Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 9

Position Papers of the 2016 Federated Conference on Computer Science and Information Systems

Comprehensive Observation and its Role in Self-Awareness; An Emotion Recognition System Example

, ,

DOI: http://dx.doi.org/10.15439/2016F588

Citation: Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 9, pages 117124 ()

Full text

Abstract. Observation plays a crucial role in self-awareness. In many scenarios, such as the Observe-Decide-Act (ODA) loops, self-awareness is founded upon observations of the system. In other words, observation generates the understanding of the system from the status and behavior of its self and its environment. Although recently more focus has been put on comprehensive and competent observations, we believe that further attention and work is due, especially in the field of cyber-physical systems. Hence, in this paper, we discuss our position on various aspects of observation methods. In a short list, the major aspects are Abstraction, Disambiguation, Desirability, Relevance, Data Reliability, Confidence, Attention, and History. We elaborate and anticipate the potential of these factors in improving the quality of the observation of the system, decreasing the processing load of higher layers, increasing the reliability of decisions, and consequently the overall performance of the system. To put these aspects into perspective, we elaborate them in the context of their potentials in our emotion recognition system under development.

References

  1. N. Dutt, A. Jantsch, and S. Sarma, “Toward smart embedded systems: A self-aware system-on-chip (SoC) perspective,” ACM Transactions on Embedded Computing Systems (TECS), vol. 15, no. 2, p. 22, 2016.
  2. J. O. Kephart and D. M. Chess, “The vision of autonomic computing,” Computer, vol. 36, no. 1, pp. 41–50, 2003.
  3. P. Mercati, A. Bartolini, F. Paterna, T. S. Rosing, and L. Benini, “A linux-governor based dynamic reliability manager for android mobile devices,” in Design, Automation and Test in Europe Conference and Exhibition (DATE), 2014. IEEE, 2014, pp. 1–4.
  4. B. Jennings and R. Stadler, “Resource management in clouds: Survey and research challenges,” Journal of Network and Systems Management, pp. 1–53, 2014. [Online]. Available: http://dx.doi.org/10.1007/s10922-014-9307-7
  5. P. eth ee Spathis and M. Bicudo, “ANA: Autonomic network architecture,” Autonomic Network Management Principles, p. 49, 2011.
  6. L. Wanner, S. Elmalaki, L. Lai, P. Gupta, and M. Srivastava, “VarEMU: An emulation testbed for variability-aware software,” in Hardware/Software Codesign and System Synthesis (CODES+ISSS), 2013 Interna- tional Conference on, 2013, pp. 1–10.
  7. J. Strassner, S.-S. Kim, and J. W.-K. Hong, “The design of an autonomic communication element to manage future internet services,” in Management Enabling the Future Internet for Changing Business and New Computing Services. Springer, 2009, pp. 122–132.
  8. H. Hoffmann, M. Maggio, M. D. Santambrogio, A. Leva, and A. Agarwal, “SEEC: A framework for self-aware computing,” MIT, Cambrige, Massachusetts, Tech. Rep. MIT-CSAIL-TR-2010-049, October 2010.
  9. W. Baek and T. M. Chilimbi, “Green: a framework for supporting energy-conscious programming using controlled approximation,” in ACM Sigplan Notices, vol. 45, no. 6. ACM, 2010, pp. 198–209.
  10. J.-S. Preden, K. Tammemäe, A. Jantsch, M. Leier, A. Riid, and E. Calis, “The benefits of self-awareness and attention in fog and mist computing,” IEEE Computer, Special Issue on Self-Aware/Expressive Computing Systems, pp. 37–45, July 2015.
  11. B. Rinner, L. Esterle, J. Simonjan, G. Nebehay, R. Pflugfelder, G. Fernandez Dominguez, and P. R. Lewis, “Self-aware and self-expressive camera networks,” Computer, vol. 48, no. 7, pp. 21–28, 2015.
  12. S. Sarma, N. Dutt, P. Gupta, A. Nicolau, and N. Venkatasubramanian, “Cyberphysical-system-on-chip (CPSoC): A self-aware MPSoC paradigm with cross-layer virtual sensing and actuation,” in Proceedngs of the Design, Automation and Test in Europe Conference and Exhibition (DATE), Grenoble, France, March 2015.
  13. L. Guang, J. Plosila, J. Isoaho, and H. Tenhunen, “Hierarchical agent monitored parallel on-chip system: A novel design paradigm and its formal specification,” International Journal of Embedded and Real-Time Communication Systems (IJERTCS), vol. 1, no. 2, 2010.
  14. A. Jantsch and K. Tammemäe, “A framework of awareness for artificial subjects,” in Proceedings of the 2014 International Conference on Hardware/Software Codesign and System Synthesis, ser. CODES ’14. New York, NY, USA: ACM, 2014, pp. 20:1–20:3. [Online]. Available: http://jantsch.se/AxelJantsch/papers/2014/AxelJantsch-CODES.pdf
  15. J.-S. Preden, J. Llinas, G. Rogava, R. Pathma, and L. Motus, “On-line data validation in distributed data fusion,” in Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IV: SPIE Defense, Security and Sensing, T. Pham, M. A. Kolodny, and K. L. Priddy, Eds. SPIE - International Society for Optics and Photonics, 2013.
  16. J.-S. Preden, “Generating situation awareness in cyber-physical systems: Creation and excahnge of situational information,” in Proceedings of the 2014 International Conference on Hardware/Software Codesign and System Synthesis. New York, NY, USA: ACM, October 2014.
  17. M. Sánchez-Escribano and R. Sanz, “Emotions and the engineering of adaptiveness,” in Procedia Computer Science: Conference on Systems Engineering Research, vol. 28. Madrid, Spain: Elsevier, 2014, pp. 473–480.
  18. H. Hoffmann, “CoAdapt: Predictable behavior for accuracy-aware applications running on power-aware systems,” in Real-Time Systems (ECRTS), 2014 26th Euromicro Conference on, July 2014, pp. 223–232.