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- W2735549848 abstract "In human computer interaction, some of the user activities are intentional, and other unintentional, but user interfaces are usually designed to react only to intentional commands. However, user's unintentional activity contains many clues about a user, that can be beneficial to take into account in designing appropriate response. Current study focuses on these unintentional traces, that left behind by use of standard input devices, keyboard and mouse, and specifically, we try to predict users age and gender. Mouse and keyboard data used in this study, are collected in six different systems between 2011 and 2017 in total from 1519 subjects. Some supervised machine learning models yield to f-scores over 0.9 when predicted both user age or gender." @default.
- W2735549848 created "2017-07-21" @default.
- W2735549848 creator A5089827244 @default.
- W2735549848 date "2017-07-09" @default.
- W2735549848 modified "2023-09-23" @default.
- W2735549848 title "Predicting Age and Gender by Keystroke Dynamics and Mouse Patterns" @default.
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- W2735549848 doi "https://doi.org/10.1145/3099023.3099105" @default.
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