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Li, \"A hierarchical method for assessing\n                    cyber security situation based on ontology and fuzzy cognitive maps,\"\n                        International Journal of Information and Computer Security,\n                    vol. 14, no. 3\/4, pp. 242-262, 2021. 10.1504\/IJICS.2021.114704","DOI":"10.1504\/IJICS.2021.114704"},{"key":"key2.0230711072135e+13_B6","doi-asserted-by":"crossref","unstructured":"K. B. Gemlau, L. Kohler, and R. Ernst, \"A platform programming\n                    paradigm for heterogeneous systems integration,\" Proceedings of the\n                        IEEE, vol. 109, no. 4, pp. 582-603, 2021. 10.1109\/JPROC.2020.3035874","DOI":"10.1109\/JPROC.2020.3035874"},{"key":"key2.0230711072135e+13_B7","doi-asserted-by":"crossref","unstructured":"R. Xu, X. Da, H. Hu, L. Ni, and Y. 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Gilardi, \"Cable control\n                    of an aerostat platform: Experimental results and model validation,\"\n                        Journal of Guidance, Control, and Dynamics, vol. 30, no. 2,\n                    pp. 620-628, 2007. 10.2514\/1.22598","DOI":"10.2514\/1.22598"},{"key":"key2.0230711072135e+13_B10","doi-asserted-by":"crossref","unstructured":"S. Jung and J. Choi, \"End-to-end reliability of satellite\n                    communication network systems,\" IEEE Systems Journal, vol. 15,\n                    no. 1, pp. 791-801, 2021. 10.1109\/JSYST.2020.2980760","DOI":"10.1109\/JSYST.2020.2980760"},{"key":"key2.0230711072135e+13_B11","unstructured":"J. Zhang, J. Pang, and Z. Zhang, \"Heterogeneity quantization method\n                    of cyberspace security system based on dissimilar redundancy structure,\"\n                        Dianzi Yu Xinxi Xuebao\/Journal of Electronics and Information\n                        Technology, vol. 41, pp. 1594-1600, 2019."},{"key":"key2.0230711072135e+13_B12","doi-asserted-by":"crossref","unstructured":"M. A. Elliott, C. Nothelfer, C. Xiong, and D. A. Szafir, \"A design\n                    space of vision science methods for visualization research,\" IEEE\n                        Transactions on Visualization and Computer Graphics, vol. 27, no.\n                    2, pp. 1117-1127, Feb. 2021. 10.1109\/TVCG.2020.3029413\n                    33090954","DOI":"10.1109\/TVCG.2020.3029413"},{"key":"key2.0230711072135e+13_B13","doi-asserted-by":"crossref","unstructured":"M. Li, F. Choudhury, Z. Bao, H. Samet, and T. 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Fazio, \"An innovative\n                    methodology for big data visualization for telemedicine,\" IEEE\n                        Transactions on Industrial Informatics, vol. 15, no. 1, pp.\n                    490-497, 2019. 10.1109\/TII.2018.2842234","DOI":"10.1109\/TII.2018.2842234"},{"key":"key2.0230711072135e+13_B26","unstructured":"Y. Cheng, X. Wang, and Y. Xia, \"Supervised t-distributed stochastic\n                    neighbor embedding for data visualization and classification,\" INFORMS\n                        Journal on Computing, vol. 33, no. 2, pp. 419-835, 2021. 10.1287\/ijoc.2020.0961\n                    34354339\n                    PMC8330414"},{"key":"key2.0230711072135e+13_B27","doi-asserted-by":"crossref","unstructured":"G. Huang and H. Qu, \"Data visualization and data fusion on the\n                    visual performance of illustration,\" Journal of Intelligent and Fuzzy\n                        Systems, vol. 39, no. 6, pp. 8795-8803, 2020. 10.3233\/JIFS-189276","DOI":"10.3233\/JIFS-189276"},{"key":"key2.0230711072135e+13_B28","unstructured":"J. Wang, S. Zhuang, C. Miao, and C. An, \"Model and application of\n                    cyberspace information system,\" Tongxin Xuebao\/Journal on\n                        Communications, vol. 41, pp. 74-83, 2021."},{"key":"key2.0230711072135e+13_B29","doi-asserted-by":"crossref","unstructured":"J. Youn, H. Oh, J. Kang, and Shin D, \"Research on cyber IPB\n                    visualization method based on BGP archive data for cyber situation awareness,\"\n                        KSII Transactions on Internet and Information Systems, vol.\n                    15, no. 2, pp. 749-766, 2020. 10.3837\/tiis.2021.02.020","DOI":"10.3837\/tiis.2021.02.020"},{"key":"key2.0230711072135e+13_B30","unstructured":"Y. Zhang, G. Si, and Y. Wang, \"Design and implementtation of\n                    cyberspace war situation visualization system for joint operations,\"\n                        Journal of Zhengzhou University (Engineering Science), vol.\n                    39, pp. 45-51, 2018."},{"key":"key2.0230711072135e+13_B31","unstructured":"Y. Wang, S. Li, and L. Ren, \"Automatic generalization methods of\n                    cyberspace point cluster features considering characteristics,\" Wuhan\n                        Daxue Xuebao (Xinxi Kexue Ban)\/Geomatics and Information Science of Wuhan\n                        University, vol. 46, pp. 427-433, 2021."},{"key":"key2.0230711072135e+13_B32","unstructured":"J. Du, F. Ao, P. Li, and H. Ma, \"Characteristics and knowledge\n                    representation of cyberspace situation information,\" Journal of Data\n                        Acquisition and Processing, vol. 34, pp. 500-508, 2019\n                    ."},{"key":"key2.0230711072135e+13_B33","unstructured":"G. U. O. Qiquan, G. A. O. Chundong, H. A. O. Mengmeng, and J. I. A.\n                    N. G. Dong, \"Develop visualization technology of cyberspace to support\n                    construction of comprehensive prevention and control system of cyber security,\"\n                        Bulletin of the Chinese Academy of Sciences, vol. 35, pp.\n                    917-924, 2019 ."},{"key":"key2.0230711072135e+13_B34","unstructured":"Y. Wang, S. Li, X. Zhang, C. Zhang, and R. Wang, \"Visualization of\n                    cyberspace information based on composite distance cartogram,\" Journal\n                        of Information Engineering University, vol. 21, no. 3, pp. 334-339,\n                    360."},{"key":"key2.0230711072135e+13_B35","unstructured":"B. Jiang, G. Wan, and R. Xu, \"Research on cyberspace division and\n                    visualization method,\" Journal of System Simulation, vol. 29,\n                    pp. 1-8, 2017."},{"key":"key2.0230711072135e+13_B36","unstructured":"X. Li, F. Yang, L. N. Wang, X. K. Yu, T. Fei, and N. Jiang, \"A\n                    survey of mapping methods for cyberspace,\" Journal of Geomatics Science\n                        and Technology, vol. 36, pp. 620-626, 631, 2019."},{"key":"key2.0230711072135e+13_B37","doi-asserted-by":"crossref","unstructured":"Y. Wang, M. Gu, J. Ma, and Q. Jin, \"DNN-DP: Differential privacy\n                    enabled deep neural network learning framework for sensitive crowdsourcing\n                    data,\" IEEE Transactions on Computational Social Systems, vol.\n                    7, no. 1, pp. 215-224, 2020. 10.1109\/TCSS.2019.2950017","DOI":"10.1109\/TCSS.2019.2950017"},{"key":"key2.0230711072135e+13_B38","doi-asserted-by":"crossref","unstructured":"P. Ferrand, A. Decurninge, and M. 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