Fabio Paternò
ABSTRACT
The combination of the Internet of Things and Artificial Intelligence has made it possible to introduce numerous automations in our daily environments. Many new interesting possibilities and opportunities have been enabled, but there are also risks and problems. Often these problems are originated from approaches that have not been able to consider the users’ viewpoint sufficiently. We need to empower people in order to actually understand the automations in their surroundings environments, modify them, and create new ones, even if they have no programming knowledge. The course discusses these problems and some possible solutions to provide people with the possibility to control and create their daily automations.
- Will Brackenbury, Abhimanyu Deora, Jillian Ritchey, Jason Vallee, Weijia He, Guan Wang, Michael L. Littman, Blase Ur: How Users Interpret Bugs in Trigger-Action Programming. CHI 2019: 552Google Scholar
- Fulvio Corno, Luigi De Russis, Alberto Monge Roffarello: Empowering End Users in Debugging Trigger-Action Rules. CHI 2019: 388Google Scholar
- Chris Elsden, Tom Feltwell, Shaun W. Lawson, John Vines: Recipes for Programmable Money. CHI 2019: 251Google Scholar
- Giuseppe Ghiani, Marco Manca, Fabio Paternò, and Carmen Santoro, 2017. Personalization of Context-dependent Applications through Trigger-Action Rules. ACM Transactions on Computer-Human Interaction, 24(2), ACM, Article 14, 33 pages. DOI:https://doi.org/10.1145/3057861Google ScholarDigital Library
- W He, J Martinez, R Padhi, L Zhang, B Ur, When smart devices are stupid: negative experiences using home smart devices, 2019 IEEE Security and Privacy Workshops (SPW), 150-155Google Scholar
- Justin Huang, and Maya Cakmak. 2015. Supporting mental model accuracy in trigger-action programming. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). ACM, New York, NY, USA, 215-225. DOI=http://dx.doi.org/10.1145/2750858.2805830Google ScholarDigital Library
- Nicola Leonardi, Marco Manca, Fabio Paternò, Carmen Santoro, Trigger-Action Programming for Personalising Humanoid Robot Behaviour, ACM Conference on Human Factors in Computing Systems (CHI'19), Glasgow, Paper 445. 1.Google Scholar
- Q. Vera Liao, Daniel M Gruen, Sarah Miller, Questioning the AI: Informing Design Practices for Explainable AI User Experiences, CHI 2020Google Scholar
- Marco Manca, Fabio Paternò, Carmen Santoro, Remote Monitoring of End-User Created Automations in Field Trials, Journal of Ambient Intelligence and Humanized Computing, 2021Google ScholarCross Ref
- Marco Manca, Fabio Paternò, Carmen Santoro, Luca Corcella, Supporting end-user debugging of trigger-action rules for IoT applications, International Journal of Human-Computer Studies, 2019, Vol.123, 56-69Google ScholarCross Ref
- Panos Markopoulos, Jeffrey Nichols, Fabio Paternò and Volkmar Pipek. 2017. End-User Development for the Internet of Things, ACM Transactions on Computer-Human Interaction (TOCHI) Vol- 24 Issue 2, 9, May 2017Google Scholar
- Andrea Mattioli and Fabio Paternò. 2020. A Visual Environment for End-User Creation of IoT Customization Rules with Recommendation Support. In International Conference on Advanced Visual Interfaces (AVI '20). https://doi.org/10.1145/3399715.3399833Google ScholarDigital Library
- Fabio Paternò and Carmen Santoro. 2019. End-user development for personalizing applications, things, and robots. International Journal of Human-Computer Studies, 131, 120-130.Google ScholarDigital Library
- Antti Salovaara, Andrea Bellucci, Andrea Vianello, and Giulio Jacucci. 2021. Programmable Smart Home Toolkits Should Better Address Households’ Social Needs. In CHI 21, May 8–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, 14 pages.Google Scholar
- Vijay Srinivasan, Christian Koehler, and Hongxia Jin. 2018. RuleSelector: Selecting Conditional Action Rules from User Behavior Patterns . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 35 (March 2018), 34 pages.Google ScholarDigital Library
- Blase Ur, Erin A McManus, Melwyn Pak Yong, Michael L Littman. 2014. Practical trigger-action programming in the smart home. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14). ACM, New York, NY, USA, 803–812. https://doi.org/10.1145/2556288.2557420Google ScholarDigital Library
- Rayoung Yang, Mark Webster Newman, Learning from a learning thermostat: lessons for intelligent systems for the home. 2013. ACM international joint conference on Pervasive and ubiquitous computing, pp. 93-102Google Scholar
Index Terms
- End-User Creation and Control of Daily Automations in Intelligent Environments
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