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2017

Float: One-Handed and Touch-Free Target Selection on Smartwatches
(CHI '17) Ke Sun, Yuntao Wang, Chun Yu, Yukang Yan, Hongyi Wen, Yuanchun Shi
Abstract
We present Float, an interaction technique that enables one-handed and touch-free input on smartwatches based on a combination of wrist tilt and PPG finger gestures.
Tap, Dwell or Gesture?: Exploring Head-Based Text Entry Techniques for HMDs
(CHI '17) Chun Yu, Yizheng Gu, Zhican Yang, Xin Yi, Hengliang Luo, Yuanchun Shi
Abstract
We investigated three head-based text entry techniques for HMDs: DwellType, TapType and GestureType. We found gesture typing on HMD using head achieved 25 words per minute.
ViVo: Video-Augmented Dictionary for Vocabulary Learning
(CHI '17) Yeshuang Zhu, Yuntao Wang, Chun Yu, Shaoyun Shi, Yankai Zhang, Shuang He, Peijun Zhao, Xiaojuan Ma, Yuanchun Shi
Abstract
We present ViVo, a novel video-augmented dictionary that provides an inexpensive, convenient, and scalable way to exploit huge online video resources for vocabulary learning. ViVo automatically generates short video clips for learning from existing movies.
Word Clarity as a Metric in Sampling Keyboard Test Sets
(CHI '17) Xin Yi, Chun Yu, Weinan Shi, Xiaojun Bi, Yuanchun Shi
Abstract
We formally define word clarity, and show that it yield 26.4% and 25% difference in error rate and input speed respectively. We propose a Pareto optimization method for sampling test sets with different sizes.
CEPT: Collaborative Editing Tool for Non-Native Authors
(CSCW '17) Yeshuang Zhu, Shichao Yue, Chun Yu, Yuanchun Shi
Abstract
Due to language deficiencies, individual non-native speakers (NNS) face many difficulties while writing. In this paper, we propose to build a collaborative editing system that aims to facilitate the sharing of language knowledge among non-native co-authors, with the ultimate goal of improving writing quality. We describe CEPT, which allows individual co-authors to generate their own revisions as well as incorporating edits from others to achieve mutual inspiration.

2016

One-Dimensional Handwriting: Inputting Letters and Words on Smart Glasses
(CHI '16, honorable mention) Chun Yu, Ke Sun, Mingyuan Zhong, Xincheng Li, Peijun Zhao, Yuanchun Shi
Abstract
We present 1D Handwriting, a unistroke gesture technique enabling text entry on a one-dimensional interface. The challenge is to map two-dimensional handwriting to a reduced one-dimensional space, while achieving a balance between memorability and performance efficiency. After an iterative design, we finally derive a set of ambiguous two-length unistroke gestures, each mapping to 1-4 letters. To input words, we design a Bayesian algorithm that takes into account the probability of gestures and the language model. To input letters, we design a pause gesture allowing users to switch into letter selection mode seamlessly. Users studies show that 1D Handwriting significantly outperforms a selection-based technique (a variation of 1Line Keyboard) for both letter input (4.67 WPM vs. 4.20 WPM) and word input (9.72 WPM vs. 8.10 WPM). With extensive training, text entry rate can reach 19.6 WPM. Users' subjective feedback indicates 1D Handwriting is easy to learn and efficient to use. Moreover, it has several potential applications for other one-dimensional constrained interfaces.
Investigating Effects of Post-Selection Feedback for Acquiring Ultra-Small Targets on Touchscreen
(CHI '16) Chun Yu, Hongyi Wen, Wei Xiong, Xiaojun Bi, Yuanchun Shi
Abstract
In this paper, we investigate the effects of post-selection feedback for acquiring ultra-small (2-4mm) targets on touchscreens. Post-selection feedback shows the contact point on touchscreen after a user lifts his/her fingers to increase users' awareness of touching. Three experiments are conducted progressively using a single crosshair target, two reciprocally acquired targets and 2D random targets. Results show that in average post-selection feedback can reduce touch error rates by 78.4%, with a compromise of target acquisition time no more than 10%. In addition, we investigate participants' adjustment behavior based on correlation between successive trials. We conclude that the benefit of post-selection feedback is the outcome of both improved understanding about finger/point mapping and the dynamic adjustment of finger movement enabled by the visualization of the touch point.
计算机学报|基于语义的英文短语检索与搭配推荐及其在辅助ESL学术写作中的应用
朱叶霜,喻纯, 史元春
Abstract
学术菜英文写作对干母语非英语的(English as a Second Language.ESL)学者而言是一项挑战,现有的ESL写作辅助系统均基于连续单词(n-gram)在语料库中出现的频率来构建,而忽略了语义信息对于ESL写作的作用. 文中提出基干语义的写作辅助方法。支持按照词性、搭配类型和概念扩展等语义条件来检索短语和自动推荐搭配,此外,为了方便英语知识有限的 ESL 学者利用语义条件进行检索,文中还设计了易于理解的短语检索界面.该界面提供包含语义修饰符的查询表达式,并可根据用户输入的部分查询表达式在线提示和补全.最后,为验证语义方法的有效性,文中实现并部署了ESLWriter 系统.用户实验表明,ESLWriter 可以有效地推荐搭配并提供短语检索结果,其查询界面直观易用;ESL.学者通过混合使用自动搭配推荐功能和短语检索功能,写作质量和写作信心得到有效提高.这些结果证明了语义信息在ESL写作辅助中的重要作用.

2015

ATK: Enabling Ten-Finger Freehand Typing in Air Based on 3D Hand Tracking Data
(UIST '15) Xin Yi, Chun Yu, Mingrui Zhang, Sida Gao, Ke Sun, Yuanchun Shi
Abstract
Ten-finger freehand mid-air typing is a potential solution for post-desktop interaction. However, the absence of tactile feedback as well as the inability to accurately distinguish tapping finger or target keys exists as the major challenge for mid-air typing. In this paper, we present ATK, a novel interaction technique that enables freehand ten-finger typing in the air based on 3D hand tracking data. Our hypothesis is that expert typists are able to transfer their typing ability from physical keyboards to mid-air typing. We followed an iterative approach in designing ATK. We first empirically investigated users' mid-air typing behavior, and examined fingertip kinematics during tapping, correlated movement among fingers and 3D distribution of tapping endpoints. Based on the findings, we proposed a probabilistic tap detection algorithm, and augmented Goodman's input correction model to account for the ambiguity in distinguishing tapping finger. We finally evaluated the performance of ATK with a 4-block study. Participants typed 23.0 WPM with an uncorrected word-level error rate of 0.3% in the first block, and later achieved 29.2 WPM in the last block without sacrificing accuracy.