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.
195349585
Identity Based Conference Key Distribution Scheme from Parings
A conference key distribution system can provide a session key of a conference with more than three participants via network. In this paper,we propose an ID-based conference key distribution scheme from pairings. The proposed scheme provides user anonymity. At the same time, we also show that the proposed scheme is secure against replaying attacks and conspiratorial impersonation attacks.
18013503
An Efficient Asynchronous Proactive RSA Scheme
In this paper, we present an efficient asynchronous proactive RSA scheme. This paper has three contributions. Firstly, we present an asynchronous verifiable secret sharing protocol. Secondly, we propose an efficient asynchronous threshold RSA signature generation protocol. Thirdly, we propose an efficient asynchronous share refreshing protocol
28980752
Exploiting the Intrinsic Irreversibility of Adaptive Technologies to Enhance the Security of Digital Watermarking
To overcome the reversibility of the widely researched adaptive watermarking, the existing method based on cryptography is first directly revised for adaptive systems. The intrinsic irreversibility of some adaptive technologies is then disclosed and evaluated. Under these technologies, attackers have great difficulty dividing a released version into their claimed original data and scaled watermarks, and in the meantime making the latter be the particular adaptive results based on the former. Their reversed solutions are violently perturbed and perceptually unacceptable. The condition number of the coefficient matrix of the reverse equations can be employed to assess the degree of the perturbation. The proposed method is more efficient because it disposes of any cryptographic processing, such as hashing and stream generation. The experiments on images support the theoretic conclusion well.
206038276
Molecular Mechanics Calculations (MM2 and MM3) on Enamines and Aniline Derivatives
The MM2 and MM3 force fields have been extended to cover this class of compounds. Structures, vibrational spectra, and other data for 13 compounds were examined and can be reproduced satisfactorily by MM3. Except for the spectra, the other data can be reproduced somewhat less well by MM2. © 1994 by John Wiley & Sons, Inc.
17091320
Universal routing in distributed networks
We show that universal routing can be achieved with low overhead in distributed networks. The validity of our results rests on a new network called the fat-stack. We show that from a routing perspective the fat-stack is efficient and is suitable for use as a baseline distributed network We prove that the fat-stack is universal by routing efficiency. A requirement for the fat-stack to be universal is that link capacities double up the levels of the network. We use methods developed in the areas of VLSI and processor interconnect for much of our analysis. Our universality proof shows that a fat-stack of area /spl Theta/(A) can simulate any competing network of area A with O(log/sup 3/2/ A) overhead independently of wire delay. The universality result 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 for an O(log/sup 3/2/ A) overhead in terms of number of links.
10895534
Aspect-Based Instrumentation for Locating Memory Leaks in Java Programs
Despite the built-in garbage collector, Java programs can still suffer the memory leak problem resulted from the unhealthy programming style of retaining unwanted references. This paper presents an aspect-based tool for assisting programmers in locating such references to fix the problem. This tool, FindLeaks, utilizes an aspect to collect memory consumption statistics and object references created during a program's execution and analyzes them for detecting memory leaks. The distinctive feature of FindLeaks is that it reports not only suspected classes of leaking objects but also where in the source the unwanted references were created. Besides, this paper also reports some preliminary experience with FindLeaks.
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.
16909772
On the use of nearest feature line for speaker identification
As a new pattern classification method, nearest feature line (NFL) provides an effective way to tackle the sort of pattern recognition problems where only limited data are available for training. In this paper, we explore the use of NFL for speaker identification in terms of limited data and examine how the NFL performs in such a vexing problem of various mismatches between training and test. In order to speed up NFL in decision-making, we propose an alternative method for similarity measure. We have applied the improved NFL to speaker identification of different operating modes. Its text-dependent performance is better than the dynamic time warping (DTW) on the Ti46 corpus, while its computational load is much lower than that of DTW. Moreover, we propose an utterance partitioning strategy used in the NFL for better performance. For the text-independent mode, we employ the NFL to be a new similarity measure in vector quantization (VQ), which causes the VQ to perform better on the KING corpus. Some computational issues on the NFL are also discussed in this paper.