Jeng-Shyang Pan

Bio: Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.

An Adaptive Approach to High-Throughput Inter-Cloud Data Transmission Based on Fast TCP

Abstract: Cloud service is a trend of promising services on Internet. Data centers with high performance are the key to realize real-time services for worldwide users. For the users travelling in global, data mobility between cloud data centers is essential to offer quality-ensured cloud services. However, existing transmission protocols such as TCP, do not support high speed and high throughput transmission on long-distance networks. Legacy TCP lacks of the ability to efficiently transport data between distant data centers. In this paper, an adaptive Fast TCP is proposed to move data faster between data centers of cloud services. The proposed approach adapts the operational parameter of Fast TCP connections to improve the throughput and the rapidness of TCP connections. Numeric results show that the throughput is improved by 2.5 times and the rapid response of flow control eliminates packet loss. The better data mobility contributes the service quality of travelling users in clouds.

Comments on Yu et al’s Shared Data Integrity Verification Protocol

Abstract: Recently, Yu et al. proposed a secure shared data integrity verification protocol called SDVIP\(^2\) to ensure the integrity of outsourced file in the cloud. Unfortunately, we exploit the vulnerability of their protocol in this paper. We demonstrate an active adversary can modify the outsourced file such that the auditor is unable to detect in the auditing process. At the end of this paper, we also provide two suggestions to fix the proposed attack.

An efficient K-medoids-based algorithm using previous medoid index, triangular inequality elimination criteria, and partial distance search

Abstract: Clustering in data mining is a discovery process that groups similar objects into the same cluster. Various clustering algorithms have been designed to fit various requirements and constraints of application. In this paper, we study several k-medoids-based algorithms including the PAM, CLARA and CLARANS algorithms. A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search. Experimental results based on elliptic, curve and Gauss-Markov databases demonstrate that the proposed algorithm applied to CLARANS may reduce the number of distance calculations by 67% to 92% while retaining the same average distance per object. In terms of the running time, the proposed algorithm may reduce computation time by 38% to 65% compared with the CLARANS algorithm.

Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition

Abstract: Dimensionality reduction is the most popular method for feature extraction and recognition Recently, Li et al (IEEE PAMI, 2009) proposed Nonparametric Discriminant Analysis (NDA) based dimensionality reduction for face recognition and reported an excellent recognition performance However, NDA has its limitations on extracting the nonlinear features of face images for recognition, and owing to the highly nonlinear and complex distribution of face images under a perceivable variation in viewpoint, illumination or facial expression In order to increase the NDA, we extend the NDA with kernel trick to propose Nonparametric Kernel Discriminant Analysis (NKDA) for feature extraction and recognition Experimental results on ORL, YALE and UMIST face databases show that NKDA outperforms NDA on recognition, which demonstrates that it is feasible to improve NDA with kernel trick for feature extraction

Improved Mean Representation Based Classification for Face Recognition

Abstract: In this paper, two new classifiers based on class mean (CM) and mean representation based classification (MRC), called improved class mean (ICM) and improved mean representation based classification (IMRC), are proposed for face recognition. ICM classifier uses the novel method to gain new mean vector of each class subspace for classification. IMRC classifier utilizes the novel class mean vector and decision rule for classification. A large number of experiments on AR face database and YaleB face database are used to assess the novel classifier. The experimental results show that the proposed classifier gains better recognition rate than MRC classifier, LRC classifier, CM classifier and nearest feature centre (NFC) classifier.

Mean shift: a robust approach toward feature space analysis

Abstract: 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.

Digital Watermarking

Abstract: 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.

PORs: Proofs of Retrievability for Large Files

Abstract: 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.