{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:40:11Z","timestamp":1773247211204,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819916382","type":"print"},{"value":"9789819916399","type":"electronic"}],"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\/978-981-99-1639-9_33","type":"book-chapter","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T07:02:39Z","timestamp":1681455759000},"page":"397-408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multivariate Time Series Retrieval with\u00a0Binary Coding from\u00a0Transformer"],"prefix":"10.1007","author":[{"given":"Zehan","family":"Tan","sequence":"first","affiliation":[]},{"given":"Mingyu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Weidong","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"key":"33_CR1","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","volume":"24","author":"T Fu","year":"2011","unstructured":"Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24, 164\u2013181 (2011)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Hallac, D., Vare, S., Boyd, S., Leskovec, J.: Toeplitz inverse covariance-based clustering of multivariate time series data. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 215\u2013223 (2017)","DOI":"10.1145\/3097983.3098060"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2\u201311 (2003)","DOI":"10.1145\/882082.882086"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Yeh, C., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1317\u20131322 (2016)","DOI":"10.1109\/ICDM.2016.0179"},{"key":"33_CR5","unstructured":"Chan, K., Fu, A.: Efficient time series matching by wavelets. In: Proceedings of the 15th International Conference on Data Engineering (Cat. No. 99CB36337), pp. 126\u2013133 (1999)"},{"key":"33_CR6","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1145\/191843.191925","volume":"23","author":"C Faloutsos","year":"1994","unstructured":"Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. ACM SIGMOD Rec. 23, 419\u2013429 (1994)","journal-title":"ACM SIGMOD Rec."},{"key":"33_CR7","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1109\/TIT.2004.831787","volume":"50","author":"M Effros","year":"2004","unstructured":"Effros, M., Feng, H., Zeger, K.: Suboptimality of the Karhunen-Loeve transform for transform coding. IEEE Trans. Inf. Theory 50, 1605\u20131619 (2004)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"33_CR8","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","author":"L De Lathauwer","year":"2000","unstructured":"De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21, 1253\u20131278 (2000)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"33_CR9","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/PL00011669","volume":"3","author":"E Keogh","year":"2001","unstructured":"Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3, 263\u2013286 (2001)","journal-title":"Knowl. Inf. Syst."},{"key":"33_CR10","unstructured":"Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, pp. 359\u2013370 (1994)"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., et al.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 262\u2013270 (2012)","DOI":"10.1145\/2339530.2339576"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Gan, J., Feng, J., Fang, Q., Ng, W.: Locality-sensitive hashing scheme based on dynamic collision counting. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 541\u2013552 (2012)","DOI":"10.1145\/2213836.2213898"},{"key":"33_CR13","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Dizaji, K., Zheng, F., Sadoughi, N., Yang, Y., Deng, C., Huang, H.: Unsupervised deep generative adversarial hashing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3664\u20133673 (2018)","DOI":"10.1109\/CVPR.2018.00386"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Zhu, D., et al.: Deep unsupervised binary coding networks for multivariate time series retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1403\u20131411 (2020)","DOI":"10.1609\/aaai.v34i02.5497"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Cao, Y., Liu, B., Long, M., Wang, J.: Hashgan: deep learning to hash with pair conditional Wasserstein GAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1287\u20131296 (2018)","DOI":"10.1109\/CVPR.2018.00140"},{"key":"33_CR17","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, pp. 151\u2013162 (2001)","DOI":"10.1145\/375663.375680"},{"key":"33_CR19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Mining Knowl. Discov. 15, 107\u2013144 (2007)","journal-title":"Data Mining Knowl. Discov."},{"key":"33_CR20","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1145\/253262.253332","volume":"26","author":"F Korn","year":"1997","unstructured":"Korn, F., Jagadish, H., Faloutsos, C.: Efficiently supporting ad hoc queries in large datasets of time sequences. ACM SIGMOD Rec. 26, 289\u2013300 (1997)","journal-title":"ACM SIGMOD Rec."},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Shasha, D.: Warping indexes with envelope transforms for query by humming. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 181\u2013192 (2003)","DOI":"10.1145\/872757.872780"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Norouzi, M., Punjani, A., Fleet, D.: Fast search in hamming space with multi-index hashing. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3108\u20133115 (2012)","DOI":"10.1109\/CVPR.2012.6248043"},{"key":"33_CR23","unstructured":"Yang, H., Lin, K., Chen, C.: Supervised learning of semantics-preserving hashing via deep neural networks for large-scale image search. arXiv Preprint arXiv:1507.00101, vol. 1, p. 3 (2015)"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Lin, K., Lu, J., Chen, C., Zhou, J.: Learning compact binary descriptors with unsupervised deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1183\u20131192 (2016)","DOI":"10.1109\/CVPR.2016.133"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Song, D., Xia, N., Cheng, W., Chen, H., Tao, D.: Deep R-th root of rank supervised joint binary embedding for multivariate time series retrieval. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2229\u20132238 (2018)","DOI":"10.1145\/3219819.3220108"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Pereira, J., Silveira, M.: Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. In: Proceedings of 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1275\u20131282 (2018)","DOI":"10.1109\/ICMLA.2018.00207"},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., Cottrell, G.: A dual-stage attention-based recurrent neural network for time series prediction. arXiv Preprint arXiv:1704.02971 (2017)","DOI":"10.24963\/ijcai.2017\/366"},{"key":"33_CR28","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"33_CR30","unstructured":"Ba, J., Kiros, J., Hinton, G.: Layer normalization. arXiv Preprint arXiv:1607.06450 (2016)"},{"key":"33_CR31","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv Preprint arXiv:1412.6980 (2014)"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1639-9_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T07:35:52Z","timestamp":1681457752000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1639-9_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819916382","9789819916399"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1639-9_33","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"0","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":"44% - 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":"2.65","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":"3","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":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","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)"}}]}}