{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:09:12Z","timestamp":1780675752635,"version":"3.54.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031700606","type":"print"},{"value":"9783031700613","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-70061-3_10","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T04:35:08Z","timestamp":1730522108000},"page":"114-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Self-supervised Spatio-Temporal Graph Mask-Passing Attention Network for\u00a0Perceptual Importance Prediction of\u00a0Multi-point Tactility"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5090-2178","authenticated-orcid":false,"given":"Dazhong","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"10_CR1","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv abs\/1803.01271 (2018)"},{"issue":"1","key":"10_CR2","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/0301-0082(94)90022-1","volume":"42","author":"J Bell","year":"1994","unstructured":"Bell, J., Bolanowski, S., Holmes, M.H.: The structure and function of pacinian corpuscles: a review. Prog. Neurobiol. 42(1), 79\u2013128 (1994)","journal-title":"Prog. Neurobiol."},{"key":"10_CR3","unstructured":"Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? arXiv abs\/2105.14491 (2021)"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Culbertson, H., L\u00f3pez\u00a0Delgado, J.J., Kuchenbecker, K.J.: One hundred data-driven haptic texture models and open-source methods for rendering on 3D objects. In: 2014 IEEE Haptics Symposium (HAPTICS), pp. 319\u2013325 (2014)","DOI":"10.1109\/HAPTICS.2014.6775475"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 922\u2013929 (2019)","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"4455","DOI":"10.1109\/TMM.2020.3042674","volume":"23","author":"R Hassen","year":"2021","unstructured":"Hassen, R., G\u00fclecy\u00fcz, B., Steinbach, E.: PVC-SLP: perceptual vibrotactile-signal compression based-on sparse linear prediction. IEEE Trans. Multimedia 23, 4455\u20134468 (2021)","journal-title":"IEEE Trans. Multimedia"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u2019ar, P., Girshick, R.B.: Masked autoencoders are scalable vision learners. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15979\u201315988 (2021)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Hou, Z., et al.: Graphmae: self-supervised masked graph autoencoders. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 594\u2013604 (2022)","DOI":"10.1145\/3534678.3539321"},{"key":"10_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/978-3-642-14075-4_12","volume-title":"Haptics: Generating and Perceiving Tangible Sensations","author":"N Landin","year":"2010","unstructured":"Landin, N., Romano, J.M., McMahan, W., Kuchenbecker, K.J.: Dimensional reduction of high-frequency accelerations for haptic rendering. In: Kappers, A.M.L., van Erp, J.B.F., Bergmann Tiest, W.M., van der Helm, F.C.T. (eds.) EuroHaptics 2010. LNCS, vol. 6192, pp. 79\u201386. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-14075-4_12"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, X., et\u00a0al.: Todynet: temporal dynamic graph neural network for multivariate time series classification. arXiv abs\/2304.05078 (2023)","DOI":"10.2139\/ssrn.4603167"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Liu, X., Dohler, M., Mahmoodi, T., Liu, H.: Challenges and opportunities for designing tactile codecs from audio codecs. In: 2017 European Conference on Networks and Communications (EuCNC), pp.\u00a01\u20135 (2017)","DOI":"10.1109\/EuCNC.2017.7980643"},{"issue":"6","key":"10_CR12","first-page":"5879","volume":"35","author":"Y Liu","year":"2023","unstructured":"Liu, Y., et al.: Graph self-supervised learning: a survey. IEEE Trans. Knowl. Data Eng. 35(6), 5879\u20135900 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10_CR13","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.E.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Noll, A., Nockenberg, L., G\u00fclecy\u00fcz, B., Steinbach, E.: VC-PWQ: vibrotactile signal compression based on perceptual wavelet quantization. In: 2021 IEEE World Haptics Conference (WHC), pp. 427\u2013432 (2021)","DOI":"10.1109\/WHC49131.2021.9517217"},{"issue":"1","key":"10_CR15","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/TOH.2012.32","volume":"6","author":"S Okamoto","year":"2013","unstructured":"Okamoto, S., Nagano, H., Yamada, Y.: Psychophysical dimensions of tactile perception of textures. IEEE Trans. Haptics 6(1), 81\u201393 (2013)","journal-title":"IEEE Trans. Haptics"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Steinbach, E., Li, S.C., et\u00a0al.: Chapter 5 - Haptic codecs for the tactile internet. In: Fitzek, F.H., Li, S.C., Speidel, S., Strufe, T., Simsek, M., Reisslein, M. (eds.) Tactile Internet, pp. 103\u2013129. Academic Press (2021)","DOI":"10.1016\/B978-0-12-821343-8.00016-2"},{"issue":"2","key":"10_CR17","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/JPROC.2018.2867835","volume":"107","author":"E Steinbach","year":"2019","unstructured":"Steinbach, E., et al.: Haptic codecs for the tactile internet. Proc. IEEE 107(2), 447\u2013470 (2019)","journal-title":"Proc. IEEE"},{"key":"10_CR18","unstructured":"Tang, W., Long, G., Liu, L., Zhou, T., Blumenstein, M., Jiang, J.: Omni-scale CNNs: a simple and effective kernel size configuration for time series classification. In: International Conference on Learning Representations (2021)"},{"key":"10_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017)"},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"87","DOI":"10.4310\/CIS.2014.v14.n2.a2","volume":"14","author":"P Yin","year":"2014","unstructured":"Yin, P., Esser, E., Xin, J.: Ratio and difference of L1 and L2 norms and sparse representation with coherent dictionaries. Commun. Inf. Syst. 14, 87\u2013109 (2014)","journal-title":"Commun. Inf. Syst."},{"key":"10_CR21","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS 2020 (2020)"},{"key":"10_CR22","unstructured":"Zhang, X., Zeman, M., Tsiligkaridis, T., Zitnik, M.: Graph-guided network for irregularly sampled multivariate time series. In: International Conference on Learning Representations, ICLR (2022)"}],"container-title":["Lecture Notes in Computer Science","Haptics: Understanding Touch; Technology and Systems; Applications and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70061-3_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T04:41:07Z","timestamp":1730522467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70061-3_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9783031700606","9783031700613"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70061-3_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EuroHaptics","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human Haptic Sensing and Touch Enabled Computer Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lille","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eurohaptics2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.eurohaptics.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}