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

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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

Ant Colony Optimization (ACO) is one of the swarm intelligent methods for solving computational problems, especially in finding the optimal paths through graphs In the past, floating point is widely used to represent the pheromone in ACO, thus requiring large amounts of memory to find the optimal solutions In this paper, the quantification-based ACS (QACS) is thus proposed to reduce the space complexity New updating rules of pheromone with no decay parameters are also designed in the proposed QACS for simplifying the updating processing of pheromone Based on the experimental results of proposed QACS, the convergence rate can be improved with less memory space for solving Traveling Salesman Problem (TSP)

Quantification-Based Ant Colony System for TSP

Since the proposition of the concept of cloud computing in the year 2006, cloud computing has drawn lots of attention from both industry and academic area. Several technologies such as virtualization formed the basis of cloud computing, while some other technologies acts as system improvement strategies in cloud computing. Among the used technologies, Load balancing is indispensable and extremely important in improving system performance and maintaining users’ experience. In this paper, we focus on load balancing algorithms commonly used in cloud computing. Through our analysis we proposed our improved online load balancing algorithm. We use several experimental results to show its power and efficiency. We use CloudSim as a simulator to verify our thoughts.

Research on Heuristic Based Load Balancing Algorithms in Cloud Computing

Privacy preserving in association rule mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.

Jeng-Shyang Pan

Papers

Quantification-Based Ant Colony System for TSP

Research on Heuristic Based Load Balancing Algorithms in Cloud Computing

BRBA: a blocking-based association rule hiding method

A compact GBMO applied to modify DV-Hop based on layers in a wireless sensor network

Surrogate-assisted Phasmatodea population evolution algorithm applied to wireless sensor networks