Scoreboard Architectural Pattern and Integration of Emotion Recognition Results - Publikacja - MOST Wiedzy

Wyszukiwarka

Scoreboard Architectural Pattern and Integration of Emotion Recognition Results

Abstrakt

This paper proposes a new design pattern, named Scoreboard , dedicated for applications solving complex, multi-stage, non-deterministic problems. The pattern provides a computational framework for the design and implementation of systems that integrate a large number of diverse specialized modules that may vary in accuracy, solution level, and modality. The Scoreboard is an extension of Blackboard design pattern and comes under behavioral type. The pattern allows for an integration of multimodal results, employing early, and/or late fusion paradigms. Additionally, it provides a framework for the evaluation of the modules, dealing with inconsistency and low accuracy. In this paper, the Scoreboard design pattern is described with the standard meta-data model, followed by a sample implementation. This paper also provides the evaluation results based on experiments and a case study. The evaluation results confirmed the robustness, modularization, ease of integration, efficiency, and adaptability of the solutions with the Scoreboard pattern in comparison with the Blackboard pattern and ‘‘no pattern’’ condition. This paper provides also a case study of Scoreboard application in an integration of emotion recognition results. There are certain complex problems in modern software engineering which require multi-stage, multi-party, multi-modal solutions, and non-deterministic control strategies. Among those are natural language processing, image processing, and emotion recognition, to name just a few. The proposed Scoreboard pattern might be used in the software addressing the problems, especially in research systems that explore large solution spaces and require runtime decisions on execution order.

Cytowania

  • 1

    CrossRef

  • 0

    Web of Science

  • 1

    Scopus

Cytuj jako

Pełna treść

pobierz publikację
pobrano 24 razy
Wersja publikacji
Accepted albo Published Version
Licencja
Creative Commons: CC-BY otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
IEEE Access nr 7, strony 7228 - 7249,
ISSN: 2169-3536
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Landowska A., Brodny G.: Scoreboard Architectural Pattern and Integration of Emotion Recognition Results// IEEE Access. -Vol. 7, (2019), s.7228-7249
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/access.2018.2889557
Bibliografia: test
  1. C. Alexander, S. Ishikawa, and M. Silverstein, A Pattern Language. Berkeley, CA, USA: Oxford Univ. Press, 1977. otwiera się w nowej karcie
  2. E. Gamma, R. Helm, R. Johnson, and J. Vlissides, Design Patterns CD: Elements of Reusable Object-Oriented Software (Addison-Wesley Profes- sional Computing). Reading, MA, USA: Addison-Wesley, 1996. otwiera się w nowej karcie
  3. D. Schmidt, M. Stal, H. Rohnert, and F. Buschmann, Pattern-Oriented Software Architecture: A System of Patterns, vol. 2. Hoboken, NJ, USA: Wiley, 2000.
  4. J. Hannemann and G. Kiczales, ''Design pattern implementation in Java and aspectJ,'' ACM SIGPLAN Notices, vol. 37, no. 11, pp. 161-173, 2002. otwiera się w nowej karcie
  5. T. Erl, SOA Design Patterns. Upper Saddle River, NJ, USA: Prentice-Hall, 2009.
  6. J. Tidwell, Designing Interfaces: Patterns for Effective Interaction Design. Newton, MA, USA: O'Reilly, 2005.
  7. E. Cambria, Y. Song, H. Wang, and N. Howard, ''Semantic multidimen- sional scaling for open-domain sentiment analysis,'' IEEE Intell. Syst., vol. 29, no. 2, pp. 44-51, Mar./Apr. 2014. otwiera się w nowej karcie
  8. S. Dublin et al., ''Natural language processing to identify pneumonia from radiology reports,'' Pharmacoepidemiol. Drug Saf., vol. 22, no. 8, pp. 834-841, Aug. 2013. otwiera się w nowej karcie
  9. R. Lienhart, L. Liang, and A. Kuranov, ''A detector tree of boosted classi- fiers for real-time object detection and tracking,'' in Proc. IEEE Int. Conf. Multimedia Expo, Jul. 2003, p. II-277. otwiera się w nowej karcie
  10. A. Landowska, ''Emotion monitor-Concept, construction and lessons learned,'' in Proc. Federated Conf. Comput. Sci. Inf. Syst. (FedCSIS), 2015, pp. 75-80. otwiera się w nowej karcie
  11. F. Buschmann, R. Meunier, and H. Rohnert, Pattern-Oriented Software Architecture: A System of Patterns. Hoboken, NJ, USA: Wiley, 1996.
  12. T. S. Levitt, ''Choosing uncertainty representations in artificial intelli- gence,'' Int. J. Approx. Reason., vol. 2, no. 3, pp. 217-232, Jul. 1988. otwiera się w nowej karcie
  13. M. Mohri, F. Pereira, and M. Riley, ''Weighted finite-state transducers in speech recognition,'' Comput. Speech Lang., vol. 16, no. 1, pp. 69-88, Jan. 2002. otwiera się w nowej karcie
  14. A. Norouzian and R. Rose, ''An approach for efficient open vocabulary spoken term detection,'' Speech Commun., vol. 57, pp. 50-62, Feb. 2014. otwiera się w nowej karcie
  15. M. O. Rabin and D. Scott, ''Finite automata and their decision problems,'' IBM J. Res. Develop., vol. 3, no. 2, pp. 114-125, 1959. otwiera się w nowej karcie
  16. A. Cleeremans, D. Servan-Schreiber, and J. L. McClelland, ''Finite state automata and simple recurrent networks,'' Neural Comput., vol. 1, no. 3, pp. 372-381, Sep. 1989. otwiera się w nowej karcie
  17. J. Daciuk, S. Mihov, B. W. Watson, and R. E. Watson, ''Incremental con- struction of minimal acyclic finite-state automata,'' Comput. Linguistics, vol. 26, no. 1, pp. 3-16, Mar. 2000. otwiera się w nowej karcie
  18. J. F. Ackermann and M. S. Landy, ''Suboptimal decision criteria are predicted by subjectively weighted probabilities and rewards,'' Attention, Perception, Psychophys., vol. 77, no. 2, pp. 638-658, Feb. 2015. otwiera się w nowej karcie
  19. A. Ampatzoglou, S. Charalampidou, and I. Stamelos, ''Research state of the art on GoF design patterns: A mapping study,'' J. Syst. Softw., vol. 86, no. 7, pp. 1945-1964, 2013. otwiera się w nowej karcie
  20. V. Lesser, R. Fennell, L. Erman, and D. Reddy, ''Organization of the hearsay II speech understanding system,'' IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-23, no. 1, pp. 11-24, Feb. 1975. otwiera się w nowej karcie
  21. A. Landowska, G. Brodny, and M. R. Wrobel, ''Limitations of emotion recognition from facial expressions in e-learning context,'' in Proc. 9th Int. Conf. Comput. Support. Educ. (CSEDU), vol. 2, 2017, pp. 383-389. otwiera się w nowej karcie
  22. S. Poria, E. Cambria, R. Bajpai, and A. Hussain, ''A review of affective computing: From unimodal analysis to multimodal fusion,'' Inf. Fusion, vol. 37, pp. 98-125, Sep. 2017. otwiera się w nowej karcie
  23. H. Gunes and B. Schuller, ''Categorical and dimensional affect analysis in continuous input: Current trends and future directions,'' Image Vis. Comput., vol. 31, no. 2, pp. 120-136, Feb. 2013. otwiera się w nowej karcie
  24. Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, ''A survey of affect recognition methods: Audio, visual, and spontaneous expressions,'' IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 1, pp. 39-58, Jan. 2009.
  25. T. Partala and A. Kallinen, ''Understanding the most satisfying and unsat- isfying user experiences: Emotions, psychological needs, and context,'' Interact. Comput., vol. 24, no. 1, pp. 25-34, Jan. 2012. otwiera się w nowej karcie
  26. R. L. Hazlett and J. Benedek, ''Measuring emotional valence to understand the user's experience of software,'' Int. J. Hum.-Comput. Stud., vol. 65, no. 4, pp. 306-314, Apr. 2007. otwiera się w nowej karcie
  27. P. G. Zimmermann, P. Gomez, B. Danuser, and S. G. Schär, ''Extending usability: Putting affect into the user-experience,'' in Proc. NordiCHI, 2006, pp. 27-32.
  28. H. H. Binali, C. Wu, and V. Potdar, ''A new significant area: Emotion detection in E-learning using opinion mining techniques,'' in Proc. IEEE Int. Conf. Digit. Ecosyst. Technol., Jun. 2009, pp. 259-264. otwiera się w nowej karcie
  29. A. Landowska, ''Affect-awareness framework for intelligent tutoring systems,'' in Proc. 6th Int. Conf. Hum. Syst. Interact. (HSI), 2013, pp. 540-547. otwiera się w nowej karcie
  30. A. Landowska, M. Szwoch, and W. Szwoch, ''Methodology of affective intervention design for intelligent systems,'' Interact. Comput., vol. 28, no. 6, pp. 737-759, 2016. otwiera się w nowej karcie
  31. K. Hone, ''Empathic agents to reduce user frustration: The effects of varying agent characteristics,'' Interact. Comput., vol. 18, no. 2, pp. 227-245, 2006. otwiera się w nowej karcie
  32. A. Kolakowska, A. Landowska, M. Szwoch, W. Szwoch, and M. R. Wro- bel, ''Emotion recognition and its applications,'' in Human-Computer Systems Interaction: Backgrounds and Applications. Cham, Switzerland: Springer, 2014, pp. 51-62. otwiera się w nowej karcie
  33. L. Chittaro and R. Sioni, ''Affective computing vs. Affective placebo: Study of a biofeedback-controlled game for relaxation training,'' Int. J. Hum.-Comput. Stud., vol. 72, pp. 663-673, Aug./Sep. 2014. otwiera się w nowej karcie
  34. T. Partala and V. Surakka, ''The effects of affective interventions in human- computer interaction,'' Interact. Comput., vol. 16, no. 2, pp. 295-309, 2004. otwiera się w nowej karcie
  35. A. Kołakowska, A. Landowska, W. Szwoch, W. R. Wróbel, and M. Szwoch, ''Emotion recognition and its application in software engineer- ing,'' in Proc. 6th Int. Conf. Hum. Syst. Interact. (HSI), 2013, pp. 532-539. otwiera się w nowej karcie
  36. M. R. Wrobel, ''Emotions in the software development process,'' in Proc. 6th Int. Conf. Hum. Syst. Interact., Apr. 2013, pp. 518-523. otwiera się w nowej karcie
  37. A. Landowska, ''Emotion monitoring-Verification of physiological char- acteristics measurement procedures,'' Metrol. Meas. Syst., vol. 21, no. 4, pp. 719-732, 2014. otwiera się w nowej karcie
  38. H. Gunes and M. Piccardi, ''Affect recognition from face and body: Early fusion vs. Late fusion,'' in Proc. IEEE Int. Conf. Syst. Man Cybern., vol. 4, Oct. 2005, pp. 3437-3443. otwiera się w nowej karcie
  39. I. Hupont, S. Ballano, S. Baldassarri, and E. Cerezo, ''Scalable multimodal fusion for continuous affect sensing,'' in Proc. IEEE Symp. Ser. Com- put. Intell. (SSCI), Workshop Affect. Comput. Intell. (WACI), Apr. 2011, pp. 1-8. otwiera się w nowej karcie
  40. S. Poria, E. Cambria, and A. Gelbukh, ''Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis,'' in Proc. Conf. Empirical Methods Natural Lang. Process., 2015, pp. 2539-2544. otwiera się w nowej karcie
  41. M. Mansoorizadeh and N. M. Charkari, ''Multimodal information fusion application to human emotion recognition from face and speech,'' Multimed. Tools Appl., vol. 49, no. 2, pp. 277-297, Aug. 2010. otwiera się w nowej karcie
  42. S. Poria, E. Cambria, N. Howard, G.-Bin Huang, and A. Hussain, ''Fusing audio, visual and textual clues for sentiment analysis from multimodal content,'' Neurocomputing, vol. 174, pp. 50-59, Jan. 2016. otwiera się w nowej karcie
  43. M. Wollmer et al., ''YouTube movie reviews: Sentiment analysis in an audio-visual context,'' IEEE Intell. Syst., vol. 28, no. 3, pp. 46-53, May 2013. otwiera się w nowej karcie
  44. P. K. Atrey, M. A. Hossain, A. El Saddik, and M. S. Kankanhalli, ''Multi- modal fusion for multimedia analysis: A survey,'' Multimedia Syst., vol. 16, no. 6, pp. 345-379, 2010. otwiera się w nowej karcie
  45. Software Engineering-Product Quality-Part 1: Quality Model, Standard ISO/IEC 9126:2001, ISO, 2001. otwiera się w nowej karcie
  46. R. G. Dromey, ''A model for software product quality,'' IEEE Trans. Softw. Eng., vol. 21, no. 2, pp. 146-162, Feb. 1995. otwiera się w nowej karcie
  47. M. H. Klein, R. Kazman, L. Bass, J. Carriere, M. Barbacci, and H. Lipson, ''Attribute-based architecture styles,'' in Proc. Work. Conf. Softw. Archit., 1999, pp. 225-243. otwiera się w nowej karcie
  48. F. Losavio, L. Chirinos, and M. A. Perez, ''Quality models to design software architectures,'' in Proc. Technol. Object-Oriented Lang. Syst. (TOOLS), 2001, pp. 123-135. otwiera się w nowej karcie
  49. B. Meyer, Object-Oriented Software Construction. Upper Saddle River, NJ, USA: Prentice-Hall, 1989.
  50. Y. Press and L. Constantine, Structured Design: Fundamentals of a Disci- pline of Computer Program and Systems Design. Upper Saddle River, NJ, USA: Prentice-Hall, 1979.
  51. A. Shaik, B. Manda, C. Prakashini, C. R. K. Reddy, and K. Deepthi, ''Metrics for object oriented design software systems: A survey,'' J. Emerg. Trends Eng. Appl. Sci., vol. 1, no. 2, pp. 190-198, 2010.
  52. M. A. Babar, L. Zhu, and R. Jeffery, ''A framework for classifying and comparing software architecture evaluation methods,'' in Proc. Softw. Eng. Conf., 2004, pp. 309-318. otwiera się w nowej karcie
  53. R. Kazman, M. Klein, and P. Clements, ''ATAM: Method for architecture evaluation,'' Softw. Eng. Inst., Carnegie-Mellon Univ, Pittsburgh, PA, USA, Tech. Rep. CMU/SEI-2000-TR-004, 2000. otwiera się w nowej karcie
  54. A. Landowska, ''Emotion monitor-concept, construction and lessons learned,'' in Proc. Federated Conf. Comput. Sci. Inf. Syst. (FedCSIS), Oct. 2015, pp. 75-80. otwiera się w nowej karcie
  55. R. Van Solingen, V. Basili, G. Caldiera, and H. D. Rombach, ''Goal ques- tion metric (GQM) approach,'' in Encyclopedia of Software Engineering. Hoboken, NJ, USA: Wiley, 2002. otwiera się w nowej karcie
  56. T. J. McCabe, ''A complexity measure,'' IEEE Trans. Softw. Eng., vol. SE-2, no. 4, pp. 308-320, Dec. 1976. otwiera się w nowej karcie
  57. K. A. M. Ferreira, M. A. S. Bigonha, R. S. Bigonha, L. F. O. Mendes, and H. C. Almeida, ''Identifying thresholds for object-oriented software metrics,'' J. Syst. Softw., vol. 85, no. 2, pp. 244-257, Feb. 2012. otwiera się w nowej karcie
  58. T. L. Alves, C. Ypma, and J. Visser, ''Deriving metric thresholds from benchmark data,'' in Proc. IEEE Int. Conf. Softw. Maintenance, Sep. 2010, pp. 1-10. otwiera się w nowej karcie
  59. P. Oliveira, M. T. Valente, and F. P. Lima, ''Extracting relative thresh- olds for source code metrics,'' in Proc. IEEE Conf. Softw. Maintenance, Reeng., Reverse Eng. Softw. Evol. Week (CSMR-WCRE), Feb. 2014, pp. 254-263. otwiera się w nowej karcie
  60. S. Herbold, J. Grabowski, and S. Waack, ''Calculation and optimization of thresholds for sets of software metrics,'' Empir. Softw. Eng., vol. 16, no. 6, pp. 812-841, Dec. 2011. otwiera się w nowej karcie
  61. A. Landowska, ''Web questionnaire as construction method of affect- annotated lexicon-Risks reduction strategy,'' in Proc. Int. Conf. Affect. Comput. Intell. Interact. (ACII), Sep. 2015, pp. 421-427. otwiera się w nowej karcie
  62. A. Kolakowska, A. Landowska, M. Szwoch, W. Szwoch, and M. R. Wrobel, ''Evaluation criteria for affect-annotated databases,'' in Beyond Databases, Architectures and Structures (Communications in Computer and Information Science). Cham, Switzerland: Springer, 2015. otwiera się w nowej karcie
  63. M. Bradley and P. Lang, ''Affective norms for English words (ANEW): Instruction manual and affective ratings,'' Center Res. Psychophysiol., Univ. Florida, Gainesville, FL, USA, Tech. Rep. C-1, 1999.
  64. G. Brodny and A. Landowska, ''Integration in multichannel emotion recognition,'' in Proc. 11th Int. Conf. Hum. Syst. Interact. (HSI), 2018, pp. 35-41. otwiera się w nowej karcie
Źródła finansowania:
Weryfikacja:
Politechnika Gdańska

wyświetlono 177 razy

Publikacje, które mogą cię zainteresować

Meta Tagi