Automatic patent classification using citation network information: an experimental study in nanotechnology
Classifying and organizing documents in repositories is an active research topic in digital library studies. Manually classifying the large volume of patents and patent applications managed by patent offices is a labor-intensive task. Many previous studies have employed patent contents for patent classification with the aim of automating this process. In this research we propose to use patent citation information, especially the citation network structure information, to address the patent classification problem. We adopt a kernel-based approach and design kernel functions to capture content information and various citation-related information in patents. These kernels. performances are evaluated on a testbed of patents related to nanotechnology. Evaluation results show that our proposed labeled citation graph kernel, which utilized citation network structures, significantly outperforms the kernels that use no citation information or only direct citation information.

title: A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana
abstract: One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.

title: RACCOON: a peer-based system for data integration and sharing
abstract: Recent database applications see the emerging need to support data integration in distributed, peer-to-peer environments, in which autonomous peers (sources) connected by a network are willing to exchange data and services with each other. To address related research challenges, we are developing a system called "RACCOON", which allows different sources to integrate and share their data. We use an application to show several important features of the RACCOON system. The system also suggests semantic mappings for the user to choose. We show the two different querying modes, particularly how a query is expanded using the semantic mappings in the extended querying mode to compute as many answers to the query as possible.

title: Real-Time Computerized Annotation of Pictures
abstract: Developing effective methods for automated annotation of digital pictures continues to challenge computer scientists. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications, including Web image search, online picture-sharing communities, and scientific experiments. In this work, the authors developed new optimization and estimation techniques to address two fundamental problems in machine learning. These new techniques serve as the basis for the automatic linguistic indexing of pictures - real time (ALIPR) system of fully automatic and high-speed annotation for online pictures. In particular, the D2-clustering method, in the same spirit as K-Means for vectors, is developed to group objects represented by bags of weighted vectors. Moreover, a generalized mixture modeling technique (kernel smoothing as a special case) for nonvector data is developed using the novel concept of hypothetical local mapping (HLM). ALIPR has been tested by thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process. Its performance has also been studied at an online demonstration site, where arbitrary users provide pictures of their choices and indicate the correctness of each annotation word. The experimental results show that a single computer processor can suggest annotation terms in real time and with good accuracy.

title: High-order Compact Schemes for Nonlinear Dispersive Waves
abstract: High-order compact finite difference schemes coupled with high-order low-pass filter and the classical fourth-order Runge–Kutta scheme are applied to simulate nonlinear dispersive wave propagation problems described the Korteweg-de Vries (KdV)-like equations, which involve a third derivative term. Several examples such as KdV equation, and KdV-Burgers equation are presented and the solutions obtained are compared with some other numerical methods. Computational results demonstrate that high-order compact schemes work very well for problems involving a third derivative term.

title: Two-way Poisson mixture models for simultaneous document classification and word clustering
abstract: An approach to simultaneous document classification and word clustering is developed using a two-way mixture model of Poisson distributions. Each document is represented by a vector with each dimension specifying the number of occurrences of a particular word in the document in question. As a collection of documents across several classes usually makes use of a large number of words, the document vectors are of high dimension. On the other hand, the number of distinct words in any single document is usually substantially smaller than the size of the vocabulary, leading to sparse document vectors. A mixture of Poisson distributions is used to model the multivariate distribution of the word counts in the documents within each class. To address the issues of high dimensionality and sparsity, the parameters in the mixture model are regularized by imposing a clustering structure on the set of words. An EM-style algorithm for the two-way mixture model will be derived for parameter estimation with the clustering of words part of the estimation process. The connection of the two-way mixture model with dimension reduction will also be elucidated. Experiments on the newsgroup data have demonstrated promising results.

title: Attribute-Based Access Control with Hidden Policies and Hidden Credentials
abstract: In an open environment such as the Internet, the decision to collaborate with a stranger (e.g., by granting access to a resource) is often based on the characteristics (rather than the identity) of the requester, via digital credentials: access is granted if Alice's credentials satisfy Bob's access policy. The literature contains many scenarios in which it is desirable to carry out such trust negotiations in a privacy-preserving manner, i.e., so as minimize the disclosure of credentials and/or of access policies. Elegant solutions were proposed for achieving various degrees of privacy-preservation through minimal disclosure. In this paper, we present protocols that protect both sensitive credentials and sensitive policies. That is, Alice gets the resource only if she satisfies the policy, Bob does not learn anything about Alice's credentials (not even whether Alice got access), and Alice learns neither Bob's policy structure nor which credentials caused her to gain access. Our protocols are efficient in terms of communication and in rounds of interaction