ArnetMiner: extraction and mining of academic social networks
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: Topic Distributions over Links on Web
abstract: It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify the strength of the influence of a web page on another if there is a link between the two linked web pages. In this paper, we focus on the problem of link semantics analysis, and propose a novel supervised learning approach to build a model, based on a training link-labeled and link-weighted graph where a link-label represents the category of a link and a link-weight represents the influence of one web page on the other in a link. Based on the model built, we categorize links and quantify the influence of web pages on the others in a large graph in the same application domain. We discuss our proposed approach, namely Pairwise Restricted Boltzmann Machines (PRBMs), and conduct extensive experimental studies to demonstrate the effectiveness of our approach using large real datasets.

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: An Automatic Voice Query System for Bank Based on Telephone Network
abstract: Automatic voice query system for bank by phone was a hi-tech product based on computer technology, electronic communication technology and speech synthesis technology. The hardware design of this system adopted modularization design scheme, which centered on SCM AT89C51, and controlled the phone transceiver chip MT8870 and voice chip ISD4004, realizing voice query for bank account by phone remote control. This paper mainly analyzed and designed several circuits such as ringing detection circuit, on-hook and off-hook control circuit, amplified-filter circuit, receiving circuit, voice circuit and the connection circuit between PC and SCM; it finally gave the realization process of the systempsilas software.

title: A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
abstract: In this paper, we propose a unified topic modeling approach and its integration into the random walk framework for academic search. Specifically, we present a topic model for simultaneously modeling papers, authors, and publication venues. We combine the proposed topic model into the random walk framework. Experimental results show that our proposed approach for academic search significantly outperforms the baseline methods of using BM25 and language model, and those of using the existing topic models (including pLSI, LDA, and the AT model).

title: A Discriminative Approach to Topic-Based Citation Recommendation
abstract: In this paper, we present a study of a novel problem, i.e. topic-based citation recommendation, which involves recommending papers to be referred to. Traditionally, this problem is usually treated as an engineering issue and dealt with using heuristics. This paper gives a formalization of topic-based citation recommendation and proposes a discriminative approach to this problem. Specifically, it proposes a two-layer Restricted Boltzmann Machine model, called RBM-CS, which can discover topic distributions of paper content and citation relationship simultaneously. Experimental results demonstrate that RBM-CS can significantly outperform baseline methods for citation recommendation.

title: Advances in Fuzzy Method for Natural Computing
abstract: Both natural computing and fuzzy method exhibit human-like mental processing. Natural computing is the field of research that works with computational techniques inspired in part by natural systems. Fuzzy method is an alternative to traditional notions of set membership and logic, and it occurs in many applications of engineering, science and medicine. This paper presents a survey of recent advances in fuzzy method for natural computing, such as fuzzy artificial neural networks, fuzzy genetic algorithms, fuzzy ant colony optimization, fuzzy artificial immune systems, etc. We utilize uncertainty handling capacity of fuzzy method for modeling ambiguity, and the learning ability of natural computing for efficiently traversing large search spaces.

title: A model-based approach to adding autonomic capabilities to network fault management system
abstract: Adding autonomic capabilities to network management systems provides great promise in delivering high QoS while lowering operation and maintenance cost. In this paper, we present a model-based approach to adding autonomic capabilities to a fault management system for cellular networks. We propose the use of modeling techniques to specify software failures and their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting. Our case study on the intelligent network fault management system illustrates the proposed approach by adding and deploying these autonomic capabilities derived from self-model specifications, to mitigate the risk of specified failures and maintain the level of healthiness of the system, dynamically and effectively.

title: A component-oriented software toolkit for patient-specific finite element model generation
abstract: A component-oriented software system, i.BioMech (image-based biomechanical modeling) is proposed for generation of patient-specific finite element model. It applies a systematic software engineering approach to patient/subject-specific meshing and assignment of material properties. The prototype program is based on the component object model (COM), which enables ease of combination of existing mesh generation algorithms and material property assignment schemes, and incorporation of new ones. It also facilitates utilization by other programming languages or platforms. Data input comprises a series of medical images captured from the patient. The output is a patient-specific finite element model for computational analysis using commercially available finite element software. The prototype software system provides a framework to compare the different finite element mesh generation methods as well as schemes for material property assignment. Our focus is on patient/subject-specific modeling of the human vertebrae.

title: Dynamic provisioning with availability guarantee for differentiated services in survivable mesh networks
abstract: Survivability is a key concern in modern network design in order to achieve fast service restorability against network failures. This paper investigates the problem of survivable dynamic connection provisioning in general telecom backbone networks, which are mesh structured. These networks employ optical fibers, which may fail due to network outages such as fiber cuts, etc. Our study applies to survivability of optical wavelength-division multiplexing (WDM) as well as multi-protocol label switching (MPLS) networks. For survivability study, we assume differentiated services where connections may have different availability requirements, so they may be provisioned differently with protection (if needed) based on their availability requirements and current network state. Therefore, it may be possible that connections with the same source, destination, and availability requirement are provisioned differently (unprotected, shared-path protected, or dedicated-path protected) at different times based on current network state. Such differentiated provisioning can provide diverse levels of service performance and achieve network resource optimization flexibly. Our main contributions are as follows. First, we develop an analytical model to quantify the availabilities of connections with various protection modes, i.e., unprotected, dedicated-path protected, and shared-path protected. Particular emphasis is placed on computing a connection's availability with shared-path protection by employing the link-vector technique, because this technique can maximally explore the sharing potential among backup paths and achieve bandwidth-assignment flexibility. Based on the mathematical model, we then present a novel provisioning strategy for dynamic connection requests in which multiple levels of services are provided and different protection schemes may be applied to different connections. The strategy jointly considers both connection availability satisfaction and resource optimization. Numerical results show very good accuracy of our model and high effectiveness of our provisioning strategy

title: A Color Indexing Scheme Using Two-level Clustering Processing for Effective and Efficient Image Retrieval
abstract: In this paper, we present a clustering-based color indexing scheme for effective amd efficient image retrieval, which is essentially an exploration on the application of clustering technique to image retrieval. In our approach, the color features are clustered automatically using a color clustering algorithm twice (called two-level clustering processing) and two color feature summarizations are obtained, e.g. Local Color Centriods (LCCs) and Global Color Centroids (GCCs). Based upon LCCs and GCCs, a three-level R-tree is bulit for indexing database images and performing effecitve and efficient image retrieval. The experiments show that this indexing scheme is effective and efficient in performing image retrieval.