3162448
A Space-Efficient Randomized DNA Algorithm for k-SAT
We present a randomized DNA algorithm for k-SAT based on the classical algorithm of Paturi et al. [8]. For an n-variable, m-clause instance of k-SAT (m > n), our algorithm finds a satisfying assignment, assuming one exists, with probability 1-e-α, in worst-case time O(k2mn) and space O(2(1-1/k)n+log α). This makes it the most space-efficient DNA k-SAT algorithm for k > 3 and k < n=log α (i.e. the clause size is small compared to the number of variables). In addition, our algorithm is the first DNA algorithm to adapt techniques from the field of randomized classical algorithms.
35381627
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.
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.
30833858
An Aspect-Oriented Approach to Declarative Access Control for Web Applications
This paper presents an aspect-oriented approach to declarative access control for Web applications that can not only realize fine-grained access control requirements but also accomplish it with very little runtime overhead. We devise a translation scheme that will automatically synthesize the desired aspect modules from access control rules in XML format and properly designed aspect templates. The generated aspect modules will then be compiled and integrated into the underlying application using standard aspect tools. At runtime, these aspect codes will be executed to enforce the required access control without any runtime interpretation overhead. Future changes of access control rules can also be effectively realized through these mechanisms without actual coding.
205219153
16S rRNA oligonucleotide catalog data base
We have developed a package of programs to create, maintain and manipulate a data base of 16S rRNA oligonucleotide catalog data. For the first time all the published catalog data is brought together in one place in a readily usable form. The package allows generation of dendrograms, facilitates searches for related oligonucleotides between catalogs, and allows construction of global and local dictionaries. Interactive capabilities allow for searches of the dictionary as well as an associated file of likely oligonucleotide families.
32262987
Speaker Modeling with Various Speech Representations
Although numerous speech representations have been reported to be useful in speaker recognition, there is much less agreement on which speech representation provides a perfect representation of speaker-specific information. In this paper, we charaterize a speaker’s identity through the simultaneous use of various speech representations of his/her voice. We present a parametric statistical model, generalized Gaussian mixture model, and develop an EM algorithm for parameter estimation. Our approach has been applied to speaker recognition and comparative results on KING corpus demonstrate its effectiveness.
41737256
On the noninvertibility of adaptive digital watermarking
This paper is intended to demonstrate that the widely researched adaptive watermarking is noninvertible in nature if it can induce and exploit the perturbation in an attacker's reverse engineering properly. The intrinsic noninvertibility of some adaptive watermarking is disclosed and evaluated. Under the technologies that exploit it, attackers have great difficulty dividing a released version into their claimed original and scaled watermark, and in the meantime making the latter be the particular adaptive result based on the former. Their reversed solutions are drastically perturbed and perceptually unacceptable. Compared with the existing noninvertible method based on cryptography, the proposed method is more efficient because it disposes of the cryptographic processing. Furthermore, it also shows a promising way of making a public watermarking scheme, which is considered intrinsically invertible, noninvertible.
38146980
A Novel Clustering Technique Based on Improved Noising Method
In this article, the clustering problem under the criterion of minimum sum of squares clustering is considered. It is known that this problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. To explore the proper result, a novel clustering technique based on improved noising method called INMC is developed, in which one-step DHB algorithm as the local improvement operation is integrated into the algorithm framework to fine-tune the clustering solution obtained in the process of iterations. Moreover, a new method for creating the neighboring solution of the noising method called mergence and partition operation is designed and analyzed in detail. Compared with two noising method based clustering algorithms recently reported, the proposed algorithm greatly improves the performance without the increase of the time complexity, which is extensively demonstrated for experimental data sets.