Existing file protection approaches and the applications are suitable for the users who own one single device but not the users with multiple devices. A novel mechanism is proposed that provides an efficient and fast cryptographic-based protection approach for files in an appointed directory called safe directory. The highlights are its ability to protect sensitive files automatically in a short time and the support for secure file sharing across devices. All operations in the safe directory are monitored in real time and the files in the directory are protected automatically. Experimental results show that proposed mechanism performs well in performance.

This study proposes a detection method of bilateral symmetry for Chinese characters that combines different types of character features, such as scale invariant feature transform (SIFT) and contour information. A directed graph is constructed with the basic symmetric elements of a character to describe the enhancement
relationships among the elements. Furthermore, the detection of the most significant axes of symmetry in one character is transformed into the problem of finding star subgraphs with local maximum weight. Experiment results show that the proposed method outperforms the existing methods on Chinese characters database.

A robust method of identifying and linking footnote and its reference in the text is proposed to solve the footnote recognition problem. Novel features of the footnote, including page layout, font information, lexical and linguistic features, are utilized for the task. Clustering is adopted to handle the features which vary in different kinds of documents but stable within one document so that the process of identification is adaptive with document types. In addition, this method leverages results from the matching process to provide feedback to the identification process and further improves the algorithm accuracy. The primary experiments in real document sets show that the proposed method is promising to identify footnote in a PDF document.

Based on the study of retrieving plane geometric figures (PGFs) in the area of computer aided instruction, a feasible solution for PGF retrieval is proposed. The authors focus on several challenging tasks such as sketch beautification, geometric primitive detection, salience analysis of the overlapped primitives, structural
relationship description between two geometric primitives, and figure similarity computing. Several algorithms are presented especially on layout description and complex shape matching. The PGFs are applied directly to content retrieval and compensate for the weaknesses in describing the query intentions using keyword-based search. Experimental results demonstrate the feasibility and significant performance of the proposed retrieval algorithm.

An innovative Supervised Dynamic Topic Model (S-DTM) is developed for overcoming the limitation of tranditional topic models. S-DTM models the time-varying language dynamics and is combined with supervised learning technology by adding label restriction in topic variational inference. It makes the topic-label mapping and improves the interpret ability of topics. A set of experiments is conducted on a twenty-five-year-spanning Chinese journal paper corpus that is mainly focusing on natural language processing. Experiment results show that compared with static supervised topic model and unsupervised dynamic topic model, S-DTM has a better semantic interpretation performance, reflects the topic structure of a document more accurately, captures the dynamic evolution of the term-distribution of topics more precisely.

To investigate the effects of experimental nitrogen additions on the species diversity of the forest understory, the authors measured the plant diversity of the primary and the secondary tropical mountain rain forests, the subtropical evergreen broad-leaved forest, the temperate broad leaf-coniferous mixed forest and the cold temperate coniferous forest before and after N additions. Results showed that 1-year N addition significantly reduced the species richness in the primary and the secondary tropical mountain rain forests and the effects of N additions would be increased with the concentration of N additions (p < 0.05). Meanwhile, the high-concentration N addition also reduced the species richness, but low-concentration N addition increased the species richness in the temperate broad leaf-coniferous mixed forest. In addition, N additions increased species richness in the subtropical evergreen broad-leaved forest and the cold temperate coniferous forest (except for the high-concentration), but not significant. Moreover, the changes of Shannon-Wiener index responded to N additions have the similar trend with the change of species richness. The results suggested that nitrogen deposition changed biodiversity in the understory forests, but effects of N addition on biodiversity differ among the dfferent forests in China. Inconsistent with the general view, N addition reduced biodiversity in some forests but increased biodiversity in the others.

For satisfying the growing needs of reading Chinese comic images on mobile devices, the authors propose an unsupervised Chinese speech text localization method which is different from the existing learning-based methods.The method consists of three major stages: 1) the first stage is to detect the white region that surrounds the text charactders (speech balloons, similarly hereinafter) using the connectivity of white region within the balloons and localize the characters within the speech balloon; 2) the detected characters are clustered into character strings (a row or column of characters aligning horizontally or vertically) based on the character shape and the consistency of typesetting, and their font features are extracted; 3) based on the features of the extracted fonts, the third stage is to detect rest of the character strings via Bayesian classifier. The proposed method is tested on a dataset consists of 900 comic images and reaches satisfactory results.

Different from the traditional formula identification methods for scanned images and Latin documents, a formula identification method which considers the characteristics of digital Chinese documents is proposed to identify both isolated and embedded formulae using both machine learning techniques and heuristic rules. Text line detection strategies and word segmentation rules are proposed towards Chinese documents, effective features and machine learning algorithms of formula identification from Chinese documents are selected, and post-processing techniques, including text line or word merging, are proposed to overcome the over-segmentation problems. The experimental results show that the proposed method achieves satisfactory results in identifying formulae from digital Chinese documents. Furthermore, a public Chinese document dataset is constructed in order to facilitate the fair comparison between different formula identification methods.

The authors present a novel method for the OFR of single Chinese character on a large font set. This method explores the specific parts of strokes of each character, called Stroke Tips, which is regarded as a good feature for font recognition. Experiments show that the recognition rate on common font sets is superior than that of other methods. Furthermore, experiments on an extended font set confirm the scalability of the proposed method, which means that this method is suitable for the OFR of single Chinese character on a large font set.

The authors propose a novel and effective table boundary detection method via visual separators and geometric content layout information, which is effective for both Chinese and English documents. Additionally, due to the lack of automatic evaluation system for table boundaries detection, the authors also provide a publicly available large-scale dataset, composed of same amount of Chinese and English pages make ground-truth and propose mobile reading oriented performance measurements. Evaluation and comparison with two other open source table boundary detection projects demonstrates effectiveness of the proposed method and practicality of the evaluation suit.

Based on the characteristics of Chinese textual images, an improved compression algorithm MC-JBIG2 is developed. First, multilevel feature data of Chinese characters are extracted; then the data are used in a cascaded clustering algorithm to replace the pattern matching procedure of JBIG2. Experiment results show MC-JBIG2 is highly efficient and can improve compression ratio in the criterion of not involving substitution errors. Despite designed for Chinese textual images, MC-JBIG2 can also improve compression ratio of English textual images.

After reviewing the existing methods on citation data extraction, the authors propose a new approach for the task depending on a common typesetting practice of bibliographies: style consistency of citation data in the same document. Citation data detection and segmentation task are described on which less attention is put in previous researches. Furthermore, the authors take advantage of the style consistency of bibliographies to enhance citation metadata tagging. Experimental results show that the proposed method performs well in citation data detection, segmentation and tagging.

The authors propose an XML compressor based on XML Schema and tiny data block optimization (XTrim), which minimizes the size of the structure in XML documents and improves the data grouping strategy by utilizing information in XML Schema. Especially, tiny data blocks in XML document are optimized by XTrim to achieve a higher compression ratio. Experimental results show that the proposed approach outperforms other compressors when handling XML documents.

After reviewing the merits and drawbacks of the existing ToC ( table of contents) recognition methods, the authors describe an automatic ToC recognition method with high efficiency and adaptability. Based on style consistency of ToC in book documents, this method employs clustering to detect decorative elements and to generate an adaptive ToC model which can be used to extract ToC entries and their hierarchies. Experimental results show that this method achieves high accuracy and efficiency. Especially, it performs well in processing complicated ToC with decorative elements, broken lines and various hierarchical structures. This method has been successfully applied in a commercial E-book production line.

To seek the good tradeoff between robustness and unobtrusiveness, the cover data is divided into nonoverlapping blocks and an independent distortion situation is assigned to each block, which is called localized quality evaluation. Two specific implementations applying the Signal to Noise Ratio and Watson's visual model as the quality metrics are respectively achieved in the sense of maximizing the detection value. And the corresponding upper limits are derived under the given distortion constraints. Then, some practical adjustment strategies of those relative parameters are provided. The approach also introduces an improved linear correlation detector using side information and analyzes its performance mathematically. A lot of experimental results show the presented schemes obtain very significant performance improvement in terms of image transparency and robustness.

Binding digital license to a unique identification of hardware configuration of the rendering device is the most popular strategy adopted in current digital rights management. But the main drawback of this scheme is that the protected content cannot be rendered any more once the user replaces any part of the hardware. By encrypting the digital content, splitting the decryption key into multiple pieces and binding them each to different hardware components of the machine, the authors propose a hardware adaptive copyright protection mechanism for digital contents. With this scheme, a buyer can change the hardware configuration of his machine within a determinate range without influencing the use of the bought digital content. Meanwhile, copyright of the content is properly protected.

Well preserved blood vessels and resemble blood corpuscles have been observed in the bone tissue of a therizinosauroid dinosaur from the lower part of the Yixian Formation(Early Cretaceous)in Liaoning Province, northeast China. The fossil therizinosauroid dinosaur is consisting of a section of vertebral column, and fragmentary limb bones. Brownish filamentous integumentary impression occurred around the limb bones. The integumentary filaments are unbranched, about 0.1mm wide and 50～60mm long. At a longitudinal section of the dinosaur's humerus bone, brownish blood vessels have been observed under the microscope. Some blood vessels contained brownish small rounded bodies; they often scattered or gathered along the blood vessels. The occurrence, shape and size of the small bodies indicate that they are possibly remains of the dinosaur's blood cells. The vascularity of the bone tissue, have been measured by using image analysis method, the relatively high vascularity of the bone tissue indicates a relatively high metabolic rate of this dinosaur. The histological features of the bone tissue and the filamentous integuments that possibly served as body heating insulation, demonstrate that the animal might be endothermal.

The size of TiO₂, SnO₂ and CuCl nanoclusters are determined using the transmission electron microscopy and the X-ray diffraction spectrum. The measured results are analyzed comparably.