International audienceA blind color image watermarking scheme based on quaternion discrete Fourier transform (QDFT) and uniform log-polar mapping (ULPM) is proposed. In this scheme, a binary meaningful watermark is first embedded into the real part of QDFT coefficient of middle frequencies. In order to make it robust to geometric attacks, a bipolar binary watermark is inserted by the following ULPM method. This second watermark can resist geometric attacks and is used so as to invert such a transformation. Once the geometric transform is reversed, the meaningful watermark can be then extracted. Furthermore, several blocking watermarking embedding approaches in QDFT domain are compared to obtain better robustness against common signal operations. Compared to other existing methods, experimental results show that the proposed method achieves better performance against common signal operations and geometric attacks

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A new four-directional total variation (4-TV) model, applicable to isotropic and anisotropic TV functions, is proposed for image denoising. A dual based fast gradient projection algorithm for the constrained 4-TV image denoising problem is also reported which combines the well-known gradient projection and the fast gradient projection methods. Experimental results show that this model provides in most cases a better signal to noise ratio when compared to previous models like the reference TV, the total generalized variation, and the nonlocal total variation.

With the intensified competition among telecommunications industry, we focused much on the quality of service. Illegal activities, especially dial-back fraud calls, may cause annoyance and inconvenience which will reduce user experience. The detection of dial-back fraud calls is an urgent issue that needs to be addressed. The rapid development of information technology which gives rise to the accumulated huge data will pose a greater challenge. However, traditional detecting methods to identify illegal activities cannot get acceptable accuracy. On the other hand, those methods become very inefficient or even unavailable when processing massive data. In this paper, we introduce a distributed outlier detection approach to locate illegal acts of the illegal users who have the characteristics as outliers. For a higher hit rate, we combine outlier detection with cluster coefficient. Besides, the method exploits parallel computation based on MapReduce in order to obtain vast time savings and improve the processing capability of the algorithm on large data. Extensive experimental results demonstrate the efficiently performances of proposed algorithm according to the evaluation criterions of speedup and scale up.

Uncertainty measures are important for knowledge discovery and data mining. Rough set theory (RST) is an important tool for measuring and processing uncertain information. Although many RST-based methods for measuring system uncertainty have been investigated, the existing measures cannot adequately characterise the imprecision of a rough set. Moreover, these methods are suitable only for complete information systems, and it is difficult to generalise methods for complete information systems to incomplete information systems. To overcome these shortcomings, we present new uncertainty measures, integrated accuracy and integrated roughness, that are based on general binary relations, and we study important properties of these measures. A theoretical analysis and examples show that the proposed integrated measures are more precise than existing uncertainty measures, they are suitable for both complete and incomplete information systems, and they are logically consistent. Therefore, integrated accuracy and integrated roughness overcome the limitations of existing measures. This research not only develops the theory of uncertainty, it also expands the application domain of uncertainty measures and provides a theoretical basis for knowledge acquisition in information systems based on general binary relations.