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吴信东 等: HAO  打卡系统: 以组织智能成就智能组织                                                   1931


                 [10]  Chen YS, Lin ZJ. Business intelligence capabilities and firm performance: A study in China. Int’l Journal of Information Management,
                     2021, 57: 102232. [doi: 10.1016/j.ijinfomgt.2020.102232]
                 [11]  Azeroual O, Theel H. The effects of using business intelligence systems on an excellence management and decision-making process by
                     start-up companies: A case study. arXiv:1901.10555, 2019.
                 [12]  Wu XD, Wang XF, Jin B, Yu Z, Wu MH. Human-machine Synergy. Beijing: Science Press, 2022 (in Chinese).
                 [13]  Moravec H. When will computer hardware match the human brain. Journal of Evolution and Technology, 1998, 1(1): 10.
                 [14]  Klien G, Woods DD, Bradshaw JM, Hoffman RR, Feltovich PJ. Ten challenges for making automation a “team player” in joint human-
                     agent activity. IEEE Intelligent Systems, 2004, 19(6): 91–95. [doi: 10.1109/MIS.2004.74]
                 [15]  Korteling JE, Brouwer AM, Toet A. A neural network framework for cognitive bias. Frontiers in Psychology, 2018, 9: 1561. [doi: 10.
                     3389/fpsyg.2018.01561]
                 [16]  Shneiderman B. Design lessons from AI’s two grand goals: Human emulation and useful applications. IEEE Trans. on Technology and
                     Society, 2020, 1(2): 73–82. [doi: 10.1109/TTS.2020.2992669]
                 [17]  Shakil  M,  Nandi  RN.  Attendance  management  system  for  industrial  worker  using  finger  print  scanner.  Global  Journal  of  Computer
                     Science and Technology, 2013, 13(6): 17–22.
                 [18]  Ahmed A, Olaniyi OM, Kolo JG, Durugo C. A multifactor student attendance management system using fingerprint biometrics and RFID
                     techniques. In: Proc. of the 2016 Int’l Conf. on Information and Communication Technology and Its Applications (ICTA 2016). Minna:
                     Federal University of Technology, 2016. 69–74.
                 [19]  Kuriakose RB, Vermaak HJ. Developing a Java based RFID application to automate student attendance monitoring. In: Proc. of the 2015
                     Pattern Recognition Association of South Africa and Robotics and Mechatronics Int’l Conf. (PRASA-RobMech). Port Elizabeth: IEEE,
                     2015. 48–53. [doi: 10.1109/RoboMech.2015.7359497]
                 [20]  Yadav V, Bhole GP. Cloud based smart attendance system for educational institutions. In: Proc. of the 2019 Int’l Conf. on Machine
                     Learning,  Big  Data,  Cloud  and  Parallel  Computing  (COMITCon).  Faridabad:  IEEE,  2019.  97 –102.  [doi:  10.1109/COMITCon.2019.
                     8862182]
                 [21]  Utomo SB, Hendradjaya B. Multifactor authentication on mobile secure attendance system. In: Proc. of the 2018 Int’l Conf. on ICT for
                     Smart Society (ICISS). Semarang: IEEE, 2018. 1–5. [doi: 10.1109/ICTSS.2018.8550017]
                 [22]  Jaikla T, Pichetjamroen S, Vorakulpipat C, Pichetjamroen A. A secure four-factor attendance system for smartphone device. In: Proc. of
                     the 22nd Int’l Conf. on Advanced Communication Technology (ICACT). Phoenix Park: IEEE, 2020. 65–68. [doi: 10.23919/ICACT48636.
                     2020.9061431]
                 [23]  Josphineleela R, Ramakrishnan M. An efficient automatic attendance system using fingerprint reconstruction technique. Int’l Journal of
                     Computer Science and Information Security, 2012, 10(3): 1–6.
                 [24]  Purohit A, Gaurav K, Bhati C, Oak A. Smart attendance. In: Proc. of the 2017 Int’l Conf. of Electronics, Communication and Aerospace
                     Technology (ICECA). Coimbatore: IEEE, 2017. 415–419. [doi: 10.1109/ICECA.2017.8203717]
                 [25]  Shinde R, Nilose A, Chandankhede P. Design and development of geofencing based attendance system for mobile application. In: Proc.
                     of  the  10th  Int ’l  Conf.  on  Emerging  Trends  in  Engineering  and  Technology —Signal  and  Information  Processing  (ICETET-SIP-22).
                     Nagpur: IEEE, 2022. 1–6. [doi: 10.1109/ICETET-SIP-2254415.2022.9791781]
                 [26]  Zhang JD, Li J. Knowledge graph embedding combining with hierarchical type information. Ruan Jian Xue Bao/Journal of Software,
                     2022, 33(9): 3331–3346 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6295.htm [doi: 10.13328/j.cnki.jos.006295]
                 [27]  Wang X, Zou L, Wang CK, Peng P, Feng ZY. Research on knowledge graph data management: A survey. Ruan Jian Xue Bao/Journal of
                     Software, 2019, 30(7): 2139–2174 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5841.htm [doi: 10.13328/j.cnki.
                     jos.005841]
                 [28]  Nadeau D, Sekine S. A survey of named entity recognition and classification. Lingvisticæ Investigationes, 2007, 30(1): 3–26. [doi: 10.
                     1075/li.30.1.03nad]
                 [29]  Lin YK, Han X, Xie RB, Liu ZY, Sun MS. Knowledge representation learning: A quantitative review. arXiv:1812.10901, 2018.
                 [30]  Zhang DH, Liu ZY, Jia WQ, Liu H, Tan JR. A review on knowledge graph and its application prospects to intelligent manufacturing.
                     Journal of Mechanical Engineering, 2021, 57(5): 90–113 (in  Chinese  with  English  abstract). [doi: 10.3901/JME.2021.05.090]
                 [31]  Ganesh A, Shanil KN, Sunitha C, Midhundas AM. OpenERP/Odoo-an open source concept to ERP solution. In: Proc. of the 6th IEEE Int’l
                     Conf. on Advanced Computing (IACC). Bhimavaram: IEEE, 2016. 112–116. [doi: 10.1109/IACC.2016.30]
                 [32]  Qi YD, Xiao X. Transformation of enterprise management in the era of digital economy. Management World, 2020, 36(6): 135–152 (in
                      Chinese  with  English  abstract). [doi: 10.3969/j.issn.1002-5502.2020.06.011]
                 [33]  Zhu LX. Analysis of flat management of enterprises from the perspective of management—Taking Xiaomi company as an example.
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