New publication: Venue Matching in Social Network APIs using Neural Networks

A multitude of social media APIs from popular services such as Facebook, Twitter and Google, allow programmers access to user generated data that is pertinent to physical venues represented within these services. In our paper, we attempt to address the issue of automatically matching venue representations from these diverse APIs, in order to obtain a more complete representation of user cyber-physical interaction with these venues. We present our work comparing a neural network approach against Nearest Point and Longest Common Substring algorithms.

Kalavrouziotis, V., Komninos A., & Garofalakis J. (2018).  Venue Matching in Social Network APIs using Neural Networks. 22nd ACM Panhellenic Conference on Informatics. Athens, Greece, ACM. DOI:https://doi.org/10.1145/3291533.3291558
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New publication: Measuring Inviscid Text Entry Using Image Description Tasks

We argue that measuring the Inviscid text entry rate requires new evaluation methods that support freeform text entry and that are not based on the traditional transcription/copy tasks. In this position paper we propose use of image description tasks and share some of our experiences of using this new language agnostic task type for free form text entry.

Dunlop, M., Nicol E., Komninos A., Dona P., & Durga N. (2016).  Measuring Inviscid Text Entry Using Image Description Tasks. CHI’16 Workshop on Inviscid Text Entry and Beyond. San Jose, CA.
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