{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":23,"n_cols":5,"rows_kept":23,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[10,2,10,3],[11,2,11,3],[14,2,14,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996478862403316"]
[null,null,"Titolo",null,"Web engineering : 22nd international conference, ICWE 2022, Bari, Italy / / Tommaso Di Noia [and three others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-09917-6"]
[null,null,"Descrizione fisica",null,"1 online resource (513 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science ; ; Volume 13362"]
[null,null,"Disciplina",null,"005.1"]
[null,null,"Soggetti",null,"Software engineering"]
[{"$nulls":4},"Web services"]
[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 -- Contents -- Recommender Systems Based on Web Technology -- MARF: User-Item Mutual Aware Representation with Feedback -- 1 Introduction -- 2 The MARF Model -- 2.1 Model Inputs and Feature Representation -- 2.2 Embedding for User and Item Profiles -- 2.3 Flexible Up-Down Sampling -- 2.4 The Sequence Extraction Layer -- 2.5 The Prediction Layer -- 3 Experiments -- 3.1 Datasets -- 3.2 Baselines -- 3.3 Evaluation Metrics -- 3.4 Hyperparameters -- 3.5 Performance Comparison -- 3.6 Ablation Study -- 3.7 Potential Transferability Analysis -- 4 Conclusion and Future Work -- References -- MRVAE: Variational Autoencoder with Multiple Relationships for Collaborative Filtering -- 1 Introduction -- 2 Preliminary -- 2.1 Notations -- 2.2 Problem Definition -- 2.3 Basics of Mult-VAE -- 3 MRVAE -- 3.1 Hybrid Encoder and Linear Decoder -- 3.2 Incorporating Item-Item Relationships -- 3.3 Discussion -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Comparison -- 4.3 Ablation Study -- 4.4 Hyperparameter Analysis -- 5 Related Works -- 5.1 VAE-Based CF Methods -- 5.2 Other CF Methods -- 6 Conclusion -- References -- Multilevel Feature Interaction Learning for Session-Based Recommendation via Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Networks (GNN) -- 2.2 GNN for Session-Based Recommendation -- 2.3 Regularization Techniques -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Overview -- 3.3 Session-Level Item Representation Learning Module -- 3.4 Global-Level Item Representation Learning Module -- 3.5 Session Representation Learning and Training -- 4 "]
[{"$nulls":4},"Experiments -- 4.1 Performance Comparisons (RQ1) -- 4.2 Impact of Side Information (RQ2) -- 4.3 Impact of Structures (RQ3) -- 4.4 Impact of Learning Strategy (RQ4) -- 5 Conclusion -- References -- Social Web Applications."]
[{"$nulls":4},"A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence -- 1 Introduction -- 2 Related Work -- 3 System Design -- 3.1 Data Collectors -- 3.2 Image Manager -- 3.3 Tweet Manager -- 4 Experiments -- 4.1 Duplicate Filtering -- 4.2 Junk Classification -- 4.3 Landslide Classification -- 4.4 Performance Evaluation and Benchmarking -- 5 Real-World Deployment -- 5.1 Data Collection and Statistics -- 5.2 Validation of the Landslide Model Predictions -- 5.3 Comparison with a Text-Based Approach -- 6 Conclusion -- References -- Online Social Event Detection via Filtering Strategy Graph Neural Network -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Problem Definition -- 3.2 Framework Overview -- 4 Methodology -- 4.1 The Initial Phase -- 4.2 The Incremental Phase -- 5 Experiments -- 5.1 Datasets -- 5.2 Baselines and Metrics -- 5.3 Experimental Settings -- 5.4 Experimental Results -- 6 Conclusions -- References -- Similarity Search with Graph Index on Directed Social Network Embedding -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Definitions -- 3.2 Directed Social Network Embedding -- 3.3 Similarity Search with Graph Index -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Experimental Results -- 5 Conclusion and Future Work -- References -- Web Application Modelling and Engineering -- An In-Depth Analysis of Web Page Structure and Efficiency with Focus on Optimization Potential for Initial Page Load -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 Structure Analysis -- 4.1 Website Selection -- 4.2 Analysis Software -- 4.3 Technical Implementation -- 4.4 Page Code Analysis -- 4.5 Desktop vs. Mobile -- 4.6 Frameworks -- 4.7 Validity of Results -- 4.8 Major Insights and Possible Improvements -- 5 Challenges and Research Roadmap -- 6 Conclusion -- References."]
[{"$nulls":4},"Automatic Web Data API Creation via Cross-Lingual Neural Pagination Recognition -- 1 Introduction -- 2 Related Work -- 3 Pagination Recognition Neural Sequence Model -- 3.1 Problem Definition -- 3.2 Proposed Method -- 4 Experiments -- 4.1 Dataset -- 4.2 Results -- 5 Case Study: Automatic Data API Constructions -- 5.1 Pagination Recognition -- 5.2 Data API Creation via Unsupervised Data Extraction -- 6 Conclusions and Future Work -- References -- Disclosure: Efficient Instrumentation-Based Web App Migration for Liquid Computing -- 1 Introduction -- 2 Background on JavaScript Runtime -- 3 Challenges for Web App Migration -- 4 Previous Approaches -- 5 The Disclosure Approach -- 5.1 Instrumentation Phase -- 5.2 Execution Phase -- 5.3 Migration Phase -- 5.4 Security and WeakMap -- 6 Evaluation -- 6.1 Instrumented Code Size -- 6.2 Execution Performance and Runtime Memory Usage -- 6.3 Multiple Migrations -- 6.4 Comparison with ThingsMigrate -- 7 Summary -- References -- Enriching Scholarly Knowledge with Context -- 1 Introduction -- 2 Related Work -- 3 Conceptual Model and Its Application -- 4 The Federated Infrastructures -- 5 Web Widgets to Enrich Knowledge with Context -- 5.1 Visualizing ORKG Scholarly Knowledge with Context -- 5.2 Enriching ORKG Contributor Profiles -- 5.3 Advanced Faceted Search for ORKG Comparisons -- 6 Discussion -- 7 Conclusions -- References -- FAIRification of Citizen Science Data Through Metadata-Driven Web API Development -- 1 Introduction -- 2 Related Work -- 3 Running Example -- 4 FAIRification of CS Platforms -- 4.1 Retrieving the Projects -- 4.2 Parsing the Metadata -- 4.3 Generating the API -- 4.4 Publishing the Web API -- 4.5 Updating Data Services Metadata -- "]
[{"$nulls":4},"4.6 Publishing Metadata -- 5 Evaluation -- 6 Conclusions -- References -- The Case for Cross-Entity Delta Encoding in Web Compression -- 1 Introduction."]
[{"$nulls":4},"2 Related Work -- 3 Cross-Entity Delta Encoding -- 4 Compression Efficiency on Real-World Traces -- 4.1 Enforcing VCDIFF -- 4.2 Case-Specific VCDIFF -- 4.3 VCDIFF with Secondary Compression -- 4.4 Impact on Different MIME-Types -- 4.5 Using Algorithms Beyond VCDIFF -- 5 Conclusion -- References -- Web Big Data and Web Data Analytics -- Dynamic Network Embedding in Hyperbolic Space via Self-attention -- 1 Introduction -- 2 Related Works -- 3 Preliminary -- 3.1 Problem Formulation -- 3.2 Hyperbolic Geometry -- 4 Proposed Model -- 4.1 Feature Map -- 4.2 Hyperbolic Structure Attention (HSA) -- 4.3 Euclidean Temporal Attention (ETA) -- 4.4 Learning Objective -- 5 Experiments and Analysis -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Link Prediction Comparison (RQ 1) -- 5.4 Ablation Study (RQ 2) -- 6 Conclusions -- References -- Engineering Annotations to Support Analytical Provenance in Visual Exploration Processes -- 1 Introduction -- 2 Foundations -- 2.1 Annotations -- 2.2 Analytical Provenance -- 3 Related Work -- 4 Our Approach -- 4.1 Extending the Conceptual Model of Annotations -- 4.2 Dependencies Between Annotation and Visualizations -- 4.3 Attributes in Data Provenance -- 5 Use Case Scenarios and Tool Support -- 5.1 Overview of Annotations -- 5.2 Managing Multiple Annotations -- 5.3 Annotating Objects in a View -- 5.4 Exporting Annotations -- 5.5 Collaborative Use of Annotations -- 5.6 Tracking Provenance Analysis -- 6 Discussion, Conclusions and Future Work -- References -- Lunatory: A Real-Time Distributed Trajectory Clustering Framework for Web Big Data -- 1 Introduction -- 2 Related Work -- 2.1 Clustering Algorithms -- 2.2 Trajectory Clustering -- 2.3 Learning-Based Trajectory Clustering -- 3 Preliminary -- 4 Trajectory Clustering Framework -- 4.1 Framework Overview -- 4.2 Pivot Trajectory Generation -- 4.3 Hexagonal-Based Partitioning Strategy."]
[{"$nulls":4},"4.4 Clustering Implementation -- 5 Experimental Evaluation -- 5.1 Experimental Setup -- 5.2 Efficiency Comparison -- 5.3 Accuracy Comparison -- 6 Conclusion -- References -- Web Mining and Knowledge Extraction -- Building Knowledge Subgraphs in Question Answering over Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 The Approach -- 3.1 Topic Entity Identification -- 3.2 Neighborhood Retrieval -- 3.3 Knowledge Subgraph Retrieval -- 4 Experiments -- 4.1 Knowledge Graphs -- 4.2 QA Datasets -- 4.3 Metric -- 4.4 Results -- 4.5 Compared Approaches -- 5 Conclusion -- References -- Dual-Attention Based Joint Aspect Sentiment Classification Model -- 1 Introduction -- 2 Model Description -- 2.1 Problem Formulation -- 2.2 Aspect-Specific Representation Module -- 2.3 ACD Module -- 2.4 ACSC Module -- 2.5 Joint Loss -- 3 Experiment Analysis -- 3.1 Data Set -- 3.2 Experimental Settings -- 3.3 Comparison Methods -- 3.4 Main Results -- 3.5 Discussions on AS-DATJM -- 3.6 Quality Analysis -- 4 Conclusion -- References -- Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers -- 1 Introduction -- 2 Related Works -- 2.1 Aspect-Based Sentiment Classification -- 2.2 Diagnostic Classifiers -- 3 Specification of the Data -- 4 Method -- 4.1 LCR-Rot-hop++ -- 4.2 Diagnostic Classifier -- 5 Evaluation -- 5.1 Overview -- 6 Conclusion -- References -- A Model for Meteorological Knowledge Graphs: Application to Météo-France Data -- 1 Introduction -- 2 Motivating Scenario and Competency Questions -- 3 Semantic Model -- 3.1 Features of Interest and Observable Properties: Describing Observations -- 3.2 Spatial "]
[{"$nulls":4},"Features: Locating the Weather Stations -- 3.3 Temporal Features: Defining Time Entities -- 3.4 Aggregated Features: Defining Observation Slices -- 4 Météo-France Weather Observations RDF Dataset."]
[{"$nulls":4},"4.1 Météo-France Dataset."]