Automatic Web page segmentation is the basis to adaptive Web browsing on mobile devices. It breaks a large page into smaller blocks, in which contents with coherent semantics are keeping together. Then, various adaptations like single column and thumbnail view can be developed. However, page segmentation remains a challenging task, and its poor result directly yields a frustrating user experience. As human usually understand the Web page well, in this paper, we start from Gestalt theory, a psychological theory that can explain human's visual perceptive processes. Four basic laws, proximity, similarity, closure, and simplicity, are drawn from Gestalt theory and then implemented in a program to simulate how human understand the layout of Web pages. The experiments show that this method outperforms existing methods.

Recovering semantic relations between different parts of web pages are of great importance for multi-platform web interface development, as they make it possible to re-distribute interaction objects and change the structure of interfaces while preserving the semantics of the UI. Important semantic relations include topic, order, hierarchy, etc. This paper presents a visual cues based approach, which is tag-tree structure independent, to automatically detect such kind of semantic relations in web pages. Comparing with other existing techniques, such as DOM-based methods, this approach mostly depends on interfaces' perceptible visual information that is more reliable. The preliminary evaluation on complex web sites shows promising results. We believe further exploration is worth taken.

Page segmentation plays a key role in browsing on small screens. It breaks a large page into smaller segments according to their semantic relationships. Then, various approaches such as single column adaptation and thumbnail view with zooming links can be implemented based on these page segments. However, for current flexible Web pages, segmentation remains a challenging task. This paper proposes an effective automatic segmentation method which combining pattern analysis and visual separators. The basic idea is that a page's semantic structure is largely reflected by repeated continuous patterns and visual separators, which coincides with human's visual perception. The proposed method works in three steps: generating a refined tag tree from the DOM tree, recognizing and merging inexact patterns recursively, and segmenting the others by visual separators. Our experimental results show that the proposed method outperforms existing methods, especially for pages automatically generated from templates

We present the uPen, a laser pointer combined with a contact-pushed switch, three press buttons and a wireless communication module. This novel interaction device allows users to interact on large displays at a distance or directly on the surface with full-function of mouse. Onboard software enable the uPen system to identify different users and provide personalized services to them, such as associating users with corresponding privileges, giving access to each participant's private content (e.g., home pages, personal calendars). Additionally, with our two-step association method, the uPen system has the ability to distinguish strokes of different uPens working simultaneously and support multi-user simultaneous interaction. A prototype system has been implemented in our Smart Classroom [1]. And user studies show the benefit of using it.