Firewall policy management is challenging and error-prone. While ample research has led to tools for policy specification, correctness analysis, and optimization, few researchers have paid attention to firewall policy deployment: the process where a management tool edits a firewall’s configuration to make it run the policies specified in the tool. In this paper, we provide the first formal definition and theoretical analysis of safety in firewall policy deployment. We show that naive deployment approaches can easily create a temporary security hole by permitting illegal traffic, or interrupt service by rejecting legal traffic during the deployment. We define safe and most-efficient deployments, and introduce the shuffling theorem as a formal basis for constructing deployment algorithms and proving their safety. We present efficient algorithms for constructing most-efficient deployments in popular policy editing languages. We show that in certain widelyinstalled policy editing languages, a safe deployment is not always possible. We also show how to leverage existing diff algorithms to guarantee a safe, mostefficient, and monotonic deployment in other editing languages. 1

Two algorithms are presented that solve the problem of recovering the longest common subsequence of two strings. The first algorithm is an improvement of Hirschberg’s divide-and-conquer algorithm. The second algorithm is an improvement of Hunt-Szymanski algorithm based on an efficient computation of all dominant match points. These two algorithms use bit-vector operations and are shown to work very efficiently in practice.

Abstract. In this paper, we study the classic and well-studied longest common subsequence (LCS) problem and a recent variant of it, namely the constrained LCS (CLCS) problem. In the CLCS problem, the computed LCS must also be a supersequence of a third given string. In this paper, we first present an efficient algorithm for the traditional LCS problem that runs in O(R log log n + n) time, where R is the total number of ordered pairs of positions at which the two strings match and n is the length of the two given strings. Then, using this algorithm, we devise an algorithm for the CLCS problem having time complexity O(pR log log n + n) in the worst case, where p is the length of the third string. 1

this paper, a scalable and efficient systolic algorithm is presented. For two given strings of length m and n,wherem # n,the algorithm can solve the LCS problem in m +2r -- 1 (respectively n +2r -- 1) time steps with r < n/2 (respectively r < m/2) processors. Experimental results show that the algorithm can be faster on multicomputers than all the previous systolic algorithms for the same problem

Abstract. We study the complexity of the longest common subsequence (LCS) problem from a new perspective. By an indeterminate string (istring, in short) we mean a sequence e X = e X[1] e X[2]... e X[n], where eX[i] ⊆ Σ for each i, and Σ is a given alphabet of potentially large size. A subsequence of e X is any usual string over Σ which is an element of the finite (but usually of exponential size) language e X[i1] e X[i2]... e X[ip], where 1 ≤ i1 < i2 < i3... < ip ≤ n, p ≥ 0. Similarly, we define a supersequence of x. Our first version of the LCS problem is Problem ILCS: for given i-strings e X and e Y, find their longest common subsequence. From the complexity point of view, new parameters of the input correspond to |Σ | and maximum size ℓ of the subsets in e X and e Y. There is also a third parameter R, which gives a measure of similarity between e X and eY. The smaller the R, the lesser is the time for solving Problem ILCS. Our second version of the LCS problem is Problem CILCS (constrained ILCS): for given i-strings e X and e Y and a plain string Z, find the longest

We propose a new, human consistent method for the evaluation of similarity of time series that uses a fuzzy quantifier base aggregation of trends (segments), within the authors’ (cf. Kacprzyk, Wilbik, Zadro˙zny [1, 2, 3, 4, 5, 6] or Kacprzyk, Wilbik [7, 8, 9]) approach to the linguistic summarization of trends based on Zadeh’s protoforms and fuzzy logic with linguistic quantifiers. The results obtain are very intuitively appealing and justified by valuable outcomes of similarity analyses between quotations of an investment fund and the two main indexes of the Warsaw Stock Exchange.

Abstract. Computing string or sequence alignments is a classical method of comparing strings and has applications in many areas of computing, such as signal processing and bioinformatics. Semi-local string alignment is a recent generalisation of this method, in which the alignment of a given string and all substrings of another string are computed simultaneously at no additional asymptotic cost. In this paper, we show that there is a close connection between semi-local string alignment and a certain class of traditional comparison networks known as transposition networks. The transposition network approach can be used to represent different string comparison algorithms in a unified form, and in some cases provides generalisations or improvements on existing algorithms. This approach allows us to obtain new algorithms for sparse semi-local string comparison and for comparison of highly similar and highly dissimilar strings, as well as of run-length compressed strings. We conclude that the transposition network method is a very general and flexible way of understanding and improving different string comparison algorithms, as well as their efficient implementation. 1