{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:56:40Z","timestamp":1767085000455,"version":"3.40.3"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031460012"},{"type":"electronic","value":"9783031460029"}],"license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-46002-9_20","type":"book-chapter","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T16:02:36Z","timestamp":1702483356000},"page":"311-330","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Benchmark: Formal Verification of\u00a0Semantic Segmentation Neural Networks"],"prefix":"10.1007","author":[{"given":"Neelanjana","family":"Pal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seojin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taylor T.","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Anderson, G., Pailoor, S., Dillig, I., Chaudhuri, S.: Optimization and abstraction: A synergistic approach for analyzing neural network robustness. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 731\u2013744. PLDI 2019, Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3314221.3314614","DOI":"10.1145\/3314221.3314614"},{"key":"20_CR2","unstructured":"Bak, S., Liu, C., Johnson, T.: The second international verification of neural networks competition (vnn-comp 2021): Summary and results. arXiv preprint arXiv:2109.00498 (2021)"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Blum, H., Sarlin, P.E., Nieto, J., Siegwart, R., Cadena, C.: Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00294"},{"issue":"04","key":"20_CR4","doi-asserted-by":"publisher","first-page":"3291","DOI":"10.1609\/aaai.v34i04.5729","volume":"34","author":"E Botoeva","year":"2020","unstructured":"Botoeva, E., Kouvaros, P., Kronqvist, J., Lomuscio, A., Misener, R.: Efficient verification of relu-based neural networks via dependency analysis. Proc. AAAI Conf. Artif. Intell. 34(04), 3291\u20133299 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i04.5729","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Brazil, G., Yin, X., Liu, X.: Illuminating pedestrians via simultaneous detection & segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4950\u20134959 (2017)","DOI":"10.1109\/ICCV.2017.530"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Brix, C., M\u00fcller, M.N., Bak, S., Johnson, T.T., Liu, C.: First three years of the international verification of neural networks competition (VNN-Comp). Int. J. Softw. Tools Technol. Transfer 25, 329\u2013339 (2023)","DOI":"10.1007\/s10009-023-00703-4"},{"issue":"2","key":"20_CR7","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","volume":"30","author":"GJ Brostow","year":"2009","unstructured":"Brostow, G.J., Fauqueur, J., Cipolla, R.: Semantic object classes in video: A high-definition ground truth database. Pattern Recogn. Lett. 30(2), 88\u201397 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"20_CR8","unstructured":"Dathathri, S., et al.: Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming (2020)"},{"key":"20_CR9","unstructured":"Demarchi, S.: VNN-LIB \u2013 vnnlib.org. https:\/\/www.vnnlib.org\/ Accessed 31 Aug 2023"},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/978-3-319-68167-2_19","volume-title":"Automated Technology for Verification and Analysis","author":"R Ehlers","year":"2017","unstructured":"Ehlers, R.: Formal verification of piece-wise linear feed-forward neural networks. In: D\u2019Souza, D., Narayan Kumar, K. (eds.) Automated Technology for Verification and Analysis, pp. 269\u2013286. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68167-2_19"},{"issue":"1","key":"20_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TAC.2020.3046193","volume":"67","author":"M Fazlyab","year":"2022","unstructured":"Fazlyab, M., Morari, M., Pappas, G.J.: Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming. IEEE Trans. Auto. Control 67(1), 1\u201315 (2022). https:\/\/doi.org\/10.1109\/TAC.2020.3046193","journal-title":"IEEE Trans. Auto. Control"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Flohr, F., Gavrila, D., et al.: Pedcut: an iterative framework for pedestrian segmentation combining shape models and multiple data cues. In: BMVC (2013)","DOI":"10.5244\/C.27.66"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., Vechev, M.: Ai2: Safety and robustness certification of neural networks with abstract interpretation. In: 2018 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE (2018)","DOI":"10.1109\/SP.2018.00058"},{"key":"20_CR14","unstructured":"Girard-Satabin, J., Alberti, M., Bobot, F., Chihani, Z., Lemesle, A.: Caisar: A platform for characterizing artificial intelligence safety and robustness. arXiv preprint arXiv:2206.03044 (2022)"},{"issue":"4","key":"20_CR15","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1109\/TMI.2015.2508280","volume":"35","author":"Y Guo","year":"2015","unstructured":"Guo, Y., Gao, Y., Shen, D.: Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans. Med. Imaging 35(4), 1077\u20131089 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5s","key":"20_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3358228","volume":"18","author":"C Huang","year":"2019","unstructured":"Huang, C., Fan, J., Li, W., Chen, X., Zhu, Q.: Reachnn: Reachability analysis of neural-network controlled systems. ACM Trans. Embedded Comput. Syst. (TECS) 18(5s), 1\u201322 (2019)","journal-title":"ACM Trans. Embedded Comput. Syst. (TECS)"},{"key":"20_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-63387-9_1","volume-title":"Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I","author":"X Huang","year":"2017","unstructured":"Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I, pp. 3\u201329. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_1"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Ivanov, R., Weimer, J., Alur, R., Pappas, G.J., Lee, I.: Verisig: verifying safety properties of hybrid systems with neural network controllers. In: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, pp. 169\u2013178 (2019)","DOI":"10.1145\/3302504.3311806"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Kamann, C., Rother, C.: Benchmarking the robustness of semantic segmentation models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8828\u20138838 (2020)","DOI":"10.1109\/CVPR42600.2020.00885"},{"key":"20_CR20","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-319-63387-9_5","volume-title":"Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I","author":"G Katz","year":"2017","unstructured":"Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I, pp. 97\u2013117. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_5"},{"key":"20_CR21","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-030-25540-4_26","volume-title":"Computer Aided Verification: 31st International Conference, CAV 2019, New York City, NY, USA, July 15-18, 2019, Proceedings, Part I","author":"G Katz","year":"2019","unstructured":"Katz, G., et al.: The Marabou framework for verification and analysis of deep neural networks. In: Dillig, I., Tasiran, S. (eds.) Computer Aided Verification: 31st International Conference, CAV 2019, New York City, NY, USA, July 15-18, 2019, Proceedings, Part I, pp. 443\u2013452. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-25540-4_26"},{"key":"20_CR22","unstructured":"LeCun, Y.: The mnist database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/ (1998)"},{"key":"20_CR23","unstructured":"Li, B., Liu, S., Xu, W., Qiu, W.: Real-time object detection and semantic segmentation for autonomous driving. In: MIPPR 2017: Automatic Target Recognition and Navigation. vol. 10608, pp. 167\u2013174. SPIE (2018)"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Liu, C., Arnon, T., Lazarus, C., Strong, C., Barrett, C., Kochenderfer, M.J., et al.: Algorithms for verifying deep neural networks. Found. Trends\u00ae in Optimization 4(3\u20134), 244\u2013404 (2021)","DOI":"10.1561\/2400000035"},{"key":"20_CR25","unstructured":"Lomuscio, A., Maganti, L.: An approach to reachability analysis for feed-forward relu neural networks. arXiv preprint arXiv:1706.07351 (2017)"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Mohapatra, J., Weng, T.W., Chen, P.Y., Liu, S., Daniel, L.: Towards verifying robustness of neural networks against a family of semantic perturbations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00032"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"20_CR28","unstructured":"M\u00fcller, M.N., Brix, C., Bak, S., Liu, C., Johnson, T.T.: The third international verification of neural networks competition (vnn-comp 2022): summary and results. arXiv preprint arXiv:2212.10376 (2022)"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Ruan, W., Huang, X., Kwiatkowska, M.: Reachability analysis of deep neural networks with provable guarantees. arXiv preprint arXiv:1805.02242 (2018)","DOI":"10.24963\/ijcai.2018\/368"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D., Kwiatkowska, M.: Global robustness evaluation of deep neural networks with provable guarantees for the $$ l_0 $$ norm. arXiv preprint arXiv:1804.05805 (2018)","DOI":"10.24963\/ijcai.2019\/824"},{"key":"20_CR31","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-81685-8_6","volume-title":"Computer Aided Verification: 33rd International Conference, CAV 2021, Virtual Event, July 20\u201323, 2021, Proceedings, Part I","author":"D Shriver","year":"2021","unstructured":"Shriver, D., Elbaum, S., Dwyer, M.B.: DNNV: a framework for deep neural network verification. In: Silva, A., Leino, K.R.M. (eds.) Computer Aided Verification: 33rd International Conference, CAV 2021, Virtual Event, July 20\u201323, 2021, Proceedings, Part I, pp. 137\u2013150. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-81685-8_6"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Singh, G., Gehr, T., P\u00fcschel, M., Vechev, M.: An abstract domain for certifying neural networks. Proc. ACM Programm. Lang. 3(POPL), 41 (2019)","DOI":"10.1145\/3290354"},{"key":"20_CR33","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Szeliski, R.: Computer vision: algorithms and applications 2nd edition (2021)","DOI":"10.1007\/978-3-030-34372-9"},{"issue":"9","key":"20_CR35","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.3390\/app8091575","volume":"8","author":"X Tao","year":"2018","unstructured":"Tao, X., Zhang, D., Ma, W., Liu, X., Xu, D.: Automatic metallic surface defect detection and recognition with convolutional neural networks. Appl. Sci. 8(9), 1575 (2018)","journal-title":"Appl. Sci."},{"key":"20_CR36","unstructured":"Thoma, M.: A survey of semantic segmentation. arXiv preprint arXiv:1602.06541 (2016)"},{"key":"20_CR37","unstructured":"Tjeng, V., Xiao, K.Y., Tedrake, R.: Evaluating robustness of neural networks with mixed integer programming. In: International Conference on Learning Representations (2019)"},{"key":"20_CR38","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-53288-8_2","volume-title":"Computer Aided Verification: 32nd International Conference, CAV 2020, Los Angeles, CA, USA, July 21\u201324, 2020, Proceedings, Part I","author":"H-D Tran","year":"2020","unstructured":"Tran, H.-D., Bak, S., Xiang, W., Johnson, T.T.: Verification of Deep Convolutional Neural Networks Using ImageStars. In: Lahiri, S.K., Wang, C. (eds.) Computer Aided Verification: 32nd International Conference, CAV 2020, Los Angeles, CA, USA, July 21\u201324, 2020, Proceedings, Part I, pp. 18\u201342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-53288-8_2"},{"key":"20_CR39","doi-asserted-by":"crossref","unstructured":"Tran, H.D., Cei, F., Lopez, D.M., Johnson, T.T., Koutsoukos, X.: Safety verification of cyber-physical systems with reinforcement learning control. In: ACM SIGBED International Conference on Embedded Software (EMSOFT\u201919). ACM (2019)","DOI":"10.1145\/3358230"},{"key":"20_CR40","doi-asserted-by":"crossref","unstructured":"Tran, H.D., et al.: Star-based reachability analysis of deep neural networks. In: Formal Methods-The Next 30 Years: Third World Congress, FM 2019, Porto, Portugal, October 7\u201311, 2019, Proceedings 3, pp. 670\u2013686. Springer (2019)","DOI":"10.1007\/978-3-030-30942-8_39"},{"key":"20_CR41","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1007\/978-3-030-30942-8_39","volume-title":"Formal Methods \u2013 The Next 30 Years: Third World Congress, FM 2019, Porto, Portugal, October 7\u201311, 2019, Proceedings","author":"H-D Tran","year":"2019","unstructured":"Tran, H.-D., et al.: Star-based reachability analysis of deep neural networks. In: ter Beek, M.H., McIver, A., Oliveira, J.N. (eds.) Formal Methods \u2013 The Next 30 Years: Third World Congress, FM 2019, Porto, Portugal, October 7\u201311, 2019, Proceedings, pp. 670\u2013686. Springer International Publishing, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30942-8_39"},{"key":"20_CR42","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-81685-8_12","volume-title":"Computer Aided Verification: 33rd International Conference, CAV 2021, Virtual Event, July 20\u201323, 2021, Proceedings, Part I","author":"H-D Tran","year":"2021","unstructured":"Tran, H.-D., et al.: Robustness verification of semantic segmentation neural networks using relaxed reachability. In: Silva, A., Leino, K.R.M. (eds.) Computer Aided Verification: 33rd International Conference, CAV 2021, Virtual Event, July 20\u201323, 2021, Proceedings, Part I, pp. 263\u2013286. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-81685-8_12"},{"key":"20_CR43","unstructured":"Tran, H.D., Xiang, W., Johnson, T.T.: Verification approaches for learning-enabled autonomous cyber-physical systems. IEEE Design & Test (2020)"},{"key":"20_CR44","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-53288-8_1","volume-title":"Computer Aided Verification: 32nd International Conference, CAV 2020, Los Angeles, CA, USA, July 21\u201324, 2020, Proceedings, Part I","author":"H-D Tran","year":"2020","unstructured":"Tran, H.-D., et al.: NNV: the neural network verification tool for deep neural networks and learning-enabled cyber-physical systems. In: Lahiri, S.K., Wang, C. (eds.) Computer Aided Verification: 32nd International Conference, CAV 2020, Los Angeles, CA, USA, July 21\u201324, 2020, Proceedings, Part I, pp. 3\u201317. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-53288-8_1"},{"key":"20_CR45","doi-asserted-by":"crossref","unstructured":"Tseng, Y.H., Jan, S.S.: Combination of computer vision detection and segmentation for autonomous driving. In: 2018 IEEE\/ION Position, Location and Navigation Symposium (PLANS), pp. 1047\u20131052. IEEE (2018)","DOI":"10.1109\/PLANS.2018.8373485"},{"issue":"2","key":"20_CR46","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TIP.2017.2768621","volume":"27","author":"Z Wang","year":"2017","unstructured":"Wang, Z., Wei, L., Wang, L., Gao, Y., Chen, W., Shen, D.: Hierarchical vertex regression-based segmentation of head and neck CT images for radiotherapy planning. IEEE Trans. Image Process. 27(2), 923\u2013937 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"20_CR47","unstructured":"Xiang, W., Johnson, T.T.: Reachability analysis and safety verification for neural network control systems. arXiv preprint arXiv:1805.09944 (2018)"},{"key":"20_CR48","unstructured":"Xiang, W., et al.: Verification for machine learning, autonomy, and neural networks survey. arXiv preprint arXiv:1810.01989 (2018)"},{"key":"20_CR49","doi-asserted-by":"crossref","unstructured":"Xie, X., Kersting, K., Neider, D.: Neuro-symbolic verification of deep neural networks. arXiv preprint arXiv:2203.00938 (2022)","DOI":"10.24963\/ijcai.2022\/503"},{"issue":"9","key":"20_CR50","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE Transac. Neural Netw. Learn. Syst. 30(9), 2805\u20132824 (2019)","journal-title":"IEEE Transac. Neural Netw. Learn. Syst."},{"key":"20_CR51","unstructured":"Zhang, H., Weng, T.W., Chen, P.Y., Hsieh, C.J., Daniel, L.: Efficient neural network robustness certification with general activation functions. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31, pp. 4939\u20134948. Curran Associates, Inc. (2018)"},{"issue":"3","key":"20_CR52","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1109\/TBME.2015.2466616","volume":"63","author":"X Zhu","year":"2015","unstructured":"Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607\u2013618 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"20_CR53","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neuroimage.2014.05.078","volume":"100","author":"X Zhu","year":"2014","unstructured":"Zhu, X., Suk, H.I., Shen, D.: A novel matrix-similarity based loss function for joint regression and classification in ad diagnosis. Neuroimage 100, 91\u2013105 (2014)","journal-title":"Neuroimage"}],"container-title":["Lecture Notes in Computer Science","Bridging the Gap Between AI and Reality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46002-9_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T16:06:05Z","timestamp":1702483565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46002-9_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"ISBN":["9783031460012","9783031460029"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46002-9_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"14 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AISoLA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bridging the Gap between AI and Reality","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aisola2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023-aisola.isola-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}