New publication: Predicting retail business success using urban social data mining



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

Smartphone Notifications and Locations Dataset

We have released a rich dataset containing 229,792 logged notifications received on the Android devices of 44 users, in the period between 2018-12-19 and 2019-05-03. The dataset accompanies our publication Komninos, A., Simou I., Frengkou E., & Garofalakis J. (2019). Discovering User Location Semantics using Mobile Notification Handling Behaviour. 15th European Conference on Ambient Intelligence (AmI'19). Rome, Italy, Springer. DOI:10.1007/978-3-030-34255-5_15

New publication: Mobile Text Entry Behaviour in Lab and In-the-Wild studies: Is it different?

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]. ..
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New publication: Investigating Error Injection to Enhance the Effectiveness of Mobile Text Entry Studies of Error Behaviour

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]. ..
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New publication: Joint Proceeding of the Poster and Workshop Sessions of AmI-2019, the 2019 European Conference on Ambient Intelligence

Joint Proceeding of the Poster and Workshop Sessions of AmI-2019, the 2019 European Conference on Ambient Intelligence, which took place in Rome, Italy, November 13-15, 2019.

Calvanese-Stratinati, E., Charitos D., Chatzigiannakis I., Ciampolini P., Cuomo F., Di Lorenzo P., et al. (2019).  Joint Proceeding of the Poster and Workshop Sessions of AmI-2019, the 2019 European Conference on Ambient Intelligence. 2019 European Conference on Ambient Intelligence. Vol. 2492, Rome, Italy, CEUR Workshop Proceedings.

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