{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T17:00:29Z","timestamp":1785603629605,"version":"3.56.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Science and Technology Innovation Committee Foundation of Shenzhen","award":["JCYJ20200109143223052"],"award-info":[{"award-number":["JCYJ20200109143223052"]}]},{"name":"Hong Kong Research Grant Council under RIF","award":["R5060-19"],"award-info":[{"award-number":["R5060-19"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,4,30]]},"abstract":"<jats:p>Robot visual servoing controls the motion of a robot through real-time visual observations. Kinematics is a key approach to achieving visual servoing. One key challenge of kinematics-based visual servoing is that it requires time-varying parameter configuration throughout the entire process of one task. Parameter tuning is also necessary when applying to different tasks. The existing work on parameter tuning either lacks adaptation or cannot automate the tuning of all parameters. Meanwhile, the transferability of existing methods from one task to another is low. This work develops a Deep Reinforcement Learning (DRL) framework for robot visual servoing, which can automate all parameters tuning for one task and across tasks. In visual servoing, forward kinematics focuses on motion speed, while inverse kinematics focuses on the smoothness of motion. Therefore, we develop two separate modules in the proposed DRL framework. One tunes time-varying Forward Kinematics parameters to accelerate the motion, and the other tunes the Inverse Kinematics parameters to ensure smoothness. Moreover, we customize a knowledge transfer method to generalize the proposed DRL models to various robot tasks without reconstructing the neural network. We verify the proposed method on simulated robot tasks. The experimental results show that the proposed method outperforms the state-of-the-art methods and manual parameter configuration in terms of movement speed and smoothness in one task and across tasks.<\/jats:p>","DOI":"10.1145\/3579829","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T16:01:02Z","timestamp":1673539262000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep Reinforcement Learning for Parameter Tuning of Robot Visual Servoing"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4857-5439","authenticated-orcid":false,"given":"Meng","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong and City University of Hong Kong Shenzhen Research Institute, Kowloon, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9318-1482","authenticated-orcid":false,"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong and City University of Hong Kong Shenzhen Research Institute, Kowloon, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRA.2002.1019475"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2006.250573"},{"key":"e_1_3_1_4_2","first-page":"776","volume-title":"Proceedings of the IEEE 15th International Conference on Control and Automation (ICCA\u201919)","author":"Copot Cosmin","year":"2019","unstructured":"Cosmin Copot, Lei Shi, and Steve Vanlanduit. 2019. Automatic tuning methodology of visual servoing system using predictive approach. In Proceedings of the IEEE 15th International Conference on Control and Automation (ICCA\u201919). IEEE, 776\u2013781."},{"key":"e_1_3_1_5_2","first-page":"518","article-title":"Hyperparameter optimization for tracking with continuous deep q-learning","author":"Dong Xingping","year":"2018","unstructured":"Xingping Dong, Jianbin Shen, Wenguan Wang, Yu Liu, Ling Shao, and Fatih Porikli.2018. Hyperparameter optimization for tracking with continuous deep q-learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201918). 518\u2013527.","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201918)"},{"issue":"5","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1109\/TPAMI.2019.2956703","article-title":"Dynamical hyperparameter optimization via deep reinforcement learning in tracking","volume":"43","author":"Dong Xingping","year":"2019","unstructured":"Xingping Dong, Jianbing Shen, Wenguan Wang, Ling Shao, Haibin Ling, and Fatih Porikli. 2019. Dynamical hyperparameter optimization via deep reinforcement learning in tracking. IEEE Trans. Pattern Anal. Mach. Intell. 43, 5 (2019), 1515\u20131529.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_1_7_2","first-page":"1329","volume-title":"International Conference on Machine Learning","author":"Duan Yan","year":"2016","unstructured":"Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel. 2016. Benchmarking deep reinforcement learning for continuous control. In International Conference on Machine Learning. PMLR, 1329\u20131338."},{"issue":"6","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1002\/asjc.865","article-title":"Optimized-based stabilization of constrained nonlinear systems: A receding horizon approach","volume":"16","author":"He De feng","year":"2014","unstructured":"De feng He, Li Yu, and Xiu lan Song.2014. Optimized-based stabilization of constrained nonlinear systems: A receding horizon approach. Asian J. Contr. 16, 6 (March2014), 1693\u20131701.","journal-title":"Asian J. Contr."},{"key":"e_1_3_1_9_2","first-page":"1","article-title":"Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: A closed-loop robotic simulation","volume":"7","author":"Garrido Jesus A.","year":"2013","unstructured":"Jesus A. Garrido, Niceto R. Luque, and Egidio D\u2019Angelo. 2013. Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: A closed-loop robotic simulation. Front. Neural Circ. 7 (October2013), 1\u201320.","journal-title":"Front. Neural Circ."},{"key":"e_1_3_1_10_2","first-page":"159","article-title":"Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: A closed-loop robotic simulation","volume":"7","author":"Alcazar Jesus A. Garrido","year":"2013","unstructured":"Jesus A. Garrido Alcazar, Niceto Rafael Luque, Egidio D\u2019Angelo, and Eduardo Ros. 2013. Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: A closed-loop robotic simulation. Front. Neural Circ. 7 (2013), 159, 1\u201320.","journal-title":"Front. Neural Circ."},{"key":"e_1_3_1_11_2","first-page":"37","article-title":"Long short-term memory. In Supervised Sequence Labelling with Recurrent Neural Networks","author":"Graves Alex","year":"2012","unstructured":"Alex Graves. 2012. Long short-term memory. In Supervised Sequence Labelling with Recurrent Neural Networks, Studies in Computational Intelligence, Vol. 385 (February2012), 37\u201345.","journal-title":"Studies in Computational Intelligence"},{"key":"e_1_3_1_12_2","first-page":"1","article-title":"Inverse kinematics using transposition method for robotic arm","author":"Hock Ondrej","year":"2018","unstructured":"Ondrej Hock and Jozef Sedo. 2018. Inverse kinematics using transposition method for robotic arm. In Proceedings of the International Conference ELEKTRO (ELEKTRO\u201918). 1\u20135.","journal-title":"Proceedings of the International Conference ELEKTRO (ELEKTRO\u201918)"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2021.3057005"},{"key":"e_1_3_1_14_2","unstructured":"Hadi S. Jomaa Josif Grabocka and Lars Schmidt-Thieme.2019. Hyp-rl: Hyperparameter optimization by reinforcement learning. arXiv:1906.11527. Retrieved from https:\/\/arxiv.org\/abs\/1906.11527."},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.neucom.2020.03.049","article-title":"Adaptive visual servoing with an uncalibrated camera using extreme learning machine and Q-leaning","volume":"402","author":"Kang Meng","year":"2020","unstructured":"Meng Kang, Hao Chen, and Jiuxiang Dong.2020. Adaptive visual servoing with an uncalibrated camera using extreme learning machine and Q-leaning. Neurocomputing 402 (March2020), 384\u2013394.","journal-title":"Neurocomputing"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/978-3-030-49186-4_6","volume-title":"Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations","author":"Kirtas M.","year":"2020","unstructured":"M. Kirtas, Konstantinos Tsampazis, Nikolaos Passalis, and Anastasios Tefas. 2020. Deepbots: A webots-based deep reinforcement learning framework for robotics. In Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, 64\u201375."},{"issue":"2","key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/JAS.2017.7510436","article-title":"Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm","volume":"6","author":"Jagatheesan K.","year":"2019","unstructured":"K. Jagatheesan, B. Anand, S. Samanta, N. Dey, A. S. Ashour, and V. E. Balas. 2019. Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm. IEEE\/CAA J. Autom. Sinica 6, 2 (March2019), 503\u2013515.","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"issue":"3","key":"e_1_3_1_18_2","first-page":"1735","article-title":"Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback","volume":"51","author":"Kong Linghuan","year":"2019","unstructured":"Linghuan Kong, Wei He, Yiting Dong, Long Cheng, Chenguang Yang, and Zhijun Li. 2019. Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback. IEEE Trans. Syst. Man Cybernet.: Syst. 51, 3 (2019), 1735\u20131746.","journal-title":"IEEE Trans. Syst. Man Cybernet.: Syst."},{"key":"e_1_3_1_19_2","article-title":"Learning visual servoing with deep features and fitted q-iteration","author":"Lee Alex X.","year":"2017","unstructured":"Alex X. Lee, Sergey Levine, and Pieter Abbeel. 2017. Learning visual servoing with deep features and fitted q-iteration. arXiv:1703.11000. Retrieved from https:\/\/arxiv.org\/abs\/1703.11000.","journal-title":"arXiv:1703.11000"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2927869"},{"issue":"5","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1109\/TII.2015.2470223","article-title":"A data-driven variable-gain control strategy for an ultra-precision wafer stage with accelerated iterative parameter tuning","volume":"11","author":"Li Min","year":"2015","unstructured":"Min Li, Yu Zhu, Kaiming Yang, and Chuxiong Hu. 2015. A data-driven variable-gain control strategy for an ultra-precision wafer stage with accelerated iterative parameter tuning. IEEE Trans. Industr. Inf. 11, 5 (October2015), 1179\u20131189.","journal-title":"IEEE Trans. Industr. Inf."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICInfA.2018.8812442"},{"key":"e_1_3_1_23_2","first-page":"658","volume-title":"Proceedings of the IEEE International Conference on Robotics and Automation (ICRA\u201920)","author":"Li Yimeng","year":"2020","unstructured":"Yimeng Li and Jana Ko\u0161ecka. 2020. Learning view and target invariant visual servoing for navigation. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA\u201920). IEEE, 658\u2013664."},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2022.3160052"},{"key":"e_1_3_1_25_2","unstructured":"Timothy P. Lillicrap Jonathan J. Hunt and Alexander Pritzel et. al. (2015). Continuous control with deep reinforcement learning. arXiv:1509.02971. Retrieved from https:\/\/arxiv.org\/abs\/1509.02971."},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.isatra.2020.12.039","article-title":"Robust predictive visual servoing control for an inertially stabilized platform with uncertain kinematics","volume":"114","author":"Liu Xiangyang","year":"2021","unstructured":"Xiangyang Liu, Jianliang Mao, Jun Yang, Shihua Li, and Kaifeng Yang. 2021. Robust predictive visual servoing control for an inertially stabilized platform with uncertain kinematics. ISA Trans. 114 (2021), 347\u2013358.","journal-title":"ISA Trans."},{"issue":"8","key":"e_1_3_1_27_2","first-page":"1","article-title":"Visual servoing for an autonomous hexarotor using a neural network based PID controller","volume":"17","author":"Lopez-Franco Carlos","year":"2017","unstructured":"Carlos Lopez-Franco, Javier Gomez-Avila, Alma Y. Alanis, and Carlos Villase\u00f1or. 2017. Visual servoing for an autonomous hexarotor using a neural network based PID controller. Sensors 17, 8 (August2017), 1\u201317.","journal-title":"Sensors"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/70.760345"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAE.2010.5451239"},{"issue":"2","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1109\/TEC.2019.2952666","article-title":"Speed-sensorless vector control of an induction generator including stray load and iron losses and online parameter tuning","volume":"35","author":"Ba\u0161i\u0107 M.","year":"2020","unstructured":"M. Ba\u0161i\u0107, D. Vukadinovi\u0107, I. Grgi\u0107, and M. Bubalo. 2020. Speed-sensorless vector control of an induction generator including stray load and iron losses and online parameter tuning. IEEE Trans. Energy Convers. 35, 2 (June2020), 724\u2013732.","journal-title":"IEEE Trans. Energy Convers."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.5772\/5618"},{"issue":"11","key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"4744","DOI":"10.1109\/TCYB.2019.2899246","article-title":"VOR adaptation on a humanoid iCub robot using a spiking cerebellar model","volume":"50","author":"Naveros Francisco","year":"2019","unstructured":"Francisco Naveros, Niceto R Luque, Eduardo Ros, and Angelo Arleo. 2019. VOR adaptation on a humanoid iCub robot using a spiking cerebellar model. IEEE Trans. Cybernet. 50, 11 (2019), 4744\u20134757.","journal-title":"IEEE Trans. Cybernet."},{"issue":"11","key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"4744","DOI":"10.1109\/TCYB.2019.2899246","article-title":"VOR adaptation on a humanoid iCub robot using a spiking cerebellar model","volume":"50","author":"Naveros Francisco","year":"2020","unstructured":"Francisco Naveros, Niceto R. Luque, Eduardo Ros, and Angelo Arleo. 2020. VOR adaptation on a humanoid iCub robot using a spiking cerebellar model. IEEE Trans. Cybernet. 50, 11 (November2020), 4744\u20134757.","journal-title":"IEEE Trans. Cybernet."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2806087"},{"issue":"12","key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1109\/TIE.2011.2179270","article-title":"Novel position-based visual servoing approach to robust global stability under field-of-view constraint","volume":"59","author":"Park Do-Hwan","year":"2011","unstructured":"Do-Hwan Park, Jeong-Hoon Kwon, and In-Joong Ha. 2011. Novel position-based visual servoing approach to robust global stability under field-of-view constraint. IEEE Trans. Industr. Electr. 59, 12 (2011), 4735\u20134752.","journal-title":"IEEE Trans. Industr. Electr."},{"key":"e_1_3_1_36_2","first-page":"979","volume-title":"Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201918)","author":"Sampedro Carlos","year":"2018","unstructured":"Carlos Sampedro, Alejandro Rodriguez-Ramos, Ignacio Gil, Luis Mejias, and Pascual Campoy. 2018. Image-based visual servoing controller for multirotor aerial robots using deep reinforcement learning. In Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201918). IEEE, 979\u2013986."},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-200823"},{"issue":"4","key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1109\/TCDS.2019.2924724","article-title":"A multiple-attribute decision-making approach to reinforcement learning","volume":"12","author":"Shi Haobin","year":"2020","unstructured":"Haobin Shi and Meng Xu. 2020. A multiple-attribute decision-making approach to reinforcement learning. IEEE Trans. Cogn. Dev. Syst. 12, 4 (December2020), 695\u2013708.","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"e_1_3_1_39_2","article-title":"A fuzzy adaptive approach to decoupled visual servoing for a wheeled mobile robot","author":"Shi Haobin","year":"2020","unstructured":"Haobin Shi, Meng Xu, and Kao-Shing Hwang. 2020. A fuzzy adaptive approach to decoupled visual servoing for a wheeled mobile robot. IEEE Trans. Fuzzy Syst. 28, 12 (December 2020), 3229\u20133243.","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.isatra.2016.11.018","article-title":"CLFs-based optimization control for a class of constrained visual servoing systems","volume":"67","author":"Song Xiulan","year":"2017","unstructured":"Xiulan Song and Miaomiao Fu.2017. CLFs-based optimization control for a class of constrained visual servoing systems. ISA Trans. 67 (March2017), 507\u2013514.","journal-title":"ISA Trans."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330724"},{"issue":"4","key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1109\/TFUZZ.2017.2752723","article-title":"A new approach to stability and stabilization analysis for continuous-time takagi\u2013sugeno fuzzy systems with time delay","volume":"26","author":"Wang Likui","year":"2020","unstructured":"Likui Wang and Hak-Keung Lam. 2020. A new approach to stability and stabilization analysis for continuous-time takagi\u2013sugeno fuzzy systems with time delay. IEEE Trans. Fuzzy Syst. 26, 4 (August2020), 2460\u20132465.","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"e_1_3_1_43_2","first-page":"10158","article-title":"Tuning-free plug-and-play proximal algorithm for inverse imaging problems","author":"Wei Kaixuan","year":"2020","unstructured":"Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schonlieb, and Hua Huang.2020. Tuning-free plug-and-play proximal algorithm for inverse imaging problems. In Proceedings of the 37th International Conference on Machine Learning. 10158\u201310169.","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3107742"},{"key":"e_1_3_1_45_2","first-page":"1241","volume-title":"Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201918)","author":"Xie Zhaoming","year":"2018","unstructured":"Zhaoming Xie, Glen Berseth, Patrick Clary, Jonathan Hurst, and Michiel van de Panne. 2018. Feedback control for cassie with deep reinforcement learning. In Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201918). IEEE, 1241\u20131246."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2007.08.008"},{"key":"e_1_3_1_47_2","first-page":"1","article-title":"Learning strategy for continuous robot visual control: A multi-objective perspective","volume":"252","author":"Xu Meng","year":"2022","unstructured":"Meng Xu and Jianping Wang. 2022. Learning strategy for continuous robot visual control: A multi-objective perspective. Knowl.-Bas. Syst. 252, 109448 (2022), 1\u201315.","journal-title":"Knowl.-Bas. Syst."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.061"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.10.048"},{"issue":"11","key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"5419","DOI":"10.1109\/TNNLS.2018.2802650","article-title":"A neural controller for image-based visual servoing of manipulators with physical constraints","volume":"29","author":"Zhang Yinyan","year":"2018","unstructured":"Yinyan Zhang and Shuai Li. 2018. A neural controller for image-based visual servoing of manipulators with physical constraints. IEEE Trans. Neural Netw. Learn. Syst. 29, 11 (2018), 5419\u20135429.","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.888718"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579829","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3579829","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:27Z","timestamp":1750182687000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579829"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":50,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4,30]]}},"alternative-id":["10.1145\/3579829"],"URL":"https:\/\/doi.org\/10.1145\/3579829","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]},"assertion":[{"value":"2022-04-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}