Abstract
There is always a component of the field of Human-Machine Interactions (HMI) which constantly tries to provide solutions to the problems relating to adaptation, in order to move towards more ergonomic approaches and more flexible and adaptive tools. It is in this context that this research work is taking place, which will consider an adaptation of User Interfaces (UIs) guided by the results of the analysis of user feelings and this, by adopting a new approach based on Multi-Agent System (MAS) and Deep Learning. To implement this approach, a system admitting a first component as a dynamic Django web application and a second component which corresponds to a SPADE Multi-Agent System, has been produced and tested and has shown effective and interesting experimental results.
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References
Beaudouin-Lafon, M.: Instrumental interaction: an interaction model for designing post-WIMP user interfaces (2000). https://doi.org/10.1145/332040.332473
Weiser, M.: L’ordinateur pour le 21e siècle. Sci. Am. 265, 94–104 (1991). https://doi.org/10.1038/scientificamerican0991-94
Calvary, G., Coutaz, J., Thevenin, D., Limbourg, Q., Bouillon, L., Vanderdonckt, J.: A unifying reference framework for multi-target user interfaces. Interact. Comput. 15(3), 289–308 (2003)
Akiki, P.A., Bandara, A.K., Yu, Y.: Adaptive model-driven user interface development systems. ACM Comput. Surv. 47(1), 9:1–9:33 (2014)
Gupta, A., Anpalagan, A., Khwaja, A-S.: Deep learning for object detection and scene perception in self-driving cars: survey, challenges, and open issues (2016)
Hachani, S., Dupuy-Chessa, S., Front, A.: A generic approach for the dynamic adaptation of UIs to the context. In: 21st Francophone Conference on Human-Machine Interaction (IHM 2009), Grenoble, France, pp. 89–96 (2009). ffhal-01002999P
Favre, J.-M.: Towards a basic theory to modeldriven engineering. In: 3rd Workshop in Software Model Engineering 2004, Lisboa, Portugal, pp. 9–17 (2004)
Giuffrida, T., Dupuy-Chessa, S., Poli, J.-P., Céret, E.: Fuzzy4U: a fuzzy logic system for the adaptation of user interfaces. In: HMI 2018, Brest, France, pp. 23–26 (2018)
Cao, J., Xing, N., Chan, A., Feng, Y., Jin, B.: Service adaptation using fuzzy theory in context-aware mobile computing middleware. In: Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2005)
Desruelle, H., Blomme, D., Gielen, F.: Adaptive mobile web applications: a quantitative evaluation approach. In: International Conference on Web Engineering (2011)
Cueva-Fernandez, G., Espada, J.P., García-Díaz, V., Crespo, R.G., GarciaFernandez, N.: Fuzzy system to adapt web voice interfaces dynamically in a vehicle sensor tracking application definition. Soft. Comput. 20(18), 3321–3334 (2016)
Nyongesa, H.O., Shicheng, T., Maleki-Dizaji, S., Huang, S.T., Siddiqi, J.: Adaptive Web interface design using fuzzy logic. In: Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003), pp. 671–674 (2016)
Papatheocharous, E., Belk, M., Germanakos, P., Samaras, G.: Proposing a fuzzy adaptation mechanism based on cognitive factors of users for web personalization. Artif. Intell. Appl. Innov., 135–144 (2012)
Soui, M., Abed, M., Ghedira, K.: Fuzzy logic approach for adaptive systems design. In: Kurosu, M. (ed.) HCI 2013. LNCS, vol. 8008, pp. 141–150. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39342-6_16
Thevenin, D., Coutaz, J.: IHM adaptation: taxonomies et software architecture. In: Acts IHM02, pp. 207–210. ACM Press (2002)
Ianjafitia, A.: Deep learning-based object detection system in an intelligent store, Master’s Thesis, universite d’antananarivo (2019)
Sharma, N., Sharma, R., Jindal, N.: Machine learning and deep learning applications-a vision. Glob. Trans. Proc. 2(1), 24–28 (2021)
Galindo, J.A., Dupuy-Chessa, S., Ceret, E.: Toward a UI adaptation approach driven by user emotions. In: ACHI 2017 - The Tenth International Conference on Advances in Computer-Human Interactions, Nice, France, pp. 12–17 (2017). ffhal-01720940
Menacer, D.-E.: A mobile Agent-Based Architecture for distributed applications, These Magister, National Institute of Informatics (INI) (2004)
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Maaloul, A., Nouri, H.E., Trifa, Z., Belkahla Driss, O. (2022). Adaptation of HMIs According to Users’ Feelings Based on Multi-agent Systems. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_35
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