8793822
An efficient clustering algorithm for large-scale topical web pages
The clustering of topic-related web pages has been recognized as a foundational work in exploiting large sets of web pages such as the cases in search engines and web archive systems, which collect and preserve billions of web pages. However, this task faces great challenges both in efficiency and accuracy. In this paper we present a novel clustering algorithm for large scale topical web pages which achieves high efficiency together with considerately high accuracy. In our algorithm, a two-phase divide and conquer framework is developed to solve the efficiency problem, in which both link analysis and content analysis are utilized in mining the topical similarity between pages to achieve a high accuracy. A comprehensive experiment was conducted to evaluate our method in terms of its effectiveness, efficiency, and quality of result.
17411195
Offer Group Generation and Delayed Processing in Multi-Issue Negotiation
In this paper we describe a mechanism for automated negotiation in which artificial agents can deal with a multi-issue negotiation in a one-to-many way based on some strategies. The negotiation process are governed dynamically by some strategies that emphasize on the diverse processing methods under the considering of different commodity attributes, including dynamic delayed evaluation and response to the received offers, generation of offer group with the same utility. Our analysis shows that the mechanism ensures that bargaining agents could reach a better agreement automatically. We also implement a prototype system to verify the advantages of our mechanism.
27036823
A new method in multi-issue negotiation
In this paper we describe a mechanism for automated negotiation in which artificial agents can deal with a multiissue negotiation in a one-to-many way based on some strategies. The negotiation process are governed dynamically by some strategies that emphasize on the diverse processing methods under the considering of different commodity attributes, including dynamic delayed evaluation and response to the received offers, generation of offer group with the same utility. Our analysis shows that the mechanism ensures that bargaining agents could reach a better agreement automatically. We also implement a prototype system to verify the advantages of our mechanism
8366599
Achieving both high precision and high recall in near-duplicate detection
To find near-duplicate documents, fingerprint-based paradigms such as Broder's shingling and Charikar's simhash algorithms have been recognized as effective approaches and are considered the state-of-the-art. Nevertheless, we see two aspects of these approaches which may be improved. First, high score under these algorithms' similarity measurement implies high probability of similarity between documents, which is different from high similarity of the documents. But how similar two documents are is what we really need to know. Second, there has to be a tradeoff between hash-code length and hash-code multiplicity in fingerprint paradigms, which makes it hard to maintain a satisfactory recall level while improving precision.
 In this paper our contributions are two-folded. First, we propose a framework for implementing the longest common subsequence (LCS) as a similarity measurement in reasonable computing time, which leads to both high precision and recall. Second, we present an algorithm to get a trustable partition from the LCS to reduce the negative impact from templates used in web page design. A comprehensive experiment was conducted to evaluate our method in terms of its effectiveness, efficiency, and quality of result. More specifically, the method has been successfully used to partition a set of 430 million web pages into 68 million subsets of similar pages, which demonstrates its effectiveness. For quality, we compared our method with simhash and a Cosine-based method through a sampling process (Cosine is compared to LCS as an alternative similarity measurement). The result showed that our algorithm reached an overall precision of 0.95 while simhash was 0.71 and Cosine was 0.82. At the same time our method obtains 1.86 times as much recall as simhash and 1.56 times as much recall as Cosine. Comparison experiment was also done for documents in the same web sites. For that, our algorithm, simhash and Cosine find almost the same number of true-positives at a precision of 0.91, 0.50 and 0.63 respectively. In terms of efficiency, our algorithm takes 118 hours to process the whole archive of 430 million topic-type pages on a cluster of six Linux boxes, at the same time the processing time of simhash and Cosine is 94 hours and 68 hours respectively. When considering the need of word segmentation for languages such as Chinese, the processing time of Cosine should be multiplied and in our experiment it is 602 hours.
4774721
An Efficient Algorithm for the Longest Pattern Subsequence Problem
A fire sensing system utilizing fiber optics to extend the physical range of radiation sensing elements into areas and environments which are physically inaccessible to, and/or destructive of, such sensors and their associated electronic circuitry. The fiber optics are combined with signal processing circuitry to achieve reliable fire sensing systems which present particular advantages in special applications.
32712174
Hailstone Detection Based on Image Mining
Hailstone is one of the main meteorological disasters and it is very difficult to be forecasted effectively. A novel method of automatic recognition hail cloud is proposed base on association rules in this paper. Firstly, the method preprocesses the original reflectivity image. Then the color features and texture features are extracted from the radar echo image. Lastly, the association rules, which are used to distinguish hailstorm from rainstorm and super refract, are constructed, namely hail echo ensemble forecast model based on image mining is constructed. The results show that hail echo ensemble forecast model based on image mining has higher accuracy compared with the PUP products.
9035268
Optimally Balanced Forward Degree Sequence
Forward degree seqences, arising from orderings of the vertices in a graph, carry a lot of vital information about the graph. In this paper, we focus our work on two special classes of forward degree sequences, which we named balanced and strongly balanced. Our main result is to prove that any chordal graph has a strongly balanced forward degree sequence and any graph with all degrees at most 3 has a balanced forward degree sequence. Moreover, we show that the strongly balanced forward degree sequence can be computed in polynomial time in the above cases.
14387828
Escape analysis for synchronization removal
In this paper we introduce our escape analysis framework for Java, which is a kind of flow-insensitive, inter-procedural, and context-sensitive data flow analysis. And we present an efficient static intra-procedural algorithm for inferring the set of types that can occur at runtime for each object. With intra-procedural type information, virtual method resolution, callee-stub inline, exception analysis, and thread allocation site analysis are implemented. Virtual method resolution and callee-stub inline effectively reduce the call graph and SCC (strongly connected components). In exception analysis, we present a more precise analysis algorithm, especially in the inter-procedural exception in SCC.Our escape analysis can be applied to the whole Java program and Java libraries to identify unnecessary synchronization in Java and remove them. We have developed an implementation on Intel's Open Runtime Platform. For our benchmarks, 7.3% to 99% (with an average of 54%) of synchronization operations are eliminated.
30078088
Image Segmentation by a Robust Clustering Algorithm Using Gaussian Estimator
This paper presents a novel clustering-based image segmentation method, which incorporates the features of robust statistics. To overcome the sensitivity to noise and outliers in fuzzy clustering, a simple but efficient M-estimator, Gaussian estimator, has been introduced to clustering analysis as weight or membership function. When applied to image analysis, the proposed Robust Gaussian Clustering/Segmentation (RGCS) algorithm exhibits more reasonable pixel classification and noise suppression performance with respect to FCM. Moreover, by using a uniform resolution parameter scheme, this method avoids producing coincident clusters, which occurs often in possibilistic clustering segmentation. Experiments on both the synthetic data and real image demonstrate the validity and power of the proposed algorithm.