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Knowledge science, engineering and management : 15th International Conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part III / / Gerard Memmi [and four others] editors
| Knowledge science, engineering and management : 15th International Conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part III / / Gerard Memmi [and four others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (769 pages) |
| Disciplina | 006.33 |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence |
| Soggetto topico |
Knowledge acquisition (Expert systems)
Knowledge management |
| ISBN | 3-031-10989-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910585789503321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge science, engineering and management : 15th International Conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part III / / Gerard Memmi [and four others] editors
| Knowledge science, engineering and management : 15th International Conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part III / / Gerard Memmi [and four others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (769 pages) |
| Disciplina | 006.33 |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence |
| Soggetto topico |
Knowledge acquisition (Expert systems)
Knowledge management |
| ISBN | 3-031-10989-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996483159703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, part ii / / Gerard Memmi, [and four others], (editors)
| Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, part ii / / Gerard Memmi, [and four others], (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (715 pages) |
| Disciplina | 658.4038 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Knowledge management |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-10986-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organizations -- Contents - Part II -- Knowledge Engineering Research and Applications (KERA) -- Multi-view Heterogeneous Network Embedding -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Methodology -- 4.1 Semantics-Based View Generation -- 4.2 View Preservation and Enhanced View Collaboration -- 4.3 Embedding Fusion -- 4.4 Optimization Objective -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Link Prediction -- 5.3 Node Classification -- 5.4 Parameter Sensitivity Analysis -- 6 Conclusion -- References -- A Multi-level Attention-Based LSTM Network for Ultra-short-term Solar Power Forecast Using Meteorological Knowledge -- 1 Introduction -- 2 Related Work -- 3 Architecture -- 3.1 Encoder -- 3.2 Decoder -- 4 Experiment: Case Study -- 4.1 Datasets Setting -- 4.2 Experimental Setting -- 4.3 Results and Analysis -- 5 Conclusions -- References -- Unsupervised Person Re-ID via Loose-Tight Alternate Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Clustering-Guided Unsupervised Person re-ID -- 2.2 Camera-Aware Unsupervised Person re-ID -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Loose and Tight Clustering Bounds -- 3.3 Loose-Tight Alternate Clustering -- 3.4 Quality Measurement Based Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Protocol -- 4.2 Implementation Details -- 4.3 Ablation Studies -- 4.4 Comparison with State-of-the-Art Methods -- 4.5 Robustness Evaluation -- 5 Conclusion -- References -- Sparse Dense Transformer Network for Video Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 CNNs in Action Recognition -- 2.2 Transformer in Action Recognition -- 3 Sparse Dense Transformer Network -- 3.1 Frame Alignment -- 3.2 Patch Crop -- 4 Experiments -- 5 Ablation Experiments -- 6 Conclusion -- References -- Deep User Multi-interest Network for Click-Through Rate Prediction.
1 Introduction -- 2 Related Works -- 3 The Proposed Model -- 3.1 Preliminaries -- 3.2 Embedding -- 3.3 Self-Interest Extractor Network -- 3.4 User-User Interest Extractor Network -- 3.5 Prediction and Optimization Objective -- 4 Experiments -- 4.1 Datasets -- 4.2 Competitors and Parameter Settings -- 4.3 Experimental Results -- 4.4 Ablation Study -- 4.5 Parameter Analysis -- 5 Conclusions -- References -- Open Relation Extraction via Query-Based Span Prediction -- 1 Introduction -- 2 Approach -- 2.1 Task Description -- 2.2 Query Template Creation -- 2.3 Encoder -- 2.4 Span Extraction Module -- 2.5 Training and Inference -- 3 Experimental Setup -- 3.1 Datasets -- 3.2 Implementations -- 3.3 Baselines -- 4 Experimental Results -- 4.1 H1: QORE for Multilingual Open Relation Extraction -- 4.2 H2: Zero-shot Domain Transferability of QORE -- 4.3 H3: Few-Shot Learning Ability of QORE -- 5 Conclusion -- References -- Relational Triple Extraction with Relation-Attentive Contextual Semantic Representations -- 1 Introduction -- 1.1 Challenge of Relation Extraction -- 1.2 Our Contribution -- 2 Related Work -- 3 Methodology -- 3.1 Representations of Token and Relation -- 3.2 Relation Prediction -- 3.3 Subject and Object Extraction -- 3.4 Training and Inference -- 4 Experiment and Analysis -- 4.1 Datasets and Settings -- 4.2 Baselines and Evaluation Metrics -- 4.3 Relation Extraction Results -- 4.4 Ablation Study -- 5 Conclusion and Future Works -- References -- Mario Fast Learner: Fast and Efficient Solutions for Super Mario Bros -- 1 Introduction -- 2 Background -- 2.1 Reinforcement Learning -- 2.2 Super Mario Bros Games -- 2.3 Leading Reinforcement Learning Methods -- 2.4 Problems of Previous Methods -- 3 Proposed Methods -- 3.1 Use Accuracy Metrics -- 3.2 Accelerated Training Solution -- 3.3 Target Function Update -- 4 Experiments. 4.1 Baseline with Accuracy Check -- 4.2 New Method -- 5 Conclusion -- References -- Few-Shot Learning with Self-supervised Classifier for Complex Knowledge Base Question Answering -- 1 Introduction -- 2 MACL -- 2.1 Overview of the Framework -- 2.2 Algorithm -- 2.3 Objective Function with Reinforcement Learning -- 3 Evaluation -- 3.1 CQA Dataset -- 3.2 Comparison Methods -- 3.3 Implementation Details -- 3.4 Performance Evaluation -- 4 Related Work -- 5 Conclusion -- References -- Data-Driven Approach for Investigation of Irradiation Hardening Behavior of RAFM Steel -- 1 Introduction -- 2 Data Set Construction -- 3 Model Construction and Application -- 3.1 Machine Learning Method -- 3.2 Feature Descriptor -- 3.3 Model Construction -- 3.4 Knowledge Reasoning and Prediction -- 4 Conclusion -- References -- Deep-to-Bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Word Embedding Distillation -- 3.2 Transformer Layer Distillation with Review Mechanism -- 3.3 Prediction Distillation -- 3.4 Total Loss -- 4 Experimental Setup -- 4.1 Experimental Data -- 4.2 Implementation Details -- 4.3 Baseline Methods -- 5 Experimental Results -- 5.1 Main Results -- 5.2 Strategy Comparison -- 6 Conclusions -- References -- Topic and Reference Guided Keyphrase Generation from Social Media -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Retriever -- 3.2 Encoder with Heterogeneous Graph -- 3.3 Contextual Neural Topic Model -- 3.4 Topic-Reference-Aware Decoder -- 3.5 Jointly Training -- 4 Experiment Settings -- 4.1 Datasets -- 4.2 Comparisons and Evaluation -- 4.3 Implementation Details -- 5 Results and Analysis -- 5.1 Performance of Keyphrase Generation -- 5.2 Prediction of Present and Absent Keyphrase -- 5.3 Ablation Study. 5.4 Influence of the Number of Topics -- 5.5 Case Study -- 6 Conclusion -- References -- DISEL: A Language for Specifying DIS-Based Ontologies -- 1 Introduction -- 2 Background -- 2.1 Domain Information System -- 2.2 Illustrative Example -- 3 Literature Review on Languages for Ontologies -- 3.1 Functional Languages -- 3.2 XML-Based Languages -- 3.3 Other Ontology Languages -- 3.4 Summary -- 4 DISEL Syntax and Support Tool -- 4.1 DISEL Editor Interface Overview -- 4.2 Name and Include Constructs -- 4.3 AtomDomain Construct -- 4.4 Concept -- 4.5 Graph -- 5 Design Decisions -- 6 Discussion -- 7 Conclusion and Future Work -- References -- MSSA-FL: High-Performance Multi-stage Semi-asynchronous Federated Learning with Non-IID Data -- 1 Introduction -- 2 Related Work -- 3 MSSA-FL: Multi-stage Semi-asynchronous Federated Learning -- 3.1 Framework Overview -- 3.2 Combination Module and Multi-stage Training -- 3.3 Semi-asynchronous Training -- 3.4 Model Assignment -- 3.5 Model Aggregation -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusion -- References -- A GAT-Based Chinese Text Classification Model: Using of Redical Guidance and Association Between Characters Across Sentences -- 1 Introduction -- 2 Methodology -- 2.1 Problem Definition -- 2.2 Technical Details of Classification Model -- 3 Evaluation -- 3.1 Dataset Description -- 3.2 Baeline -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Incorporating Explanations to Balance the Exploration and Exploitation of Deep Reinforcement Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 Reinforcement Learning -- 2.2 Variational Inference -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Explanation of Actions with Activation Maps -- 3.3 Fusion Activation Maps and States -- 3.4 Encoding the Fused State with Variational Inference. 4 Experiments and Results -- 4.1 Environment and Experimental Settings -- 4.2 Comparisons with Benchmark Algorithms -- 4.3 Analysis of Explainability -- 5 Conclusion -- References -- CLINER: Clinical Interrogation Named Entity Recognition -- 1 Introduction -- 2 Proposed Method -- 2.1 Model Design -- 3 Experiments -- 3.1 Baselines and Evaluation Metrics -- 3.2 Experimental Settings -- 3.3 Results and Analysis -- 4 Conclusion -- References -- CCDC: A Chinese-Centric Cross Domain Contrastive Learning Framework -- 1 Introduction -- 2 Related Work -- 2.1 Contrastive Learning -- 2.2 Unsupervised SimCSE -- 2.3 Supervised SimCSE -- 2.4 Sentence Contrastive Learning with PLMs -- 3 CCDC Framework -- 3.1 Cross-Domain Sentences as Hard-Negative Samples -- 3.2 Hard NLI Data Builder -- 3.3 Soft NLI Data Builder -- 4 Experiment -- 4.1 Data Preparation -- 4.2 Training Details -- 4.3 CCDC with One-Domain Training and In-Domain/Out-Domain Testing -- 4.4 CCDC with the Hard/Soft NLI Data Builder -- 5 Analysis -- 6 Conclusion -- 7 Appendix -- 7.1 CCDC with Different PLM and Different Pooling Layer -- 7.2 Case Analysis -- References -- A Multi-objective Optimization Method for Joint Feature Selection and Classifier Parameter Tuning -- 1 Introduction -- 2 Problem Formulation -- 3 The Proposed Approach -- 3.1 Traditional MOGWO -- 3.2 IMOGWO -- 4 Experimental Results and Analysis -- 4.1 Datasets and Setups -- 4.2 Feature Selection Results -- 5 Conclusion -- References -- Word Sense Disambiguation Based on Memory Enhancement Mechanism -- 1 Introduction -- 2 Related Word -- 2.1 Knowledge-Based WSD -- 2.2 Supervised WSD -- 3 Methodology -- 3.1 Task Definition -- 3.2 Model Architecture -- 3.3 Context-Encoder and Gloss-Encoder Units -- 3.4 Memory-Enhancement Unit -- 3.5 Prediction Unit -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details. 4.3 Comparison with the State-of-the-Art Baselines. |
| Record Nr. | UNINA-9910585774603321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part I / / Gerard Memmi [and four others] editors
| Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part I / / Gerard Memmi [and four others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (778 pages) |
| Disciplina | 006.312 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Data mining
Decision making - Data processing |
| ISBN | 3-031-10983-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organizations -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Knowledge Science with Learning and AI (KSLA) -- A Decoupled YOLOv5 with Deformable Convolution and Multi-scale Attention -- 1 Introduction -- 2 Related Work -- 3 Our Method -- 3.1 Decoupled Head -- 3.2 Deformable Convolution -- 3.3 Multi-scale Attention -- 4 Experiments -- 4.1 Data, Parameter Settings and Performance Metrics -- 4.2 Results -- 5 Conclusions -- References -- OTE: An Optimized Chinese Short Text Matching Algorithm Based on External Knowledge -- 1 Introduction -- 2 Related Work -- 2.1 Pre-trained Models -- 2.2 Data Augmentation -- 2.3 Multi-granularity Information -- 3 Model -- 3.1 Data Augmentation Model -- 3.2 Input Model -- 3.3 Semantic Information Transformer -- 3.4 Sentence Matching Layer -- 3.5 Relation Classifier Layer -- 4 Experiment -- 4.1 Experiment Dataset -- 4.2 Experiment Result -- 5 Conclusion -- References -- KIR: A Knowledge-Enhanced Interpretable Recommendation Method -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 4 Method -- 4.1 KIR Framework -- 4.2 Category Feature -- 4.3 Attention Mechanism -- 4.4 Building User Preferences -- 4.5 Learning Algorithm -- 5 Experiments and Results -- 5.1 Datasets and Experimental Settings -- 5.2 Results and Analysis -- 6 Conclusion -- References -- ICKEM: A Tool for Estimating One's Understanding of Conceptual Knowledge -- 1 Introduction -- 1.1 Procedural and Conceptual Knowledge -- 1.2 The Framework -- 2 Evaluate Familiarity Degree -- 2.1 Definition of Knowledge and Learning -- 2.2 Discriminate Learning Sessions -- 2.3 Capture the Text Learning Content -- 2.4 Calculate a Knowledge Point's Share -- 2.5 The Subject's State and Learning Method -- 2.6 A Knowledge Point's Learning History -- 2.7 Memory Retention of a Learning Experience.
2.8 Calculate the Familiarity Degree -- 3 Estimate Understanding Degree -- 3.1 Calculation of Understanding Degree -- 4 Discussion -- 4.1 Trade-Offs Between Different Methods -- 4.2 Privacy Issues -- 4.3 Analyzing with Topic Models -- 5 Related Work -- 6 Conclusion -- References -- Cross-perspective Graph Contrastive Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Perspective-specific Convolution Module -- 3.2 Cross-perspective Contrastive Learning Module -- 3.3 Information Fusion Module -- 3.4 Optimization Objective -- 4 Experimental Analysis -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Parameters Setting -- 4.4 Node Classification Results -- 4.5 Ablation Study -- 4.6 Parameter Sensitivity -- 5 Conclusion -- References -- A Multi-scale Convolution and Gated Recurrent Unit Based Network for Limit Order Book Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Problem Formulation -- 3.2 Independent Feature Representation -- 3.3 Market State Representation -- 3.4 Feature Fusion -- 3.5 Temporal Attention Module -- 4 Experiments -- 4.1 Experiments Settings -- 4.2 Dataset -- 4.3 Results -- 5 Conclusion -- References -- Pre-train Unified Knowledge Graph Embedding with Ontology -- 1 Introduction -- 2 Related Work on Entity Typing Task -- 3 Background and Methodology -- 3.1 Rot-Pro Model -- 4 Method -- 4.1 Relation Patterns Within Each Layer -- 4.2 Relation Patterns Between Layers -- 4.3 Optimization Objective -- 5 Experiment -- 5.1 Datasets -- 5.2 Experimental Settings -- 5.3 Experimental Results -- 6 Conclusion -- References -- Improving Dialogue Generation with Commonsense Knowledge Fusion and Selection -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Task Formulation and Model Overview -- 3.2 Context Encoder -- 3.3 Knowledge-Enriched Encoder -- 3.4 Topic Fact Predictor -- 3.5 Response Generator. 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experimental Setup -- 4.4 Evaluation Metrics -- 4.5 Results and Analysis -- 5 Conclusion and Future Work -- References -- A Study of Event Multi-triple Extraction Methods Based on Edge-Enhanced Graph Convolution Networks -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Indicator for Event Argument Detection Based on EE-GCN -- 3.2 Event Argument and Role Match Based on ACE2005 -- 3.3 Event Argument and Role Extraction Based on NER -- 3.4 Event Multi-triple Generation -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Design -- 4.3 Generate Indicator for Event Argument Detection -- 4.4 Argument Extraction -- 4.5 Generate Event Multi-triple -- 5 Conclusion and Future Works -- References -- Construction Research and Applications of Industry Chain Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Construction Method of Industry Chain Knowledge Graph -- 3.1 Ontology Modeling of Knowledge Graphs in Financial Field -- 3.2 Knowledge Extraction Based on Dependency Parsing -- 3.3 Automatic Labeling and Named Entity Recognition -- 4 Experiment -- 5 Conclusion -- References -- Query and Neighbor-Aware Reasoning Based Multi-hop Question Answering over Knowledge Graph -- 1 Introduction -- 2 Related Work -- 2.1 Multi-hop KGQA -- 2.2 Attention in KGQA -- 2.3 GNN in KGQA -- 3 Methodology -- 3.1 Preliminary -- 3.2 Instruction Module and KG Initialization -- 3.3 CoAttention Module -- 3.4 Neighbor-Aware Reasoning -- 4 Experiment -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results and Analysis -- 4.4 Ablation Study -- 4.5 Influence of Parameters -- 4.6 Case Study -- 5 Conclusion -- References -- Question Answering over Knowledge Graphs with Query Path Generation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Related Definition. 3.2 The Framework of KGQA Based on Query Path Generation -- 4 Experimental Results and Analysis -- 4.1 Experimental Datasets -- 4.2 Baseline and Evaluation Metrics -- 4.3 Experimental Results and Analysis -- 4.4 Case Study -- 5 Conclusion -- References -- Improving Parking Occupancy Prediction in Poor Data Conditions Through Customization and Learning to Learn -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 The Model Training Module -- 3.2 The Model Pretraining Module -- 3.3 The Client Selection Module -- 4 Experiments and Results -- 4.1 Evaluation Preparation -- 4.2 Result and Discussions -- 5 Conclusions and Future Works -- References -- Knowledge Concept Recommender Based on Structure Enhanced Interaction Graph Neural Network -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Problem Statement -- 3.2 Key Concept -- 4 Proposed Method -- 4.1 Entity Feature Extraction and Entity Relation Extraction -- 4.2 Knowledge Concept Representation Learning Based on Structure-Enhanced Interaction Graph Neural Network -- 4.3 User Representation Learning Based on Heterogeneous Graph Neural Network -- 4.4 Extended Matrix Factorization for Knowledge Concept Recommendation -- 5 Experiments -- 5.1 Dataset Description -- 5.2 Evaluation Metrics -- 5.3 Evaluation of Model Parameters -- 5.4 Baseline Methods -- 5.5 Experiment Setup -- 5.6 Results -- 6 Conclusion -- References -- Answering Complex Questions on Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Topic Entity Recognition -- 3.2 Core Path Generation -- 3.3 Constraints Selection -- 4 Experimental Studies -- 4.1 Settings -- 4.2 Model Training -- 4.3 Results and Analysis -- 5 Conclusion -- References -- Multi-attention User Information Based Graph Convolutional Networks for Explainable Recommendation -- 1 Introduction -- 2 Preliminaries -- 2.1 Foggy Feedback. 2.2 Knowledge Graph Integration -- 3 Methodology -- 3.1 KG Embedding Layer -- 3.2 Perceptual Bias Layer -- 3.3 Attention Characteristic Aggregation Layer -- 3.4 Prediction Layer -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experiments Setup -- 4.4 Performance Comparison -- 4.5 Specific Comparison -- 5 Conclusion and Future Work -- References -- Edge-Shared GraphSAGE: A New Method of Buffer Calculation for Parallel Management of Big Data Project Schedule -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Schedule Network -- 3.2 Critical Chain Method -- 3.3 Buffer Size -- 3.4 Evaluation Index -- 4 Edge-Shared GraphSAGE -- 4.1 Global Network Without Resource Sharing -- 4.2 Global Network with Resource Sharing -- 4.3 Features of the Node -- 4.4 Edge-Shared GraphSAGE -- 4.5 Calculate the Project Buffer and Import Buffer -- 5 Experiment -- 5.1 Data -- 5.2 Method -- 5.3 Comparison -- 6 Conclusion -- References -- Tackling Solitary Entities for Few-Shot Knowledge Graph Completion -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Local Pattern Graph Construction -- 3.2 Message Passing over Local Pattern Graph -- 3.3 HGAT for Encoding One-Hop Neighbors -- 3.4 Query-Aware Gating Mechanism -- 3.5 Transformer Relation Learner -- 3.6 Attentive Prototypical Network -- 3.7 Model Training -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementation Details -- 4.3 Main Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- CP Tensor Factorization for Knowledge Graph Completion -- 1 Introduction -- 2 Related Work -- 3 Knowledge Graph Completion Based on CP Decomposition -- 3.1 Problem Definition -- 3.2 Model Definition -- 3.3 Model Learning -- 3.4 Model Analysis -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Implementation and Evaluation -- 4.3 Experiment Setting -- 4.4 Link Prediction Result -- 5 Conclusion. References. |
| Record Nr. | UNINA-9910585790603321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part I / / Gerard Memmi [and four others] editors
| Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, Part I / / Gerard Memmi [and four others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (778 pages) |
| Disciplina | 006.312 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Data mining
Decision making - Data processing |
| ISBN | 3-031-10983-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organizations -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Knowledge Science with Learning and AI (KSLA) -- A Decoupled YOLOv5 with Deformable Convolution and Multi-scale Attention -- 1 Introduction -- 2 Related Work -- 3 Our Method -- 3.1 Decoupled Head -- 3.2 Deformable Convolution -- 3.3 Multi-scale Attention -- 4 Experiments -- 4.1 Data, Parameter Settings and Performance Metrics -- 4.2 Results -- 5 Conclusions -- References -- OTE: An Optimized Chinese Short Text Matching Algorithm Based on External Knowledge -- 1 Introduction -- 2 Related Work -- 2.1 Pre-trained Models -- 2.2 Data Augmentation -- 2.3 Multi-granularity Information -- 3 Model -- 3.1 Data Augmentation Model -- 3.2 Input Model -- 3.3 Semantic Information Transformer -- 3.4 Sentence Matching Layer -- 3.5 Relation Classifier Layer -- 4 Experiment -- 4.1 Experiment Dataset -- 4.2 Experiment Result -- 5 Conclusion -- References -- KIR: A Knowledge-Enhanced Interpretable Recommendation Method -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 4 Method -- 4.1 KIR Framework -- 4.2 Category Feature -- 4.3 Attention Mechanism -- 4.4 Building User Preferences -- 4.5 Learning Algorithm -- 5 Experiments and Results -- 5.1 Datasets and Experimental Settings -- 5.2 Results and Analysis -- 6 Conclusion -- References -- ICKEM: A Tool for Estimating One's Understanding of Conceptual Knowledge -- 1 Introduction -- 1.1 Procedural and Conceptual Knowledge -- 1.2 The Framework -- 2 Evaluate Familiarity Degree -- 2.1 Definition of Knowledge and Learning -- 2.2 Discriminate Learning Sessions -- 2.3 Capture the Text Learning Content -- 2.4 Calculate a Knowledge Point's Share -- 2.5 The Subject's State and Learning Method -- 2.6 A Knowledge Point's Learning History -- 2.7 Memory Retention of a Learning Experience.
2.8 Calculate the Familiarity Degree -- 3 Estimate Understanding Degree -- 3.1 Calculation of Understanding Degree -- 4 Discussion -- 4.1 Trade-Offs Between Different Methods -- 4.2 Privacy Issues -- 4.3 Analyzing with Topic Models -- 5 Related Work -- 6 Conclusion -- References -- Cross-perspective Graph Contrastive Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Perspective-specific Convolution Module -- 3.2 Cross-perspective Contrastive Learning Module -- 3.3 Information Fusion Module -- 3.4 Optimization Objective -- 4 Experimental Analysis -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Parameters Setting -- 4.4 Node Classification Results -- 4.5 Ablation Study -- 4.6 Parameter Sensitivity -- 5 Conclusion -- References -- A Multi-scale Convolution and Gated Recurrent Unit Based Network for Limit Order Book Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Problem Formulation -- 3.2 Independent Feature Representation -- 3.3 Market State Representation -- 3.4 Feature Fusion -- 3.5 Temporal Attention Module -- 4 Experiments -- 4.1 Experiments Settings -- 4.2 Dataset -- 4.3 Results -- 5 Conclusion -- References -- Pre-train Unified Knowledge Graph Embedding with Ontology -- 1 Introduction -- 2 Related Work on Entity Typing Task -- 3 Background and Methodology -- 3.1 Rot-Pro Model -- 4 Method -- 4.1 Relation Patterns Within Each Layer -- 4.2 Relation Patterns Between Layers -- 4.3 Optimization Objective -- 5 Experiment -- 5.1 Datasets -- 5.2 Experimental Settings -- 5.3 Experimental Results -- 6 Conclusion -- References -- Improving Dialogue Generation with Commonsense Knowledge Fusion and Selection -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Task Formulation and Model Overview -- 3.2 Context Encoder -- 3.3 Knowledge-Enriched Encoder -- 3.4 Topic Fact Predictor -- 3.5 Response Generator. 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experimental Setup -- 4.4 Evaluation Metrics -- 4.5 Results and Analysis -- 5 Conclusion and Future Work -- References -- A Study of Event Multi-triple Extraction Methods Based on Edge-Enhanced Graph Convolution Networks -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Indicator for Event Argument Detection Based on EE-GCN -- 3.2 Event Argument and Role Match Based on ACE2005 -- 3.3 Event Argument and Role Extraction Based on NER -- 3.4 Event Multi-triple Generation -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Design -- 4.3 Generate Indicator for Event Argument Detection -- 4.4 Argument Extraction -- 4.5 Generate Event Multi-triple -- 5 Conclusion and Future Works -- References -- Construction Research and Applications of Industry Chain Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Construction Method of Industry Chain Knowledge Graph -- 3.1 Ontology Modeling of Knowledge Graphs in Financial Field -- 3.2 Knowledge Extraction Based on Dependency Parsing -- 3.3 Automatic Labeling and Named Entity Recognition -- 4 Experiment -- 5 Conclusion -- References -- Query and Neighbor-Aware Reasoning Based Multi-hop Question Answering over Knowledge Graph -- 1 Introduction -- 2 Related Work -- 2.1 Multi-hop KGQA -- 2.2 Attention in KGQA -- 2.3 GNN in KGQA -- 3 Methodology -- 3.1 Preliminary -- 3.2 Instruction Module and KG Initialization -- 3.3 CoAttention Module -- 3.4 Neighbor-Aware Reasoning -- 4 Experiment -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results and Analysis -- 4.4 Ablation Study -- 4.5 Influence of Parameters -- 4.6 Case Study -- 5 Conclusion -- References -- Question Answering over Knowledge Graphs with Query Path Generation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Related Definition. 3.2 The Framework of KGQA Based on Query Path Generation -- 4 Experimental Results and Analysis -- 4.1 Experimental Datasets -- 4.2 Baseline and Evaluation Metrics -- 4.3 Experimental Results and Analysis -- 4.4 Case Study -- 5 Conclusion -- References -- Improving Parking Occupancy Prediction in Poor Data Conditions Through Customization and Learning to Learn -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 The Model Training Module -- 3.2 The Model Pretraining Module -- 3.3 The Client Selection Module -- 4 Experiments and Results -- 4.1 Evaluation Preparation -- 4.2 Result and Discussions -- 5 Conclusions and Future Works -- References -- Knowledge Concept Recommender Based on Structure Enhanced Interaction Graph Neural Network -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Problem Statement -- 3.2 Key Concept -- 4 Proposed Method -- 4.1 Entity Feature Extraction and Entity Relation Extraction -- 4.2 Knowledge Concept Representation Learning Based on Structure-Enhanced Interaction Graph Neural Network -- 4.3 User Representation Learning Based on Heterogeneous Graph Neural Network -- 4.4 Extended Matrix Factorization for Knowledge Concept Recommendation -- 5 Experiments -- 5.1 Dataset Description -- 5.2 Evaluation Metrics -- 5.3 Evaluation of Model Parameters -- 5.4 Baseline Methods -- 5.5 Experiment Setup -- 5.6 Results -- 6 Conclusion -- References -- Answering Complex Questions on Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Topic Entity Recognition -- 3.2 Core Path Generation -- 3.3 Constraints Selection -- 4 Experimental Studies -- 4.1 Settings -- 4.2 Model Training -- 4.3 Results and Analysis -- 5 Conclusion -- References -- Multi-attention User Information Based Graph Convolutional Networks for Explainable Recommendation -- 1 Introduction -- 2 Preliminaries -- 2.1 Foggy Feedback. 2.2 Knowledge Graph Integration -- 3 Methodology -- 3.1 KG Embedding Layer -- 3.2 Perceptual Bias Layer -- 3.3 Attention Characteristic Aggregation Layer -- 3.4 Prediction Layer -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experiments Setup -- 4.4 Performance Comparison -- 4.5 Specific Comparison -- 5 Conclusion and Future Work -- References -- Edge-Shared GraphSAGE: A New Method of Buffer Calculation for Parallel Management of Big Data Project Schedule -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Schedule Network -- 3.2 Critical Chain Method -- 3.3 Buffer Size -- 3.4 Evaluation Index -- 4 Edge-Shared GraphSAGE -- 4.1 Global Network Without Resource Sharing -- 4.2 Global Network with Resource Sharing -- 4.3 Features of the Node -- 4.4 Edge-Shared GraphSAGE -- 4.5 Calculate the Project Buffer and Import Buffer -- 5 Experiment -- 5.1 Data -- 5.2 Method -- 5.3 Comparison -- 6 Conclusion -- References -- Tackling Solitary Entities for Few-Shot Knowledge Graph Completion -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Local Pattern Graph Construction -- 3.2 Message Passing over Local Pattern Graph -- 3.3 HGAT for Encoding One-Hop Neighbors -- 3.4 Query-Aware Gating Mechanism -- 3.5 Transformer Relation Learner -- 3.6 Attentive Prototypical Network -- 3.7 Model Training -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementation Details -- 4.3 Main Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- CP Tensor Factorization for Knowledge Graph Completion -- 1 Introduction -- 2 Related Work -- 3 Knowledge Graph Completion Based on CP Decomposition -- 3.1 Problem Definition -- 3.2 Model Definition -- 3.3 Model Learning -- 3.4 Model Analysis -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Implementation and Evaluation -- 4.3 Experiment Setting -- 4.4 Link Prediction Result -- 5 Conclusion. References. |
| Record Nr. | UNISA-996483161703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, part ii / / Gerard Memmi, [and four others], (editors)
| Knowledge science, engineering and management : 15th international conference, KSEM 2022, Singapore, August 6-8, 2022, proceedings, part ii / / Gerard Memmi, [and four others], (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (715 pages) |
| Disciplina | 658.4038 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Knowledge management |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-10986-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organizations -- Contents - Part II -- Knowledge Engineering Research and Applications (KERA) -- Multi-view Heterogeneous Network Embedding -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Methodology -- 4.1 Semantics-Based View Generation -- 4.2 View Preservation and Enhanced View Collaboration -- 4.3 Embedding Fusion -- 4.4 Optimization Objective -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Link Prediction -- 5.3 Node Classification -- 5.4 Parameter Sensitivity Analysis -- 6 Conclusion -- References -- A Multi-level Attention-Based LSTM Network for Ultra-short-term Solar Power Forecast Using Meteorological Knowledge -- 1 Introduction -- 2 Related Work -- 3 Architecture -- 3.1 Encoder -- 3.2 Decoder -- 4 Experiment: Case Study -- 4.1 Datasets Setting -- 4.2 Experimental Setting -- 4.3 Results and Analysis -- 5 Conclusions -- References -- Unsupervised Person Re-ID via Loose-Tight Alternate Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Clustering-Guided Unsupervised Person re-ID -- 2.2 Camera-Aware Unsupervised Person re-ID -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Loose and Tight Clustering Bounds -- 3.3 Loose-Tight Alternate Clustering -- 3.4 Quality Measurement Based Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Protocol -- 4.2 Implementation Details -- 4.3 Ablation Studies -- 4.4 Comparison with State-of-the-Art Methods -- 4.5 Robustness Evaluation -- 5 Conclusion -- References -- Sparse Dense Transformer Network for Video Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 CNNs in Action Recognition -- 2.2 Transformer in Action Recognition -- 3 Sparse Dense Transformer Network -- 3.1 Frame Alignment -- 3.2 Patch Crop -- 4 Experiments -- 5 Ablation Experiments -- 6 Conclusion -- References -- Deep User Multi-interest Network for Click-Through Rate Prediction.
1 Introduction -- 2 Related Works -- 3 The Proposed Model -- 3.1 Preliminaries -- 3.2 Embedding -- 3.3 Self-Interest Extractor Network -- 3.4 User-User Interest Extractor Network -- 3.5 Prediction and Optimization Objective -- 4 Experiments -- 4.1 Datasets -- 4.2 Competitors and Parameter Settings -- 4.3 Experimental Results -- 4.4 Ablation Study -- 4.5 Parameter Analysis -- 5 Conclusions -- References -- Open Relation Extraction via Query-Based Span Prediction -- 1 Introduction -- 2 Approach -- 2.1 Task Description -- 2.2 Query Template Creation -- 2.3 Encoder -- 2.4 Span Extraction Module -- 2.5 Training and Inference -- 3 Experimental Setup -- 3.1 Datasets -- 3.2 Implementations -- 3.3 Baselines -- 4 Experimental Results -- 4.1 H1: QORE for Multilingual Open Relation Extraction -- 4.2 H2: Zero-shot Domain Transferability of QORE -- 4.3 H3: Few-Shot Learning Ability of QORE -- 5 Conclusion -- References -- Relational Triple Extraction with Relation-Attentive Contextual Semantic Representations -- 1 Introduction -- 1.1 Challenge of Relation Extraction -- 1.2 Our Contribution -- 2 Related Work -- 3 Methodology -- 3.1 Representations of Token and Relation -- 3.2 Relation Prediction -- 3.3 Subject and Object Extraction -- 3.4 Training and Inference -- 4 Experiment and Analysis -- 4.1 Datasets and Settings -- 4.2 Baselines and Evaluation Metrics -- 4.3 Relation Extraction Results -- 4.4 Ablation Study -- 5 Conclusion and Future Works -- References -- Mario Fast Learner: Fast and Efficient Solutions for Super Mario Bros -- 1 Introduction -- 2 Background -- 2.1 Reinforcement Learning -- 2.2 Super Mario Bros Games -- 2.3 Leading Reinforcement Learning Methods -- 2.4 Problems of Previous Methods -- 3 Proposed Methods -- 3.1 Use Accuracy Metrics -- 3.2 Accelerated Training Solution -- 3.3 Target Function Update -- 4 Experiments. 4.1 Baseline with Accuracy Check -- 4.2 New Method -- 5 Conclusion -- References -- Few-Shot Learning with Self-supervised Classifier for Complex Knowledge Base Question Answering -- 1 Introduction -- 2 MACL -- 2.1 Overview of the Framework -- 2.2 Algorithm -- 2.3 Objective Function with Reinforcement Learning -- 3 Evaluation -- 3.1 CQA Dataset -- 3.2 Comparison Methods -- 3.3 Implementation Details -- 3.4 Performance Evaluation -- 4 Related Work -- 5 Conclusion -- References -- Data-Driven Approach for Investigation of Irradiation Hardening Behavior of RAFM Steel -- 1 Introduction -- 2 Data Set Construction -- 3 Model Construction and Application -- 3.1 Machine Learning Method -- 3.2 Feature Descriptor -- 3.3 Model Construction -- 3.4 Knowledge Reasoning and Prediction -- 4 Conclusion -- References -- Deep-to-Bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Word Embedding Distillation -- 3.2 Transformer Layer Distillation with Review Mechanism -- 3.3 Prediction Distillation -- 3.4 Total Loss -- 4 Experimental Setup -- 4.1 Experimental Data -- 4.2 Implementation Details -- 4.3 Baseline Methods -- 5 Experimental Results -- 5.1 Main Results -- 5.2 Strategy Comparison -- 6 Conclusions -- References -- Topic and Reference Guided Keyphrase Generation from Social Media -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Retriever -- 3.2 Encoder with Heterogeneous Graph -- 3.3 Contextual Neural Topic Model -- 3.4 Topic-Reference-Aware Decoder -- 3.5 Jointly Training -- 4 Experiment Settings -- 4.1 Datasets -- 4.2 Comparisons and Evaluation -- 4.3 Implementation Details -- 5 Results and Analysis -- 5.1 Performance of Keyphrase Generation -- 5.2 Prediction of Present and Absent Keyphrase -- 5.3 Ablation Study. 5.4 Influence of the Number of Topics -- 5.5 Case Study -- 6 Conclusion -- References -- DISEL: A Language for Specifying DIS-Based Ontologies -- 1 Introduction -- 2 Background -- 2.1 Domain Information System -- 2.2 Illustrative Example -- 3 Literature Review on Languages for Ontologies -- 3.1 Functional Languages -- 3.2 XML-Based Languages -- 3.3 Other Ontology Languages -- 3.4 Summary -- 4 DISEL Syntax and Support Tool -- 4.1 DISEL Editor Interface Overview -- 4.2 Name and Include Constructs -- 4.3 AtomDomain Construct -- 4.4 Concept -- 4.5 Graph -- 5 Design Decisions -- 6 Discussion -- 7 Conclusion and Future Work -- References -- MSSA-FL: High-Performance Multi-stage Semi-asynchronous Federated Learning with Non-IID Data -- 1 Introduction -- 2 Related Work -- 3 MSSA-FL: Multi-stage Semi-asynchronous Federated Learning -- 3.1 Framework Overview -- 3.2 Combination Module and Multi-stage Training -- 3.3 Semi-asynchronous Training -- 3.4 Model Assignment -- 3.5 Model Aggregation -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusion -- References -- A GAT-Based Chinese Text Classification Model: Using of Redical Guidance and Association Between Characters Across Sentences -- 1 Introduction -- 2 Methodology -- 2.1 Problem Definition -- 2.2 Technical Details of Classification Model -- 3 Evaluation -- 3.1 Dataset Description -- 3.2 Baeline -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Incorporating Explanations to Balance the Exploration and Exploitation of Deep Reinforcement Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 Reinforcement Learning -- 2.2 Variational Inference -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Explanation of Actions with Activation Maps -- 3.3 Fusion Activation Maps and States -- 3.4 Encoding the Fused State with Variational Inference. 4 Experiments and Results -- 4.1 Environment and Experimental Settings -- 4.2 Comparisons with Benchmark Algorithms -- 4.3 Analysis of Explainability -- 5 Conclusion -- References -- CLINER: Clinical Interrogation Named Entity Recognition -- 1 Introduction -- 2 Proposed Method -- 2.1 Model Design -- 3 Experiments -- 3.1 Baselines and Evaluation Metrics -- 3.2 Experimental Settings -- 3.3 Results and Analysis -- 4 Conclusion -- References -- CCDC: A Chinese-Centric Cross Domain Contrastive Learning Framework -- 1 Introduction -- 2 Related Work -- 2.1 Contrastive Learning -- 2.2 Unsupervised SimCSE -- 2.3 Supervised SimCSE -- 2.4 Sentence Contrastive Learning with PLMs -- 3 CCDC Framework -- 3.1 Cross-Domain Sentences as Hard-Negative Samples -- 3.2 Hard NLI Data Builder -- 3.3 Soft NLI Data Builder -- 4 Experiment -- 4.1 Data Preparation -- 4.2 Training Details -- 4.3 CCDC with One-Domain Training and In-Domain/Out-Domain Testing -- 4.4 CCDC with the Hard/Soft NLI Data Builder -- 5 Analysis -- 6 Conclusion -- 7 Appendix -- 7.1 CCDC with Different PLM and Different Pooling Layer -- 7.2 Case Analysis -- References -- A Multi-objective Optimization Method for Joint Feature Selection and Classifier Parameter Tuning -- 1 Introduction -- 2 Problem Formulation -- 3 The Proposed Approach -- 3.1 Traditional MOGWO -- 3.2 IMOGWO -- 4 Experimental Results and Analysis -- 4.1 Datasets and Setups -- 4.2 Feature Selection Results -- 5 Conclusion -- References -- Word Sense Disambiguation Based on Memory Enhancement Mechanism -- 1 Introduction -- 2 Related Word -- 2.1 Knowledge-Based WSD -- 2.2 Supervised WSD -- 3 Methodology -- 3.1 Task Definition -- 3.2 Model Architecture -- 3.3 Context-Encoder and Gloss-Encoder Units -- 3.4 Memory-Enhancement Unit -- 3.5 Prediction Unit -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details. 4.3 Comparison with the State-of-the-Art Baselines. |
| Record Nr. | UNISA-996483162003316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Mobile Computing, Applications, and Services [[electronic resource] ] : 5th International Conference, MobiCase 2013, Paris, France, November 7-8, 2013, Revised Selected Papers / / edited by Gerard Memmi, Ulf Blanke
| Mobile Computing, Applications, and Services [[electronic resource] ] : 5th International Conference, MobiCase 2013, Paris, France, November 7-8, 2013, Revised Selected Papers / / edited by Gerard Memmi, Ulf Blanke |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XVI, 334 p. 127 illus.) |
| Disciplina | 004.65 |
| Collana | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
| Soggetto topico |
Computer organization
Information storage and retrieval Application software User interfaces (Computer systems) Computers and civilization Computer Systems Organization and Communication Networks Information Storage and Retrieval Information Systems Applications (incl. Internet) User Interfaces and Human Computer Interaction Computers and Society |
| ISBN | 3-319-05452-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Towards User Interface Components for Dashboard Applications on Smartphones -- User-Centric Quality of Experience Measurement -- Online Reviews as First Class Artifacts in Mobile App Development -- SaSYS: A Swipe Gesture-Based System for Exploring Urban Environments for the Visually Impaired -- Smartphone Interactions Change for Different Intimacy Contexts -- Geometric Layout Analysis in a Wearable Reading Device for the Blind and Visually Impaired -- Intelligent Energy-Efficient Triggering of Geolocation Fix Acquisitions Based on Transitions between Activity Recognition States -- Fine-Grained Activity Recognition of Pedestrians Travelling by Subway -- Reconciling Cloud and Mobile Computing Using Activity-Based Predictive Caching -- A Study of Graphical Password for Mobile Devices -- LOX Framework: Designing Human Computation Games to Update Street Views -- Towards Unsupervised Remote Therapy for Individuals with Aphasia. |
| Record Nr. | UNINA-9910299058003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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