Shu-Tao Xia

An effective puncturing scheme for rate-compatible LDPC codes

We consider the problem of finding good puncturing patterns for rate-compatible LDPC codes over additive white Gaussian noise (AWGN) channels. When studying the recovered process of punctured nodes, Ha et al maximized the number of lower Ä-SR nodes and obtained a so-called grouping and sorting puncturing scheme, where only single survived check node was guaranteed. In this paper, we investigate the effect of multiple survived check nodes and afford theoretical analysis to them. Based on the analysis, we propose an effective puncturing scheme called MSCN, which maximizes the number of survived check nodes. The MSCN scheme is quite different from the grouping and sorting scheme in the rules of finding nodes and update. By simulations, the MSCN scheme is shown to be superior to the grouping and sorting scheme over AWGN channels at low rates for rate-compatible LDPC codes.

Multi-document Summarization Based on Sentence Clustering

A main task of multi-document summarization is sentence selection. However, many of the existing approaches only select top ranked sentences without redundancy detection. In addition, some summarization approaches generate summaries with low redundancy but they are supervised. To address these issues, we propose a novel method named Redundancy Detection-based Multi-document Summarizer (RDMS). The proposed method first generates an informative sentence set, then applies sentence clustering to detect redundancy. After sentence clustering, we conduct cluster ranking, candidate selection, and representative selection to eliminate redundancy. RDMS is an unsupervised multi-document summarization system and the experimental results on DUC 2004 and DUC 2005 datasets indicate that the performance of RDMS is better than unsupervised systems and supervised systems in terms of ROUGE-1, ROUGE-L and ROUGE-SU.

A traffic localization strategy for peer-to-peer live streaming

Current P2P applications are based on random connected overlays, which lead to generating a significant amount of inter-ISP traffic. Asking for more QoS requirements, such as a short delay and a stable streaming rate, few studies are dedicated to optimizing P2P live streaming applications, despite that recent work have proposed some solutions for P2P file distribution applications. In this paper, current traffic localization strategy is analyzed at first, and its two inherent flaws are revealed. Then we propose a novel strategy for ISP-friendly live streaming: based on a hybrid overlay, which is a two-tier structure, all ISPs are organized into an ISP-tree, and then local peers in each ISP form a mesh overlay. In each tier, a data scheduling is designed for inter-ISP traffic reduction and performance guarantee respectively. Compared with a famous live streaming strategy, R2, simulation results demonstrate that our strategy generates much less inter-ISP traffic and achieves a higher system performance.

Exploiting Multiple Features for Learning to Rank in Expert Finding

Expert finding is the process of identifying experts given a particular topic. In this paper, we propose a method called Learning to Rank for Expert Finding LREF attempting to leverage learning to rank to improve the estimation for expert finding. Learning to rank is an established means of predicting ranking and has recently demonstrated high promise in information retrieval. LREF first defines representations for both topics and experts, and then collects the existing popular language models and basic document features to form feature vectors for learning purpose from the representations. Finally, LRER adopts RankSVM, a pair wise learning to rank algorithm, to generate the lists of experts for topics. Extensive experiments in comparison with the language models profile based model and document based model, which are state-of-the-art expert finding methods, show that LREF enhances expert finding accuracy.

Keyword Proximity Search over Large and Complex RDF Database

In this paper, we propose a keyword proximity search approach that can be applied to large and complex RDF database. We model RDF database as undirected data graph, construct three indexes for each data graph, only one index need be loaded into memory. Keyword graph is defined as search result, keyword tree and minimal keyword tree are proposed as middle structures for Keyword graph extraction, and we present a link join operation based algorithm to retrieve Keyword trees in this paper. We employ a technique of keyword node pruning to accelerate keyword tree retrieval and define a scoring function to rank search results. In experiments, our approach achieves both high efficiency and high accuracy, outperforms the existing approaches.

An adaptive approach to chinese semantic advertising

Semantic Advertising is a new kind of web advertising to find the most related advertisements for web pages semantically. In this way, users are more likely to be interest in the related advertisements when browsing the web pages. A big challenge for semantic advertising is to match advertisements and web pages in a conceptual level. Especially, there are few studies proposed for Chinese semantic advertising. To address this issue, we proposed an adaptive method to construct an ontology automatically for matching Chinese advertisements and web pages semantically. Seven distance functions are exploited to measure the similarity between advertisements and web pages. Based on the empirical experiments, we found the proposed method shows a promising result in terms of precision, and among the distance functions, the Tanimoto distance function outperforms the other six distance functions.

An effective transmission scheduling mechanism with network coding for adaptive P2P streaming

The peer-to-peer technology has been successfully used in live multimedia streaming. SVC (the scalable extension of the H.264/AVC standard) video streaming is more scalable to the network fluctuations with the multi-layer structure. The transmission scheduling mechanism of SVC P2P streaming is different with traditional single layer P2P streaming. To fully explore the advantage of SVC and P2P to transmit stream, the scheduling problem is urgent to be solved. Several solutions have been proposed, but these solutions are confined to the traditional methods and without fully considering the characteristics of SVC. In this paper, we present a novel transmission scheduling mechanism based on intra-layer network coding scheme for SVC P2P streaming, called random layer selection with random push (R&R). In R&R, when a peer pushes a packet, it will randomly choose a layer and then encoding a packet using random network encode. Through the theoretical analysis we demonstrate the R&R is feasible. The simulation results verify our theoretical analysis and show that R&R has a better performance in terms of latency and bandwidth utilization.

An Ontology-Based Approach to Query Suggestion Diversification

Query suggestion is proposed to generate alternative queries and help users explore and express their information needs. Most existing query suggestion methods generate query suggestions based on document information or search logs without considering the semantic relationships between the original query and the suggestions. In addition, existing query suggestion diversifying methods generally use greedy algorithm, which has high complexity. To address these issues, we propose a novel query suggestion method to generate semantically relevant queries and diversify query suggestion results based on the WordNet ontology. First, we generate the query suggestion candidates based on Markov random walk. Second, we diversify the candidates according the different senses of original query in the WordNet. We evaluate our method on a large-scale search log dataset of a commercial search engine. The outstanding feature of our method is that our query suggestion results are semantically relevant belonging to different topics. The experimental results show that our method outperforms the two well-known query suggestion methods in terms of precision and diversity with lower time consumption.

Exploiting Level-Wise Category Links for Semantic Relatedness Computing

Explicit Semantic Analysis(ESA) is an effective method that adopts Wikipedia articles to represent text and compute semantic relatedness(SR). Most related studies do not take advantage of the semantics carried by Wikipedia categories. We develop a SR computing framework exploiting Wikipedia category structure to generate abstract features for texts and considering the lexical overlap between a pair of text. Experiments on three datasets show that our framework could gain better performance against ESA and most other methods. It indicates that Wikipedia category graph is a promising resource to aid natural language text analysis.

An optimal segment replication strategy in P2P-VoD systems

In this paper, we address the problem of content replication in segmented peer-to-peer on-demand systems, with the objective of minimizing the content servers workload. We consider the system performance under heterogeneous environment. In this P2P-VoD system, multimedia content is divided into segments and peers can seek and cache any segments. Because different segments may be of different popularity, badly designed segment replication may lead to great servers workload. We deduce the “optimal replication ratio” in segmented P2P-VoD system such that peers will receive upload bandwidth from each other and at the same time, minimize the servers workload. We formulate the segment replication as an optimization problem and propose a model to solve it. We show that the proportional replication strategy is not optimal for segmented P2P-VoD systems and the segmented system can lead to less servers workload than non-segmented. We simulate our model, evaluate the performance of segmented P2P-VoD systems and show that our algorithm can greatly reduce the servers workload.