640529
Heuristic Discovery of Role-Based Trust Chains in Peer-to-Peer Networks
Credential chains are needed in trusted peer-to-peer (P2P) applications, where trust delegation must be established between each pair of peers at specific role level. Role-based trust is refined from the coarse-grained trust model used in most P2P reputation systems. This paper offers a novel heuristic-weighting approach to selecting the most likely path to construct a role-based trust chain. We apply history-sensitive heuristics to measure the path complexity and assess the chaining efficiency. We discover successive edges of a trust chain, adaptively, to match with the demands from various P2P applications. New heuristic chaining algorithms are developed for backward, forward, and bi-directional discovery of trust chains. Our heuristic chain discovery scheme shortens the search time, reduces the memory requirement, and enhances the chaining accuracy in scalable P2P networks. Consider a trust graph over N credentials and M distinct role nodes. Our heuristic trust-chain discovery algorithms require O(N2 logN) search time and O(M) memory space, if the secondary heuristics are generated off-line in advance. These are improved from O(N3) search time and O(NM) space required in non-heuristic discovery algorithms by Li, Winsborough, and Mitchell (2003). Our analytical results are verified by extensive simulation experiments over typical classes of role-based trust graphs.
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.
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
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
15894335
An adaptive load balancing framework for parallel database systems based on collaborative agents
Load balancing is a key technique in parallel computer supported collaborative work (CSCW) systems, parallel database system and P2P system for instance, to boost performance and improve scalability. In order to reduce total cost of ownership (TCO), adaptive/self-tuning administration techniques are gradually and extensively expected in the cyberspace. In parallel database systems, adaptive load balancing techniques are proposed to face the change in data storage patterns and access patterns in a dynamic real environment. The techniques utilized in both shared-nothing and shared-disk parallel database systems are discussed, and a general flexible framework based on collaborative agents is studied to support these techniques in both architectures. The framework supports two kinds of load balancing - one is passively executing query statements balancedly, and the other one is proactively adjusting data placement and task execution scheme, by means of data and task migration, whenever load unbalance is detected. Three categories of agents, scheduling agents, monitoring agents and task agents, are identified in the framework. The collaboration protocols and scheduling algorithms to support adaptive load balancing are described. The framework also applies to other parallel systems such as P2P systems and shared file processing systems due to their underlying commonness.
16833900
Securely Sharing Data in Encrypted Databases
Collaborative design and application integration emphasize the demand for database security. Database encryption is widely adopted to ensure data privacy, which can prevent attacks from both outside intruders and inside malicious users. Current researches on this area are mainly focusing on encryption algorithms, key management and encryption efficiency. However, data sharing nature of database system is usually neglected. In this paper, we propose an access control model, 3S-RBAC, which enables data sharing while guaranteeing privacy. The model has many features: the novel concept of strong permission and weak permission; the hierarchy of database objects and keys; the permission and key inheritance; the binding of keys and permissions. Implementation in OSCAR Secure DBMS shows that the model is flexible, secure, practical, and can be integrated easily into existing enterprise applications
13931461
Accurate sequence-based prediction of catalytic residues
MOTIVATION
Prediction of catalytic residues provides useful information for the research on function of enzymes. Most of the existing prediction methods are based on structural information, which limits their use. We propose a sequence-based catalytic residue predictor that provides predictions with quality comparable to modern structure-based methods and that exceeds quality of state-of-the-art sequence-based methods.


RESULTS
Our method (CRpred) uses sequence-based features and the sequence-derived PSI-BLAST profile. We used feature selection to reduce the dimensionality of the input (and explain the input) to support vector machine (SVM) classifier that provides predictions. Tests on eight datasets and side-by-side comparison with six modern structure- and sequence-based predictors show that CRpred provides predictions with quality comparable to current structure-based methods and better than sequence-based methods. The proposed method obtains 15-19% precision and 48-58% TP (true positive) rate, depending on the dataset used. CRpred also provides confidence values that allow selecting a subset of predictions with higher precision. The improved quality is due to newly designed features and careful parameterization of the SVM. The features incorporate amino acids characterized by the highest and the lowest propensities to constitute catalytic residues, Gly that provides flexibility for catalytic sites and sequence motifs characteristic to certain catalytic reactions. Our features indicate that catalytic residues are on average more conserved when compared with the general population of residues and that highly conserved amino acids characterized by high catalytic propensity are likely to form catalytic sites. We also show that local (with respect to the sequence) hydrophobicity contributes towards the prediction.
3182057
A method of combining multiple probabilistic classifiers through soft competition on different feature sets
Abstract A novel method is proposed for combining multiple probabilistic classifiers on different feature sets. In order to achieve the improved classification performance, a generalized finite mixture model is proposed as a linear combination scheme and implemented based on radial basis function networks. In the linear combination scheme, soft competition on different feature sets is adopted as an automatic feature rank mechanism so that different feature sets can be always simultaneously used in an optimal way to determine linear combination weights. For training the linear combination scheme, a learning algorithm is developed based on Expectation–Maximization (EM) algorithm. The proposed method has been applied to a typical real-world problem, viz., speaker identification, in which different feature sets often need consideration simultaneously for robustness. Simulation results show that the proposed method yields good performance in speaker identification.
108588022
Bonding of Cu-based high aspect ratio microscale structures with Sn intermediate layers
Simple and effective protocols for bonding Cu-based high-aspect-ratio microscale structures (HARMSs) are essential for assembly of Cu-based microdevices. In this paper, Cu-to-Cu bonding with thin intermediate layers of elemental Sn is explored. Bonding quality is assessed through quantitative evaluation of tensile bond strength as a function of bonding parameters such as temperature and applied pressure. Structural examination of the Cu/Sn/Cu interface region is accomplished by combining focused ion beam sectioning and imaging with ion-induced secondary electrons. Successful bonding of Cu-based HARMSs to flat Cu plates with Sn intermediate layers is demonstrated, as well as assembly of all-Cu microfluidic prototypes.
35419934
MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion
This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.
1757195
Combining linear discriminant functions with neural networks for supervised learning
A novel supervised learning method is proposed by combining linear discriminant functions with neural networks. The proposed method results in a tree-structured hybrid architecture. Due to constructive learning, the binary tree hierarchical architecture is automatically generated by a controlled growing process for a specific supervised learning task. Unlike the classic decision tree, the linear discriminant functions are merely employed in the intermediate level of the tree for heuristically partitioning a large and complicated task into several smaller and simpler subtasks in the proposed method. These subtasks are dealt with by component neural networks at the leaves of the tree accordingly. For constructive learning, growing and credit-assignment algorithms are developed to serve for the hybrid architecture. The proposed architecture provides an efficient way to apply existing neural networks (e.g. multi-layered perceptron) for solving a large scale problem. We have already applied the proposed method to a universal approximation problem and several benchmark classification problems in order to evaluate its performance. Simulation results have shown that the proposed method yields better results and faster training in comparison with the multilayered perceptron.