String Subsequence Research Articles

A Fast Algorithm for Finding a Maximal Common Subsequence of Multiple Strings

A Fast Algorithm for Finding a Maximal Common Subsequence of Multiple Strings

Return of CFA: call-site sensitivity can be superior to object sensitivity even for object-oriented programs

In this paper, we challenge the commonly-accepted wisdom in static analysis that object sensitivity is superior to call-site sensitivity for object-oriented programs. In static analysis of object-oriented programs, object sensitivity has been established as the dominant flavor of context sensitivity thanks to its outstanding precision. On the other hand, call-site sensitivity has been regarded as unsuitable and its use in practice has been constantly discouraged for object-oriented programs. In this paper, however, we claim that call-site sensitivity is generally a superior context abstraction because it is practically possible to transform object sensitivity into more precise call-site sensitivity. Our key insight is that the previously known superiority of object sensitivity holds only in the traditional k -limited setting, where the analysis is enforced to keep the most recent k context elements. However, it no longer holds in a recently-proposed, more general setting with context tunneling. With context tunneling, where the analysis is free to choose an arbitrary k -length subsequence of context strings, we show that call-site sensitivity can simulate object sensitivity almost completely, but not vice versa. To support the claim, we present a technique, called Obj2CFA, for transforming arbitrary context-tunneled object sensitivity into more precise, context-tunneled call-site-sensitivity. We implemented Obj2CFA in Doop and used it to derive a new call-site-sensitive analysis from a state-of-the-art object-sensitive pointer analysis. Experimental results confirm that the resulting call-site sensitivity outperforms object sensitivity in precision and scalability for real-world Java programs. Remarkably, our results show that even 1-call-site sensitivity can be more precise than the conventional 3-object-sensitive analysis.

SQL Injection Attack classification through the feature extraction of SQL query strings using a Gap-Weighted String Subsequence Kernel

SQL Injection Attacks are one of the most common methods behind data security breaches. Previous research has attempted to produce viable detection solutions in order to filter SQL Injection Attacks from regular queries. Unfortunately it has proven to be a challenging problem with many solutions suffering from disadvantages such as being unable to process in real time as a preventative solution, a lack of adaptability to differing types of attack and the requirement for access to difficult-to-obtain information about the source application. This paper presents a novel solution of classifying SQL queries purely on the features of the initial query string. A Gap-Weighted String Subsequence Kernel algorithm is implemented to identify subsequences of shared characters between query strings for the output of a similarity metric. Finally a Support Vector Machine is trained on the similarity metrics between known query strings which are then used to classify unknown test queries. By gathering all feature data from the query strings, additional information from the source application is not required. The probabilistic nature of the learned models allows the solution to adapt to new threats whilst in operation. The proposed solution is evaluated using a number of test datasets derived from the Amnesia testbed datasets. The demonstration software achieved 97.07% accuracy for Select type queries and 92.48% accuracy for Insert type queries. This limited success rate is due to unsanitized quotation marks within legitimate inputs confusing the feature extraction. Using a test dataset that denies legitimate queries the use of unsanitized quotation marks, the Select and Insert query accuracy rose.

Efficient geometric-based computation of the string subsequence kernel

Kernel methods are powerful tools in machine learning. They have to be computationally efficient. This paper builds on our previous work which proposed a list-based approach to compute efficiently the string subsequence kernel (SSK). In this paper we present a novel Geometric-based approach, our main idea is that the SSK computation reduces to the range query problem. We started with the construction of a match list $$L(s,t)=\left\{ (i,j):s_{i}=t_{j}\right\} $$ where s and t are the strings to be compared; such a match list contains only the required data that contribute to the result. To compute the SSK efficiently, we extended the layered range tree data structure to a layered range sum tree, a range-aggregation data structure. The SSK computation takes $$O(p|L|\log |L|)$$ time and $$O(|L|\log |L|)$$ space, where |L| is the size of the match list and p is the length of the SSK. We present an empirical evaluation of our approach against the dynamic and the sparse dynamic programming approaches both on synthetically generated data and on newswire article data. Experimental results show the efficiency of our approach for large alphabets except for very short strings. So it can be used in many applications like text categorization, information extraction and music genre classification. Moreover, compared to the sparse dynamic approach, the proposed approach outperforms also for long strings.

Detection of malicious behavior in android apps through API calls and permission uses analysis

SummaryIn recent years, with the prevalence of smartphones, the number of Android malware shows explosive growth. As malicious apps may steal users' sensitive data and even money from mobile and bank accounts, it is important to detect potential malicious behaviors so as to block them. To achieve this goal, we propose a dynamic behavior inspection and analysis framework for malicious behavior detection. A customized Android system is built to record apps' API calls, permission uses, and some other runtime features. We also develop an automated app behavior inspection platform to install and inspect massive samples so as to collect apps' dynamic behavior records. Then these records are exploited to train a string subsequence kernel–based Support Vector Machine (SVM) model, which can be used to classify benign and malicious behaviors offline. To realize online detection, we further extract apps' runtime features including sensitive permission combination uses, sensitive behavior sequences, and user interactions for behavior classification. The classification results can reach an accuracy of 84.9% in offline phase and 99.0% in online phase. Besides, we verify our scheme for identifying malicious apps, and the results show that 71.8% instances of malware samples are identified by running each app for only 18 minutes.

Lower Bounds on the Generalized Central Moments of the Optimal Alignments Score of Random Sequences

We present a general approach to the problem of determining tight asymptotic lower bounds for generalized central moments of the optimal alignment score of two independent sequences of i.i.d. random variables. At first, these are obtained under a main assumption for which sufficient conditions are provided. When the main assumption fails, we nevertheless develop a “uniform approximation” method leading to asymptotic lower bounds. Our general results are then applied to the length of the longest common subsequences of binary strings, in which case asymptotic lower bounds are obtained for the moments and the exponential moments of the optimal score. As a by-product, a local upper bound on the rate function associated with the length of the longest common subsequences of two binary strings is also obtained.

A real-time searching and sequencing assembly Platform based on an fpga implementation for Bioinformatics applications

DNA database is increasing day by day. The DNA sequence comparison is used for classification of species, finding heredity and disease detection. This similarity search for identifying similarities between subsequences of strings is called as sequence alignment. Gene sequence alignment is a tough task in bioinformatics due to huge data and high searching time. Researchers have used various bioinformatics algorithms for sequencing, but it takes an enormous amount of time for computations. The bloom filter which is commonly used for searching in Intrusion Detection System (IDS) is proposed with FPGA to overcome the space and time complexity problems in bioinformatics FPGAs have high-performance computing accelerators and their reprogramming ability allows different algorithms to be implemented using the same hardware. For realizing the biological mysteries to meet the necessary requirements such as time, memory and speed, an efficient searching method has been used and compared with FPGA.

A reconfigurable approach for Dnasequencing and Searching methods

DNA database are increasing in day by day. The DNA sequences comparison is used for classification of species, finding heredity and disease detection. This similarity search for identify similarities between subsequences of strings is called as sequence alignment. Gene sequence alignment is a tough task in bioinformatics due to huge data and high searching time. Researchers have used various bioinformatics algorithms for sequencing but it takes enormous amount of time for computations. The bloom filter which is commonly used for searching in Intrusion Detection System (IDS) is proposed with FPGA to overcome the space and time complexity problems in bioinformatics FPGAs have high-performance computing accelerators and their reprogramming ability allows different algorithms to be implemented using the same hardware. For realizing the biological mysteries to meet the necessary requirements such as time, memory and speed, an efficient searching methodshas been used and compared with FPGA.

Towards Topic-to-Question Generation

This paper is concerned with automatic generation of all possible questions from a topic of interest. Specifically, we consider that each topic is associated with a body of texts containing useful information about the topic. Then, questions are generated by exploiting the named entity information and the predicate argument structures of the sentences present in the body of texts. The importance of the generated questions is measured using Latent Dirichlet Allocation by identifying the subtopics (which are closely related to the original topic) in the given body of texts and applying the Extended String Subsequence Kernel to calculate their similarity with the questions. We also propose the use of syntactic tree kernels for the automatic judgment of the syntactic correctness of the questions. The questions are ranked by considering both their importance (in the context of the given body of texts) and syntactic correctness. To the best of our knowledge, no previous study has accomplished this task in our setting. A series of experiments demonstrate that the proposed topic-to-question generation approach can significantly outperform the state-of-the-art results.

Asymmetric signature schemes for efficient exact edit similarity query processing

Given a query string Q , an edit similarity search finds all strings in a database whose edit distance with Q is no more than a given threshold τ. Most existing methods answering edit similarity queries employ schemes to generate string subsequences as signatures and generate candidates by set overlap queries on query and data signatures. In this article, we show that for any such signature scheme, the lower bound of the minimum number of signatures is τ + 1, which is lower than what is achieved by existing methods. We then propose several asymmetric signature schemes, that is, extracting different numbers of signatures for the data and query strings, which achieve this lower bound. A basic asymmetric scheme is first established on the basis of matching q -chunks and q -grams between two strings. Two efficient query processing algorithms (IndexGram and IndexChunk) are developed on top of this scheme. We also propose novel candidate pruning methods to further improve the efficiency. We then generalize the basic scheme by incorporating novel ideas of floating q -chunks, optimal selection of q -chunks, and reducing the number of signatures using global ordering. As a result, the Super and Turbo families of schemes are developed together with their corresponding query processing algorithms. We have conducted a comprehensive experimental study using the six asymmetric algorithms and nine previous state-of-the-art algorithms. The experiment results clearly showcase the efficiency of our methods and demonstrate space and time characteristics of our proposed algorithms.

Query-focused multi-document summarization: automatic data annotations and supervised learning approaches

AbstractIn this paper, we apply different supervised learning techniques to build query-focused multi-document summarization systems, where the task is to produce automatic summaries in response to a given query or specific information request stated by the user. A huge amount of labeled data is a prerequisite for supervised training. It is expensive and time-consuming when humans perform the labeling task manually. Automatic labeling can be a good remedy to this problem. We employ five different automatic annotation techniques to build extracts from human abstracts using ROUGE, Basic Element overlap, syntactic similarity measure, semantic similarity measure, and Extended String Subsequence Kernel. The supervised methods we use are Support Vector Machines, Conditional Random Fields, Hidden Markov Models, Maximum Entropy, and two ensemble-based approaches. During different experiments, we analyze the impact of automatic labeling methods on the performance of the applied supervised methods. To our knowledge, no other study has deeply investigated and compared the effects of using different automatic annotation techniques on different supervised learning approaches in the domain of query-focused multi-document summarization.

A Fast Multiple Longest Common Subsequence (MLCS) Algorithm

Finding the longest common subsequence (LCS) of multiple strings is an NP-hard problem, with many applications in the areas of bioinformatics and computational genomics. Although significant efforts have been made to address the problem and its special cases, the increasing complexity and size of biological data require more efficient methods applicable to an arbitrary number of strings. In this paper, we present a new algorithm for the general case of multiple LCS (or MLCS) problem, i.e., finding an LCS of any number of strings, and its parallel realization. The algorithm is based on the dominant point approach and employs a fast divide-and-conquer technique to compute the dominant points. When applied to a case of three strings, our algorithm demonstrates the same performance as the fastest existing MLCS algorithm designed for that specific case. When applied to more than three strings, our algorithm is significantly faster than the best existing sequential methods, reaching up to 2-3 orders of magnitude faster speed on large-size problems. Finally, we present an efficient parallel implementation of the algorithm. Evaluating the parallel algorithm on a benchmark set of both random and biological sequences reveals a near-linear speedup with respect to the sequential algorithm.

Improving graph-based random walks for complex question answering using syntactic, shallow semantic and extended string subsequence kernels

The task of answering complex questions requires inferencing and synthesizing information from multiple documents that can be seen as a kind of topic-oriented, informative multi-document summarization. In generic summarization the stochastic, graph-based random walk method to compute the relative importance of textual units (i.e. sentences) is proved to be very successful. However, the major limitation of the TF *IDF approach is that it only retains the frequency of the words and does not take into account the sequence, syntactic and semantic information. This paper presents the impact of syntactic and semantic information in the graph-based random walk method for answering complex questions. Initially, we apply tree kernel functions to perform the similarity measures between sentences in the random walk framework. Then, we extend our work further to incorporate the Extended String Subsequence Kernel (ESSK) to perform the task in a similar manner. Experimental results show the effectiveness of the use of kernels to include the syntactic and semantic information for this task.

A Fast Heuristic Search Algorithm for Finding the Longest Common Subsequence of Multiple Strings

Finding the longest common subsequence (LCS) of multiple strings is an NP-hard problem, with many applications in the areas of bioinformatics and computational genomics. Although significant efforts have been made to address the problem and its special cases, the increasing complexity and size of biological data require more efficient methods applicable to an arbitrary number of strings. In this paper, a novel search algorithm, MLCS-A*, is presented for the general case of multiple LCS (or MLCS) problems. MLCS-A* is a variant of the A* algorithm. It maximizes a new heuristic estimate of the LCS in each search step so that the longest common subsequence can be found. As a natural extension of MLCS-A*, a fast algorithm, MLCS-APP, is also proposed to deal with large volume of biological data for which finding a LCS within reasonable time is impossible. The benchmark test shows that MLCS-APP is able to extract common subsequences close to the optimal ones and that MLCS-APP significantly outperforms existing heuristic approaches. When applied to 8 protein domain families, MLCS-APP produced more accurate results than existing multiple sequence alignment methods.

Semi-local String Comparison: Algorithmic Techniques and Applications

Given two strings, the longest common subsequence (LCS) problem consists in computing the length of the longest string that is a subsequence of both input strings. Its generalisation, the all semi-local LCS problem, requires computing the LCS length for each string against all substrings of the other string, and for all prefixes of each string against all suffixes of the other string. We survey a number of algorithmic techniques related to the all semi-local LCS problem. We then present a number of algorithmic applications of these techniques, both existing and new. In particular, we obtain a new all semi-local LCS algorithm, with asymptotic running time matching (in the case of an unbounded alphabet) the fastest known global LCS algorithm by Masek and Paterson. We conclude that semi-local string comparison turns out to be a useful algorithmic plug-in, which unifies, and often improves on, a number of previous approaches to various substring- and subsequence-related problems.

Lambda pruning: an approximation of the string subsequence kernel for practical SVM classification and redundancy clustering

The support vector machine (SVM) is a powerful learning algorithm, e.g., for classification and clustering tasks, that works even for complex data structures such as strings, trees, lists and general graphs. It is based on the usage of a kernel function for measuring scalar products between data units. For analyzing string data Lodhi et al. (J Mach Learn Res 2:419–444, 2002) have introduced a String Subsequence kernel (SSK). In this paper we propose an approximation to SSK based on dropping higher orders terms (i.e., subsequences which are spread out more than a certain threshold) that reduces the computational burden of SSK. As we are also concerned with practical application of complex kernels with high computational complexity and memory consumption, we provide an empirical model to predict runtime and memory of the approximation as well as the original SSK, based on easily measurable properties of input data. We provide extensive results on the properties of the proposed approximation, SSK-LP, with respect to prediction accuracy, runtime and memory consumption. Using some real-life datasets of text mining tasks, we show that models based on SSK and SSK-LP perform similarly for a set of real-life learning tasks, and that the empirical runtime model is also useful in roughly determining total learning time for a SVM using either kernel.

Finding a longest common subsequence between a run-length-encoded string and an uncompressed string

In this paper, we propose an O ( min { mN , Mn } ) time algorithm for finding a longest common subsequence of strings X and Y with lengths M and N , respectively, and run-length-encoded lengths m and n , respectively. We propose a new recursive formula for finding a longest common subsequence of Y and X which is in the run-length-encoded format. That is, Y = y 1 y 2 ⋯ y N and X = r 1 l 1 r 2 l 2 ⋯ r m l m , where r i is the repeated character of run i and l i is the number of its repetitions. There are three cases in the proposed recursive formula in which two cases are for r i matching y j . The third case is for r i mismatching y j . We will look specifically at the prior two cases that r i matches y j . To determine which case will be used when r i matches y j , we have to find a specific value which can be obtained by using another of our proposed recursive formulas.

On composition and lookahead delegation of [formula omitted]-services modeled by automata

Let M be a class of (possibly nondeterministic) language acceptors with a one-way input tape. A system ( A ; A 1 , … , A r ) of automata in M is composable if for every string w = a 1 ⋯ a n of symbols accepted by A , there is an assignment of each symbol a j in w to one of the A i 's such that for each 1 ⩽ i ⩽ r , the subsequence of w assigned to A i is accepted by A i . For a nonnegative integer k , a k - lookahead delegator for ( A ; A 1 , … , A r ) is a deterministic machine D in M which, knowing (a) the current states of A , A 1 , … , A r and the accessible “local” information of each machine (e.g., the top of the stack if each machine is a pushdown automaton, whether a counter is zero or nonzero if each machine is a multicounter automaton, etc.), and (b) the k lookahead symbols to the right of the current input symbol being processed, can uniquely determine the A i to assign the current symbol. Moreover, every string w accepted by A is also accepted by D ; i.e., the subsequence of string w delegated by D to each A i is accepted by A i . Thus, k -lookahead delegation is a stronger requirement than composability, since the delegator D must be deterministic. A system that is composable may not have a k -delegator for any k . We study the decidability of composability and existence of k -delegators for various classes of machines M . Our results generalize earlier ones (and resolve some open questions) concerning composability of deterministic finite automata as e-services to finite automata that are augmented with unbounded storage (e.g., counters and pushdown stacks) and finite automata with discrete clocks (i.e., discrete timed automata). The results have applications to automated composition of e-services.

Matching for Run-Length Encoded Strings

Measuring the similarity between two strings, through such standard measures as Hamming distance, edit distance, and longest common subsequence, is one of the fundamental problems in pattern matching. We consider the problem of finding the longest common subsequence of two strings. A well-known dynamic programming algorithm computes the longest common subsequence of strings X and Y in O(|X|/spl middot/|Y|) time. We develop significantly faster algorithms for a special class of strings which emerge frequently in pattern matching problems. A string S is run-length encoded if it is described as an ordered sequence of pairs (/spl sigma/,i), each consisting of an alphabet symbol /spl sigma/ and an integer i. Each pair corresponds to a run in S consisting of i consecutive occurrences of /spl sigma/. For example, the string aaaabbbbcccabbbbcc can be encoded as a/sup 4/b/sup 4/c/sup 3/a/sup 1/b/sup 4/c/sup 2/. Such a run-length encoded string can be significantly shorter than the expanded string representation. Indeed, runlength coding serves as a popular image compression technique, since many classes of images, such as binary images in facsimile transmission, typically contain large patches of identically-valued pixels.

Optimal correspondence of string subsequences

The definition of optimal correspondent subsequence (OCS), which extends the finite alphabet editing error minimization matching to infinite alphabet penalty minimization matching, is given. The authors prove that the string distance derived from OCS is a metric. An algorithm to compute the string-to-string OCS is given. The computational complexity of OCS is analyzed. OCS is more efficient than relaxation and elastic matching for 1D problems. An algorithm combining syntactic information in template matching is given to show the ease of integrating regular grammar into the OCS technique. Since in different applications different penalty functions may be required, two of them are discussed: one pointwise and the other piecewise. The pointwise application consists of a stereo epipolar line matching problem solved by using string-to-string OCS. The feasibility of applying OCS to UPC bar-code recognition is investigated, showing the elegance of string-to-regular-expression OCS compared to the relaxation and elastic matching techniques.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>