{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:06:45Z","timestamp":1759334805266,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032013767"},{"type":"electronic","value":"9783032013774"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Here we present a new approach to training and operationalizing segmentation models for de-arraying Tissue Micro Arrays (TMAs). The scarcity of large, high-quality datasets in sensitive domains such as human tissue samples, coupled with strict privacy regulations to protect doner interests, poses significant obstacles to training robust and generalised segmentation models. To address these challenges, we introduce a new Low-Code\/No-Code (LCNC) Domain-Specific Language (DSL) integrated into the Cinco de Bio (CdB) platform. The DSL consists of multiple Service-Independent Building Blocks (SIBs), each providing a distinct functionality essential to creating a pipeline. LCNC enables biologists to train and deploy de-arraying models without writing code. Our methodology incorporates a domain-specific data augmentation technique that generates pseudo-synthetic samples from a minimal set of real data. It also leverages AutoML techniques, including Neural Architecture Search (NAS) and hyperparameter optimisation, to automate the model development process. Furthermore, we present an architectural update to the Cinco de Bio platform, adopting a \u201cModel as Data\u201d paradigm that treats neural network models as dynamic, versioned data assets that can be used as inputs to SIBs. This work provides a practical solution to the challenges of distribution shift and data scarcity in sensitive health domains, where building sufficiently sized datasets to train generalise robust models is infeasible. The proposed LCNC DSL and accompanying pipeline enables domain experts to effectively leverage Artificial Intelligence (AI) technologies and tailor them to their own data.<\/jats:p>","DOI":"10.1007\/978-3-032-01377-4_5","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:32:12Z","timestamp":1759271532000},"page":"104-121","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LC\/NC Pipeline for\u00a0Training and\u00a0Operationalising Segmentation Models in\u00a0a\u00a0Data Scarce Domain: De-arraying Tissue MicroArrays"],"prefix":"10.1007","author":[{"given":"Colm","family":"Brandon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c9anna","family":"Fennell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amandeep","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiziana","family":"Margaria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"issue":"1","key":"5_CR1","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR2","unstructured":"Bergstra, J., Yamins, D., Cox, D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: International Conference on Machine Learning, pp. 115\u2013123. PMLR (2013)"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Bo\u00dfelmann, S.: Evolution of Ecosystems for Language-Driven Engineering. Phd thesis, TU Dortmund University (2023). https:\/\/doi.org\/10.17877\/DE290R-23218","DOI":"10.17877\/DE290R-23218"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Brandon, C., et al.: Cinco de bio: a low-code platform for domain-specific workflows for biomedical imaging research. BioMedInformatics 4(3), 1865\u20131883 (2024)","DOI":"10.3390\/biomedinformatics4030102"},{"key":"5_CR5","unstructured":"Community, O.: Open neural network exchange (onnx). https:\/\/github.com\/onnx\/onnx (2025), version: 1.18.0"},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MMUL.2003.1195157","volume":"10","author":"C Dorai","year":"2003","unstructured":"Dorai, C., Venkatesh, S.: Bridging the semantic gap with computational media aesthetics. IEEE Multimedia 10(2), 15\u201317 (2003). https:\/\/doi.org\/10.1109\/MMUL.2003.1195157","journal-title":"IEEE Multimedia"},{"issue":"55","key":"5_CR7","first-page":"1","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(55), 1\u201321 (2019)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR8","unstructured":"\u00c9anna Fennell, C.B..: cellmaps: development repository for cellmaps data process services, data and process ontologies and the sdk(s). https:\/\/github.com\/colm-brandon-ul\/cellmaps (2024)"},{"key":"5_CR9","doi-asserted-by":"crossref","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","volume":"212","author":"X He","year":"2021","unstructured":"He, X., Zhao, K., Chu, X.: Automl: a survey of the state-of-the-art. Knowl.-Based Syst. 212, 106622 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"5_CR10","unstructured":"Hein, A.M.: Identification and bridging of semantic gaps in the context of multi-domain engineering (2010)"},{"key":"5_CR11","unstructured":"International Telecommunication union: q-series intelligent network recommendation structure. https:\/\/www.itu.int\/rec\/T-REC-Q.1200-199303-S\/en (1993), Accessed 11 Mar 2025"},{"issue":"6","key":"5_CR12","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","volume":"2","author":"GA Kaissis","year":"2020","unstructured":"Kaissis, G.A., Makowski, M.R., R\u00fcckert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2(6), 305\u2013311 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"5_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-642-34026-0_5","volume-title":"Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change","author":"A-L Lamprecht","year":"2012","unstructured":"Lamprecht, A.-L., Margaria, T.: Scientific workflows: eternal components, changing interfaces, varying compositions. In: Margaria, T., Steffen, B. (eds.) ISoLA 2012. LNCS, vol. 7609, pp. 47\u201363. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-34026-0_5"},{"key":"5_CR14","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-45006-2","volume-title":"Process Design for Natural Scientists","year":"2014","unstructured":"Lamprecht, A.-L., Margaria, T. (eds.): Process Design for Natural Scientists. CCIS, vol. 500. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45006-2"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Lamprecht, A.L., Margaria, T.: Scientific Workflows with XMDD: a way to use process modeling in computational science education. Procedia Comput. Sci. 51, 1927\u20131936 (2015). https:\/\/doi.org\/10.1016\/j.procs.2015.05.457, 15th International Conference On Computational Science (ICCS 2015): Computational Science at the Gates of Nature","DOI":"10.1016\/j.procs.2015.05.457"},{"key":"5_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/978-3-540-79450-9_42","volume-title":"Bioinformatics Research and Applications","author":"A-L Lamprecht","year":"2008","unstructured":"Lamprecht, A.-L., Margaria, T., Steffen, B.: Seven variations of an alignment workflow - an illustration of agile process design and management in Bio-jETI. In: M\u0103ndoiu, I., Sunderraman, R., Zelikovsky, A. (eds.) ISBRA 2008. LNCS, vol. 4983, pp. 445\u2013456. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-79450-9_42"},{"key":"5_CR17","unstructured":"Lamprecht, A.L., Margaria, T., Steffen, B.: Supporting process development in Bio-jETI by model checking and synthesis. In: Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2009). CEUR Workshop Proceedings, vol.\u00a0435 (2008)"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Lamprecht, A.L., Margaria, T., Steffen, B.: Bio-jETI: a framework for semantics-based service composition. BMC Bioinf. 10(Suppl 10), S8 (2009). https:\/\/doi.org\/10.1186\/1471-2105-10-S10-S8","DOI":"10.1186\/1471-2105-10-S10-S8"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Lamprecht, A.L., Margaria, T., Steffen, B.: From Bio-jETI process models to native code. In: 14th IEEE International Conference on Engineering of Complex Computer Systems, ICECCS 2009, Potsdam, Germany, 2\u20134 June 2009, pp. 95\u2013101. IEEE Computer Society, June 2009","DOI":"10.1109\/ICECCS.2009.50"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Lamprecht, A.L., Margaria, T., Steffen, B.: Bioinformatics: processes and workflows. In: Laplante, P.A. (ed.) Encyclopedia of Software Engineering, chap.\u00a011, pp. 118\u2013130. Taylor & Francis, November 2010. https:\/\/doi.org\/10.1081\/E-ESE-120044612","DOI":"10.1081\/E-ESE-120044612"},{"issue":"1","key":"5_CR21","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"issue":"11","key":"5_CR22","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MC.2007.398","volume":"40","author":"T Margaria","year":"2007","unstructured":"Margaria, T.: Service is in the eyes of the beholder. Computer 40(11), 33\u201337 (2007)","journal-title":"Computer"},{"issue":"3","key":"5_CR23","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-642-34032-1_8","volume":"17","author":"T Margaria","year":"2013","unstructured":"Margaria, T., Bo\u00dfelmann, S., Kujath, B.: Simple modeling of executable role-based workflows: an application in the healthcare domain. J. Integr. Des. Process. Sci. 17(3), 25\u201345 (2013). https:\/\/doi.org\/10.1007\/978-3-642-34032-1_8","journal-title":"J. Integr. Des. Process. Sci."},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Margaria, T., Bo\u00dfelmann, S., Doedt, M., Floyd, B.D., Steffen, B.: Customer-oriented business process management: visions and obstacles. In: Hinchey, M., Coyle, L. (eds.) Conquering Complexity, pp. 407\u2013429. Springer London (2012). https:\/\/doi.org\/10.1007\/978-1-4471-2297-5_16","DOI":"10.1007\/978-1-4471-2297-5_16"},{"issue":"10","key":"5_CR25","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MC.2006.355","volume":"39","author":"T Margaria","year":"2006","unstructured":"Margaria, T., Steffen, B.: Service engineering: linking business and it. Computer 39(10), 45\u201355 (2006)","journal-title":"Computer"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Margaria, T., Steffen, B.: Business process modelling in the jABC: the one-thing-approach. In: Cardoso, J., van\u00a0der Aalst, W. (eds.) Handbook of Research on Business Process Modeling. IGI Global (2009)","DOI":"10.4018\/978-1-60566-288-6.ch001"},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"Margaria, T., Steffen, B.: Service-orientation: conquering complexity with XMDD. In: Hinchey, M., Coyle, L. (eds.) Conquering Complexity, pp. 217\u2013236. Springer, London (2012). https:\/\/doi.org\/10.1007\/978-1-4471-2297-5_10","DOI":"10.1007\/978-1-4471-2297-5_10"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Margaria, T., Steffen, B.: Extreme model-driven development (xmdd) technologies as a hands-on approach to software development without coding. In: Encyclopedia of Education and Information Technologies, pp. 732\u2013750 (2020)","DOI":"10.1007\/978-3-030-10576-1_208"},{"key":"5_CR29","unstructured":"Mellor, S.J., Balcer, M.J.: Executable UML: A Foundation for Model-Driven Architecture. Addison-Wesley Professional (2002)"},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Mussbacher, G., et al.: The relevance of model-driven engineering thirty years from now. In: Proceeding of the 17th International Conference on Model Driven Engineering Languages and Systems (MODELS 2014), pp. 183\u2013200. No.\u00a08767 in LNCS, Springer (2014). https:\/\/doi.org\/10.1007\/978-3-319-11653-2_12","DOI":"10.1007\/978-3-319-11653-2_12"},{"key":"5_CR31","unstructured":"Naur, P., Randell, B. (eds.): Software Engineering: Report of a Conference Sponsored by the NATO Science Committee, Garmisch, Germany, 7\u201311 October 1968. Scientific Affairs Division, NATO, Brussels 39 Belgium (1969)"},{"key":"5_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-017-2006-0","volume":"19","author":"HN Nguyen","year":"2018","unstructured":"Nguyen, H.N., Paveau, V., Cauchois, C., Kervrann, C.: Atmad: robust image analysis for automatic tissue microarray de-arraying. BMC Bioinf. 19, 1\u201323 (2018)","journal-title":"BMC Bioinf."},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Parida, S.K., Gerostathopoulos, I., Bogner, J.: How do model export formats impact the development of ml-enabled systems? a case study on model integration. arXiv preprint arXiv:2502.00429 (2025)","DOI":"10.1109\/CAIN66642.2025.00014"},{"issue":"1","key":"5_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke, N., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3(1), 1\u20137 (2020)","journal-title":"NPJ Digit. Med."},{"key":"5_CR35","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"5_CR36","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1038\/s41592-021-01308-y","volume":"19","author":"D Schapiro","year":"2022","unstructured":"Schapiro, D., et al.: Mcmicro: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat. Methods 19(3), 311\u2013315 (2022)","journal-title":"Nat. Methods"},{"issue":"1","key":"5_CR37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4103\/0971-6203.58777","volume":"35","author":"N Sharma","year":"2010","unstructured":"Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. J. Med. Phys. 35(1), 3\u201314 (2010)","journal-title":"J. Med. Phys."},{"key":"5_CR38","unstructured":"Stefani, D., Peroni, S., Turchet, L., et\u00a0al.: A comparison of deep learning inference engines for embedded real-time audio classification. In: Proceedings of the International Conference on Digital Audio Effects, DAFx, vol.\u00a03, pp. 256\u2013263. MDPI (Multidisciplinary Digital Publishing Institute) (2022)"},{"key":"5_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-319-91908-9_17","volume-title":"Computing and Software Science","author":"B Steffen","year":"2019","unstructured":"Steffen, B., Gossen, F., Naujokat, S., Margaria, T.: Language-driven engineering: from general-purpose to purpose-specific languages. In: Steffen, B., Woeginger, G. (eds.) Computing and Software Science. LNCS, vol. 10000, pp. 311\u2013344. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-91908-9_17"},{"key":"5_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-540-70889-6_7","volume-title":"Hardware and Software, Verification and Testing","author":"B Steffen","year":"2007","unstructured":"Steffen, B., Margaria, T., Nagel, R., J\u00f6rges, S., Kubczak, C.: Model-driven development with the jABC. In: Bin, E., Ziv, A., Ur, S. (eds.) HVC 2006. LNCS, vol. 4383, pp. 92\u2013108. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-70889-6_7"},{"key":"5_CR41","unstructured":"Team, P.: Torchscript. https:\/\/github.com\/pytorch\/pytorch (2024)"},{"key":"5_CR42","doi-asserted-by":"crossref","unstructured":"Vaidya, G., Ilg, L., Kshirsagar, M., Naredo, E., Ryan, C.: Hyperestimator: evolving computationally efficient cnn models with grammatical evolution. In: ICSBT, pp. 57\u201368 (2022)","DOI":"10.5220\/0011324800003280"},{"issue":"7","key":"5_CR43","doi-asserted-by":"crossref","first-page":"319","DOI":"10.3390\/a16070319","volume":"16","author":"G Vaidya","year":"2023","unstructured":"Vaidya, G., Kshirsagar, M., Ryan, C.: Grammatical evolution-driven algorithm for efficient and automatic hyperparameter optimisation of neural networks. Algorithms 16(7), 319 (2023)","journal-title":"Algorithms"},{"issue":"10","key":"5_CR44","doi-asserted-by":"crossref","first-page":"e26007","DOI":"10.1371\/journal.pone.0026007","volume":"6","author":"Y Wang","year":"2011","unstructured":"Wang, Y., et al.: A tma de-arraying method for high throughput biomarker discovery in tissue research. PLoS ONE 6(10), e26007 (2011)","journal-title":"PLoS ONE"},{"key":"5_CR45","doi-asserted-by":"crossref","unstructured":"White, C., Neiswanger, W., Savani, Y.: Bananas: Bayesian optimization with neural architectures for neural architecture search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 10293\u201310301 (2021)","DOI":"10.1609\/aaai.v35i12.17233"},{"key":"5_CR46","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","volume":"415","author":"L Yang","year":"2020","unstructured":"Yang, L., Shami, A.: On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415, 295\u2013316 (2020)","journal-title":"Neurocomputing"},{"key":"5_CR47","unstructured":"Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., Hutter, F.: Understanding and robustifying differentiable architecture search. arXiv preprint arXiv:1909.09656 (2019)"},{"key":"5_CR48","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.compbiomed.2019.02.017","volume":"108","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., Sejdi\u0107, E.: Radiological images and machine learning: trends, perspectives, and prospects. Comput. Biol. Med. 108, 354\u2013370 (2019)","journal-title":"Comput. Biol. Med."},{"key":"5_CR49","unstructured":"Zhou, H., Yang, M., Wang, J., Pan, W.: Bayesnas: a bayesian approach for neural architecture search. In: International Conference on Machine Learning, pp. 7603\u20137613. PMLR (2019)"},{"key":"5_CR50","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)"},{"key":"5_CR51","doi-asserted-by":"publisher","unstructured":"Zweihoff, P., Tegeler, T., Sch\u00fcrmann, J., Bainczyk, A., Steffen, B.: Aligned, purpose-driven cooperation: the future way of system development. In: Margaria, T., Steffen, B. (eds.) ISoLA 2021. LNCS, vol. 13036, pp. 426\u2013449. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-89159-6_27","DOI":"10.1007\/978-3-030-89159-6_27"}],"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\/978-3-032-01377-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:32:20Z","timestamp":1759271540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-01377-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9783032013767","9783032013774"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-01377-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aisola2024","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"}}]}}