Research

Intelligent Text Entry Application

  • Type, Then Correct: Intelligent Text Correction Techniques for Mobile Text Entry Using Neural Networks Mingrui Zhang, He Wen, Jacob O. Wobbrock
    The ACM Symposium on User Interface Software and Technology (UIST), 2019
    Instead of normal touch+cursor based correction process, why cannot we rethink of the correction interaction? In this paper, we present three novel interactions that allow the user to type the correction first, then apply it to the error place. Furthermore, we applied deep learning technology to enable automatic error detection for the interaction.
  • ATK: Enabling Ten-Finger Freehand Typing in Air Based on 3D Hand Tracking Data Xin Yi, Chun Yu, Mingrui Zhang, Sida Gao
    The ACM Symposium on User Interface Software and Technology (UIST), 2015
    A novel air-typing method, Leapmotion tracking fingers, improved Bayes prediction model with application developed. Users reached the speed of 29.2 WPM on average.
  • Text Entry Evaluation

  • Beyond the Input Stream: Making Text Entry Evaluations More Flexible with Transcription Sequences Mingrui Zhang, Jacob O. Wobbrock
    The ACM Symposium on User Interface Software and Technology (UIST), 2019
    In this work, we present a new underlying model that supersedes the input stream model for general-purpose method-independent character-level text entry evaluation. Specifically, we present an approach that replaces the input stream with transcription sequences, or “T-sequences” for short. In brief, T-sequences are snapshots of the entire transcribed string after each text-changing action is taken by the user. Every pair of successive snapshots are then analyzed to compute character-level text entry metrics.
  • Text Entry Throughput: Towards Unifying Speed and Accuracy in a Single Performance Metric Mingrui Zhang, Shumin Zhai, Jacob O. Wobbrock
    The ACM CHI Conference on Human Factors in Computing Systems (CHI), 2019 Related Blog Post
    We define the text entry Throughput as a performance metric combining the speed and accuracy. Throughput is derived from the transmission ratio in the information theory. Unlike other metrics, throughput is less affected by speed-accuracy tradeoffs, thus it enables cross-device, cross-publication comparison.
  • Voice User Interface

  • Communication Breakdowns Between Families and Alexa Erin Beneteau, Olivia K. Richards, Mingrui Zhang, Julie A. Kientz, Jason Yip, Alexis Hiniker
    The ACM CHI Conference on Human Factors in Computing Systems (CHI), 2019
    We investigated different types of communication breakdowns and the repairing strategies between the conversation of family members and Alexa. Our findings indicates that improving technology’s ability to identify the communication partners and to provide specific clarification responses will ultimately improve the conversational interaction experience.
  • MISC

  • Anchored Audio Sampling: A Seamless Method for Exploring Children’s Thoughts During Deployment Studies Alexis Hiniker, Jon E. Froehlich, Mingrui Zhang, Erin Beneteau
    The ACM CHI Conference on Human Factors in Computing Systems (CHI), 2019 Best Paper Award
    We present Anchored Audio Smapling (AAS) method for collecting remote data of qualitative audio samples during field development with young children. The anchor event triggers the recording, and a sliding window surrounding this anchor captures both antecedent and ensuing recording. Our AAS Library for Android

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