A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

A morphable model for the synthesis of 3D faces

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

When data is released or shared among different organizations, some sensitive or confidential information may be subject to be exposed by using data mining tools. Thus, a question arises: how can we protect sensitive knowledge while allowing other parties to extract the knowledge behind the shared data. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A sensitive rule can be hidden by adding items into the dataset to make the support of the antecedent part of the sensitive rule increase and accordingly the confidence of the sensitive rule decrease. The evolutionary multi-objective optimization (EMO) algorithm is utilized to find suitable transactions (or tuples) to be modified so as the side effects to be minimized. Experiments on real datasets demonstrated the effectiveness of the proposed method.

Use EMO to protect sensitive knowledge in association rule mining by adding items

Cloud Computing has earned more and more attention for its important role in the modern network development process. It can be seen as a new distributed computing mode based on virtualization process. One of the problems for Cloud Computing is load balance. An improved k-subset algorithm is proposed in this paper for this problem. Good performance in experiments on CloudSim platform shows its efficiency

An improved k-subset algorithm for load balance problems in Cloud Computing

In this paper, an optimization of adaptive transmission with guarantee connection degree of nodes for wireless sensor networks is proposed. The proposed optimization realizes a novel approach to topology control based a power-adaptation scheme. According to the density of deployed nodes, an expected connection degree of nodes and the operational condition are derived. Afterward, the transmission power is adapted to the minimum strength but is sufficient to guarantee node degrees. The benefits of the proposed approach include the reduction and the equalization of power dissipation in nodes. The proposal is applied on a hierarchical network composed of node clusters to manage transmission distances of nodes in a cluster. The present approach guarantees each pair of nodes in a cluster to be connected. The simulation results are obtained in case of uniform-distribution node deployment. The error, defined as the difference of the actual connection degree of nodes and the expected, is around 10%. Besides, the error decreases as the expected connection degree of nodes increasing. The evidence shows that the proposed approach is reliable and stable.

An optimization of adaptive transmission with guarantee connection degree for wireless sensor networks

Using color QR codes for QR code secret sharing

In this paper, a double domain based robust digital image watermarking scheme is proposed. Up to now, the existing robust watermarking algorithms can only resist a part of geometrical attacks and some common image processing attacks, while the proposed method can resist almost all geometrical attacks and a lot of common image processing attacks. Firstly, the watermark is embedded in the 3-level DWT coefficients by the parity modulation method, and then the same watermark is embedded in the pixel domain by the similar method. At the detector side, the watermark is extracted twice, one is in the DWT domain, the other is in the pixel domain. Select the watermark which has better visual quality as the final result. By carefully regulating the parameters and selecting the embedding sequence, the watermarks in the two domains are compatible with each other. Experimental results show the effectiveness of the proposed method.

Jeng-Shyang Pan

Papers

Use EMO to protect sensitive knowledge in association rule mining by adding items

An improved k-subset algorithm for load balance problems in Cloud Computing

An optimization of adaptive transmission with guarantee connection degree for wireless sensor networks

Using color QR codes for QR code secret sharing

A Double Domain Based Robust Digital Image Watermarking Scheme