Issue
Security and Safety
Volume 2, 2023
Security and Safety in the "Metaverse"
Article Number 2023012
Number of page(s) 13
Section Other Fields
DOI https://doi.org/10.1051/sands/2023012
Published online 30 June 2023
  1. Brown T, Mann B, Ryder N et al. Language models are few-shot learners. Adv Neural Inf Process Syst 2020; 33: 1877–901. [Google Scholar]
  2. Chowdhery A, Narang S, Devlin J et al. Palm: Scaling language modeling with pathways, arXiv preprint arXiv:2204.02311, 2022. [Google Scholar]
  3. OpenAI. ChatGPT: Optimizing language models for dialogue, OpenAI Blog, 2022. [Google Scholar]
  4. OpenAI, GPT-4 technical report. OpenAI, 2023. [Google Scholar]
  5. Guo B, Ding Y and Sun Y et al. The mass, fake news, and cognition security. Front Comput Sci 2021; 15: 1–13. [Google Scholar]
  6. Andrade RO and Yoo SG. Cognitive security: A comprehensive study of cognitive science in cybersecurity. J Inf Secur Appl 2019; 48: 102352. [Google Scholar]
  7. Jobson KO and Hartley DS. Achieving cognitive warfare superiority amidst accelerating change. Phalanx 2022; 55: 28–31. [Google Scholar]
  8. Claverie B, Prébot B, Buchler N and Du Cluzel F. Cognitive Warfare: The Future of Cognitive Dominance, NATO Collaboration Support Office, 2022; 2: 1–7. [Google Scholar]
  9. Chen YF, Ye H and Wang DD. Theories of social preferences beyond homo economicus: A review based on the experimental economics. Nankai Econ Stud 2012; 01: 63–100. [Google Scholar]
  10. Zhou LN, Yang Z and Chu BL, et al. Overview of multimedia cognition security. J Signal Process. 2021; 37: 2440–2456. https://doi.org/10.16798/j.issn.1003-0530.2021.12.012 [Google Scholar]
  11. Fan WJ and Wang YB, Cognition security protection about the mass: A survey of key technologies. J Commun Univ China Sci Technol 2022; 29: 1–8. [Google Scholar]
  12. Toffler A. The third wave: The classic study of tomorrow. Bantam, 2022. [Google Scholar]
  13. Ke P, Zou JH and Sun XN. Launch a new round of strategy for the transformation and upgrading of cultural industry: Analysis and inspiration of “opinions on promoting the implementation of the national cultural digitalization strategy”. Inf Stud: Theory Appl 2022; 45: 1. [Google Scholar]
  14. Li Y, Li X and Shen S, et al. DTBVis: An interactive visual comparison system for digital twin brain and human brain[J]. Visual Informatics, 2023; ISSN 2468-502X, https://doi.org/10.1016/j.visinf.2023.02.002. [Google Scholar]
  15. Wang WX, Zhou F and Wan YL et al. A survey of metaverse technology. Chin J Eng 2022; 44: 744–56. [Google Scholar]
  16. Parthasarathy PK, Mantri A and Mittal A et al. Digital brain building a key to improve cognitive functions by an EEG–controlled videogames as interactive learning platform. In: Congress on Intelligent Systems: Proceedings of CIS 2020. Singapore: Springer, 2021, vol. 1, 241–52. [CrossRef] [Google Scholar]
  17. D’Angelo E and Jirsa V. The quest for multiscale brain modeling[J]. Trends Neurosci, 2022; Oct, 45: 777–790. https://doi.org/10.1016/j.tins.2022.06.007. Epub 2022 Jul 27. PMID: 35906100 [CrossRef] [PubMed] [Google Scholar]
  18. Avramovic P, Rietdijk R and Attard M et al. Cognitive and behavioral digital health interventions for people with traumatic brain injury and their caregivers: A systematic review. J Neurotrauma 2023; 40: 159–94. [CrossRef] [PubMed] [Google Scholar]
  19. Roetzer-Pejrimovsky T, Moser AC and Atli B et al. The digital brain tumour atlas, an open histopathology resource. Sci Data 2022; 9: 55. [CrossRef] [PubMed] [Google Scholar]
  20. O’Sullivan ED and Schofield SJ. Cognitive bias in clinical medicine. J R Coll Physicians Edinb 2018; 48: 225–32. [CrossRef] [PubMed] [Google Scholar]
  21. MacLeod C and Mathews A, Cognitive bias modification approaches to anxiety. Annu Rev Clin Psychol 2012; 8: 189–217. [CrossRef] [PubMed] [Google Scholar]
  22. Acciarini C, Brunetta F and Boccardelli P, Cognitive biases and decision-making strategies in times of change: A systematic literature review. Manag Decis 2021; 59: 638–52. [CrossRef] [Google Scholar]
  23. Binz M and Schulz E. Using cognitive psychology to understand GPT-3. Proc Natl Acad Sci 2023; 120: e2218523120. [CrossRef] [PubMed] [Google Scholar]
  24. Szegedy C, Zaremba W and Sutskever I et al. Intriguing properties of neural networks. In: 2nd International Conference on Learning Representations, ICLR 2014, 2014. [Google Scholar]
  25. Ian JG, Jonathon S and Christian S, Explaining and harnessing adversarial examples. In: Proceedings of the International Conference on Learning Representations, 2015. [Google Scholar]
  26. Jia R and Liang P. Adversarial examples for evaluating reading comprehension systems. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, 2021–2031. [Google Scholar]
  27. Li Y, Lyu X and Koren N et al. Anti-backdoor learning: Training clean models on poisoned data. Adv Neural Inf Process Syst 2021; 34: 14900–12. [Google Scholar]
  28. Li Y, Jiang Y and Li Z et al. Backdoor learning: A survey. In: IEEE Transactions on Neural Networks and Learning Systems, 2022. [Google Scholar]
  29. Lyu L, Yu H and Ma X et al. Privacy and robustness in federated learning: Attacks and defenses. In: IEEE Transactions on Neural Networks and Learning Systems, 2022. [PubMed] [Google Scholar]
  30. Xiao H, Biggio B and Brown G et al.. Is feature selection secure against training data poisoning? In: International Conference on Machine Learning. PMLR, 2015, 1689–98. [Google Scholar]
  31. Zhengli Z, Dheeru D and Sameer S. Generating natural adversarial examples. In: Proceedings of the International Conference on Learning Representations, 2018. [Google Scholar]
  32. Yuan X, He P and Zhu Q et al. Adversarial examples: Attacks and defenses for deep learning. IEEE Trans Neural Netw Learn Syst 2019; 30: 2805–24. [CrossRef] [PubMed] [Google Scholar]
  33. Saha A, Subramanya A and Pirsiavash H. Hidden trigger backdoor attacks. Proc AAAI Conf Artif Intell. 2020; 34: 11957–65. [Google Scholar]
  34. Minhao C, Wei W and Cho-Jui H. Evaluating and enhancing the robustness of dialogue systems: A case study on a negotiation agent. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019. [Google Scholar]
  35. Quiring E and Rieck K. Backdooring and poisoning neural networks with image-scaling attacks. In: 2020 IEEE Security and Privacy Workshops (SPW). IEEE, 2020, 41–7. [CrossRef] [Google Scholar]
  36. Kahn J. Move over photoshop: OpenAI has just revolutionized digital image making. Fortune, 2022. [Google Scholar]
  37. Rombach R, Blattmann A and Lorenz D et al. High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, 10684–95. [Google Scholar]
  38. Baidu: Baidu to launch ERNIE Bot-integrated cloud service on March 27. [2023-03-21], https://finance.sina.cn/2023-03-21/detail-imymqrre3128262.d.html [Google Scholar]
  39. Ouyang L, Wu J and Jiang X et al. Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst 2022; 35: 27730–44. [Google Scholar]
  40. Fei N, Lu Z and Gao Y et al. Towards artificial general intelligence via a multimodal foundation model. Nat Commun 2022; 13: 3094. [CrossRef] [PubMed] [Google Scholar]
  41. Ororbia A and Kifer D. The neural coding framework for learning generative models. Nat Commun 2022; 13: 2064. [CrossRef] [PubMed] [Google Scholar]
  42. Le Cun Y. A path towards autonomous machine intelligence version 0.9. 2, 2022–06-27. Open Review, 62. Corpus ID: 251881108 Computer Science. https://openreview.net/pdf?id=BZ5a1r-kVsf. [Google Scholar]
  43. Williamson B. Brain data: Scanning, scraping and sculpting the plastic learning brain through neurotechnology. Postdigital Sci Edu 2019; 1: 65–86. [CrossRef] [Google Scholar]
  44. Mehonic A and Kenyon AJ, Brain-inspired computing needs a master plan. Nature 2022; 604: 255–60. [CrossRef] [PubMed] [Google Scholar]
  45. Ji X, Dong Z and Lai CS et al. A brain-inspired in-memory computing system for neuronal communication via memristive circuits. IEEE Commun Mag 2022; 60: 100–6. [CrossRef] [Google Scholar]
  46. Szabo B, Valencia-Aguilar A and Damas-Moreira I et al. Wild cognition–linking form and function of cognitive abilities within a natural context. Curr Opin Behav Sci 2022; 44: 101115. [CrossRef] [Google Scholar]
  47. Hutchins E. The distributed cognition perspective on human interaction. In: Roots of Human Sociality. Routledge, 2020, 375–98. [CrossRef] [Google Scholar]
  48. Magnani L and Magnani L, AlphaGo, locked strategies, and eco-cognitive openness. Eco-Cognit Comput: Cognit Domest Ignorant Entities 2022; 43: 45–71. [Google Scholar]
  49. Zhao Z and Zhang X, A continuous heterogeneous-agent model for the co-evolution of asset price and wealth distribution in financial market. Chaos Solitons Fractals 2022; 155: 111543. [CrossRef] [Google Scholar]
  50. Fu Q, Ai X and Yi J et al. Learning heterogeneous agent cooperation via multiagent league training, arXiv preprint arXiv:2211.11616 2022. [Google Scholar]
  51. Rizk Y, Awad M and Tunstel EW. Cooperative heterogeneous multi-robot systems: A survey. ACM Comput Surv (CSUR) 2019; 52: 1–31. [Google Scholar]

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