{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:01:12Z","timestamp":1750309272963,"version":"3.41.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T00:00:00Z","timestamp":1718150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council of Taiwan","doi-asserted-by":"crossref","award":["111-2636-E-006-026-, 112-2221-E-006-100-, and 112-2221-E-006-150-MY3"],"award-info":[{"award-number":["111-2636-E-006-026-, 112-2221-E-006-100-, and 112-2221-E-006-150-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            Financial forecasting is an important task for urban development. In this article, we propose a novel deep learning framework to predict the future financial potential of urban spaces. To be more precise, our target is to infer the number of financial institutions in the future for any arbitrary location with environmental and geographical data. We propose a novel local-regional model, the\n            <jats:italic>L<\/jats:italic>\n            ocal-Regional\n            <jats:italic>I<\/jats:italic>\n            nterpretable\n            <jats:italic>M<\/jats:italic>\n            ulti-\n            <jats:italic>A<\/jats:italic>\n            ttention (LIMA) model, which considers multiple aspects of a location\u2014the place itself and its surroundings. Besides, our model offers three kinds of interpretability, providing a superior way for decision makers to understand how the model determines the prediction: critical rules learned from the tree-based module, surrounding locations that are highly correlated with the prediction, and critical regional features. Our module not only takes advantage of a tree-based model, which can effectively extract cross features, but also leverages convolutional neural networks to obtain more complex and inclusive features around the target location. Experimental results on real-world datasets demonstrate the superiority of our proposed LIMA model against existing state-of-the-art methods. The LIMA model has been deployed as a web system for assisting one of the largest bank companies in Taiwan to select locations for building new branches in major cities since 2020.\n          <\/jats:p>","DOI":"10.1145\/3656479","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T12:10:42Z","timestamp":1712578242000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating Future Financial Development of Urban Areas for Deploying Bank Branches: A Local-Regional Interpretable Model"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6014-4191","authenticated-orcid":false,"given":"Pei-Xuan","family":"Li","sequence":"first","affiliation":[{"name":"National Cheng Kung University, Tainan, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4540-5702","authenticated-orcid":false,"given":"Yu-En","family":"Chang","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9032-2050","authenticated-orcid":false,"given":"Ming-Chun","family":"Wei","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-1337","authenticated-orcid":false,"given":"Hsun-Ping","family":"Hsieh","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan Taiwan"}]}],"member":"320","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"issue":"3","key":"e_1_3_1_2_2","first-page":"36","article-title":"Role of financial institutions in the growth of micro and small enterprises in Assosa zone","volume":"8","author":"Abara Gudata","year":"2017","unstructured":"Gudata Abara and Tadele Banti. 2017. Role of financial institutions in the growth of micro and small enterprises in Assosa zone. Research Journal of Finance and Accounting 8, 3 (2017), 36\u201340.","journal-title":"Research Journal of Finance and Accounting"},{"issue":"6","key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.3846\/16111699.2015.1113198","article-title":"Geomarketing models in supermarket location strategies","volume":"17","author":"Baviera-Puig Amparo","year":"2016","unstructured":"Amparo Baviera-Puig, Juan Buitrago-Vera, and Carmen Escriba-Perez. 2016. Geomarketing models in supermarket location strategies. Journal of Business Economics and Management 17, 6 (2016), 1205\u20131221.","journal-title":"Journal of Business Economics and Management"},{"key":"e_1_3_1_4_2","article-title":"ABC-CNN: An attention based convolutional neural network for visual question answering","author":"Chen Kan","year":"2015","unstructured":"Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, and Ram Nevatia. 2015. ABC-CNN: An attention based convolutional neural network for visual question answering. arXiv preprint arXiv:1511.05960 (2015).","journal-title":"arXiv preprint arXiv:1511.05960"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804291"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.667"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/2988450.2988454","volume-title":"Proceedings of the 1st Workshop on Deep Learning for Recommender Systems","author":"Cheng Heng-Tze","year":"2016","unstructured":"Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 7\u201310."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0144-8"},{"key":"e_1_3_1_10_2","article-title":"Towards a rigorous science of interpretable machine learning","author":"Doshi-Velez Finale","year":"2017","unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017).","journal-title":"arXiv preprint arXiv:1702.08608"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3359786"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3332281"},{"issue":"4","key":"e_1_3_1_13_2","first-page":"1","article-title":"CityTransfer: Transferring inter- and intra-city knowledge for chain store site recommendation based on multi-source urban data","volume":"1","author":"Guo Bin","year":"2018","unstructured":"Bin Guo, Jing Li, Vincent W. Zheng, Zhu Wang, and Zhiwen Yu. 2018. CityTransfer: Transferring inter- and intra-city knowledge for chain store site recommendation based on multi-source urban data. Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies 1, 4 (2018), 1\u201323.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies"},{"key":"e_1_3_1_14_2","article-title":"DeepFM: A factorization-machine based neural network for CTR prediction","author":"Guo Huifeng","year":"2017","unstructured":"Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).","journal-title":"arXiv preprint arXiv:1703.04247"},{"key":"e_1_3_1_15_2","first-page":"77","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics","author":"Hara Satoshi","year":"2018","unstructured":"Satoshi Hara and Kohei Hayashi. 2018. Making tree ensembles interpretable: A Bayesian model selection approach. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 77\u201385."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.dss.2010.12.003","article-title":"An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models","volume":"51","author":"Huysmans Johan","year":"2011","unstructured":"Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, and Bart Baesens. 2011. An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems 51, 1 (2011), 141\u2013154.","journal-title":"Decision Support Systems"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.74.035101"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487616"},{"issue":"3","key":"e_1_3_1_20_2","first-page":"1003","article-title":"Financial institutions and economic growth: An empirical analysis of Indian economy in the post liberalized era","volume":"6","author":"Kaushal Shrutikeerti","year":"2016","unstructured":"Shrutikeerti Kaushal and Amlan Ghosh. 2016. Financial institutions and economic growth: An empirical analysis of Indian economy in the post liberalized era. International Journal of Economics and Financial Issues 6, 3 (2016), 1003\u20131013.","journal-title":"International Journal of Economics and Financial Issues"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330858"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9272-4"},{"key":"e_1_3_1_23_2","article-title":"Visualizing the loss landscape of neural nets","author":"Li Hao","year":"2018","unstructured":"Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. 2018. Visualizing the loss landscape of neural nets. In Proceedings of the 32nd Conference on Neural Information Processing Systems. 1\u201311.","journal-title":"Proceedings of the 32nd Conference on Neural Information Processing Systems."},{"key":"e_1_3_1_24_2","first-page":"149","volume-title":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct","author":"Li Jing","year":"2016","unstructured":"Jing Li, Bin Guo, Zhu Wang, Mingyang Li, and Zhiwen Yu. 2016. Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. 149\u2013152."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"key":"e_1_3_1_26_2","article-title":"Recurrent neural network for text classification with multi-task learning","author":"Liu Pengfei","year":"2016","unstructured":"Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101 (2016).","journal-title":"arXiv preprint arXiv:1605.05101"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2916143"},{"key":"e_1_3_1_28_2","first-page":"5188","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Mahendran Aravindh","year":"2015","unstructured":"Aravindh Mahendran and Andrea Vedaldi. 2015. Understanding deep image representations by inverting them. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5188\u20135196."},{"key":"e_1_3_1_29_2","article-title":"Deep learning recommendation model for personalization and recommendation systems","author":"Naumov Maxim","year":"2019","unstructured":"Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Illia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, and Misha Smelyanskiy. 2019. Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091 (2019).","journal-title":"arXiv preprint arXiv:1906.00091"},{"key":"e_1_3_1_30_2","article-title":"In-BoXBART: Get instructions into biomedical multi-task learning","author":"Parmar Mihir","year":"2022","unstructured":"Mihir Parmar, Swaroop Mishra, Mirali Purohit, Man Luo, M. Hassan Murad, and Chitta Baral. 2022. In-BoXBART: Get instructions into biomedical multi-task learning. arXiv preprint arXiv:2204.07600 (2022).","journal-title":"arXiv preprint arXiv:2204.07600"},{"key":"e_1_3_1_31_2","first-page":"304","volume-title":"Proceedings of the 10th International Joint Conference on Artificial Intelligence","volume":"1","author":"Quinlan J. Ross","year":"1987","unstructured":"J. Ross Quinlan. 1987. Generating production rules from decision trees. In Proceedings of the 10th International Joint Conference on Artificial Intelligence, Vol. 1. 304\u2013307."},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/ICDM.2010.127","volume-title":"Proceedings of the 2010 IEEE International Conference on Data Mining","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle. 2010. Factorization machines. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, 995\u20131000."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_1_34_2","article-title":"An overview of multi-task learning in deep neural networks","author":"Ruder Sebastian","year":"2017","unstructured":"Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017).","journal-title":"arXiv preprint arXiv:1706.05098"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.03.013"},{"issue":"4","key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1177\/0739456X14550401","article-title":"Location and agglomeration: The distribution of retail and food businesses in dense urban environments","volume":"34","author":"Sevtsuk Andres","year":"2014","unstructured":"Andres Sevtsuk. 2014. Location and agglomeration: The distribution of retail and food businesses in dense urban environments. Journal of Planning Education and Research 34, 4 (2014), 374\u2013393.","journal-title":"Journal of Planning Education and Research"},{"issue":"7587","key":"e_1_3_1_37_2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver David","year":"2016","unstructured":"David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484\u2013489.","journal-title":"Nature"},{"key":"e_1_3_1_38_2","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan Karen","year":"2013","unstructured":"Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013).","journal-title":"arXiv preprint arXiv:1312.6034"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matcom.2020.04.031"},{"key":"e_1_3_1_40_2","first-page":"2371","volume-title":"Proceedings of the 25th ACM International Conference on Information and Knowledge Management","author":"Wang Feng","year":"2016","unstructured":"Feng Wang, Li Chen, and Weike Pan. 2016. Where to place your next restaurant? Optimal restaurant placement via leveraging user-generated reviews. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 2371\u20132376."},{"key":"e_1_3_1_41_2","first-page":"Article 12, 1\u20137","volume-title":"Proceedings of the ADKDD\u201917","author":"Wang Ruoxi","year":"2017","unstructured":"Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD\u201917. Article 12, 1\u20137."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450078"},{"key":"e_1_3_1_43_2","first-page":"1543","volume-title":"Proceedings of the 2018 World Wide Web Conference","author":"Wang Xiang","year":"2018","unstructured":"Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, and Tat-Seng Chua. 2018. TEM: Tree-enhanced embedding model for explainable recommendation. In Proceedings of the 2018 World Wide Web Conference. 1543\u20131552."},{"key":"e_1_3_1_44_2","first-page":"1563","volume-title":"Proceedings of the 2018 IEEE 20th International Conference on High Performance Computing and Communications, the IEEE 16th International Conference on Smart City, and the IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS)","author":"Yang Chengliang","year":"2018","unstructured":"Chengliang Yang, Anand Rangarajan, and Sanjay Ranka. 2018. Global model interpretation via recursive partitioning. In Proceedings of the 2018 IEEE 20th International Conference on High Performance Computing and Communications, the IEEE 16th International Conference on Smart City, and the IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS). IEEE, 1563\u20131570."},{"key":"e_1_3_1_45_2","first-page":"10790","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Yu Wenmeng","year":"2021","unstructured":"Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu. 2021. Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 10790\u201310797."},{"issue":"1","key":"e_1_3_1_46_2","first-page":"1","article-title":"Shop-type recommendation leveraging the data from social media and location-based services","volume":"11","author":"Yu Zhiwen","year":"2016","unstructured":"Zhiwen Yu, Miao Tian, Zhu Wang, Bin Guo, and Tao Mei. 2016. Shop-type recommendation leveraging the data from social media and location-based services. ACM Transactions on Knowledge Discovery from Data 11, 1 (2016), 1\u201321.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_47_2","first-page":"8827","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Zhang Quanshi","year":"2018","unstructured":"Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu. 2018. Interpretable convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8827\u20138836."},{"issue":"32","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"4790","DOI":"10.1364\/AO.29.004790","article-title":"Parallel distributed processing model with local space-invariant interconnections and its optical architecture","volume":"29","author":"Zhang Wei","year":"1990","unstructured":"Wei Zhang, Kazuyoshi Itoh, Jun Tanida, and Yoshiki Ichioka. 1990. Parallel distributed processing model with local space-invariant interconnections and its optical architecture. Applied Optics 29, 32 (1990), 4790\u20134797.","journal-title":"Applied Optics"},{"key":"e_1_3_1_49_2","volume-title":"Proceedings of Annual Conference of the Japan Society of Applied Physics","volume":"564","author":"Zhang Wei","year":"1988","unstructured":"Wei Zhang, Jun Tanida, Kazuyoshi Itoh, and Yoshiki Ichioka. 1988. Shift-invariant pattern recognition neural network and its optical architecture. In Proceedings of Annual Conference of the Japan Society of Applied Physics, Vol. 564."},{"issue":"12","key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","article-title":"A survey on multi-task learning","volume":"34","author":"Zhang Yu","year":"2021","unstructured":"Yu Zhang and Qiang Yang. 2021. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering 34, 12 (2021), 5586\u20135609.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101918"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656479","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3656479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:57:31Z","timestamp":1750291051000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,12]]},"references-count":50,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3656479"],"URL":"https:\/\/doi.org\/10.1145\/3656479","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"type":"print","value":"2158-656X"},{"type":"electronic","value":"2158-6578"}],"subject":[],"published":{"date-parts":[[2024,6,12]]},"assertion":[{"value":"2022-12-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-20","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}