and Analysis -- 4 Conclusion -- References -- A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification -- 1 Introduction -- 2 Model Description -- 2.1 Transformer-Based Encoder -- 2.2 Language Model Layer -- 2.3 Classification Layer -- 2.4 K- Augmentation -- 2.5 Regularization -- 2.6 Training Process -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Main Results -- 3.3 Ablation Study -- 3.4 Importance of Diversity and Constraints -- 4 Conclusion -- References -- Neural Demographic Prediction in Social Media with Deep Multi-view Multi-task Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Context View. 327 $a3.2 Sentiment View. 410 0$aLecture notes in computer science ;$v12682. 606 $aDatabase management$vCongresses 606 $aDatabases$vCongresses 606 $aDatabase management 615 0$aDatabase management 615 0$aDatabases 615 0$aDatabase management. 676 $a005.74 702 $aJensen$b Christian S. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464484703316 996 $aDatabase Systems for Advanced Applications$9772450 997 $aUNISA LEADER 05742nam 2200769Ia 450 001 9910824765803321 005 20200520144314.0 010 $a9786612684203 010 $a9781282684201 010 $a1282684205 010 $a9780470531778 010 $a0470531770 010 $a9780470531761 010 $a0470531762 035 $a(CKB)2670000000019279 035 $a(EBL)533938 035 $a(SSID)ssj0000415006 035 $a(PQKBManifestationID)11286173 035 $a(PQKBTitleCode)TC0000415006 035 $a(PQKBWorkID)10409648 035 $a(PQKB)10533721 035 $a(Au-PeEL)EBL533938 035 $a(CaPaEBR)ebr10388344 035 $a(CaONFJC)MIL268420 035 $a(FINmELB)ELB178440 035 $a(MiAaPQ)EBC533938 035 $a(OCoLC)632157442 035 $a(Perlego)2755646 035 $a(EXLCZ)992670000000019279 100 $a20090410d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCorrosion resistance of aluminum and magnesium alloys $eunderstanding, performance, and testing /$fEdward Ghali 205 $a1st ed. 210 $aHoboken, N.J. $cWiley$dc2010 215 $a1 online resource (743 p.) 225 1 $aWiley series on corrosion 300 $aDescription based upon print version of record. 311 08$a9780471715764 311 08$a047171576X 320 $aIncludes bibliographical references and index. 327 $aCorrosion Resistance of Aluminum and Magnesium Alloys; Contents; Preface; Acknowledgments; Part One Electrochemical Fundamentals and Active-Passive Corrosion Behaviors; 1. Fundamentals of Electrochemical Corrosion; Overview; A. Thermodynamic Considerations of Corrosion; 1.1. Electrolytic Conductance; 1.1.1. Faraday Laws; 1.2. Tendency to Corrosion; 1.3. The Electrochemical Interface; 1.3.1. Electric Double Layer; 1.3.2. Equivalent Circuit of the Electric Double Layer; 1.4. Nernst Equation; 1.5. Standard Potentials of Electrodes; 1.5.1. Standard States in Solution; 1.5.2. Hydrogen Electrode 327 $a1.5.3. Positive and Negative Signs of Potentials1.5.4. Graphical Presentation; B. Activity and Conductance of the Electrolyte; 1.6. Activity of the Electrolyte; 1.6.1. Constant and Degree of Dissociation; 1.6.2. Activity and Concentration; 1.6.3. Theory of More Concentrated Solutions; 1.6.4. Electrolytic Conduction; 1.7. Mobility of Ions; 1.7.1. Law of Additivity of Kohlrausch; 1.7.2. Ion Transport Number or Index; 1.8. Conductance; 1.9. Potential of Decomposition; C. The Different Types of Electrodes; 1.10. Gas Electrodes; 1.11. Metal-Metal Ion Electrodes; 1.11.1. Alloyed Electrodes 327 $a1.12. Metal-Insoluble Salt or Oxide Electrodes1.12.1. Metal-Insoluble Salt Electrodes; 1.12.2. Metal-Insoluble Oxide Electrodes; 1.13. Electrodes of Oxidation-Reduction; 1.14. Selective Ion Electrodes; 1.14.1. Glass Electrodes; 1.14.2. Copper Ion-Selective Electrodes; D. Electrochemical and Corrosion Cells; 1.15. Chemical Cells; 1.15.1. Chemical Cell with Transport; 1.15.2. Chemical Cell Without Transport; 1.16. Concentration Cells; 1.16.1. Concentration Cell with Difference of Activity at the Electrode and Electrolyte; 1.16.2. Junction Potential; 1.17. Solvent Corrosion Cells 327 $a1.17.1. Cathodic Oxidoreduction Reaction1.17.2. Displacement Cell; 1.17.3. Complexing Agent Cells; 1.17.4. Stray Current Corrosion Cell; 1.18. Temperature Differential Cells; 1.19. Overlapping of Different Corrosion Cells; E. Chemical and Electrochemical Corrosion; 1.20. Definition and Description of Corrosion; 1.21. Electrochemical and Chemical Reactions; 1.21.1. Electrochemical Corrosion; 1.21.2. Film-Free Chemical Interactions; References; 2. Aqueous and High-Temperature Corrosion; Overview; 2.1. Atmospheric Media; 2.1.1. Description; 2.1.2. Types of Corrosion 327 $a2.1.3. Atmospheric Contaminants2.1.4. Corrosion Prevention and Protection; 2.2. Aqueous Environments; 2.3. Organic Solvent Properties; 2.4. Underground Media; 2.5. Water Media Properties; 2.5.1. Water Composition; 2.5.2. The Oxidizing Power of Solution; 2.5.3. Scale Formation and Water Indexes; 2.6. Corrosion at High Temperatures; 2.6.1. Description; 2.6.2. The Pilling-Bedworth Ratio (PBR); 2.6.3. Kinetics of Formation; 2.6.4. Corrosion Behaviors of Some Alloys at Elevated Temperatures; References; 3. Active and Passive Behaviors of Aluminum and Magnesium and Their Alloys; Overview 327 $a3.1. Potential-pH Diagrams of Aluminum and Magnesium 330 $aValuable information on corrosion fundamentals and applications of aluminum and magnesium Aluminum and magnesium alloys are receiving increased attention due to their light weight, abundance, and resistance to corrosion. In particular, when used in automobile manufacturing, these alloys promise reduced car weights, lower fuel consumption, and resulting environmental benefits. Meeting the need for a single source on this subject, Corrosion Resistance of Aluminum and Magnesium Alloys gives scientists, engineers, and students a one-stop reference for understanding both the corrosion f 410 0$aWiley series on corrosion. 606 $aAluminum alloys$xCorrosion 606 $aMagnesium alloys$xCorrosion 606 $aCorrosion and anti-corrosives 615 0$aAluminum alloys$xCorrosion. 615 0$aMagnesium alloys$xCorrosion. 615 0$aCorrosion and anti-corrosives. 676 $a620.1/8623 700 $aGhali$b Edward$01674908 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824765803321 996 $aCorrosion resistance of aluminum and magnesium alloys$94059306 997 $aUNINA