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A system for real-time intervention in negative emotional contagion in a smart classroom deployed under edge computing service infrastructure

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Abstract

Literature has indicated that negative emotions may lead students to disengagement in teaching activities. Furthermore, the contagion of negative emotion is similar to infectious disease diffusion that drives more students into negative emotions. However, few methods have been brought forward to intervene in negative emotional contagion in real time, and most of them are limited to interventions of teachers, which are often not timely and even cause students to resist. Intervention in negative emotional contagion in classroom imposes several fundamental challenges on model and system design. In this paper, we address these issues from the following three aspects: (1) to design an emotional contagion model for classroom scene to locate the source of negative emotional contagion; (2) to develop deep learning-based visual emotion recognition system to recognize emotions of all students in the classroom; (3) to design and deploy the emotion recognition system as an edge computing-based service for minimizing response time to achieve multi-person emotional recognition and intervene in real time. We have applied the system to real-world classroom. Our results have shown that the system achieved two objectives: (1) reducing the number of students with negative emotions; (2) reducing response time to achieve real-time recognition and intervention. Meanwhile, this work provides a new perspective on research into emotional contagion in the classroom and smart education.

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References

  1. Delplanque S et al (2004) Modulation of cognitive processing by emotional valence studied through event-related potentials in humans. Neurosci Lett 356(1):1–4

    Article  Google Scholar 

  2. Delplanque S et al (2005) Event-related P3a and P3b in response to unpredictable emotional stimuli. Biol Psychol 68(2):0–120

    Article  Google Scholar 

  3. Huang YX, Luo Y (2006) Temporal course of emotional negativity bias: An ERP study. Neurosci Lett 398 (1-2):91–96

    Article  Google Scholar 

  4. Meyer DK, Turner JC (2002) Discovering Emotion in Classroom Motivation Research. Educ Psychol 37 (2):107–114

    Article  Google Scholar 

  5. Meyer DK, Turner JC (2006) Re-conceptualizing emotion and motivation to learn in classroom contexts. Educ Psychol Rev 18(4):377–390

    Article  Google Scholar 

  6. Vassiou AAEK (2016) Students’ achievement goals, emotion perception ability and affect and performance in the classroom: A multilevel examination. Educ Psychol 36(5):879–897

    Article  Google Scholar 

  7. Bowen J (2015) Emotion in the classroom: An update. To Improve the Academy 33(2):196–219

    Article  Google Scholar 

  8. Mouratidis A et al (2009) Beyond positive and negative affect: Achievement goals and discrete emotions in the elementary physical education classroom. Psychology of Sport and Exercise 10(3):0–343

    Article  Google Scholar 

  9. Ntika M et al (2014) [ACM Press the 4th International Conference - Thessaloniki, Greece (2014.06.02-2014.06.04)] Proceedings of the 4th international conference on web intelligence, mining and semantics (WIMS14) - WIMS 14 - Experiments with emotion contagion in emergency evacuation simulation, pp 1–11

  10. Ren-Jia W et al (2014) Design and implementation about emotion recognition module of online learning system. Modern educational technology

  11. Mehrabian A (2008) Communication without words. Communication theory

  12. Okosun KO, Makinde OD, Takaidza I (2013) Impact of optimal control on the treatment of HIV/AIDS and screening of unaware infectives. Appl Math Model 37(6):3802–3820

    Article  MathSciNet  MATH  Google Scholar 

  13. Gan C et al (2014) A propagation model of computer virus with nonlinear vaccination probability. Commun Nonlinear Sci Numer Simul 19(1):92–100

    Article  MathSciNet  Google Scholar 

  14. Xiaoming W, Zaobo HE, Lichen Z (2014) A pulse immunization model for inhibiting malware propagation in mobile wireless sensor networks. Chinese J Electron 23(4):810–815

    Google Scholar 

  15. Li H et al (2017) Teaching effect analysis based on the facial expression recognition in classroom. Modern distance education research

  16. Kerkeni L et al (2017) A review on speech emotion recognition: Case of pedagogical interaction in classroom 2017 international conference on advanced technologies for signal and image processing (ATSIP). IEEE

  17. Whitehill J, Bartlett M, Movellan J (2008) Automatic facial expression recognition for intelligent tutoring systems

  18. Shi W et al (2016) Edge computing: Vision and challenges. IEEE Internet of Things Journal 3(5):637–646

    Article  Google Scholar 

  19. Zhang D, Tan L, Ren J et al (2019) Near-optimal and truthful online auction for computation offloading in green edge-computing systems[J]. IEEE Trans Mob Comput 2019:1–1

    Google Scholar 

  20. Zhang D, Shen R, Ren J, Zhang Y (2018) Delay-optimal proactive service framework for block-stream as a service. IEEE Wirel Commun Lett 7:298–601. https://doi.org/10.1109/LWC.2018.2799935

    Google Scholar 

  21. Duan S, Zhang D, Wang Y, Li L, Zhang Y (2019) JointRec: A deep learning-based joint cloud video recommendation framework for mobile IoTs. IEEE Internet of Things Journal 2019:1–1. https://doi.org/10.1109/JIOT.2019.2944889

    Google Scholar 

  22. Tang W, Ren J, Zhang K, Zhang D, Zhang Y, Shen X (2019) Efficient and privacy-preserving fog-assisted health data sharing scheme. ACM Trans Intell Syst Technol 10:1–23. https://doi.org/10.1145/3341104

    Article  Google Scholar 

  23. Zhang D, Qiao Y, She L, Shen R, Ren J, Zhang Y (2018) Two time-scale resource management for green internet of things networks. IEEE Internet of Things Journal 6:545–556. https://doi.org/10.1109/JIOT.2018.2842766

    Article  Google Scholar 

  24. Li H, Ota K, Dong M (2019) Deep reinforcement scheduling for mobile crowdsensing in fog computing. ACM Trans Internet Technol 19:1–18. https://doi.org/10.1145/3234463

    Article  Google Scholar 

  25. Li H, Ota K, Dong M (2018) Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Netw 32:96–101. https://doi.org/10.1109/MNET.2018.1700202

    Article  Google Scholar 

  26. Li H, Ota K, Dong M (2018) ECCN: Orchestration of edge-centric computing and content-centric networking in the 5G radio access network. IEEE Wirel Commun 25:88–93. https://doi.org/10.1109/MWC.2018.1700315

    Article  Google Scholar 

  27. Li L, Ota K, Dong M (2018) Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Trans Indust Inform 14:4665–4673. https://doi.org/10.1109/TII.2018.2842821

    Article  Google Scholar 

  28. Ota K, Dao M, Mezaris V, Natale F (2017) Deep learning for mobile multimedia: A survey. ACM Trans Multimed Comput Commun Appl 13:1–22. https://doi.org/10.1145/3092831

    Google Scholar 

  29. Satyanarayanan M et al (2014) Cloudlets: At the leading edge of mobile-cloud convergence. International Conference on Mobile Computing IEEE Computer Society

  30. Shi W, Dustdar S (2016) The promise of edge computing. Computer 49(5):78–81

    Article  Google Scholar 

  31. Yi S, Li C, Li Q (2015) A survey of fog computing: Concepts, applications and issues. Proceedings of the 2015 workshop on mobile big data. ACM

  32. Garcia Lopez P et al (2015) Edge-centric computing: Vision and challenges. ACM SIGCOMM Comput Commun Rev 45(5):37–42

    Article  Google Scholar 

  33. Kaehler A, Bradski G (2016) Learning OpenCV 3: Computer vision in C++ with the openCV library. O’ReillyMedia Inc.

  34. Pekrun R et al (2002) Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educ Psychol 37(2):91–105

    Article  Google Scholar 

  35. Tato R et al (2002) Emotional space improves emotion recognition. 7th International Conference on Spoken Language Processing

  36. Montagne B et al (2007) The emotion recognition task: A paradigm to measure the perception of facial emotional expressions at different intensities. Perceptual and motor skills 104(2):589–598

    Article  Google Scholar 

  37. Zhang S et al (2016) Multimodal deep convolutional neural network for audio-visual emotion recognition. Proceedings of the 2016 ACM on international conference on multimedia retrieval. ACM

  38. Li S et al (2017) A method of emotional analysis of movie based on convolution neural network and bi-directional LSTM RNN. 2017 IEEE 2nd international conference on data science in cyberspace (DSC). IEEE

  39. Wright J et al (2008) Demo: Robust face recognition via sparse representation. Demo: Robust face recognition via sparse representation. IEEE

  40. Kulkarni SS, Reddy NP, Hariharan SI (2009) Facial expression (mood) recognition from facial images using committee neural networks. BioMedical Engineer OnLine 8(1):16

    Article  Google Scholar 

  41. Tian Y, Kanade T, Cohn JF (2011) Facial expression recognition. International Journal on Computer Science & Engineering

  42. Cohen I et al (2003) Facial expression recognition from video sequences. Comput Vis Image Underst 91:160–187

    Article  Google Scholar 

  43. Hatfield E, Cacioppo JT, Rapson RL (1993) Emotional contagion. Current Directions in Psychological Science 2(3):96–100

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by Jilin Province Development and Reform Commission, China (No. 2019C053-1), Development Project of Jilin Province of China (20170101006JC), the National Natural Science Foundation of China (No. 71620107001) and Jilin Provincial Key Laboratory of Big Date Intelligent Computing (20180622002JC).

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All authors have contributed to the intellectual content of this paper. Jian LI designed and conducted the models and experiments. Daqian Shi implemented the algorithms and analyzed the data. Piyaporn Tumnark designed the experiments and intervention mechanisms. Hao XU designed the research and conducted the project.

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Correspondence to Hao Xu.

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This article belongs to the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge

Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong

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Li, J., Shi, D., Tumnark, P. et al. A system for real-time intervention in negative emotional contagion in a smart classroom deployed under edge computing service infrastructure. Peer-to-Peer Netw. Appl. 13, 1706–1719 (2020). https://doi.org/10.1007/s12083-019-00863-8

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  • DOI: https://doi.org/10.1007/s12083-019-00863-8

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