{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:29:03Z","timestamp":1775579343584,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T00:00:00Z","timestamp":1608595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.<\/jats:p>","DOI":"10.3390\/s21010015","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T20:39:29Z","timestamp":1608669569000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Real-Time Single Image Depth Perception in the Wild with Handheld Devices"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8911-3241","authenticated-orcid":false,"given":"Filippo","family":"Aleotti","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giulio","family":"Zaccaroni","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5509-437X","authenticated-orcid":false,"given":"Luca","family":"Bartolomei","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3337-2236","authenticated-orcid":false,"given":"Matteo","family":"Poggi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6276-5282","authenticated-orcid":false,"given":"Fabio","family":"Tosi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3681-7704","authenticated-orcid":false,"given":"Stefano","family":"Mattoccia","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets Robotics: The KITTI Dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res. (IJRR)"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_3","unstructured":"Wang, C., Lin, C.H., and Lucey, S. (2020). Deep nrsfm++: Towards 3d reconstruction in the wild. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.1007\/s11042-014-2191-z","article-title":"In the wild image retrieval and clustering for 3D cultural heritage landmarks reconstruction","volume":"75","author":"Makantasis","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1049\/iet-cvi.2017.0536","article-title":"Human pose estimation method based on single depth image","volume":"12","author":"Wu","year":"2018","journal-title":"IET Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Poggi, M., Aleotti, F., Tosi, F., and Mattoccia, S. (2018, January 1\u20135). Towards Real-Time Unsupervised Monocular Depth Estimation on CPU. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593814"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TPAMI.2008.132","article-title":"Make3d: Learning 3d scene structure from a single still image","volume":"31","author":"Saxena","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ladicky, L., Shi, J., and Pollefeys, M. (2014, January 23\u201328). Pulling things out of perspective. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.19"},{"key":"ref_9","unstructured":"Eigen, D., Puhrsch, C., and Fergus, R. (2014, January 8\u201313). Depth map prediction from a single image using a multi-scale deep network. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., and Navab, N. (2016, January 25\u201328). Deeper Depth Prediction with Fully Convolutional Residual Networks. Proceedings of the Fourth International Conference on 3D Vision (3DV 2016), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.32"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4676","DOI":"10.1109\/TIP.2018.2832296","article-title":"Learning depth from single images with deep neural network embedding focal length","volume":"27","author":"He","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., and Brostow, G.J. (2017, January 21\u201326). Unsupervised Monocular Depth Estimation with Left-Right Consistency. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.699"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tosi, F., Aleotti, F., Poggi, M., and Mattoccia, S. (2019, January 16\u201320). Learning monocular depth estimation infusing traditional stereo knowledge. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01003"},{"key":"ref_14","unstructured":"Watson, J., Firman, M., Brostow, G.J., and Turmukhambetov, D. (November, January 27). Self-Supervised Monocular Depth Hints. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, N., Wang, R., Stuckler, J., and Cremers, D. (2018, January 8\u201314). Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_50"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Andraghetti, L., Myriokefalitakis, P., Dovesi, P.L., Luque, B., Poggi, M., Pieropan, A., and Mattoccia, S. (2019, January 16\u201319). Enhancing self-supervised monocular depth estimation with traditional visual odometry. Proceedings of the 7th International Conference on 3D Vision (3DV), Quebec City, QC, Canada.","DOI":"10.1109\/3DV.2019.00054"},{"key":"ref_17","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., and Koltun, V. (2019). Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., and Lowe, D.G. (2017, January 21\u201326). Unsupervised Learning of Depth and Ego-Motion From Video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.700"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, C., Miguel Buenaposada, J., Zhu, R., and Lucey, S. (2018, January 18\u201322). Learning depth from monocular videos using direct methods. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00216"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mahjourian, R., Wicke, M., and Angelova, A. (2018, January 18\u201322). Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00594"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yin, Z., and Shi, J. (2018, January 18\u201322). GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00212"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zou, Y., Luo, Z., and Huang, J.B. (2018, January 8\u201314). DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_3"},{"key":"ref_23","unstructured":"Chen, Y., Schmid, C., and Sminchisescu, C. (November, January 27). Self-Supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_24","unstructured":"Anurag, R., Jampani, V., Kim, K., Sun, D., Wulff, J., and Black, M.J. (2019, January 16\u201320). Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tosi, F., Aleotti, F., Zama Ramirez, P., Poggi, M., Salti, S., Di Stefano, L., and Mattoccia, S. (2020, January 14\u201319). Distilled Semantics for Comprehensive Scene Understanding from Videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Conference, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00471"},{"key":"ref_26","unstructured":"Godard, C., Mac Aodha, O., Firman, M., and Brostow, G.J. (November, January 27). Digging into self-supervised monocular depth estimation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_27","unstructured":"Dijk, T.V., and Croon, G.D. (November, January 27). How Do Neural Networks See Depth in Single Images?. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_28","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Lai, Z., Huang, G., Wang, B.H., Van Der Maaten, L., Campbell, M., and Weinberger, K.Q. (2018). Anytime Stereo Image Depth Estimation on Mobile Devices. arXiv.","DOI":"10.1109\/ICRA.2019.8794003"},{"key":"ref_30","unstructured":"Diana, W., Ma, F., Yang, T.-J., Karaman, S., and Sze, V. (2019, January 20\u201324). FastDepth: Fast Monocular Depth Estimation on Embedded Systems. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada."},{"key":"ref_31","unstructured":"Garg, R., Wadhwa, N., Ansari, S., and Barron, J.T. (November, January 27). Learning Single Camera Depth Estimation using Dual-Pixels. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wadhwa, N., Orts-Escolano, S., H\u00e4ne, C., Fanello, S., and Garg, R. (2020). Du2 Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels. arXiv.","DOI":"10.1007\/978-3-030-58452-8_34"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., and Zhang, Y. (2017, January 10\u201312). Matterport3D: Learning from RGB-D Data in Indoor Environments. Proceedings of the International Conference on 3D Vision (3DV), Qingdao, China.","DOI":"10.1109\/3DV.2017.00081"},{"key":"ref_34","unstructured":"Vasiljevic, I., Kolkin, N., Zhang, S., Luo, R., Wang, H., Dai, F.Z., Daniele, A.F., Mostajabi, M., Basart, S., and Walter, M.R. (2019). DIODE: A Dense Indoor and Outdoor DEpth Dataset. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Z., Dekel, T., Cole, F., Tucker, R., Snavely, N., Liu, C., and Freeman, W.T. (2019, January 16\u201320). Learning the depths of moving people by watching frozen people. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00465"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guo, X., Li, H., Yi, S., Ren, J., and Wang, X. (2018, January 18\u201322). Learning monocular depth by distilling cross-domain stereo networks. Proceedings of the European Conference on Computer Vision (ECCV), Salt Lake City, UT, USA.","DOI":"10.1007\/978-3-030-01252-6_30"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Poggi, M., Aleotti, F., Tosi, F., and Mattoccia, S. (2020, January 14\u201319). On the uncertainty of self-supervised monocular depth estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Conference, Washington, DC, USA.","DOI":"10.1109\/CVPR42600.2020.00329"},{"key":"ref_38","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017). Automatic differentiation in PyTorch. NIPS-W, Available online: https:\/\/openreview.net\/forum?id=BJJsrmfCZ."},{"key":"ref_39","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016, January 2\u20134). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_40","unstructured":"Lee, J., Chirkov, N., Ignasheva, E., Pisarchyk, Y., Shieh, M., Riccardi, F., Sarokin, R., Kulik, A., and Grundmann, M. (2019). On-device neural net inference with mobile gpus. arXiv."},{"key":"ref_41","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FA, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Peluso, V., Cipolletta, A., Calimera, A., Poggi, M., Tosi, F., and Mattoccia, S. (2019). Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms, IEEE. Design Automation and Test in Europe (DATE).","DOI":"10.23919\/DATE.2019.8714893"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, T.J., Howard, A., Chen, B., Zhang, X., Go, A., Sandler, M., Sze, V., and Adam, H. (2018, January 8\u201314). Netadapt: Platform-aware neural network adaptation for mobile applications. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_18"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2012, January 7\u201312). A benchmark for the evaluation of RGB-D SLAM systems. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Silberman, N., Derek Hoiem, P.K., and Fergus, R. (2012, January 7\u201313). Indoor Segmentation and Support Inference from RGBD Images. Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Menze, M., and Geiger, A. (2015, January 7\u201312). Object Scene Flow for Autonomous Vehicles. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_49","unstructured":"Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., and Kamali, S. (2017, December 21). OpenImages: A Public Dataset for Large-Scale Multi-Label and Multi-Class Image Classification. Available online: https:\/\/storage.googleapis.com\/openimages\/web\/index.html."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","unstructured":"Kingma, D., and Ba, J. (2014, January 14\u201316). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_52","unstructured":"Sch\u00f6nberger, J.L., and Frahm, J.M. (July, January 26). Structure-from-Motion Revisited. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_53","unstructured":"Sch\u00f6nberger, J.L., Zheng, E., Pollefeys, M., and Frahm, J.M. (July, January 26). Pixelwise View Selection for Unstructured Multi-View Stereo. Proceedings of the European Conference on Computer Vision (ECCV), Las Vegas, NV, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, Z., and Snavely, N. (2018, January 18\u201322). MegaDepth: Learning Single-View Depth Prediction from Internet Photos. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00218"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Holynski, A., and Kopf, J. (2018). Fast Depth Densification for Occlusion-aware Augmented Reality, ACM. ACM Transactions on Graphics (Proc. SIGGRAPH Asia).","DOI":"10.1145\/3272127.3275083"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Luo, X., Huang, J., Szeliski, R., Matzen, K., and Kopf, J. (2020). Consistent Video Depth Estimation, ACM. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH).","DOI":"10.1145\/3386569.3392377"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:48:20Z","timestamp":1760179700000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,22]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21010015"],"URL":"https:\/\/doi.org\/10.3390\/s21010015","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,22]]}}}