Kuei-Chun Kao’s research while affiliated with National Yang Ming Chiao Tung University and other places

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Publications (1)


Figure 1: User interfaces for the main functions of the Killing Time Labeling application
Figure 2: Illustration for the architecture of our proposed model, which takes the input composed of the phone-sensor data and the screenshots (collected within a certain time window, e.g., 30 seconds) and predicts the users' time-killing moments.
Figure 3: Two performance measurements of our proposed fusion model (i.e., Sensor+Screenshot), its variants (i.e., SensorOnly and ScreenshotOnly). Note. Point on the curves represents a classifcation threshold equal to 0.5.
Figure 4: Example attention maps, produced by Grad-CAM [79] and the ScreenshotOnly model, comprising a sequence of time-killing screenshots in the top row, and two sequence of non-time-killing ones in the middle and bottom row. The sensitive content on the images have been blurred for privacy reasons.
Figure 5: Scatter plot of session clusters, grouped based on in-session behavioral characteristics

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Are You Killing Time? Predicting Smartphone Users’ Time-killing Moments via Fusion of Smartphone Sensor Data and Screenshots
  • Conference Paper
  • Full-text available

April 2023

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109 Reads

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9 Citations

Yu-Chun Chen

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Yu-Jen Lee

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Kuei-Chun Kao

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[...]

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Citations (1)


... Besides contextual factors, Vanden Abeele [99] hints that the content consumed during infinite scrolling might also influence reactions towards an intervention (e.g., engaging content might cause higher reactance than boring content). Hence, future work should look into the influence of the consumed content, e.g., via screenshots [12,68]. ...

Reference:

Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social Media
Are You Killing Time? Predicting Smartphone Users’ Time-killing Moments via Fusion of Smartphone Sensor Data and Screenshots