{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:05:33Z","timestamp":1773414333667,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user\u2019s face and accept commands from the user to do an action.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM\/F1 score than using the CNN encoder.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40537-020-00341-6","type":"journal-article","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T12:53:29Z","timestamp":1600260809000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Deep learning-based question answering system for intelligent humanoid robot"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2681-0901","authenticated-orcid":false,"given":"Widodo","family":"Budiharto","sequence":"first","affiliation":[]},{"given":"Vincent","family":"Andreas","sequence":"additional","affiliation":[]},{"given":"Alexander Agung Santoso","family":"Gunawan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,16]]},"reference":[{"key":"341_CR1","doi-asserted-by":"crossref","unstructured":"Perera V, Veloso M. Learning to Understand Questions on the Task History of a Service Robot. In: IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Portugal. 2017; pp. 304-309.","DOI":"10.1109\/ROMAN.2017.8172318"},{"key":"341_CR2","doi-asserted-by":"crossref","unstructured":"Kwon D. Self-Taught Robots, Scientific American; 2018\u00a0pp. 26-31.","DOI":"10.1038\/scientificamerican0318-26"},{"key":"341_CR3","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1007\/978-3-540-30301-5_60","volume-title":"Handbook of Robotics","author":"A Billard","year":"2008","unstructured":"Billard A, Calinon S, Dillmann R, Schaal S. Robot programming by demonstration. In: Siciliano B, Khatib O, editors. Handbook of Robotics. Springer: USA; 2008. p. 1371\u201394."},{"key":"341_CR4","unstructured":"Retto J. Sophia, first citizen robot of the world. ResearchGate, URL: https:\/\/www.researchgate.net 2017."},{"key":"341_CR5","doi-asserted-by":"crossref","unstructured":"Oh HJ, Lee CH, Hwang YG, Jang MG, Park JG, Lee YK. A case study of edutainment robot: Applying voice question answering to intelligent robot. InRO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication 2007 Aug 26 (pp. 410-415). IEEE.","DOI":"10.1109\/ROMAN.2007.4415119"},{"key":"341_CR6","doi-asserted-by":"crossref","unstructured":"Ahn HS, Yep W, Lim J, Ahn BK, Johanson DL, Hwang EJ, Lee MH, Broadbent E, MacDonald BA. Hospital Receptionist Robot v2: Design for Enhancing Verbal Interaction with Social Skills. In2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2019 Oct 14 (pp. 1-6). IEEE.","DOI":"10.1109\/RO-MAN46459.2019.8956300"},{"key":"341_CR7","unstructured":"Wilcock G, Jokinen K, Yamamoto S. What topic do you want to hear about?: A bilingual talking robot using English and Japanese Wikipedias. InThe 26th International Conference on Computational Linguistics, Proceedings of COLING 2016 System Demonstrations 2016 Dec 11. Association for Computational Linguistics."},{"key":"341_CR8","doi-asserted-by":"crossref","unstructured":"Feng M, Xiang B, Glass MR, Wang L, Zhou B. Applying deep learning to answer selection: A study and an open task. In IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU); 2015\u00a0pp. 813-820.","DOI":"10.1109\/ASRU.2015.7404872"},{"key":"341_CR9","unstructured":"Yin W, Kann K, Yu M, Sch\u00fctze H. Comparative study of cnn and rnn for natural language processing; 2017. arXiv preprint arXiv:1702.01923."},{"key":"341_CR10","doi-asserted-by":"crossref","unstructured":"Iyyer M, Boyd-Graber J, Claudino L, Socher R, Daum\u00e9 III H. A neural network for factoid question answering over paragraphs. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Qatar; 2014. pp. 633-644.","DOI":"10.3115\/v1\/D14-1070"},{"key":"341_CR11","doi-asserted-by":"crossref","unstructured":"Yin J, Jiang X, Lu Z, Shang L, Li H, Li X. Neural generative question answering. arXiv preprint arXiv:1512.01337; 2015.","DOI":"10.18653\/v1\/W16-0106"},{"key":"341_CR12","doi-asserted-by":"crossref","unstructured":"Chen D, Fisch A, Weston J, Bordes A. Reading Wikipedia to answer open-domain questions. arXiv preprint arXiv:1704.00051; 2017.","DOI":"10.18653\/v1\/P17-1171"},{"issue":"12","key":"341_CR13","doi-asserted-by":"publisher","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","volume":"105","author":"V Sze","year":"2017","unstructured":"Sze V, Chen YH, Yang TJ, Emer JS. Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE. 2017;105(12):2295\u2013329.","journal-title":"Proc IEEE"},{"issue":"11","key":"341_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278\u2013324.","journal-title":"Proc IEEE"},{"issue":"2","key":"341_CR15","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL. Finding structure in time. Cognitive Sci. 1990;14(2):179\u2013211.","journal-title":"Cognitive Sci"},{"key":"341_CR16","unstructured":"Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press; 2016 Nov 10."},{"issue":"3","key":"341_CR17","first-page":"173","volume":"16","author":"EJ Rhee","year":"2018","unstructured":"Rhee EJ. A deep learning approach for classification of cloud image patches on small datasets. J Inform Commun Converg Eng. 2018;16(3):173\u20138.","journal-title":"J Inform Commun Converg Eng"},{"issue":"8","key":"341_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"key":"341_CR19","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 2014."},{"key":"341_CR20","unstructured":"Allahyari M, Pouriyeh S, Assefi M, Safaei S, Trippe ED, Gutierrez JB, Kochut K. A brief survey of text mining: Classification, clustering, and extraction techniques. arXiv preprint arXiv:1707.02919. 2017 Jul 10."},{"key":"341_CR21","doi-asserted-by":"crossref","unstructured":"Suchanek F, Weikum G. Knowledge harvesting in the big-data era. InProceedings of the 2013 ACM SIGMOD International Conference on Management of Data 2013 Jun 22 (pp. 933-938).","DOI":"10.1145\/2463676.2463724"},{"key":"341_CR22","doi-asserted-by":"crossref","unstructured":"Reshmi S, Balakrishnan K. Implementation of an inquisitive chatbot for database supported knowledge bases. s\u0101dhan\u0101. 2016 Oct 1;41(10):1173-8.","DOI":"10.1007\/s12046-016-0544-1"},{"key":"341_CR23","unstructured":"Mizuno J, Tanaka M, Ohtake K, Oh JH, Kloetzer J, Hashimoto C, Torisawa K. WISDOM X, DISAANA, and D-SUMM: Large-scale NLP systems for analyzing textual big data. InProceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations 2016 Dec (pp. 263-267)."},{"key":"341_CR24","unstructured":"de Jong M, Zhang K, Roth AM, Rhodes T, Schmucker R, Zhou C, Ferreira S, Cartucho J, Veloso M. Towards a robust interactive and learning social robot. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, Sweden; 2018\u00a0pp. 883-891."},{"key":"341_CR25","unstructured":"NAOqi developer Guide. http:\/\/doc.aldebaran.com\/2-5\/index_dev_guide.html Accessed on 1 June 2019."},{"key":"341_CR26","unstructured":"Google Speech to Text using Python. https:\/\/pythonspot.com\/speech-recognition-using-google-speech-api\/ Accessed on 1 June 2019."},{"issue":"1","key":"341_CR27","first-page":"33","volume":"9","author":"W Budiharto","year":"2017","unstructured":"Budiharto W, Cahyani AD. Behavior-Based Humanoid Robot for Teaching Basic Mathematics, Internetwork Indonesia Journal. Indonesia. 2017;9(1):33\u20137.","journal-title":"Indonesia"},{"key":"341_CR28","doi-asserted-by":"crossref","unstructured":"Garc\u00eda DH, Monje CA, Balaguer C. Knowledge Base Representation for Humanoid Robot Skills, IFAC Proceedings Volumes; 2014 Vol 47(3), pp 3042-3047. Baru sampe sini.","DOI":"10.3182\/20140824-6-ZA-1003.02229"},{"key":"341_CR29","doi-asserted-by":"crossref","unstructured":"Andreas V, Gunawan AA, Budiharto W. Anita: Intelligent Humanoid Robot with Self-Learning Capability Using Indonesian Language. In 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Tokyo; 2019. pp. 144-147. IEEE.","DOI":"10.1109\/ACIRS.2019.8935964"},{"key":"341_CR30","doi-asserted-by":"crossref","unstructured":"Rajpurkar P, Zhang J, Lopyrev K, Liang P. Squad: 100,000\u2009+\u2009questions for machine comprehension of text. arXiv preprint arXiv:1606.05250. 2016.","DOI":"10.18653\/v1\/D16-1264"},{"key":"341_CR31","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. InProceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Qatar; 2014. pp. 1532-1543.","DOI":"10.3115\/v1\/D14-1162"},{"key":"341_CR32","unstructured":"Seo M, Kembhavi A, Farhadi A, Hajishirzi H. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603; 2016."},{"issue":"15","key":"341_CR33","first-page":"42","volume":"08","author":"U Sasikumar","year":"2014","unstructured":"Sasikumar U, Sindhu L. A survey of natural language question answering system. Int J Comput Appl. 2014;08(15):42\u20136.","journal-title":"Int J Comput Appl"},{"key":"341_CR34","unstructured":"NLP\u2013Building a Question-Answering model. https:\/\/towardsdatascience.com\/nlp-building-a-question-answering-model-ed0529a68c54 Accessed on 1 June 2019."},{"key":"341_CR35","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 2018 ."},{"key":"341_CR36","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. InAdvances in neural information processing systems 2017 (pp. 5998-6008)."},{"key":"341_CR37","doi-asserted-by":"crossref","unstructured":"Rajpurkar P, Jia R, Liang P. Know what you don\u2019t know: Unanswerable questions for SQuAD. arXiv preprint arXiv:1806.03822. 2018.","DOI":"10.18653\/v1\/P18-2124"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00341-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-020-00341-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00341-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T12:38:57Z","timestamp":1668775137000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-020-00341-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,16]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["341"],"URL":"https:\/\/doi.org\/10.1186\/s40537-020-00341-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-26510\/v2","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-26510\/v1","asserted-by":"object"}]},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,16]]},"assertion":[{"value":"18 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"77"}}