15844013
Survey of Disjoint NP-pairs and Relations to Propositional Proof Systems
We survey recent results on disjoint NP-pairs. In particular, we survey the relationship of disjoint NP-pairs to the theory of proof systems for propositional calculus.
11528752
Redundancy in Complete Sets
We show that a set is m-autoreducible if and only if it is m-mitotic. This solves a long standing open question in a surprising way. As a consequence of this unconditional result and recent work by Glaser et al. [12], complete sets for all of the following complexity classes are m-mitotic: NP, coNP, $\bigoplus$P, PSPACE, and NEXP, as well as all levels of PH, MODPH, and the Boolean hierarchy over NP. In the cases of NP, PSPACE, NEXP, and PH, this at once answers several well-studied open questions. These results tell us that complete sets share a redundancy that was not known before. In particular, every NP-complete set A splits into two NP-complete sets A1 and A2.
16393711
Salient Feature Selection for Visual Concept Learning
Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval. In this paper, we propose a novel image classification framework by learning semantic concepts of image categories. To choose representative features for an image category and meanwhile reduce noisy features, a three-step salient feature selection strategy is proposed. In the feature selection stage, salient patches are first detected and clustered. Then the region of dominance and salient entropy measures are calculated to reduce non-common salient patches for the category. Based on the selected visual keywords, SVM and keyword frequency model categorization method are applied to classification, respectively. The experimental results on Corel image database demonstrate that the proposed salient feature selection approach is very effective in image classification and visual concept learning.
35345915
Ranking Component Retrieval Results by Leveraging User History Information
21758360
WS-trustworthy: a framework for Web services centered trustworthy computing
The emerging paradigm of Web services has been gaining significant momentum in the recent years since it offers a promising way to facilitate business-to-business (B2B) collaboration. However, it is not clear that this new model of Web services provides any measurable increase in computing trustworthiness. We propose a generic framework to control the trustworthiness of computing in the domain of Web services. A layered model is established to highlight four key elements: resources, policies, validation processes, and management. The robustness of this model exhibits its flexibility and extensibility. Examples utilizing our framework are reported.
52862068
Robust face alignment based on local texture classifiers
We propose a robust face alignment algorithm with a novel discriminative local texture model. Different from the conventional descriptive PCA local texture model in ASM, classifiers using LUT-type Haar-like features are trained from a large data set as local texture model. The strong discriminative power of the classifier greatly improves the accuracy and robustness of local searching on faces with expression variation and ambiguous contours. A Bayesian framework is configured for shape parameter optimization and the algorithm is implemented in a hierarchical structure for efficiency. Extensive experiments are reported to show its accuracy and robustness.
13978949
An experimental study of two graph analysis based component capture methods for object-oriented systems
The problem of how to partition a software system and thus capture its overall architecture and its constituent components has become a research focus in the community of software engineering. In the literature, many methods have been proposed for solving this problem. For example, both top-down and bottom-up methods based on analyzing the graph representation of software systems have been proposed. We report an experimental study of a top-down method and a bottom-up method. In our study, we focus on the capability of component capture, the capability of architecture recovery and the time complexity for the two methods. According to our results on two real world systems, the studied bottom-up method is superior to the studied top-down method in both aspects, although the time complexity of the bottom-up method remains a big concern for large systems.