{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:53:52Z","timestamp":1768204432060,"version":"3.49.0"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262014"],"award-info":[{"award-number":["62262014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Cyber\u2010Physical\u2010Social System (CPSS) which refers\u00a0to the complex interaction of cyber, physical and social systems, has the important purpose to provide personalized intelligent services. CPSS data, generated from every aspect of people living life, are mainly in the form of time series multimodal data with characteristic of high order and high dimension. How to efficiently process these CPSS data is one of the fundamental ways for the intelligent services. In this paper, a highly\u2010efficient attention driven Quantized Tensor Train Recursive Neural Network is proposed, in which the CPSS data is decomposed into the form of tensor train cores. In this way, the proposed method is composed of lightweight high\u2010order neural network units, which better preserves the multi\u2010attribute features of the original data and the correlation between different dimensions by using tensors with its calculations, and implicitly trims the dense vector\u2010matrix connections in the fully connected network by using the form of quantization tensor train decomposition, which greatly reduces the model parameters, shortens the training time and improves the efficiency. Also, an effective attentional feature enhancement module is constructed to assist the high\u2010order neural network, so that the overall model can achieve a balance between low parameter number and accuracy. The network structure proposed in this paper realizes an efficient and lossless high\u2010order tensor recurrent neural network model with a small number of parameters. Finally, experiments on the UCF50 action video dataset, CWRU bearing dataset, and image generation tasks are conducted. Comparative analyses with vanilla LSTM and other tensorized LSTMs in terms of training time, accuracy, error, and compression ratio validate the reliability of the proposed model.<\/jats:p>","DOI":"10.1002\/cpe.70477","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:01:49Z","timestamp":1765267309000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["QTTARNN: A Highly\u2010Efficient Attention Driven Quantized Tensor Train Recursive Neural Network for Cyber\u2010Physical\u2010Social Intelligence"],"prefix":"10.1002","volume":"38","author":[{"given":"Tinghua","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Hainan University  Hainan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junxin","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science Dalhousie University  Halifax Nova Scotia Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4919-3274","authenticated-orcid":false,"given":"Xiaosong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hainan University  Hainan China"},{"name":"School of Computer Science and Artificial Intelligence Zhengzhou University  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hainan University  Hainan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hainan University  Hainan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biyuan","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hainan University  Hainan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaokang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence Zhengzhou University  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.07.015"},{"key":"e_1_2_11_3_1","doi-asserted-by":"crossref","unstructured":"R.Rajkumar I.Lee L.Sha et al. \u201cCyberphysical Systems: The Next Computing Revolution. I \u201d2010.","DOI":"10.1145\/1837274.1837461"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.512"},{"issue":"8","key":"e_1_2_11_5_1","first-page":"321374","article-title":"Tensor Analysis on Manifolds","volume":"76","author":"Bishop R. L.","year":"1968","journal-title":"American Mathematical Monthly"},{"issue":"1","key":"e_1_2_11_6_1","first-page":"25","article-title":"Industrial Cyberphysical Systems: Realizing Cloud\u2010Based Big Data Infras\u2010Tructures","volume":"12","author":"Cheng B.","year":"2018","journal-title":"IEEE Industry Applications Magazine"},{"issue":"5","key":"e_1_2_11_7_1","article-title":"Big Data Analytics for Large\u2010Scale Wireless Networks: Challenges and Opportunities","volume":"52","author":"Dai H.\u2010N.","year":"2019","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"e_1_2_11_8_1","first-page":"25","article-title":"Industrial Cyberphysical Systems: Realizing Cloud\u2010Based Big Data Infrastructures","volume":"12","author":"Cheng B.","year":"2018","journal-title":"IEEE Industry Applications Magazine"},{"key":"e_1_2_11_9_1","first-page":"1591","volume-title":"Tensor Analysis and Continuum Mechanics","author":"Talpaert Y. R.","year":"2013"},{"key":"e_1_2_11_10_1","unstructured":"ADTT: A Highly Efficient Distributed Tensor\u2010Train Decomposition Method for IIoT Big Data."},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.137"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_11_13_1","first-page":"5142","article-title":"Predicting Human Eye Fixations via an LSTM\u2010Based Saliency Attentive Model","volume":"27","author":"Marcella C.","year":"2016","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21233-3_6"},{"issue":"99","key":"e_1_2_11_15_1","first-page":"11","article-title":"A Data Driven Approach of Product Quality Prediction for Complex Production Systems","author":"Ren L.","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_2_11_16_1","unstructured":"T.Garipov D.Podoprikhin A.Novikov andD.Vetrov \u201cUltimate Tensorization: Compressing Convolutional and FC Layers Alike \u201d arXiv:1611.03214 (2016)."},{"key":"e_1_2_11_17_1","first-page":"3891","volume-title":"International conference on machine learning","author":"Yang Y.","year":"2017"},{"key":"e_1_2_11_18_1","first-page":"9378","article-title":"Learning Compact Recurrent Neural Networks With Blockterm Tensor Decomposition","volume":"18","author":"Ye J.","year":"2018","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPP.2016.19"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2503737"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8127066"},{"key":"e_1_2_11_22_1","unstructured":"K.Gregor I.Danihelka A.Graves D. J.Rezende andD.Wierstra \u201cDRAW: A Recurrent Neural Network for Image Generation \u201d arXiv preprint arXiv:1502.04623 (2015)."},{"key":"e_1_2_11_23_1","first-page":"6655","volume-title":"Acoustics, Speech and Signal Processing (ICASSP)","author":"Sainath T. N.","year":"2013"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3058103"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2639466"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1137\/090752286"},{"key":"e_1_2_11_27_1","first-page":"497","volume-title":"IEEE 1988 International Conference on Neural Networks","author":"Lee J.","year":"1988"},{"key":"e_1_2_11_28_1","first-page":"3185","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Liang X.","year":"2016"},{"key":"e_1_2_11_29_1","first-page":"6000","article-title":"Attention Is All You Need","volume":"30","author":"Vaswani A.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_30_1","unstructured":"S.Targ D.Almeida andK.Lyman \u201cResnet in resnet: Generalizing residual architectures \u201d arXiv preprint arXiv:1603.08029 (2016)."}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.70477","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T04:49:57Z","timestamp":1768193397000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.70477"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/cpe.70477"],"URL":"https:\/\/doi.org\/10.1002\/cpe.70477","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"value":"1532-0626","type":"print"},{"value":"1532-0634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]},"assertion":[{"value":"2025-02-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70477"}}