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BackgroundEmotion recognition technology plays the essential role of enhancement in Human-Computer Interaction (HCI). In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. This approach can be considered to be rather desirable in HCI because the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. This study aims to examine the source of variance in keystroke typing patterns caused by emotions.MethodsA controlled experiment to collect subjects’ keystroke data in different emotional states induced by International Affective Picture System (IAPS) was conducted. Two-way Valence (3) × Arousal (3) ANOVAs were used to examine the collected dataset.ResultsThe results of the experiment indicate that the effect of emotion is significant (p < .001) in the keystroke duration, keystroke latency, and accuracy rate of the keyboard typing. However, the size of the emotional effect is small, compare to the individual variability.ConclusionsOur findings support the conclusion that the keystroke duration, keystroke latency, and also the accuracy rate of typing, are influenced by emotional states. Notably, the finding about the size of effect suggests that the accuracy rate of the emotion recognition could be further improved if personalized models are utilized. On the other hand, the finding also provides an explanation of why real-world applications which authenticate the identity of users by monitoring keystrokes may not be interfered by the emotional states of users. The experiment was conducted using standard instruments and hence is expected to be highly reproducible.

Creating a pyramidal structure on an n-GaN surface is considered the most effective approach for maximizing light extraction of n-side-up GaN-based light-emitting diodes (LEDs). This letter shows that the light extraction efficiency of a pyramidal n-GaN surface can be further enhanced by growing ZnO nano-rods (NRs) specifically on the tips of the pyramids on the n-GaN surface. Using tip-only ZnO NRs, the light-output power of an n-side-up LED with a pyramidal n-GaN surface can be further enhanced by 49.6% at 250 mA. This improved light extraction efficiency is due to the multiple facets on the ZnO NRs.

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By utilizing a code-fragmented representation of Extended Classifier System (XCS) condition in conjunction with buildingblock extraction technique, autonomous scaling has been realized in the latest work of XCS. The technique substantially reduces the number of training instances required in various benchmark problems. However, the subsumption mechanism was not included in the former report of the technique. Therefore, we invented the subsumption mechanism for XCS with such technique, and observed the characteristics of such the system in multiplexer problems. The finding indicates that our subsumption mechanism decreased the number of macro-classifiers.