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Metastimuli: An Introduction to PIMS Filtering

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12197)

Abstract

A system design for correlating information stimuli and a user’s personal information management system (PIMS) is introduced. This is achieved via a deep learning classifier for textual data, a recently developed PIMS graph information architecture, and a principle component analysis (PCA) reduction thereof. The system is designed to return unique and meaningful signals from incoming textual data in or near realtime. The classifier uses a recurrent neural network to determine the location of a given atom of information in the user’s PIMS. PCA reduction of the PIMS graph to \(\mathbb {R}^m\), with m the actuator (haptic) dimensionality, is termed a PIMS filter. Demonstrations are given of the classifier and PIMS filter. The haptic stimuli, then, are correlated with the user’s PIMS and are therefore termed “metastimuli.” Applications of this system include educational environments, where human learning may be enhanced. We hypothesize a metastimulus bond effect on learning that has some support from the analogous haptic bond effect. A study is outlined to test this hypothesis.

Keywords

Design Human centered design and user centered design Design Information design Technology Augmented reality and environments Technology Haptic user interface Technology Intelligent and agent systems Technology Natural user interfaces (NUI) 

References

  1. 1.
    Fredembach, B., de Boisferon, A.H., Gentaz, E.: Learning of arbitrary association between visual and auditory novel stimuli in adults: the “bond effect” of haptic exploration. PloS one 4(3), e4844 (2009)CrossRefGoogle Scholar
  2. 2.
    Jung, J., et al.: Speech communication through the skin: design of learning protocols and initial findings. In: Marcus, A., Wang, W. (eds.) DUXU 2018. LNCS, vol. 10919, pp. 447–460. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91803-7_34CrossRefGoogle Scholar
  3. 3.
    Critique of Pure Reason. Palgrave Macmillan, London (2007).  https://doi.org/10.1007/978-1-137-10016-0_3
  4. 4.
    Karim, M.R.: Deep-learning-with-tensorflow, April 2017. https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow/graphs/contributors
  5. 5.
    Lehoucq, R., Maschhoff, K., Sorensen, D., Yang, C.: Arpack software. https://www.caam.rice.edu/software/ARPACK/
  6. 6.
    Picone, R.A.: ricopicone/pims-filter: Pims filter, January 2020.  https://doi.org/10.5281/zenodo.3633355
  7. 7.
    Picone, R.A.R., Lentz, J., Powell, B.: The fuzzification of an information architecture for information integration. In: Yamamoto, S. (ed.) HIMI 2017. LNCS, vol. 10273, pp. 145–157. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58521-5_11CrossRefGoogle Scholar
  8. 8.
    Picone, R.A.R., Powell, B.: A new information architecture: a synthesis of structure, flow, and dialectic. In: Yamamoto, S. (ed.) HIMI 2015. LNCS, vol. 9172, pp. 320–331. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-20612-7_31CrossRefGoogle Scholar
  9. 9.
    Saerens, M., Fouss, F., Yen, L., Dupont, P.: The principal components analysis of a graph, and its relationships to spectral clustering. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 371–383. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30115-8_35CrossRefGoogle Scholar
  10. 10.
    Scipy: Sparse eigenvalue problems with arpack. https://docs.scipy.org/doc/scipy/reference/tutorial/arpack.html
  11. 11.
    Sporleder, C., Lapata, M.: Automatic paragraph identification: a study across languages and domains. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 72–79 (2004)Google Scholar
  12. 12.
    Stein, B.E., Meredith, M.A., Wallace, M.T.: Development and neural basis of multisensory integration. In: The Development of Intersensory Perception: Comparative Perspectives, pp. 81–105 (1994)Google Scholar
  13. 13.
    Virtanen, P., et al.: SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python. arXiv e-prints arXiv:1907.10121 (2019)
  14. 14.
    Webb, D.: danewebb/Tag-Classification: Initial release of Tag-Classification, January 2020.  https://doi.org/10.5281/zenodo.3633402
  15. 15.
    Webb, D., Picone, R.A.: danewebb/tex-tagging: Initial release of Tex- Tagging, January 2020.  https://doi.org/10.5281/zenodo.3633400
  16. 16.
    Wittgenstein, L., Anscombe, G.: Philosophical Investigations: The German Text, with a Revised English Translation. Blackwell, Oxford (2001)Google Scholar
  17. 17.
    Zaccone, G., Karim, M.: Deep Learning with TensorFlow: Explore Neural Networks and Build Intelligent Systems with Python, 2nd edn. Packt Publishing, Birmingham (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Saint Martin’s UniversityLaceyUSA
  2. 2.Dialectica LLCOlympiaUSA

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