18102767
Resilient Rights Protection for Product Data
With the development of advance manufacture applications, enterprise must interact with their suppliers and customers to share more product data information. Consequently, copyright and integrity protection over product data is becoming an urgent requirement. Product data information consists of two parts: product structure and part content, besides protecting the three-dimensional (3D) model of part by watermarking, we also need to protect product structure information, which describes the relations among product parts and can be represented as a tree. In this paper, based on the 3D polygonal meshes watermark technology, the structure/semi-structure data watermark technologies and the idea of Fractal, we propose a novel watermark scheme for tree-structure product data with the value lying both in product structure and in part node content, which gives a more comprehensive and resilient right protection for both part node content and product structure. Moreover it is particularly effective when resisting invertibility attacks
15447660
A logical replication-based intrusion detection approach for CSCW system
With the rapid development of CSCW techniques, CSCW system security has been of much attention. Its major challenge comes from malicious attacks, which cannot be handled by traditional security mechanisms, such as authorization, access control, encryption, etc. Although there existed some intrusion detection systems, current researches on intrusion detection are still insufficient in accuracy and efficiency for two reasons: firstly, most of them implement malicious transaction detection only by matching samples with predefined patterns; secondly, most current researches mainly resort to semantic analyzing or even only manual interventions to judge the malicious attacks. Inspired by the replication technique, we propose and implement a novel logical replication-based intrusion detection solution for CSCW system in this paper. It provides an additional layer of defense against application attacks, especially program attacks. The approach entitles the system to detect malicious intrusions efficiently at low false positive rate.
24158595
A Fractal Watermark Solution for Product Data
Copyright and integrity protection over product data is becoming an urgent requirement, because in a Distributed Networked Manufacturing environment, enterprises have to interact with each other to share product data. In this paper, we introduce a novel watermarking solution for product data based on the idea of fractal, which classifies product data watermarks into three tiers: fractal watermark, tree-structure watermark, and tree-node watermark. It embeds watermarks not only in value-carrying nodes but also in product structures that "glue" all the nodes together by embedding multiple structure watermarks in product data. Simulation results show that the proposed technique is robust against various forms of malicious attacks of product data, and naturally immune from structure transformation attacks and subtractive (sub-tree) attacks. Moreover, the solution can protect tree-structure data whose knowledge is mainly embodied in the tree structure efficiently. It is very easy to implement and popularize.
40247854
Fast iterative and preconditioning methods for linear and non-linear systems
Apparatus for unloading a top unloading mine car that is mounted to pivot about a horizontal axis extending along one side of the base of the mine car includes a hoist chain detachably connected to the mine car at the side opposite the pivotal axis. The hoist chain extends upwardly over a pulley mounted on a positioning crank arm. The pulley moves on rotation of the crank arm from a position substantially vertically above the connection means when the mine car is in an upright position to a position above the pivotal axis. The hoist chain continues around a pulley mounted on a drive crank arm and then to an attachment of restricted mobility. The drive crank arm and the positioning crank arm are rotated in a single direction by drive means which are adapted to rotate the drive crank arm at twice the angular velocity of the positioning crank arm. On rotation of the crank arms the hoist chain rotates the top unloading car past the axis of rotation to dump the contents of the car and returns the car to the upright position. In a preferred embodiment, the attachment of restricted mobility of the hoist chain is a differential pulley. A compensating chain runs from the differential pulley over an idler pulley to a connection on the positioning arm to adjust the effective length of the hoist chain.
17327326
UBM-based incremental speaker adaptation
This paper addresses a novel algorithm of incremental speaker adaptation (ISA) based on universal background model (UBM) for saving storage and real-time processing. This algorithm can be seen as an extension of traditional speaker adaptation. It consists of two steps, adaptation and combination. It not only considers the speaker's characteristics in limited training data, but also prohibits over-fitting of the updated model. The incremental adaptation algorithm needs little storage and meets the requirement of real-time processing. In order to evaluate the efficiency and effectivity of the proposed approach, a real-time speaker segmentation system for broadcasting news is built. Experiment results demonstrate that our approach yields real time operation and achieves satisfactory performance.
38388542
On a Class of Preconditioning Methods for Dense Linear Systems from Boundary Elements
In this paper we discuss several sparse-matrix-based preconditioners suitable for preconditioning dense linear systems of boundary element equations. All preconditioners involve only O(n) nonzeros. We provide a framework for constructing operator-splitting-based preconditioners and use it to analyze a class of sparse preconditioners. For singular integral equations, a more efficient preconditioner is proposed that has a band-2 structure.
15647217
Common Nature of Learning Exemplified by BP and Hopfield Neural Networks for Solving Online a System of Linear Equations
Many computational problems widely encountered in scientific and engineering applications could finally be transformed to the online linear-equations solving. Classic numerical methods for solving linear equations include Gaussian elimination and matrix factorization methods, which are usually of O(n3) operations. Being important parallel-computational models, both BP (back propagation) and Hopfield neural networks could be exploited for solving such linear equations. BP neural network is evidently different from Hopfield neural network in terms of network definition, architecture and learning pattern. However, both of these two neural networks could have a common nature of learning (i.e., governed by the same mathematical iteration formula) during the online solution of linear equations. In addition, computer-simulation results substantiate the theoretical analysis of both BP and Hopfield neural networks for solving online such a set of linear equations.
15363041
An Ensemble of Competitive Learning Networks with Different Representations for Temporal Data Clustering
Temporal data clustering provides useful techniques for condensing and summarizing information conveyed in temporal data, which is demanded in various fields ranging from time series analysis to sequential data understanding. In this paper, we propose a novel approach to temporal data clustering by an ensemble of competitive learning networks incorporated by different representations of temporal data. In our approach, competitive learning networks of the rival-penalized learning mechanism are employed for clustering analyses based on different temporal data representations while an optimal selection function is applied to find out a final consensus partition from multiple partition candidates yielded by applying alternative consensus functions to results of competitive learning on different representations. Thanks to its capability of the rival penalized learning rules in automatic model selection and the synergy of fusing diverse partitions on different representations, our ensemble approach yields favorite results, which has been demonstrated in time series and motion trajectory clustering tasks.