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

In this paper, a Reduce Identical Composite Event Transmission (RICET) algorithm is proposed to solve the problem of detecting composite events in wireless sensor networks. The RICET algorithm extends the traditional data aggregation algorithm to detect composite events, and this algorithm can eliminate redundant transmission and save power consumption, thereby extending the lifetime of the entire wireless sensor network. According to the experimental results, the proposed algorithm not only reduces power consumption by approximately 64.78% and 62.67%, but it also enhances the sensor node's lifetime by up to 8.97 times compared with some traditional algorithms.

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A Reduce Identical Composite Event Transmission Algorithm for Wireless Sensor Networks

A single-phase-to-ground fault location method based on convolutional deep belief network

With the development of convolutional neural networks and deep learning and a series of very significant breakthroughs in computer speech, many new models and methods have been provided for the field of Natural language processing. Natural language processing is a very important branch of artificial intelligence, and its application requirements and relevant fields are also becoming wider and wider. This paper first summarizes the related concepts of Natural language processing; then introduces in detail the development process of Natural language processing; then elaborates on the research progress of the application field of Natural language processing, including lexical analysis, syntactic analysis, machine translation and other fields; finally, the semantic understanding, the problem of low resources and the development direction of other fields are summarized and forecasted.

A Review of the Development and Application of Natural Language Processing

With the development of network technology, database-driven web applications (apps) provide flexible, convenient, available, and various services for users. User can send requests to these web apps by using browser over the Internet to get services such as e-commerce services, entertainments, and financial services. Though web environments have several advantages, various security threats have been described. Among these threats, SQL injection attack (SQLIA) is one of the most serious threats. SQLIA is a code injection attack that exploits secure vulnerabilities consisting in source codes to attack databases. SQLIA allows attackers to bypass authentication, access private information, modify data, and even destroy databases. Since many sensitive and confidential data stored in database must be kept private and secure, a mechanism to detect SQLIAs for web environments is necessary. In this paper, we define a framework named DSD (Dynamic SQLIAs Detection) to counter SQLIAs in web environments. Then, a concrete detection mechanism based on DSD is proposed to detect SQLIAs by using parse tree. The experimental results are demonstrated that our mechanism has higher accuracy, lower false positive rate, and false negative rate.

Towards SQL Injection Attacks Detection Mechanism Using Parse Tree

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Jeng-Shyang Pan

Papers

A Reduce Identical Composite Event Transmission Algorithm for Wireless Sensor Networks

A single-phase-to-ground fault location method based on convolutional deep belief network

A Review of the Development and Application of Natural Language Processing

Towards SQL Injection Attacks Detection Mechanism Using Parse Tree

No-Reference Quality Metric of Blocking Artifacts Based on Color Discontinuity Analysis