Data Value as the basis for Human Behavior Modeling

  • Agostino G. Bruzzone 
  • b Francesco Longo, 
  • Giulio Franzinetti, 
  • d Alberto De Paoli, 
  • eEnrico Ferrari
  • Simulation Team, SIM4Future, via Trento 43, 16145 Genova, Italy
  • MSC-LES, DIMEG, University of Calabria, Via Pietro Bucci, Cubo 45 C, 87036 Rende
  • Lio-Tech ltd., London, UK
  • d,e Simulation Team, Italy
Cite as
Bruzzone A.G., Longo F., Franzinetti G., De Paoli A., Ferrari E. (2021). Data Value as the basis for Human Behavior Modeling. Proceedings of the 23rd International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation(HMS 2021), pp. 83-89. DOI: https://doi.org/10.46354/i3m.2021.hms.011

Abstract

This paper introduces the importance of creating models to evaluate the value of data regarding their potential to extract information to identify human behaviors, attitudes, and characteristics. This is just a preliminary overview on this potential and consider that in the future, these values could become parts of the assets of companies if properly acquired and processed in order to respect all regulations and all rights of all the parties.

References

  1. Abdelhedi, S., Wali, A., & Alimi, A. M. (2016). Fuzzy logic-based human activity recognition in video surveillance applications. In Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015 (pp. 227-235). Springer, Cham.
  2. Abdulsalam, W. H., Alhamdani, R. S., & Abdullah, M. N. (2019). Facial emotion recognition from videos using deep convolutional neural networks. International Journal of Machine Learning and Computing, 9(1), 14-19.
  3. Achara, J.P., Acs, G. and Castelluccia, C., 2015, October. On the unicity of smartphone applications. In Proceedings of the 14th ACM Workshop on Privacy in the Electronic Society (pp. 27-36).
  4. Aho, B., & Duffield, R. (2020). Beyond surveillance capitalism: Privacy, regulation and big data in Europe and China. Economy and Society, 49(2), 187-212.
  5. Aridor, G., Che, Y. K., & Salz, T. (2020). The economic consequences of data privacy regulation: Empirical evidence from GDPR. NBER working paper, (w26900).
  6. Bhardway J., Long J. (2021) "What are the different kinds of data profiling?", Proc. of Open Data Science Conference, September 15-16 
  7. Bloor R. (2020) "How Much Is Data Worth? The Value of Your Personal Data", Permission, April 8
  8. Blumenstock, J., Cadamuro, G. and On, R., 2015. Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), pp.1073-1076.
  9. Broekens, J., Jonker, C. M., & Meyer, J. J. C. (2010). Affective negotiation support systems. Journal of Ambient Intelligence and Smart Environments, 2(2), 121-144.
  10. Bruzzone, A. G., Massei, M., Sinelshchikov, K., Fadda, P., Fancello, G., Fabbrini, G. & Gotelli, M. (2019). Extended reality, intelligent agents, and simulation to improve efficiency, safety, and security in harbors and port plants. In 21st International Conference on Harbor, Maritime and Multimodal Logistics Modeling and Simulation, HMS 2019 (pp. 88-91). DIME University of Genoa.
  11. Bruzzone, A. G., Di Matteo, R., & Sinelshchikov, K. (2018) Strategic Engineering & Innovative Modeling Paradigms. In Workshop on Applied Modelling & Simulation, Praha, CZ
  12. Bruzzone, A.G., Massei, M., Sinelshchikov, K. & Di Matteo, R. (2018). Population behavior, social networks, transportations, infrastructures, industrial and urban simulation. Proceedings of 30th European Modeling and Simulation Symposium, EMSS 2018, Budapest, Hungary. pp. 401-404
  13. Bruzzone, A., Massei, M., Longo, F., Poggi, S., Agresta, M., Bartolucci, C. & Nicoletti, L. (2014). Human behavior simulation for complex scenarios based on intelligent agents. Proceedings of ANSS2014, Spring Simulation Multi-Conference (SpringSim'14) April 13 – 16, Tampa, FL; USA
  14. Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75-86.
  15. Cavanillas, J. M., Curry, E., & Wahlster, W. (2016). The big-data value opportunity. In New horizons for a data-driven economy (pp. 3-11). Springer, Cham.
  16. Chakraborty, A., Konar, A., Chakraborty, U. K., & Chatterjee, A. (2009). Emotion recognition from facial expressions and their control using fuzzy logic. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39(4), 726-743.
  17. Chittaranjan, G., Blom, J. and Gatica-Perez, D., 2011, June. Who is who with big-five: Analyzing and classifying personality traits with smartphones. In Wearable Computers (ISWC), 2011 15th Annual International Symposium on (pp. 29-36). IEEE.
  18. Crowley-Sweet Davin (2021) "Proving Data’s Strategic Value To Get You Board-Level Buy-In," DataIQ
  19. De Silva, L. C., Miyasato, T., & Nakatsu, R. (1997, September). Facial emotion recognition using multi-modal information. In Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat. (Vol. 1, pp. 397-401). IEEE.
  20. Isaak, J., & Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), 56-59.
  21. Jeffay J. (2017) "Who Spends on Jewelry: Income Levels Are Key," Index Online, February 8
  22. Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W. and Torralba, A., 2016. Eye-tracking for everyone. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2176-2184)
  23. Kaya, M. F., & Schoop, M. (2019, June). Application of data mining methods for pattern recognition in negotiation support systems. In International conference on group decision and negotiation (pp. 223-237). Springer, Cham.
  24. Laney D.B. (2021) Data Valuation Paves The Road To The Future For Highways England", Forbes, February 1
  25. Leonelli, S. (2019). Data—from objects to assets, Nature, October, 574, pp. 317-320
  26. Maier, J., Schlechte, D., Fernandes, M., & Theissler, A. (2020). A deep learning approach to prepare participants for negotiations by recognizing emotions with voice analysis. In Proceedings of the 20th International Conference on Group Decision and Negotiation, Toronto, Canada.
  27. Mandryk, R. L., & Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International journal of human-computer studies, 65(4), 329-347.
  28. Mavriki P, Karyda M. (2018) "Profiling with big data: Identifying privacy implications for individuals, groups, and society," Proc. of MCIS, Corfu, Greece
  29. Nemitz, P. (2018). Constitutional democracy and technology in the age of artificial intelligence. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180089.
  30. Nicola, F. G., & Pollicino, O. (2020). The Balkanization of Data Privacy Regulation. W. Va. L. Rev., 123, 61.
  31. Pantic, M., & Rothkrantz, L. (2004, June). Case-based reasoning for user-profiled recognition of emotions from face images. In 2004 IEEE International Conference on Multimedia and Expo (ICME)(IEEE Cat. No. 04TH8763) (Vol. 1, pp. 391-394). IEEE. 
  32. Pennebaker J.W., M.R. Mehl, K. Niederhoffer (2003) Psychological aspects of natural language use: Our words, our selves, Annual Review of Psychology (54) (2003), pp. 547-577
  33. Ploug, T., & Holm, S. (2021). The Right to Contest AI Profiling Based on Social Media Data. The American Journal of Bioethics, 21(7), 21-23.
  34. Poyraz, O. I., Canan, M., McShane, M., Pinto, C. A., & Cotter, T. S. (2020). Cyber assets at risk: monetary impact of US personally identifiable information mega data breaches. The Geneva Papers on Risk and Insurance-Issues and Practice, 45(4), 616-638.
  35. Rangel, F., & Rosso, P. (2016). On the impact of emotions on author profiling. Information processing & management, 52(1), 73-92.
  36. Rossi, F., Fasel, I. and Sanfey, A.G., 2011. Inscrutable games? Facial expressions predict economic behavior. BMC Neuroscience, 12(1), p.P281.
  37. Saha, A. K. (2017). Review of Design of Speech Recognition and Text Analytics based Digital Banking Customer Interface and Future Directions of Technology Adoption. Computer Science, December, arXiv preprint arXiv:1712.04640.
  38. Schler, J., Koppel, M., Argamon, S., & Pennebaker, J. W. (2006, March). Effects of age and gender on blogging. In AAAI spring symposium: Computational approaches to analyzing weblogs (Vol. 6, pp. 199-205).
  39. Sengupta S., Rusli E.M. (2012) "Personal Data's Value? Facebook Is Set to Find Out", The New York Times, January 31
  40. Singh, V.K., Freeman, L., Lepri, B. and Pentland, A.P., 2013. Classifying spending behavior using socio-mobile data.
  41. Sjöberg, M., Chen, H. H., Floréen, P., Koskela, M., Kuikkaniemi, K., Lehtiniemi, T., & Peltonen, J. (2016, September). Digital me: Controlling and making sense of my digital footprint. In International Workshop on Symbiotic Interaction (pp. 155-167). Springer, Cham
  42. Sottilare, R. A., & Proctor, M. (2012). Passively classifying student mood and performance within intelligent tutors. Journal of Educational Technology & Society, 15(2), 101-114.
  43. US Census Bureaus (2016) "Person Income in 2015 - 15 Years and Over - All Races", Technical Report US Census Bureau. 2016, Suitland, MD
  44. Williams N. (2017)"What exactly is 'profiling' under the GDPR," Data & Marketing Association, June 20
  45. Wimmer, K. (2018). Free expression and EU privacy regulation: Can the GDPR reach US publishers. Syracuse L. Rev., 68, 547.
  46. Wingenbach T. S., C. Ashwin, and M. Brosnan, (2016) "Correction: Validation of the Amsterdam dynamic facial expression set–bath intensity variations (ADFES-BIV): A set of videos expressing low, intermediate, and high-intensity emotions," PloS One, vol. 11, no. 12, p. e0168891
  47. Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F. and Freeman, W., 2012. Eulerian