Therefore, we performed an eye-tracking study with 47 users to determine the users' path of attention when solving three low-level analytical tasks using 30 different charts from the MASSVIS database [1].
Tourassi, Mazurowski, Harrawood, and Krupinski (2010) examined the potential of a context-sensitive computer-assisted detection (CADe) system that is guided by the user's focus of attention.
The main research questions were whether an attention-aware display design can capture the user's focus of attention and whether this has an influence on the knowledge gain.
While Facebook's total user count gets lots of attention, daily active user is a much more accurate reflection of its health.
If Facebook can strike the right balance between the urgency of Live streaming and being respectful of users' attention, it could grow Live into a powerful communication medium between celebrities and their followers.
This suggests that the combination of CoPerplexity and an RW based algorithm can effectively identify a set of users for collective attention monitoring.
To develop a practical motor imagery-based brain-controlled switch as functional as a real-world switch that is reliable with a minimal false positive operation rate and convenient for users without the need of attention to the switch during a 'No Control' state (when not to activate the switch).
Eivazi and Bednarik (2011) proposed the use of real-time tracking of users' visual attention patterns to model users' high-level cognitive states and performance.
To calculate the similarity between items, the authors derive two features from X c (t i ): one that compares if two items receive the same proportion of users' attention and another one that measures if the two items experience a similar popularity growth.
These ad units, which require at least 15 seconds' of users attention, can now include things like a swipeable slide gallery or a 360-degree product spotlight, in addition to video – the latter which was the only ad format initially available when the product first launched into beta.
Average weighted degree: the edge weights capture the number of users whose attention shifted from one hashtag to another; hence the average weighted degree of a network for a particular period of time reflects the attention shift frequency or rate.
