Have a personal or library account? Click to login
Opportunities for the Development of Military Cognitive Skills II (Practical Approach) Cover

Opportunities for the Development of Military Cognitive Skills II (Practical Approach)

By: Imre Négyesi  
Open Access
|Jun 2024

References

  1. Blankenbeckler, P.N., Graves, T.R., & Wampler, R.L. (2014). Designing interactive multimedia instruction to address soldiers’ learning needs. Alexandria, VA, ARI Research Report #1979.
  2. Bostrom, N., & Sandberg, A. (2009). Cognitive Enhancement: Methods, Ethics, Regulatory Challenges. Science and Engineering Ethics, Vol. 15, Issue 3, 311-41. DOI:10.1007/s11948-009-9142-5.
  3. Brunyé, T.T., et al. (2020). Retrieval practice enhances near but not far transfer of spatial memory. Journal of Experimental Psychology: Learning Memory and Cognition, Vol. 46, 24-45. Available at: https://doi.org/10.1016/j.bandc.2018.09.008.
  4. Brunyé, T.T., Smith, A.M., Horner, C.B., & Thomas, A.K. (2018). Verbal long-term memory is enhanced by retrieval practice but impaired by prefrontal direct current stimulation. Brain and Cognition, Vol. 128, 80-88.
  5. Campbell, C., Cantrell, G., Generalao, T., Sawyer, A., & Takitch, J. (2006). Interactive multimedia instruction for US Army training. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, 1105-1110. Waynesville, NC: Association for the Advancement of computing in education (AACE).
  6. Chase, W.G., & Simon, H.A. (1973). The MIND’S eye in chess. Visual Information Processing, Proceedings of the Eighth Annual Carnegie Symposium on Cognition, 215-281. Available at: https://doi.org/10.1016/B978-0-12-170150-5.50011-1.
  7. Chuang, H.M., & Cheng, D.W. (2022). Conversational AI over military scenarios using intent detection and response generation. Applied Sciences, Vol. 12, Issue 5, 2494. Available at: https://doi.org/10.3390/app12052494.
  8. Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations & Trends in Signal Processing, Vol. 7, Issue 3-4, 197-387.
  9. Gao, L., Chen, Y., Zhang, B., & Gao, Y. (2019). A real-time target detection and recognition system for UAVs based on improved YOLOv3 and ST-C3D. IEEE Access, Vol. 7, 35028-35036.
  10. Lackey, S.J., Salcedo, J.N., Matthews, G., & Maxwell, D.B. (2014). Virtual world room clearing: A study in training effectiveness. In Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Orlando, FL.
  11. Li, E., Zhou, Z., & Chen, X. (2018). Edge intelligence: On-demand deep learning model co-inference with device-edge synergy. In Proceedings Workshop Mobile Edge Commun/MECOMM, 31-36.
  12. Li, L., Ota, K., & Dong, M. (2018). Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics, Vol. 14, Issue 10, 4665-4673. DOI: 10.1109/TII.2018.2842821.
  13. McDaniel, M.A., & Einstein, G.O. (2006). Material appropriate difficulty: A framework for determining when difficulty is desirable for improving learning. In Healy, A.F. (Ed.), Decades of behavior. Experimental cognitive psychology and its applications, 73-85. Washington, D.C.: American Psychological Association. Available at: https://doi.org/10.1037/10895-006.
  14. NATO. (2020). Science & Technology Trends 2020-2040, Exploring the S&T Edge, NATO Science & Technology Organization. Available at: https://www.nato.int/nato_static_fl2014/assets/pdf/2020/4/pdf/190422-ST_Tech_Trends_Report_2020-2040.pdf.
  15. O’Hanlon, M. (2019a). Forecasting change in military technology, 2020-2040. Foreign Policy at Brookings.
  16. O’Hanlon, M. (2019b). The Senkaku Paradox: Risking Great Power War Over Small Stakes. Washington, DC: Brookings Institution Press.
  17. Paisner, M., Cox, M.T., Maynord, M., & Perlis, D. (2014). Goal-driven autonomy for cognitive systems. Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 36. Available at: https://escholarship.org/uc/item/5vq1h9jc.
  18. Porkoláb, I., & Négyesi, I. (2019). A mesterséges intelligencia alkalmazási lehetőségeinek kutatása a haderőben. Honvédségi Szemle. Available at: https://honvedelem.hu/images/media/5f2bd1646eeb8298912683.pdf.
  19. Prelipcean, G., Boscoianu, M., & Moisescu, F. (2010). New Ideas on the Artificial Intelligence Support in Military Applications. AIKED’10: Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases, 34-39. Available at: https://dl.acm.org/doi/10.5555/1808036.1808044.
  20. Sha, J., Chen, Y., Gao, Y., & Li, X. (2021). Deep neural networks-based target detection and recognition for UAV. IEEE Access, Vol. 9, 92960-92968.
  21. Spain, R.D., Priest, H.A., & Murphy, J.S. (2012). Current trends in adaptive training with military applications: An introduction. Military Psychology, Vol. 24, Issue 2, 87-95.
  22. Swets, J.A., & Bjork, R.A. (1990). Enhancing human performance: An evaluation of “new age” techniques considered by the U.S. Army. Psychological Science, Vol. 1, Issue 2, 85-96.
  23. Zhang, T., et al. (2017). Current trends in the development of intelligent unmanned autonomous systems. Frontiers of Information Technology & Electronic Engineer, Vol. 18, Issue 1, 68-85.
DOI: https://doi.org/10.2478/bsaft-2024-0009 | Journal eISSN: 3100-5098 | Journal ISSN: 3100-508X
Language: English
Page range: 80 - 90
Published on: Jun 7, 2024
Published by: Nicolae Balcescu Land Forces Academy
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2024 Imre Négyesi, published by Nicolae Balcescu Land Forces Academy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.