{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"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":38,"n_cols":5,"rows_kept":38,"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],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[8,2,8,3],[9,2,9,3],[14,2,14,3],[14,4,14,5],[15,2,15,3],[15,4,15,5],[16,2,16,3],[17,2,17,3],[18,2,18,3],[19,2,19,3],[20,2,20,3],[21,2,21,3],[22,2,22,3],[31,2,31,3],[32,2,32,3],[35,2,35,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910713950803321"]
[null,null,"Autore",null,"Saur Genevieve"]
[null,null,"Titolo",null,"Fueling station component validation / / Genevieve Saur, Spencer Gilleon, and Sam Sprik"]
[null,null,"Pubbl/distr/stampa",null,"[Golden, Colo.] : , : National Renewable Energy Laboratory, , 2020"]
[null,null,"Descrizione fisica",null,"1 online resource (21 pages, 1 unnumbered page) : color illustrations, color maps"]
[null,null,"Collana",null,"NREL/PR ; ; 5400-76846"]
[null,null,"Soggetti",null,"Fuel pumps - United States"]
[{"$nulls":4},"Hydrogen as fuel"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Slideshow presentation."]
[{"$nulls":4},"\"H2@Scale working group, May 19, 2020.\""]
[{"$nulls":4},"\"Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Fuel Cell Technologies Office\"--Page 4 of cover."]
[null,"2.","Record Nr.",null,"UNINA9910585789503321"]
[null,null,"Titolo",null,"Knowledge Science, Engineering and Management : 15th International Conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part III / / edited by Gerard Memmi, Baijian Yang, Linghe Kong, Tianwei Zhang, Meikang Qiu"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"3-031-10989-9"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (769 pages)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 13370"]
[null,null,"Disciplina",null,"006.33"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computing Milieux"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Knowledge Management with Optimization and Security (KMOS) -- Study on Chinese Named Entity Recognition Based on Dynamic Fusion and Adversarial Training -- Spatial Semantic Learning for Travel Time Estimation -- A Fine-Grained Approach for Vulnerabilities Discovery using Augmented Vulnerability Signatures -- PPBR-FL: a Privacy-preserving and Byzantine-robust Federated Learning System -- GAN-Based Fusion Adversarial Training -- MAST-NER: A Low-Resource Named Entity Recognition Method based on Trigger Pool -- Fuzzy information measures feature selection using descriptive statistics data -- Prompt-Based Self-Training Framework for Few-Shot Named Entity Recognition -- Learning Advisor-Advisee Relationship from Multiplex Network Structure -- CorefDRE: Coref-aware Document-level Relation Extraction -- Single Pollutant Prediction Approach by Fusing MLSTM and CNN -- A Multi-objective Evolutionary Algorithm Based on Multi-layer Network Reduction for Community Detection -- Detection DDoS "]
[{"$nulls":4},"of attacks based on federated learning with Digital Twin Network -- A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy."]
[null,null,null,"Sommario/riassunto","The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6–8, 2022. The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections: Volume I: Knowledge Science with Learning and AI (KSLA) Volume II: Knowledge Engineering Research and Applications (KERA) Volume III: Knowledge Management with Optimization and Security (KMOS)."]