15408418
CatRelate: A New Hierarchical Document Category Integration Algorithm by Learning Category Relationships
We address the problem of integrating documents from a source catalog into a master catalog. Current technologies for solving the problem deem it as a flat category integration problem without considering the useful hierarchy information in the catalog, or deal with it hierarchically but without a rigorous model. In contrast, our method is based on correctly identifying relationships among categories, such as Match, Disjoint, SubConcept, SuperConcept, and Overlap, which come from the relations of sets in Set theory. Compared with traditional Match/NotMatch relationship in literature, our approach is more expressive in defining the relationship. The relationships among categories are first learned in a probabilistic way, and then refined by considering the hierarchy context. Our preliminary experiments show that it can help to correctly identify category relationships, and thus increase the accuracy of document integration.
15850495
Automatic Summarization for Financial News Delivery on Mobile Devices
Christopher C. C. Yang and Fu Lee Wang Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China {yang, flwang}@se.cuhk.edu.hk ABSTRACT Wireless access with mobile devices is a promising addition to the WWW and traditional electronic business. Mobile devices provide convenience and portable access to the huge information space on the Internet. It is desire to access the most updated financial information through mobile devices in order to make critical and urgent decision for most of the investors. In this paper, we present a financial news delivery system on mobile devices based on the fractal summarization model. Fractal summarization is developed based on the fractal theory. It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of the document are generated on demands of users. Such interactive summarization reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional summarization, which is ideal for wireless access.
17665082
Generating Cross-lingual Concept Space from Parallel Corpora on the Web
The information available in languages other than English on the World Wide Web is increasing significantly. To cross language boundaries between different languages, dictionaries are the most typical tools. However, the general-purpose dictionary is less sensitive in genre and domain and it is impractical to manually construct tailored bilingual dictionaries or sophisticated multilingual thesauri for large applications. Corpus-based approaches, which do not have the limitation of dictionaries, provide a statistical translation model to cross the language boundary. The objective of this research work is to mine English/Chinese parallel documents automatically from the World Wide Web and generate a crosslingual concept space automatically for cross-lingual information retrieval. The alignment method is developed based on dynamic programming to identify the one-to-one Chinese and English title pairs for building parallel corpus. The Hopfield network is then employed to generate the cross-lingual concept space based on the statistical correlation analysis of the semantics (knowledge) embedded in the bilingual press release corpus. The research output consisted of a thesaurus-like, semantic network knowledge base, which can aid in semantics-based cross-lingual information management and retrieval.
15657957
Automatic Summarization of Chinese and English
As a result of the rapid growth in Internet access, significantly more information has become available online in real time. However, there is not sufficient time for users to read large volumes of information and make decisions accordingly. The problem of information-overloading can be resolved through the application of automatic summarization. Many summarization systems for documents in different languages have been implemented. However, the performance of summarization system on documents in different languages has not yet been investigated. In this paper, we compare the result of fractal summarization technique on parallel documents in Chinese and English. The grammatical and lexical differences between Chinese and English have significant effect on the summarization processes. Their impact on the performances of the summarization for the Chinese and English parallel documents is compared.
1680199
A heuristic method based on a statistical approach for Chinese text segmentation
The authors propose a heuristic method for Chinese automatic text segmentation based on a statistical approach. This method is developed based on statistical information about the association among adjacent characters in Chinese text. Mutual information of bi-grams and significant estimation of tri-grams are utilized. A heuristic method with six rules is then proposed to determine the segmentation points in a Chinese sentence. No dictionary is required in this method. Chinese text segmentation is important in Chinese text indexing and thus greatly affects the performance of Chinese information retrieval. Due to the lack of delimiters of words in Chinese text, Chinese text segmentation is more difficult than English text segmentation. Besides, segmentation ambiguities and occurrences of out-of-vocabulary words (i.e., unknown words) are the major challenges in Chinese segmentation. Many research studies dealing with the problem of word segmentation have focused on the resolution of segmentation ambiguities. The problem of unknown word identification has not drawn much attention. The experimental result shows that the proposed heuristic method is promising to segment the unknown words as well as the known words. The authors further investigated the distribution of the errors of commission and the errors of omission caused by the proposed heuristic method and benchmarked the proposed heuristic method with a previous proposed technique, boundary detection. It is found that the heuristic method outperformed the boundary detection method.
1362369
Watermarking in online media e-business
The need for copyright protection has become a detachable requirement for online media e-business as online multimedia businesses and enabling technologies become viable in recent years. Watermarking enables copyright protection in e-business but it also slows down the packaging process for delivery. The requirement of instant delivery is a prime requirement for online business. In the literature, extensive research on watermarking performance has been emphasized on imperceptibility and robustness but not the efficiency factor for online e-business, for example processing time and storage requirement that are crucial to the success of online e-business. We investigate four watermarking schemes derived from one-bit watermarking, multiple-bit watermarking and their combinations in order to identify a viable watermarking scheme that can meet the requirements of online e-business. The performance of these schemes in terms of preparation time, delivery time and storage requirement are measured and reported. Our analysis proves that the watermarking schemes with multiple-bit watermarking can ensure instant delivery with a penalty of some extra storage requirements.
12121845
Automatic image orientation detection
We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.
818365
Effects of luminance quantization error on color image processing
A common approach to color image processing is to apply monochrome techniques to the quantized intensity component. However, the quantization error in the intermediate intensity image propagates to the final processed image. This can lead to significant distortion, depending on the input RGB values and on the particular image processing function being applied. A theoretical analysis of the worst-case quantization error is presented. Experimental results with histogram equalization demonstrate how the histogram resolution affects the performance.
46320705
Intelligent Internet searching engine based on hybrid simulated annealing
The World Wide Web (WWW) based Internet services have become a major channel for information delivery. For the same reason, information overload also has become a serious problem to the users of such services. It has been estimated that the amount of information stored on the Internet doubled every 18 months. The speed of increase of home pages can be even faster, some people estimated that it doubled every six months. Therefore, a scalable approach to support Internet searching is critical to the success of Internet services and other current or future National Information Infrastructure (NII) applications. We discuss using a modified version of simulated annealing algorithm to develop an intelligent personal spider (agent), which is based on automatic textual analysis of the Internet documents and hybrid simulated annealing.
23496769
Image Enhancement for Segmentation by Self-Induced Autoregressive Filtering
A filter induced by the autoregressive pattern from an image is used to enhance the image itself. The enhanced image usually reveals the wave pattern and nonstationary structures more clearly than the original. Unlike the Fourier analysis, local structures in the image are still retained. The merits have been demonstrated on simulated data and synthetic-aperture radar (SAR) ocean imagery. >
20253419
Grid computing in Taiwan
Grid computing focuses on aggregating resources (e.g., processor cycles, disk storage and contents) from a large-scale computing environment. It intends to deliver high-performance distributed platforms for computationand/or data-intensive applications. In this paper, we study the enabling techniques for Grid computing for high-performance computing. Our goals are to: (1) understand the design of these key components; (2) set up a Grid computing platform; (3) learn how to create Grid-enabled high-performance applications; and (4) share experiences on constructing such platforms and applications from the status of Taiwan.