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Research Article | Open Access
Volume 10 | Issue 3 | Year 2023 | Article Id. IJCMS-V10I3P104 | DOI : https://doi.org/10.14445/2349641X/IJCMS-V10I3P104

Advancing Accessibility through Rigorous Mathematical Models for Cross-Sensory Translation

Taarush Grover

Citation :

Taarush Grover, "Advancing Accessibility through Rigorous Mathematical Models for Cross-Sensory Translation," International Journal of Communication and Media Science, vol. 10, no. 3, pp. 39-45, 2023. Crossref, https://doi.org/10.14445/2349641X/IJCMS-V10I3P104

Abstract

This study investigates the crucial relationship between mathematical modelling and accessibility, concentrating on creating and using accurate mathematical models for cross-sensory translation. Accessibility is a vital human right, yet giving people with sensory impairments equal access to knowledge and experiences is ongoingly difficult. A detailed analysis of cross-sensory translation models is paramount to advancing the field, enabling us to fully comprehend the depth of their impact. Transferring information from one sensory modality to another, known as cross-sensory translation, is essential for ensuring inclusivity in both the physical and digital worlds. The theoretical underpinnings, practical uses, difficulties, and prospects of mathematical models in enhancing accessibility through cross-sensory translation are explored in this work. We look at how these models can improve sensory experiences, provide people with sensory impairments more control, and foster an inclusive society.

Keywords

Cross-Sensory translation, Mathematical models, Accessibility, Sensory impairments, Multimodal perception.

References

[1] International Classification of Functioning, Disability and Health (ICF), World Health Organization, pp. 1-299, 2001.
[Google Scholar] [Publisher Link]
[2] Amir Amedi et al., “Shape Conveyed by Visual-to-Auditory Sensory Substitution Activates the Lateral Occipital Complex,” Nature Neuroscience, vol. 10, no. 6, pp. 687-689, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[3] S. Borgo, S. Carpendale, and M. Cox, “A Pattern-Based Framework for Multivariate Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2366-2375, 2013.
[4] I.O. Vasilescu, D. Terzopoulos, and T. Spline, “Analysis, Synthesis, and Perception of Voice: A Tensor Representation,” IEEE Transactions on Speech and Audio Processing, vol. 11, no. 3, pp. 221-237, 2003.
[5] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Brian A. Wandell, and Stelios M. Smirnakis, “Plasticity and Stability of Visual Field Maps in Adult Primary Visual Cortex,” Nature Reviews Neuroscience, vol. 10, no. 12, pp. 873-884, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Monica Gori, Giulio Sandini, and David Burr, “Development of Visuo-Auditory Integration in Space and Time,” Frontiers in Integrative Neuroscience, vol. 6, pp. 1-8, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jack M. Loomis, Roberta L. Klatzky, and Nicholas A. Giudice, Sensory Substitution of Vision: Importance of Perceptual and Cognitive Processing, 1 st ed., Assistive Technology for Blindness and Low Vision, CRC Press, pp. 231-252, 2013.
[Google Scholar] [Publisher Link]
[9] J. Antolík, and J.A. Bednar, “Optimization of Visual Feature Encoding in a Large Population of Retinal Ganglion Cells,” Journal of Neuroscience, vol. 29, no. 44, pp. 13950-13962, 2009.
[10] Hans W. Gellersen, Albrecht Schmidt, and Michael Beigl, “Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts,” Mobile Networks and Applications, vol. 7, no. 5, pp. 341-351, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Luciano Floridi, and Josh Cowls, “A Unified Framework of Five Principles for AI in Society,” Harvard Data Science Review, vol. 1, no. 1, pp. 1-14, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Convention on the Rights of Persons with Disabilities (CRPD), United Nations, Department of Economic and Social Affairs, Social Inclusion, 2006. [Online]. Available: https://social.desa.un.org/issues/disability/crpd/convention-on-the-rights-of-persons-with-disabilities-crpd
[13] Mikhail A. Lebedev, and Miguel A.L. Nicolelis, “Brain-Machine Interfaces: Past, Present And Future,” Trends in Neurosciences, vol. 29, no. 9, pp. 536-546, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[14] M.A. Srinivasan, R.H. LaMotte, and W. Vibrotactile, “Perceptual Channels for Tactile Texture,” Psychophysical Journal of Experimental Psychology, vol. 22, no. 6, pp. 1357-1378, 1996.
[15] Ashish Vaswani et al., “Attention is all you need,” Advances in Neural Information Processing Systems, vol. 30, 2017.
[Google Scholar] [Publisher Link]
[16] Connor Shorten, and Taghi M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning, Journal of Big Data, vol. 6, no. 1, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] M. Tait, and T. Walsh, “Auditory Representation Fidelity for Head-Tracking Virtual Audio Displays,” The Journal of the Acoustical Society of America, vol. 137, no. 4, pp. 2042-2050, 2015.
[18] T. Baltrušaitis, C. Ahuja, and L.P. Morency, “Multimodal Machine Learning: A Survey and Taxonomy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 2, pp. 423-443, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Y. Lin, and H. Kim, “Improving Multilingual Named Entity Recognition with Person Names,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 274-280, 2018.
[20] Anna Jobin, Marcello Ienca, and Effy Vayena, “The Global Landscape of AI Ethics Guidelines,” Nature Machine Intelligence, vol. 1, no. 9, pp. 389-399, 2019.
[CrossRef] [Google Scholar] [Publisher Link]