32371187
Exploit Semantic Information for Category Annotation Recommendation in Wikipedia
Compared with plain-text resources, the ones in "semi-semantic" web sites, such asWikipedia, contain high-level semantic information which will benefit various automatically annotating tasks on themself. In this paper, we propose a "collaborative annotating" approach to automatically recommend categories for a Wikipedia article by reusing category annotations from its most similar articles and ranking these annotations by their confidence. In this approach, four typical semantic features in Wikipedia, namely incoming link, outgoing link, section heading and template item, are investigated and exploited as the representation of articles to feed the similarity calculation. The experiment results have not only proven that these semantic features improve the performance of category annotating, with comparison to the plain text feature; but also demonstrated the strength of our approach in discovering missing annotations and proper level ones for Wikipedia articles.
495169
EachWiki: Suggest to Be an Easy-To-Edit Wiki Interface for Everyone
In this paper, we present EachWiki, an extension of Semantic Media Wiki characterized by an intelligent suggestion mechanism. It aims to facilitate the wiki authoring by recommending the following elements: links, categories, and properties. We exploit the semantics of Wikipedia data and leverage the collective wisdom of web users to provide high quality annotation suggestions. The proposed mechanism not only improves the usability of Semantic MediaWiki but also speeds up its converging use of terminology. The suggestions are applied to relieve the burden of wiki authoring and attract more inexperienced contributors, thus making Semantic MediaWiki even better Semantic Web proto types and data source.
35950271
Partition Triangle Meshes into Coarsely Quadrangular Segmentation
A feature captured approach was presented to automatically partition a triangle meshes into a coarsely quadrangular segmentation. The triangle meshes was firstly partitioned into a polygonal segmentation which was further split into triangular and quadrangular patches. After the convex and concave features were identified, the triangular patches were eliminated according to their topological relations to achieve the quadrangular segmentation. The optimization methods based on globally smooth parameterization and active contour model were also adopted to improve the qualities of quadrangular patches and their edges. The final segmentation consisted of pure-quadrangular patches with appropriate sizes according to the model's geometric characteristics, and the features of input model werewell captured.
642679
Representing Discovered Patterns Using Attributed Hypergraph
One of the fundamental problems in knowledge discovery in databases and other applications of AI is how to represent knowledge and patterns. Existing representation schemes have various shortcomings. In this paper, we propose a new knowledge representation scheme using attributed hypergraph (AHG), which is simple yet general enough to directly encode different order patterns discovered from large databases. In AHG, both the qualitative and quantitative relations are represented as attributed hyperedges. Such representation is lucid and transparent for visualization. Besides, patterns in AHG are easy to understand. In the discussion, some basic manipulations of AHG for data mining tasks are briefly addressed. The paper ends with examples of pattern representation using AHG.
5756583
Face Relighting from a Single Image under Arbitrary Unknown Lighting Conditions
In this paper, we present a new method to modify the appearance of a face image by manipulating the illumination condition, when the face geometry and albedo information is unknown. This problem is particularly difficult when there is only a single image of the subject available. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using a spherical harmonic representation. Moreover, morphable models are statistical ensembles of facial properties such as shape and texture. In this paper, we integrate spherical harmonics into the morphable model framework by proposing a 3D spherical harmonic basis morphable model (SHBMM). The proposed method can represent a face under arbitrary unknown lighting and pose simply by three low-dimensional vectors, i.e., shape parameters, spherical harmonic basis parameters, and illumination coefficients, which are called the SHBMM parameters. However, when the image was taken under an extreme lighting condition, the approximation error can be large, thus making it difficult to recover albedo information. In order to address this problem, we propose a subregion-based framework that uses a Markov random field to model the statistical distribution and spatial coherence of face texture, which makes our approach not only robust to extreme lighting conditions, but also insensitive to partial occlusions. The performance of our framework is demonstrated through various experimental results, including the improved rates for face recognition under extreme lighting conditions.
16388786
Collecting Internet Malware Based on Client-side Honeypot
With the improvement of software security, attacks based on RPC vulnerabilities declined, however, attacks based on client application software vulnerabilities have increased. Such client application software includes Web browsers, email client and office. The spread of malware using these software vulnerabilities has become a severe threat to todaypsilas Internet. In allusion to this kind of threat, this paper designed an Internet malware collecting system based on client-side honeypot. This system can not only collect malware but also detect malicious Web site. It uses a unique network crawler based on client-side attack techniques to collect source of URL, and it collects URL and attachments from emails, then it creates software processes to open URL or files, and uses a device-drive monitor to detect malicious behaviors. It gives an alarm and locates the malicious file, and sends the malware coming through the Internet to the collecting server. We introduce the design and implement of this system and give the results.
5597681
An Ensemble Approach to Learning to Rank
In recent years, 'learning to rank' is a focused approach for information retrieval, which can learn the ranking order given by experts and construct a uniform model to rank for new query. But in practice user queries vary in large diversity, it makes a single learned ranker not representative. Therefore, we propose an ensemble approach to 'learning to rank,' in which a lower generalization error can be gotten by generating a set of rankers and leveraging these rankers for the final prediction. Moreover, two strategies of creating multiple base rankers are proposed to make the ensemble more effective for information retrieval. The experiment results on two real world datasets indicate that the proposed approach can outperform the original 'learning to rank' methods significantly.