Abstract
In this chapter, we introduce the main technologies that underpin parallel services, including decentralized technology, multi-agent simulation, and data fusion techniques.
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References
Abney, S.: Bootstrap**. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 360–367. Association for Computational Linguistics, Philadelphia, Pennsylvania (2002). https://doi.org/10.3115/1073083.1073143
Balmer, M., Nagel, K., Raney, B.: Large-scale multi-agent simulations for transportation applications. In: Intelligent Transportation Systems, vol. 8, pp. 205–221. Taylor & Francis, Milton Park (2004)
Bao, J., Zheng, Y.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems (2012)
Baxter, J.: A model of inductive bias learning. J. Artif. Intell. Res. 12(1), 149–198 (2000)
Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A.: Affective computing and sentiment analysis. In: A Practical Guide to Sentiment Analysis, pp. 1–10. Springer, Berlin (2017)
Chamikara, M.A.P., Bertok, P., Khalil, I., Liu, D., Camtepe, S.: Privacy preserving distributed machine learning with federated learning. Comput. Commun. 171, 112–125 (2021)
Chen, Y., Lu, Y., Bulysheva, L., Kataev, M.Y.: Applications of blockchain in industry 4.0: A review. Inf. Syst. Front. 1–15 (2022). https://doi.org/10.1007/s10796-022-102
Ding, W.W., Liang, X., Hou, J., Wang, G., Yuan, Y., Li, J., Wang, F.Y.: Parallel governance for decentralized autonomous organizations enabled by blockchain and smart contracts. In: 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), pp. 1–4. IEEE (2021)
Drakatos, P., Demetriou, E., Koumou, S., Konstantinidis, A., Zeinalipour-Yazti, D.: Triastore: A web 3.0 blockchain datastore for massive iot workloads. In: 2021 22nd IEEE International Conference on Mobile Data Management (MDM), pp. 187–192. IEEE (2021)
Firdausiyah, N., Taniguchi, E., Qureshi, A.: Modeling city logistics using adaptive dynamic programming based multi-agent simulation. Transpor. Res. Part E Logist. Transpor. Rev. 125, 74–96 (2019)
Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Ranzato, M.A., Mikolov, T.: DeViSE: A Deep Visual-Semantic Embedding Model. In: Advances in Neural Information Processing Systems, vol. 26. Curran Associates, Red Hook (2013)
Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. Numer. Math. 14(5), 403–420 (1970). https://doi.org/10.1007/BF02163027
Han, S., Wang, X., Zhang, J.J., Cao, D., Wang, F.Y.: Parallel vehicular networks: a CPSS-based approach via multimodal big data in IoV. IEEE Int. Things J. 6(1), 1079–1089 (2019). https://doi.org/10.1109/JIOT.2018.2867039. 10 citations (Crossref) [2022-06-30] Conference Name: IEEE Internet of Things Journal
Hou, J., Ding, W., Liang, X., Zhu, F., Yuan, Y., Wang, F.: A study on decentralized autonomous organizations based intelligent transportation system enabled by blockchain and smart contract. In: 2021 China Automation Congress (CAC), pp. 967–971. IEEE (2021)
Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)
Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., Ramchandran, K.: Speeding up distributed machine learning using codes. IEEE Trans. Inf. Theory 64(3), 1514–1529 (2017)
Lu, Y.: The blockchain: State-of-the-art and research challenges. J. Ind. Inf. Integr. 15, 80–90 (2019)
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: ICML (2011)
Nguyen, D.C., Ding, M., Pham, Q.V., Pathirana, P.N., Le, L.B., Seneviratne, A., Li, J., Niyato, D., Poor, H.V.: Federated learning meets blockchain in edge computing: opportunities and challenges. IEEE Int. Things J. 8(16), 12806–12825 (2021)
Ouyang, L., Wang, S., Yuan, Y., Ni, X., Wang, F.: Smart contracts: architecture and research progresses. Acta Automatica Sinica 45(3), 445–457 (2019)
Paranjape, R., Sadanand, A.: Multi-Agent Systems for Healthcare Simulation and Modeling: Applications for System Improvement: Applications for System Improvement. IGI Global, Pennsylvania (2009)
Pokhrel, S.R., Choi, J.: Federated learning with blockchain for autonomous vehicles: analysis and design challenges. IEEE Trans. Commun. 68(8), 4734–4746 (2020)
Siebers, P.O., Aickelin, U.: Introduction to multi-agent simulation. In: Encyclopedia of Decision Making and Decision Support Technologies, pp. 554–564. IGI Global, Pennsylvania (2008)
Siebers, P.O., Aickelin, U., Celia, H., Clegg, C.: A multi-agent simulation of retail management practices (2008). Preprint ar**v:0803.1598
Srivastava, N., Salakhutdinov, R.R.: Multimodal learning with deep boltzmann machines. In: Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Red Hook (2012)
Uhrmacher, A.M., Weyns, D.: Multi-Agent Systems: Simulation and Applications. CRC Press, Boca Raton (2009)
Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., Rellermeyer, J.S.: A survey on distributed machine learning. ACM Comput. Surveys (CSUR) 53(2), 1–33 (2020)
Wang, F.: Parallel philosophy and intelligent technology: dual equations and testing systems for parallel industries and smart societies. Chin. J. Intell. Sci. Technol. 3(3), 245–255 (2021)
Wang, F.Y., Wang, Y.: Parallel ecology for intelligent and smart cyber–physical–social systems. IEEE Trans. Comput. Soc. Syst. 7(6), 1318–1323 (2020)
Wang, X., Li, L., Yuan, Y., Ye, P., Wang, F.Y.: Acp-based social computing and parallel intelligence: societies 5.0 and beyond. CAAI Trans. Intell. Technol. 1(4), 377–393 (2016)
Wang, S., Ding, W., Li, J., Yuan, Y., Ouyang, L., Wang, F.Y.: Decentralized autonomous organizations: concept, model, and applications. IEEE Trans. Comput. Soc. Syst. 6(5), 870–878 (2019)
Wang, F., Wang, Y., Chen, Y., Tian, Y., Qi, H., Wang, X., Zhang, W., Zhang, J., Yuan, Y.: Federated ecology: From federated data to federated intelligence. Chinese J. Intell. Sci. Technol. 2(4), 305 (2020)
Yong, Y., Liwei, O., **ao, W., Feiyue, W.: Blockchain-based intelligent-ware: a novel paradigm for distributed artificial intelligence research. Front. Data Comput. 3(1), 1–14 (2021)
Zhang, J.J., Wang, F.Y., Wang, X., **ong, G., Zhu, F., Lv, Y., Hou, J., Han, S., Yuan, Y., Lu, Q., et al.: Cyber-physical-social systems: the state of the art and perspectives. IEEE Trans. Comput. Soc. Syst. 5(3), 829–840 (2018)
Zheng, Y.: Methodologies for cross-domain data fusion: an overview. IEEE Trans. Big Data 1(1), 16–34 (2015). https://doi.org/10.1109/TBDATA.2015.2465959. 211 citations (Crossref) [2022-07-07] Conference Name: IEEE Transactions on Big Data
Zheng, Z., **e, S., Dai, H., Chen, X., Wang, H.: An overview of blockchain technology: architecture, consensus, and future trends. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 557–564. IEEE (2017)
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Li, L., Wang, FY. (2023). Enabling Technology. In: Parallel Services. SpringerBriefs in Service Science. Springer, Cham. https://doi.org/10.1007/978-3-031-25333-1_6
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DOI: https://doi.org/10.1007/978-3-031-25333-1_6
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