742966
Clustering with Noising Method
The minimum sum of squares clustering problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. In this article, a recent metaheuristic technique, the noising method, is introduced to explore the proper clustering of data sets under the criterion of minimum sum of squares clustering. Meanwhile, K-means algorithm as a local improvement operation is integrated into the noising method to improve the performance of the clustering algorithm. Extensive computer simulations show that the proposed approach is feasible and effective.
41230139
Comments on an access control model in semantic grid
In this note, we show that there are some significant design weaknesses in the access control model that proposed to address the convergence and consistence problems in semantic grid by Bu and Xu in a recent paper.
44251234
Efficient and Proactive Threshold Signcryption
To make the system more secure and robust, threshold schemes are proposed to avoid single point failure. At the same time, there are more and more applications which utilize the two basic blocks encryption and digital signature to secure message delivery (such as SSL, SSH). Combining the three tools organically leads to an interesting security tool termed as threshold signcryption which can be used in distributed systems especially the mobile networks. In this paper, we present an efficient threshold signcryption scheme. The scheme is designed for an asynchronous network model which may better present practical distributed systems, especially Internet or mobile ad hoc networks. In order to resist mobile attacks, we add proactive property to our scheme. To the best of our knowledge, the proposed scheme is the first threshold signcryption scheme which is noninteractive, proactive and provably secure and works on asynchronous network models.
875545
Multiplex Encryption: A Practical Approach to Encrypting Multi-recipient Emails
Efficiently protecting the privacy of multi-recipient emails is not as trivial as it seems. The approach proposed by S/MIME is to concatenate all ciphertexts. However, it suffers from poor scalability due to its linear computation and communication cost. In this paper, we propose a new practical and secure scheme, called multiplex encryption. By combining the ideas of identity-based mediated RSA and re-encryption mix network, our framework only requires constant computation and communication cost to encrypt a multi-recipient email.
39321946
Attacks on the (enhanced) Yang-Shieh authentication
The Yang-Shieh authentication is a time-stamp based password authentication scheme that uses smart cards [1]. In [2,3], various attacks on this scheme are described. However, an enhancement of the scheme is proposed in [3] and enables the scheme to resist these existing attacks. In this paper, we show two new attack that can break the enhanced scheme. We further point out that the fundamental computational assumption of the Yang-Shieh authentication scheme is incorrect.
12064638
SDD: Secure Distributed Diffusion Protocol for Sensor Networks
A sensor network is a collection of tiny sensor nodes, which consists of sensing, data processing and communicating components. Directed Diffusion is an important data-centric routing protocol of sensor networks. In this paper we present the design of a new secure Directed Diffusion protocol (SDD), which provides a secure extension for the Directed Diffusion protocol. We mainly focus on secure routing and give a simple scheme to securely diffuse data. We have not considered with the in-network aggregation as a goal. In order to support the use of SDD for sensor nodes with extremely limited CPU processing capability, we use an efficient one-way chain and do not use asymmetric cryptographic operations in this protocol. Our security analyses show that SDD is robust against any active attackers or compromised nodes in the network.
15289343
A connectionist method for pattern classification with diverse features
A novel connectionist method is proposed to simultaneously use diverse features in an optimal way for pattern classification. Unlike methods of combining multiple classifiers, a modular neural network architecture is proposed through use of soft competition among diverse features. Parameter estimation in the proposed architecture is treated as a maximum likelihood problem, and an Expectation-Maximization (EM) learning algorithm is developed for adjusting the parameters of the architecture. Comparative simulation results are presented for the real world problem of speaker identification.
18568401
Capture inter-speaker information with a neural network for speaker identification
Many speaker identification systems are created by model-based approaches, where a statistical model is used to characterize a speaker's voice and no inter-speaker information is used in parameter estimation. It is well known that inter-speaker information is very helpful in discrimination of different speakers. We propose a method for the use of inter-speaker information to improve performance of a model-based speaker identification system. A neural network is employed to capture inter-speaker information from output space of those statistical models. In order to sufficiently utilize inter-speaker information, a rival penalized encoding rule is proposed to design supervised learning pairs for training the neural network. Comparative results in the KING speech corpus show that our method leads to a considerable improvement for a model-based speaker identification system.
5211158
Finding Frequent Items in Data Streams
We present a 1-pass algorithm for estimating the most frequent items in a data stream using limited storage space. Our method relies on a data structure called a COUNT SKETCH, which allows us to reliably estimate the frequencies of frequent items in the stream. Our algorithm achieves better space bounds than the previously known best algorithms for this problem for several natural distributions on the item frequencies. In addition, our algorithm leads directly to a 2-pass algorithm for the problem of estimating the items with the largest (absolute) change in frequency between two data streams. To our knowledge, this latter problem has not been previously studied in the literature. c © 2003 Elsevier B.V. All rights reserved.
94451
Lower Bounds for Maximum Parsimony with Gene Order Data
In this paper, we study lower bound techniques for branch-and-bound algorithms for maximum parsimony, with a focus on gene order data. We give a simple O(n3) time dynamic programming algorithm for computing the maximum circular ordering lower bound, where n is the number of leaves. The well-known gene order phylogeny program, GRAPPA, currently implements two heuristic approximations to this lower bounds. Our experiments show a significant improvement over both these methods in practice. Next, we show that the linear programming-based lower bound of Tang and Moret (Tang and Moret, 2005) can be greatly simplified, allowing us to solve the LP in O*n3) time in the worst case, and in O*(n2.5) time amortized over all binary trees. Finally, we formalize the problem of computing the circular ordering lower bound, when the tree topologies are generated bottom-up, as a Path-Constrained Traveling Salesman Problem, and give a polynomial-time 3-approximation algorithm for it. This is a special case of the more general Precedence-Constrained Travelling Salesman Problem and has not previously been studied, to the best of our knowledge.
666073
A Feasible Baseline Architecture for Building and Evaluating Distributed Systems
Although distributed networks and systems are widely deployed, an effective overall benchmark architecture is still lacking. Recently we defined and studied the fat-stack network architecture and found that it is both efficient and scalable to distributed systems of various scales. In precedent work, we theoretically proved that the fat-stack is universal by measure of routing efficiency with a low 1.5-powered logarithmic overhead. Universality implies that the fat-stack of a given size is nearly the best routing network of that size. The fat-stack is also the minimal universal network in terms of number of links and nodes used. In this paper, we add more analytical proof and report our simulation results. Simulations show that the fat-stack outperforms a mesh-based distributed network of comparable hardware usage. We show how the fat-stack can be an effective benchmark architecture and how to scale the network from a VLSI graph layout to a large-scale distributed topology. Our work helps explain why some deployed networks function in the way they do in terms of routing. It also provides an exemplary network of proven efficiency and scalability for building new distributed systems.