{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:51:31Z","timestamp":1742964691055,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031371288"},{"type":"electronic","value":"9783031371295"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-37129-5_3","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T05:01:46Z","timestamp":1688014906000},"page":"29-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Approach to\u00a0Decentralized Hybrid Question Answering Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6650-6942","authenticated-orcid":false,"given":"Dilan","family":"Bak\u0131r","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7908-5067","authenticated-orcid":false,"given":"Mehmet S.","family":"Aktas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"3_CR1","unstructured":"McMahan, B., Ramage, D.: Google Ai Blog. Communication-Efficient Learning with Federated Learning: An Overview (2020). https:\/\/ai.googleblog.com\/2020\/05\/communication-efficient-learning-with.html"},{"key":"3_CR2","unstructured":"Tensorflow Federated: Machine Learning on Decentralized Data (2020). https:\/\/www.tensorflow.org\/federated\/. Accessed 16 Apr 2023"},{"key":"3_CR3","unstructured":"Federated AI Ecosystem-Collaborative Learning and Knowledge Transfer With Data Protection (2020). https:\/\/www.fedai.org\/. Accessed 16 Apr 2023"},{"key":"3_CR4","unstructured":"PySyft: A Library for Encrypted, Privacy Preserving Machine Learning (2020). https:\/\/github.com\/OpenMined\/PySyft. Accessed 16 Apr 2023"},{"key":"3_CR5","unstructured":"PaddleFL: Federated Deep Learning in PaddlePaddle (2020). https:\/\/github.com\/PaddlePaddle\/PaddleFL. Accessed 16 Apr 2023"},{"key":"3_CR6","unstructured":"Nvidia Developer Blog: Federated Learning Powered by Nvidia Clara (2020). https:\/\/developer.nvidia.com\/blog\/federated-learning-clara\/. Accessed 16 Apr 2023"},{"key":"3_CR7","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.: Communication efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) (2017)"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Ekmefjord, M., et al.: Scalable federated machine learning with FEDN. In: 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (2022). https:\/\/doi.org\/10.1109\/CCGrid54584.2022.00065","DOI":"10.1109\/CCGrid54584.2022.00065"},{"key":"3_CR9","unstructured":"Hard, A., et al.: Federated learning for mobile keyboard prediction (2019). ArXiv:1811.03604"},{"key":"3_CR10","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data. ArXiv:1806.00582 (2018)"},{"key":"3_CR11","unstructured":"Bonawitz, K., et al.: Towards federated learning at scale: System design. ArXiv: 1902.01046 (2019)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Bakir, D., Aktas, M.: A systematic literature review of question answering: research trends, datasets, methods and frameworks. In: The 22nd International Conference on Computational Science and Its Applications (ICCSA), Malaga, Spain, 4\u201307 July 2022, pp. 1\u201316 (2022)","DOI":"10.1007\/978-3-031-10536-4_4"},{"key":"3_CR13","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Chen, D., Bolton, J., Manning, C.D.: A thorough examination of the CNN\/daily mail reading comprehension task. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), In ACL, pp. 2358\u20132367 (2016)","DOI":"10.18653\/v1\/P16-1223"},{"key":"3_CR15","unstructured":"Seo, M.J., et al.: Bidirectional attention flow for machine comprehension. ICLR. arxiv:1611.01603 (2017)"},{"key":"3_CR16","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bidirectional encoder representations from transformers. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"3_CR17","unstructured":"Liu, Y., Liu, D., Li, D., Lv, Y.: Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"3_CR18","unstructured":"Qi, Y., Wang, W., Zhang, B., Dauphin, Y.: A Lite BERT for Self-supervised Learning of Language Representations. arXiv preprint arXiv:2004.10948 (2020)"},{"key":"3_CR19","unstructured":"Xu, K., Zhu, W., Yang, Z., Bai, X.: Efficiently Learning an Encoder that Classifies Token Replacements Accurately. arXiv preprint arXiv:2009.13258 (2020)"},{"key":"3_CR20","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)"},{"key":"3_CR21","unstructured":"Ghazvininejad, M., Levy, O., Liu, Y., Zettlemoyer, L.: A Knowledge-Grounded Autoencoder for Commonsense Inference. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, pp. 5669\u20135674"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Baeth, M.J., Aktas, M.S.: An approach to custom privacy policy violation detection problems using big social provenance data. Concurr. Comput. Pract. Exp. 30(21) (2018)","DOI":"10.1002\/cpe.4690"},{"issue":"7","key":"3_CR23","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1002\/cpe.1276","volume":"20","author":"MS Aktas","year":"2008","unstructured":"Aktas, M.S., Fox, G.C., Pierce, M., Oh, S.: XML metadata services. Concurr. Comput. Pract. Exp. 20(7), 801\u2013823 (2008)","journal-title":"Concurr. Comput. Pract. Exp."},{"issue":"15","key":"3_CR24","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1002\/cpe.1557","volume":"22","author":"MS Aktas","year":"2010","unstructured":"Aktas, M.S., Pierce, M.: High-performance hybrid information service architecture. Concurr. Comput. Pract. Exp. 22(15), 2095\u20132123 (2010)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"3_CR25","doi-asserted-by":"publisher","unstructured":"Fox, G.C., et al.: Real time streaming data grid applications. In: Davoli, F., Palazzo, S., Zappatore, S. (eds.) Distributed Cooperative Laboratories: Networking, Instrumentation, and Measurements. Signals and Communication Technology, pp. 253\u2013267. Springer, Boston, MA (2006). https:\/\/doi.org\/10.1007\/0-387-30394-4_17","DOI":"10.1007\/0-387-30394-4_17"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Tufek, A., Gurbuz, A., Ekuklu, O.F., Aktas, M.S.: provenance collection platform for the weather research and forecasting model. In: 2018 14th International Conference on Semantics, Knowledge and Grids (2018)","DOI":"10.1109\/SKG.2018.00009"},{"issue":"3","key":"3_CR27","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.future.2006.05.009","volume":"23","author":"MS Aktas","year":"2007","unstructured":"Aktas, M.S., Fox, G.C., Pierce, M.: Fault tolerant high performance information services for dynamic collections of grid and web services. Futur. Gener. Comput. Syst. 23(3), 317\u2013337 (2007)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"14","key":"3_CR28","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1002\/cpe.1312","volume":"20","author":"G Aydin","year":"2008","unstructured":"Aydin, G., et al.: Building and applying geographical information system Grids. Concurr. Comput. Pract. Exp. 20(14), 1653\u20131695 (2008)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"3_CR29","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s00791-007-0083-8","volume":"12","author":"GC Fox","year":"2009","unstructured":"Fox, G.C., et al.: Algorithms and the Grid. Comput. Vis. Sci. 12, 115\u2013124 (2009)","journal-title":"Comput. Vis. Sci."},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Aktas, M., et al.: ISERVO: implementing the international solid earth research virtual observatory by integrating computational grid and geographical information Web Services. Comput. Earthq. Phys. Simul. Anal. Infrastruct. Part II, 2281\u20132296 (2007)","DOI":"10.1007\/978-3-7643-8131-8_3"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Aydin, G., Aktas, M.S., Fox, G.C., Gadgil, H., Pierce, M., Sayar, A.: SERVOGrid complexity computational environments CCE integrated performance analysis. In: The 6th IEEE\/ACM International Workshop on Grid Computing (2005)","DOI":"10.1109\/GRID.2005.1542750"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Pierce, M.E., et al.: The QuakeSim project: Web services for managing geophysical data and applications. Earthq. Simul. Sourc. Tsunamis. 635\u2013651 (2008)","DOI":"10.1007\/978-3-7643-8757-0_11"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Uygun, Y., Oguz, R.F., Olmezogullari, E., Aktas, M.S.: On the large-scale graph data processing for user interface testing in big data science projects. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 2049\u20132056 (2020)","DOI":"10.1109\/BigData50022.2020.9378153"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Sahinoglu, M., Incki, K., Aktas, M.S.: Mobile application verification: a systematic mapping study. In: Computational Science and Its Applications-ICCSA: 15th International Conference, Banff, AB, Canada, June 22\u201325, 2015, Proceedings. Part V, vol. 15(2015)","DOI":"10.1007\/978-3-319-21413-9_11"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Kapdan, M., Aktas, M., Yigit, M.: On the structural code clone detection problem: a survey and software metric based approach. In: Computational Science and Its Applications-ICCSA,: 14th International Conference, Guimar\u00e3es, Portugal, June 30-July 3, 2014, Proceedings. Part V, vol. 14 (2014)","DOI":"10.1007\/978-3-319-09156-3_35"},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Olmezogullari, E., Aktas, M.S.: Pattern2Vec: representation of clickstream data sequences for learning user navigational behavior. Concurr. Comput. Pract. Exp. 34(9) (2022)","DOI":"10.1002\/cpe.6546"},{"key":"3_CR37","doi-asserted-by":"crossref","unstructured":"Olmezogullari, E., Aktas, M.S.: Representation of click-stream datasequences for learning user navigational behavior by using embeddings. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 3173\u20133179 (2020)","DOI":"10.1109\/BigData50022.2020.9378437"},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Nacar, M.A.et al.: VLab: collaborative Grid services and portals to support computational material science. Concurr. Comput. Pract. Exp. 19(12), 1717\u20131728 (2007)","DOI":"10.1002\/cpe.1199"},{"key":"3_CR39","unstructured":"Dundar, B., Astekin, M., Aktas, M.S.: A big data processing framework for self-healing internet of things applications. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 2353\u20132361 (2021)"},{"key":"3_CR40","doi-asserted-by":"crossref","unstructured":"Baeth, M.J., Aktas, M.S.: Detecting misinformation in social networks using provenance data. In: 2017 13th International Conference on Semantics, Knowledge and Grids (2017)","DOI":"10.1109\/SKG.2017.00022"},{"key":"3_CR41","unstructured":"Aktas, M., et al.: Implementing geographical information system grid services to support computational geophysics in a service-oriented environment. NASA Earth-Sun System Technology Conference, University of Maryland, Adelphi, Maryland (2005)"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37129-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:41:34Z","timestamp":1702687294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37129-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031371288","9783031371295"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37129-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Custom based on Cyberchair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"283","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PHD Showcase Papers: 6(for main conf) \/ For ICCSA 2023 Workshops 876 subm sent, 350 full papers and 29 short papers accepted, additional PHD Showcase Papers: 2","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}