The manufacturing environment undergoes a dis- ruptive evolution due to the Fourth Industrial Revolution driven by the Industrial Internet of Things and Cyber Physical Systems technologies. This evolution is applicable to further sectors comprising similar requirements, involving large numbers of devices that need to interoperate, exchange their data and be controlled. Integration at the manufacturing environment remains a challenge taking into account the diversity of equipment / devices, the existence of legacy systems, and the need to integrate IoT devices participating in the production paradigm. This paper presents an AutomationML based approach for this integration, modeling the industrial manufacturing environment, and enabling its emulation through a 3D Virtual Environment.

Alexakos, C., Komninos A., Anagnostopoulos C., Kalogeras G., & Kalogeras A. (2020).  IoT Integration in the Manufacturing Environment Towards Industry 4.0 Applications. 18th IEEE International Conference on Industrial Informatics. Warwick, UK, IEEE.

Papadimitriou, G., Komninos A., & Garofalakis J. (2020).  Predicting retail business success using urban social data mining. Journal of Ambient Intelligence and Smart Environments. Preprint, 1–15., IOS Press. DOI:10.3233/AIS-200561

Text entry in smartphones remains a critical element of mobile HCI. It has been widely studied in lab settings, using primarily transcription tasks, and to a far lesser extent through in-the-wild (field) experiments. So far it remains unknown how well user behaviour during lab transcription tasks approximates real use. In this paper, we present a study that provides evidence that lab text entry behaviour is clearly distinguishable from real world use. Using machine learning techniques, we show that it is possible to accurately identify the type of study in which text entry sessions took place. The implications of our findings relate to the design of future studies in text entry, aiming to support input with virtual smartphone keyboards. Work carried out Oct 2016-Jun 2017, manuscript produced in June 2018

Komninos, A., Katsaris K., Nicol E., Dunlop M., & Garofalakis J. (2020).  Mobile Text Entry Behaviour in Lab and In-the-Wild studies: Is it different?. arXiv:2003.06323 [cs.HC]. ..

During lab studies of text entry methods it is typical to observer very few errors in participants' typing - users tend to type very carefully in labs. This is a problem when investigating methods to support error awareness or correction as support mechanisms are not tested. We designed a novel evaluation method based around injection of errors into the users' typing stream and report two user studies on the effectiveness of this technique. Injection allowed us to observe a larger number of instances and more diverse types of error correction behaviour than would normally be possible in a single study, without having a significant impact on key input behaviour characteristics. Qualitative feedback from both studies suggests that our injection algorithm was successful in creating errors that appeared realistic to participants. The use of error injection shows promise for the investigation of error correction behaviour in text entry studies. Work originally conducted in between Sept 2013 - Jan 2016, document prepared Feb 2016

Komninos, A., Nicol E., & Dunlop M. (2020).  Investigating Error Injection to Enhance the Effectiveness of Mobile Text Entry Studies of Error Behaviour. arXiv:2003.06318 [cs.HC]. ..