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.
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- 1.
The dialectical architecture’s structural aspect can be considered to estimate the structure of a language game: a communally developed set of language rules of usage [16]. It is, then, important to recognize that there are many language games and therefore many structures to be estimated. Furthermore, the rules of these games evolve with use. Therefore, a user’s PIMS should not be isolated from others’, but neither should there be only one such structure. Additionally, a PIMS should evolve with the language game, a feature that can be detected through collective user estimation.
- 2.
Kant claims the mind has a priori “intuitions” for space and time, but for our purposes we can take a priori to mean pre-existing.
- 3.
Here we use “crisp” set notation; however, fuzzy set notation can also be used (see [7]).
References
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)
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_34
Critique of Pure Reason. Palgrave Macmillan, London (2007). https://doi.org/10.1007/978-1-137-10016-0_3
Karim, M.R.: Deep-learning-with-tensorflow, April 2017. https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow/graphs/contributors
Lehoucq, R., Maschhoff, K., Sorensen, D., Yang, C.: Arpack software. https://www.caam.rice.edu/software/ARPACK/
Picone, R.A.: ricopicone/pims-filter: Pims filter, January 2020. https://doi.org/10.5281/zenodo.3633355
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_11
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_31
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_35
Scipy: Sparse eigenvalue problems with arpack. https://docs.scipy.org/doc/scipy/reference/tutorial/arpack.html
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)
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)
Virtanen, P., et al.: SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python. arXiv e-prints arXiv:1907.10121 (2019)
Webb, D.: danewebb/Tag-Classification: Initial release of Tag-Classification, January 2020. https://doi.org/10.5281/zenodo.3633402
Webb, D., Picone, R.A.: danewebb/tex-tagging: Initial release of Tex- Tagging, January 2020. https://doi.org/10.5281/zenodo.3633400
Wittgenstein, L., Anscombe, G.: Philosophical Investigations: The German Text, with a Revised English Translation. Blackwell, Oxford (2001)
Zaccone, G., Karim, M.: Deep Learning with TensorFlow: Explore Neural Networks and Build Intelligent Systems with Python, 2nd edn. Packt Publishing, Birmingham (2018)
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Picone, R.A.R., Webb, D., Powell, B. (2020). Metastimuli: An Introduction to PIMS Filtering. In: Schmorrow, D., Fidopiastis, C. (eds) Augmented Cognition. Human Cognition and Behavior. HCII 2020. Lecture Notes in Computer Science(), vol 12197. Springer, Cham. https://doi.org/10.1007/978-3-030-50439-7_8
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