21909317
Generating Concept Hierarchies from Text for Intelligence Analysis
It is important to automatically extract key information from sensitive text documents for intelligence analysis. Text documents are usually unstructured and information extraction from texts usually focuses on named entity extraction that is insufficient to reflect the concepts contained within documents. Other research on texts focuses on classifying the whole text into categories. However, texts might be diverse in subjects and suffer from classification accuracy. In this paper, our goal is to present a feasible approach to extract relevant information from texts and organize into concept hierarchies with the help of Web corpora. The approach is designed considering the need of information intelligence and the adaptation to different application domains.
94229
Translating unknown queries with web corpora for cross-language information retrieval
It is crucial for cross-language information retrieval (CLIR) systems to deal with the translation of unknown queries due to that real queries might be short. The purpose of this paper is to investigate the feasibility of exploiting the Web as the corpus source to translate unknown queries for CLIR. We propose an online translation approach to determine effective translations for unknown query terms via mining of bilingual search-result pages obtained from Web search engines. This approach can alleviate the problem of the lack of large bilingual corpora, translate many unknown query terms, provide flexible query specifications, and extract semantically-close translations to benefit CLIR tasks -- especially for cross-language Web search.
44717149
Peer-to-Peer Support for File Transfer and Caching Mechanism
In existing Internet file transfer mechanism, proxy servers play a major role in load balancing and reducing duplicate file access requests for services like FTP and WWW. However, proxy servers are usually unaware of the availability of cached contents on other peer proxy servers. This is a waste of time since duplicate requests are needed. Unnecessary traffic can be reduced if cooperation and coordination among peer proxies can be utilized. In this paper, peer-to-peer support was incorporated in ordinary file transfer and caching mechanism to reduce unnecessary processing time and storage. Through the location service, hosts requesting file services can dynamically determine if a copy is available and its current location. Work load for file servers will be greatly reduced, and personalization of file transfer configuration can be fully supported.
10320126
Resolving the unencoded character problem for chinese digital libraries
Constructing a Chinese digital library, especially for historical article archiving, is often hindered by the small character sets supported by current computer systems. This paper is aimed at resolving the unencoded character problem with a practical and composite approach for Chinese digital libraries. The proposed approach consists of the glyph expression model, glyph structure database, and supporting tools. With this approach, the following problems can be resolved. First, the extensibility of Chinese characters can be preserved. Second, it would be as easy to generate, input, display, and search unencoded characters as existing ones. Third, it is compatible with existing encoding schemes that most computers use. This approach has been utilized by organizations and projects in various application domains including archeology, linguistics, ancient texts, calligraphy and paintings, and stone and bronze rubbings. For example, in Academia Sinica, a very large full-text database of ancient texts called Scripta Sinica has been created using this approach. The Union Catalog of National Digital Archives Project (NDAP) dealt with the unencoded characters encountered when merging the metadata of 12 different thematic domains from various organizations. Also, in Bronze Inscriptions Research Team (BIRT) of Academia Sinica, 3,459 bronze inscriptions were added, which is very helpful to the education and research in historic linguistics
14678674
Enhanced intranet management in a DHCP-enabled environment
The DHCP (dynamic host configuration protocol) is widely deployed in resource allocation and intranet management. However the DHCP mechanism is not mandatory and the DHCP server can neither force DHCP clients to release their leases, nor enforce cooperation from externally configured hosts that are DHCP-unaware. Although new DHCP options such as DHCP reconfigure extension have been proposed, the basic problems inherent in DHCP mechanism cannot be solved without first strengthening its operations. In the paper a DHCP-based infrastructure for intranet management is proposed by combining the resource allocation functions of the DHCP server with the packet filtering features of MAC (Medium Access Control) bridges such as Ethernet switches and wireless access points. DHCP clients and DHCP-unaware hosts that do not abide by the DHCP mechanism or our management policy will be denied network accesses by MAC bridges. Resource allocation and access control can be integrated and local configuration conflicts can be reduced to the minimum.
44970162
Toward Web mining of cross-language query translations in digital libraries
This paper proposes an effective query-translation approach that enables a cross-language information retrieval (CLIR) service to be more easily supported in digital library systems that only contain monolingual content. A query-translation engine called LiveTrans is used to process the translation requests of cross-lingual queries from connected digital library systems. To automatically extract translations not covered by standard dictionaries, the engine is developed based on a novel integration of dictionary resources and Web mining approaches, including anchor-text and search-result methods. The engine exploits a broad range of multilingual Web resources used as live bilingual corpora to alleviate translation difficulties. It is shown to be particularly effective for extracting multilingual translation equivalents of query terms containing proper names or new terminology. The obtained results show the feasibility of and great potential for creating English-Chinese CLIR services in existing digital libraries and new applications in cross-language Web searching, although difficulties still remain that need to be investigated further.
11368060
Packet classifiers in ternary CAMs can be smaller
Serving as the core component in many packet forwarding, differentiating and filtering schemes, packet classification continues to grow its importance in today's IP networks. Currently, most vendors use Ternary CAMs (TCAMs) for packet classification. TCAMs usually use brute-force parallel hardware to simultaneously check for all rules. One of the fundamental problems of TCAMs is that TCAMs suffer from range specifications because rules with range specifications need to be translated into multiple TCAM entries. Hence, the cost of packet classification will increase substantially as the number of TCAM entries grows. As a result, network operators hesitate to configure packet classifiers using range specifications. In this paper, we optimize packet classifier configurations by identifying semantically equivalent rule sets that lead to reduced number of TCAM entries when represented in hardware. In particular, we develop a number of effective techniques, which include: trimming rules, expanding rules, merging rules, and adding rules. Compared with previously proposed techniques which typically require modifications to the packet processor hardware, our scheme does not require any hardware modification, which is highly preferred by ISPs. Moreover, our scheme is complementary to previous techniques in that those techniques can be applied on the rule sets optimized by our scheme. We evaluate the effectiveness and potential of the proposed techniques using extensive experiments based on both real packet classifiers managed by a large tier-1 ISP and synthetic data generated randomly. We observe significant reduction on the number of TCAM entries that are needed to represent the optimized packet classifier configurations.
14843520
Deriving knowledge from figures for digital libraries
Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We present our system on automatic categorization of figures and extraction of data from 2-D plots. A machine-learning based method is used to categorize figures into a set of predefined types based on image features. An automated algorithm is designed to extract data values from solid line curves in 2-D plots. The semantic type of figures and extracted data values from 2-D plots can be integrated with textual information within documents to provide more effective document retrieval services for digital library users. Experimental evaluation has demonstrated that our system can produce results suitable for real-world use.
550060
Automatic categorization of figures in scientific documents
Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for real- world use. Our tools can be integrated into a scientific-document digital library
6599744
A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations
A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the recurrent neural network and solvability of algebraic Riccati equations by use of the recurrent neural network are discussed. The operating characteristics of the recurrent neural network are also demonstrated through three illustrative examples.
2016479
Shape-Based Curve Growing Model and Adaptive Regularization for Pulmonary Fissure Segmentation in CT
This paper presents a shape-based curve-growing algorithm for object recognition in the field of medical imaging. The proposed curve growing process, modeled by a Bayesian network, is influenced by both image data and prior knowledge of the shape of the curve. A maximum a posteriori (MAP) solution is derived using an energy-minimizing mechanism. It is implemented in an adaptive regularization framework that balances the influence of image data and shape prior in estimating the curve, and reflects the causal dependencies in the Bayesian network. The method effectively alleviates over-smoothing, an effect that can occur with other regularization methods. Moreover, the proposed framework also addresses initialization and local minima problems. Robustness and performance of the proposed method are demonstrated by segmentation of pulmonary fissures in computed tomography (CT) images.