{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T20:39:04Z","timestamp":1757450344635,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"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":["J Supercomput"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s11227-022-04899-1","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:23:37Z","timestamp":1667420617000},"page":"6309-6346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quadro-W learning for human behavior prediction in an evolving environment: a case study of the intelligent butler technology"],"prefix":"10.1007","volume":"79","author":[{"given":"Sheng-Tzong","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Wei","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0193-2104","authenticated-orcid":false,"given":"Gwo-Jiun","family":"Horng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan-Ting","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Wang H, Schmid C (2013) Action recognition with improved trajectories. In: 2013 IEEE international conference on computer vision, pp 3551\u20133558. https:\/\/doi.org\/10.1109\/ICCV.2013.441","key":"4899_CR1","DOI":"10.1109\/ICCV.2013.441"},{"unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: NIPS'14: proceedings of the 27th international conference on neural information processing systems, vol 1, pp 568\u2013576","key":"4899_CR2"},{"doi-asserted-by":"crossref","unstructured":"Yue-Hei Ng J, Hausknecht M, Vijayanarasimhan S, Vinyals O, Monga R, Toderici G (2015) Beyond short snippets: deep networks for video classification. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 4694\u20134702","key":"4899_CR3","DOI":"10.1109\/CVPR.2015.7299101"},{"doi-asserted-by":"crossref","unstructured":"Qiu Z, Yao T, Mei T (2017) Learning spatio-temporal representation with pseudo-3d residual networks. In: Proceedings of the IEEE international conference on computer vision, pp 5533\u20135541","key":"4899_CR4","DOI":"10.1109\/ICCV.2017.590"},{"doi-asserted-by":"crossref","unstructured":"Shou Z, Wang D, Chang S-F (2016) Temporal action localization in untrimmed videos via multi-stage CNNs. In: IEEE conference on computer vision and pattern recognition, pp 1049\u20131058","key":"4899_CR5","DOI":"10.1109\/CVPR.2016.119"},{"doi-asserted-by":"crossref","unstructured":"Lin T, Zhao X, Su H, Wang C, Yang M (2018) BSN: boundary sensitive network for temporal action proposal generation. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","key":"4899_CR6","DOI":"10.1007\/978-3-030-01225-0_1"},{"issue":"11","key":"4899_CR7","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 (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"6","key":"4899_CR8","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: The 3rd international conference on learning representations (ICLR2015). https:\/\/arxiv.org\/abs\/1409.1556","key":"4899_CR9"},{"doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","key":"4899_CR10","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3\u20134","key":"4899_CR11","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF00992698","volume":"8","author":"CJ Watkins","year":"1992","unstructured":"Watkins CJ, Dayan P (1992) Q-learning. Mach Learn 8(3\u20134):279\u2013292","journal-title":"Mach Learn"},{"issue":"5","key":"4899_CR12","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard E, Adhikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Gr Appl 21(5):34\u201341","journal-title":"IEEE Comput Gr Appl"},{"issue":"13","key":"4899_CR13","doi-asserted-by":"publisher","first-page":"2006","DOI":"10.1016\/j.patrec.2005.02.010","volume":"26","author":"Z Li","year":"2005","unstructured":"Li Z, Jing Z, Yang X, Sun S (2005) Color transfer based remote sensing image fusion using non-separable wavelet frame transform. Pattern Recogn Lett 26(13):2006\u20132014. https:\/\/doi.org\/10.1016\/j.patrec.2005.02.010","journal-title":"Pattern Recogn Lett"},{"issue":"6","key":"4899_CR14","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/34.87344","volume":"13","author":"L Vincent","year":"1991","unstructured":"Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583\u2013598","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","key":"4899_CR15","DOI":"10.1109\/CVPR.2017.106"},{"doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","key":"4899_CR16","DOI":"10.1109\/CVPR.2014.81"},{"unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS\u201915: proceedings of the 28th international conference on neural information processing systems, vol 1, pp 91\u201399","key":"4899_CR17"},{"doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779\u2013788","key":"4899_CR18","DOI":"10.1109\/CVPR.2016.91"},{"doi-asserted-by":"crossref","unstructured":"Liu W et al (2016) SSD: Single shot multibox detector. In: European conference on computer vision, vol 9905. Springer, pp 21\u201337","key":"4899_CR19","DOI":"10.1007\/978-3-319-46448-0_2"},{"doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 234\u2013241","key":"4899_CR20","DOI":"10.1007\/978-3-319-24574-4_28"},{"unstructured":"Google, Speech-to-Text: Automatic Speech Recognition, Cloud Speech-to-Text. https:\/\/cloud.google.com\/speech-to-text. erests include mobile computing, intelligent systems, and multimedia","key":"4899_CR21"},{"key":"4899_CR22","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518:529\u2013533. https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"issue":"2","key":"4899_CR23","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1109\/TCSS.2020.2969484","volume":"7","author":"Y Xiao","year":"2020","unstructured":"Xiao Y, Li J, Zhu Y, Li Q (2020) User behavior prediction of social hotspots based on multimessage interaction and neural network. IEEE Trans Comput Soc Syst 7(2):536\u2013545. https:\/\/doi.org\/10.1109\/TCSS.2020.2969484","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"4899_CR24","doi-asserted-by":"publisher","first-page":"171357","DOI":"10.1109\/ACCESS.2019.2937508","volume":"7","author":"D Li","year":"2019","unstructured":"Li D, Shen D, Kou Y, Nie T (2019) Integrating sign prediction with behavior prediction for signed heterogeneous information networks. IEEE Access 7:171357\u2013171371. https:\/\/doi.org\/10.1109\/ACCESS.2019.2937508","journal-title":"IEEE Access"},{"issue":"1","key":"4899_CR25","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TCSS.2020.3017818","volume":"8","author":"W Jiang","year":"2021","unstructured":"Jiang W, Lv S, Wang Y, Chen J, Liu X, Sun Y (2021) Computational experimental study on social organization behavior prediction problems. IEEE Trans Comput Soc Syst 8(1):148\u2013160. https:\/\/doi.org\/10.1109\/TCSS.2020.3017818","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"4","key":"4899_CR26","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1109\/TII.2019.2951089","volume":"16","author":"M Wo\u017aniak","year":"2020","unstructured":"Wo\u017aniak M, Po\u0142ap D (2020) Intelligent home systems for ubiquitous user support by using neural networks and rule-based approach. IEEE Trans Ind Inf 16(4):2651\u20132658. https:\/\/doi.org\/10.1109\/TII.2019.2951089","journal-title":"IEEE Trans Ind Inf"},{"doi-asserted-by":"crossref","unstructured":"Liu Y, Xie DY, Gao Q, Han J, Wang S, Gao X (2019) Graph and autoencoder based feature extraction for zero-shot learning. In: IJCAI, vol 1, no 2, p 6","key":"4899_CR27","DOI":"10.24963\/ijcai.2019\/421"},{"unstructured":"Library of Congress (2020) Who is credited with inventing the telephone? https:\/\/www.loc.gov\/everyday-mysteries\/item\/who-is-credited-with-inventing-the-telephone\/. Accessed 07 Jan 2020","key":"4899_CR28"},{"issue":"9","key":"4899_CR29","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Analysis Mach Intell 37(9):1904\u20131916","journal-title":"IEEE Trans Pattern Analysis Mach Intell"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04899-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-022-04899-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04899-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T19:14:47Z","timestamp":1677784487000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-022-04899-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,2]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["4899"],"URL":"https:\/\/doi.org\/10.1007\/s11227-022-04899-1","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"type":"print","value":"0920-8542"},{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2022,11,2]]},"assertion":[{"value":"13 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}