A Mixture Model for Expert Finding
This paper addresses the issue of identifying persons with expertise knowledge on a given topic. Traditional methods usually estimate the relevance between the query and the support documents of candidate experts using, for example, a language model. However, the language model lacks the ability of identifying semantic knowledge, thus results in some right experts cannot be found due to not occurrence of the query terms in the support documents. In this paper, we propose a mixture model based on Probabilistic Latent Semantic Analysis (PLSA) to estimate a hidden semantic theme layer between the terms and the support documents. The hidden themes are used to capture the semantic relevance between the query and the experts. We evaluate our mixture model in a real-world system, ArnetMiner. Experimental results indicate that the proposed model outperforms the language models.

title: MicroRNA identification based on sequence and structure alignment
abstract: MicroRNAs (miRNA) are approximately 22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences. Therefore, secondary structural features should be more fully exploited in the homologue search for new miRNA genes.



In this paper, we present a novel genome-wide computational approach to detect miRNAs in animals based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and comparable specificity than other reported homologue searching methods. We applied this method on Anopheles gambiae and detected 59 new miRNA genes.



This program is available at http://bioinfo.au.tsinghua.edu.cn/miralign.



Supplementary information is available at http://bioinfo.au.tsinghua.edu.cn/miralign/supplementary.htm.

title: htSNPer1.0: software for haplotype block partition and htSNPs selection
abstract: BackgroundThere is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection. heuristic algorithm, generalized branch-and-bound algorithm, is applied to the searching of minimal set of haplotype tagging SNPs (htSNPs) according to different htSNPs performance criteria. We develop a software htSNPer1.0 to implement the algorithm, and integrate three htSNPs performance criteria and four haplotype block definitions for haplotype block partitioning. It is a software with powerful Graphical User Interface (GUI), which can be used to characterize the haplotype block structure and select htSNPs in the candidate gene or interested genomic regions. It can find the global optimization with only a fraction of the computing time consumed by exhaustive searching algorithm.ConclusionhtSNPer1.0 allows molecular geneticists to perform haplotype block analysis and htSNPs selection using different definitions and performance criteria. The software is a powerful tool for those focusing on association mapping based on strategy of haplotype block and htSNPs.

title: ArnetMiner: extraction and mining of academic social networks
abstract: This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.

title: ArnetMiner: An Expertise Oriented Search System for Web Community
abstract: Expertise Oriented Search aims at providing comprehensive analysis and mining for people from distributed sources. In this paper, we give an overview of the expertise oriented search system (ArnetMiner). The system addresses several key research issues in extraction and mining of a researcher social network. The system is in operation on the internet for more than one year and receives accesses from about 1,500 users per month. Feedbacks from users and system logs indicate that users consider the system can really help people to find and share information in the web community.

title: Computing Exact p-Value for Structured Motif
abstract: Extracting motifs from a set of DNA sequences is important in computational biology. Occurrence probability is a common used statistics to evaluate the statistical significance of a motif. A main problem is how to calculate the occurrence probability of the motif on the random model of DNA sequence efficiently and accurately. In this paper, we are interested in a particular motif model which is useful in transcription process. This motif, which is called structured motif, is composed two motif words on single nucleotide alphabet and with fixed spacers between them. We present an efficient algorithm to calculate the exact occurrence probability of a structured motif on a given sequence. It is the first nontrivial algorithm to calculate the exact p-value for such kind of motifs.

title: A Novel Video Searching Model Based on Ontology Inference and Multimodal Information Fusion
abstract: Video comprises multiple types of textual, audio and visual information, and each of them contains abundant semantic information. Therefore multimodal features query and fusion are necessary in video retrieval. In this paper, we propose a new video retrieval model, which adopts multi-model including text, image, semantic concept and camera motion to query video. Then relation algebra expression is advanced to fuse multimodal information instead of traditional linear fusion method. In semantic concept detection model, Bayesian network based ontology is proposed to extract concepts. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared with exiting key approaches of video retrieval by multimodal information fusion.

title: Model-Driven Adaptive Self-healing for Autonomic Computing
abstract: Self-healing is a vital property that an autonomic system must possess in order to provide robust performance and survivability. The promise of self-healing depends on other properties that the system should provide, which include self-monitoring and self-configuring. Autonomic systems further require self-healing behavior to adapt to changes in user needs, business goals, and environmental conditions such that self-healing decisions are made dynamically and adaptively according to the system context. In this paper, we propose a model-driven approach that leverages modeling techniques, reliability engineering methodologies, and aspect-oriented development to realize an adaptive self- healing paradigm for autonomic computing.

title: A dynamic scalable service model for SIP-based video conference
abstract: As an extensible signaling protocol, SIP (session initiation protocol) can be applied in developing video conference system. Traditionally, for SIP-based centralized video conference system, conferencing scale is limited by both the capability of conference server and the availability of bandwidth. The paper focuses on how to keep conferencing when the number of conference users increases. Based on the study of the SIP protocol, we analyze the drawback of the existing video conference model, and propose a dynamic scalable service model for SIP-based video conference. By this model, the extra service requests can be transferred and served in the cooperated conference servers. The paper presents the conferencing control policy of the model in detail. We developed a prototype of video conference system based on the above model. Experimental results show this service model works well.

title: Metamodel-driven model interpreter evolution
abstract: Domain-specific modeling (DSM) raises the level of abstraction by specifying a metamodel that is aligned to a particular problem domain. A key feature of DSM is the construction of model interpreters that synthesize the domain models into software artifacts. In the presence of new stakeholder requirements, it is possible that a metamodel undergoes numerous changes during periods of evolution (a new instance of the classic schema evolution problem). Consequently, there is a fundamental problem in keeping the model interpreters up to date with metamodel changes. This research abstract outlines the technical challenges in formalizing model interpreter implementation with the intent to facilitate interpreter evolution in terms of metamodel migration.

title: Automatic detection of signs with affine transformation
abstract: In this paper, we propose an approach for detecting signs from natural scenes. The approach efficiently embeds multiresolution, adaptive search, and affine rectification algorithms in a hierarchical framework, with different emphases at each layer. We combine in multi-resolution and multi-scale edge detection techniques to effectively detect text in different sizes. By using the cites from text inside the image, we introduce affine rectification transformation to recover deformation of the text region caused by air inappropriate camera view angle. This procedure can significantly improve text detection rate and OCR (Optical Character Recognition) accuracy. Experimental results have demonstrated feasibility of the proposed algorithms. We have applied the proposed approach to a Chinese sign translation system, which can automatically detect Chinese text input from a camera, recognize the text, and translate the recognized text into English or voice stream.