15271218
Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring
Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source.
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.
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.
9978380
Learning a discriminative hidden part model for human action recognition
We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (hCRF) for object recognition. Similar to hCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Different from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying hCRF on local patches alone.
2310778
Boosting an associative classifier
Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent development of the AdaBoost algorithm indicates that boosting simple rules could often achieve better classification results than the use of complex rules. In view of this, we apply the AdaBoost algorithm to an associative classification system for both learning time reduction and accuracy improvement. In addition to exploring many advantages of the boosted associative classification system, this paper also proposes a new weighting strategy for voting multiple classifiers.
14397325
Constraint-Sensitive Privacy Management for Personalized Web-Based Systems
This research aims at reconciling web personalization with privacy constraints imposed by legal restrictions and by users' privacy preferences. We propose a software product line architecture approach, where our privacyenabling user modeling architecture can dynamically select personalization methods that satisfy current privacy constraints to provide personalization services. A feasibility study is being carried out with the support of an existing user modeling server and a software architecture based development environment.
1772294
Scalable real-time system design using preemption thresholds
The maturity of schedulability analysis techniques for fixed-priority preemptive scheduling has enabled the consideration of timing issues at design time using a specification of the tasking architecture and estimates of execution times for tasks. While successful, this approach has limitations since the preemptive multi-tasking model does not scale well for a large number of tasks, and the fixed-priority scheduling theory does not work well with many object-oriented design methods. In this paper, we present an approach that scales well even when the design consists of a large number of concurrent jobs. The approach avoids any unnecessary preemptability in the system, thereby resulting in reduced run-time overheads from preemptions and associated context switches. It also allows significant memory savings by grouping jobs into non-preemptive groups and then sharing the stack space between them. Our approach is based on our earlier work on scheduling using preemption thresholds that allows parametric control over preemptability in a priority-based system. We show that our approach provides significant advantages over one using a fixed-priority preemptive scheduling architecture. The benefits include higher schedulability for small numbers of tasks and lower run-time overheads, and hence better scalability. We develop algorithms that allow design-time consideration of schedulability and automatic synthesis of an implementation model to minimize run-time overheads.
15938722
An Implicit Finite Difference Scheme with Preconditioning for Convection Dominated Diffusion Equation
An implicit finite difference scheme for solving time-dependent convection dominated diffusion equations in two space variables is presented. A one-sided difference approximation is used for the convection terms and a second-order central difference approximationfor the diffusion term. The implicit scheme is consistent,unconditionally stable and its $L^2$ error estimation is optimal. By preconditioning technique of incremental unknowns, the implicit scheme is an efficient one.