16272457
SimFusion: measuring similarity using unified relationship matrix
In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user click-through sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com- bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm.
15310025
A study of relevance propagation for web search
Different from traditional information retrieval, both content and structure are critical to the success of Web information retrieval. In recent years, many relevance propagation techniques have been proposed to propagate content information between web pages through web structure to improve the performance of web search. In this paper, we first propose a generic relevance propagation framework, and then provide a comparison study on the effectiveness and efficiency of various representative propagation models that can be derived from this generic framework. We come to many conclusions that are useful for selecting a propagation model in real-world search applications, including 1) sitemap-based propagation models outperform hyperlink-based models in sense of both effectiveness and efficiency, and 2) sitemap-based term propagation is easier to be integrated into real-world search engines because of its parallel offline implementation and acceptable complexity. Some other more detailed study results are also reported in the paper.
459986
A New Statistical Approach To Chinese Pinyin Input
Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.
17711119
IRC: an iterative reinforcement categorization algorithm for interrelated Web objects
Most existing categorization algorithms deal with homogeneous Web data objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspects of these interrelationships and objects does not fully reveal their true categories. In this paper, we propose a categorization algorithm, the iterative reinforcement categorization algorithm (IRC), to exploit the full interrelationships between the heterogeneous objects on the Web. IRC attempts to classify the interrelated Web objects by iterative reinforcement between individual classification results of different types via the interrelationships. Experiments on a clickthrough log dataset from MSN search engine show that, with the Fl measures, IRC achieves a 26.4% improvement over a pure content-based classification method, a 21% improvement over a query metadata-based method, and a 16.4% improvement over a virtual document-based method. Furthermore, our experiments show that IRC converges rapidly.
2801577
Scalable collaborative filtering using cluster-based smoothing
Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approach has been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approach has been proposed to alleviate these problems, but this approach tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two approaches by introducing a smoothing-based method. In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. As a result, we provide higher accuracy as well as increased efficiency in recommendations. Empirical studies on two datasets (EachMovie and MovieLens) show that our new proposed approach consistently outperforms other state-of-art collaborative filtering algorithms.
5754331
Web-page summarization using clickthrough data
Most previous Web-page summarization methods treat a Web page as plain text. However, such methods fail to uncover the full knowledge associated with a Web page needed in building a high-quality summary, because many of these methods do not consider the hidden relationships in the Web. Uncovering the hidden knowledge is important in building good Web-page summarizers. In this paper, we extract the extra knowledge from the clickthrough data of a Web search engine to improve Web-page summarization. Wefirst analyze the feasibility in utilizing the clickthrough data to enhance Web-page summarization and then propose two adapted summarization methods that take advantage of the relationships discovered from the clickthrough data. For those pages that are not covered by the clickthrough data, we design a thematic lexicon approach to generate implicit knowledge for them. Our methods are evaluated on a dataset consisting of manually annotated pages as well as a large dataset that is crawled from the Open Directory Project website. The experimental results indicate that significant improvements can be achieved through our proposed summarizer as compared to the summarizers that do not use the clickthrough data.
7363283
Self-adapting software for numerical linear algebra and LAPACK for clusters
This article describes the context, design, and recent development of the LAPACK for clusters (LFC) project. It has been developed in the framework of Self-Adapting Numerical Software (SANS) since we believe such an approach can deliver the convenience and ease of use of existing sequential environments bundled with the power and versatility of highly tuned parallel codes that execute on clusters. Accomplishing this task is far from trivial as we argue in the paper by presenting pertinent case studies and possible usage scenarios.
20004613
Disk Embeddings of Planar Graphs
Abstract 
Given a planar graph $G=(V,E)$ and a rooted forest ${\FF}=(V_{\FF}, A_{\FF})$ 
with leaf set $V$, we wish to decide whether $G$ has a plane embedding $\GG$ 
satisfying the following condition: There are $|V_{\FF}|-|V|$ pairwise noncrossing 
Jordan curves in the plane one-to-one corresponding to the nonleaf vertices of 
${\FF}$ such that for every nonleaf vertex $f$ of ${\FF}$, the interior of the curve 
$\JJ_f$ corresponding to $f$ contains all the leaf descendants of $f$ in ${\FF}$
but contains no other leaves of ${\FF}$. 
This problem arises from theoretical studies in geographic database systems. 
It is unknown whether this problem can be solved in polynomial time. 
This paper presents an almost linear-time algorithm for a nontrivial special case 
where the set of leaf descendants of each nonleaf vertex $f$ in ${\FF}$ induces 
a connected subgraph of $G$. 


17657159
A Chinese-character thinning algorithm based on global features and contour information
This paper proposes a method of using run-length coding to perform thinning. First, we construct graphs from characters. The attributes (vertical lines, horizontal lines or points) of each node in the graph are determined according to the node's relationship to the nodes above and below it (we will refer to these relationships as global features) and the black runs within the node. Intersections between two adjacent segments are determined on the basis of the graph constructed and contour information. The thinning algorithm thus employs global features and contour information to produce a more accurate skeleton.
7983044
Algorithm-based checkpoint-free fault tolerance for parallel matrix computations on volatile resources
As the size of today's high performance computers increases from hundreds, to thousands, and even tens of thousands of processors, node failures in these computers are becoming frequent events. Although checkpoint/rollback-recovery is the typical technique to tolerate such failures, it often introduces a considerable overhead. Algorithm-based fault tolerance is a very cost-effective method to incorporate fault tolerance into matrix computations. However, previous algorithm-based fault tolerance methods for matrix computations are often derived using algorithms that are seldomly used in the practice of today's high performance matrix computations and have mostly focused on platforms where failed processors produce incorrect calculations. To fill this gap, this paper extends the existing algorithm-based fault tolerance to the volatile computing platform where the failed processor stops working and applies it to scalable high performance matrix computations with two dimensional block cyclic data distribution. We show the practicality of this technique by applying it to the ScaLAPACK/PBLAS matrix-matrix multiplication kernel. Experimental results demonstrate that the proposed approach is able to survive process failures with a very low performance overhead
37360166
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