As large-screen smartphones are trending, they bring a new set of challenges such as acquiring unreachable screen targets using one hand. To understand users' touch behavior on large mobile touchscreens, we conducted an empirical experiment to discover their usage patterns of tilting devices toward their thumbs to touch screen regions. Exploiting this natural tilting behavior, we designed three novel mobile interaction techniques: TiltSlide, TiltReduction, and TiltCursor. We conducted a controlled experiment to compare our methods with other existing methods, and then evaluated them in real mobile phone scenarios such as sending an e-mail and web surfing. We constructed a design space for one-hand targeting interactions and proposed design considerations for onehand targeting in real mobile phone circumstances.

We present three novel methods to facilitate one hand targeting at discomfort regions on a mobile touch screen using tilting gestures; TiltSlide, TiltReduction, and TiltCursor. We conducted a controlled user study to evaluate them in terms of their performance and user preferences by comparing them with other related methods, i.e. ThumbSpace, Edge Triggered with Extendible Cursor (ETEC), and Direct Touch (directly touching with a thumb). All three methods showed better performance than ThumbSpace in terms of speed and accuracy. Moreover, TiltReduction led users to require less thumb/grip movement than Direct Touch while showing comparable performance in speed and accuracy.

Most visualization recommendation systems predominantly rely on graphical previews to describe alternative visual encodings. However, since InfoVis novices are not familiar with visual representations (e.g., interpretation barriers [GTS10]), novices might have difficulty understanding and choosing recommended visual encodings. As an initial step toward understanding effective representation methods for visualization recommendations, we investigate the effectiveness of three representation methods (i.e., previews, animated transitions, and textual descriptions) under scatterplot construction tasks. Our results show how different representations individually and cooperatively help users understand and choose recommended visualizations, for example, by supporting their expect‐and‐confirm process. Based on our study results, we discuss design implications for visualization recommendation interfaces.

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Content based image retrieval (CBIR) has been one of the most important research areas in computer science for the last decade. A retrieval method which combines color and texture feature is proposed in this paper. According to the characteristic of the image texture, we can represent the information of texture by Dual-Tree Complex Wavelet (DT-CWT) transform and rotated wavelet filter (RWF). We choose the color histogram in RGB and HSV color space as the color feature. The experiment results show that this method is more efficient than the traditional CBIR method based on the single visual feature and other methods combining color and texture.

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