Input String Research Articles

Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning

Grounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years. In most work on this topic, however, utterances in a conversation are treated independently and discourse structure information is largely ignored. In the context of language acquisition, this independence assumption discards cues that are important to the learner, e.g., the fact that consecutive utterances are likely to share the same referent (Frank et al., 2013). The current paper describes an approach to the problem of simultaneously modeling grounded language at the sentence and discourse levels. We combine ideas from parsing and grammar induction to produce a parser that can handle long input strings with thousands of tokens, creating parse trees that represent full discourses. By casting grounded language learning as a grammatical inference task, we use our parser to extend the work of Johnson et al. (2012), investigating the importance of discourse continuity in children’s language acquisition and its interaction with social cues. Our model boosts performance in a language acquisition task and yields good discourse segmentations compared with human annotators.

Adaptive non-critical alarm reduction using hash-based contextual signatures in intrusion detection

Signature-based intrusion detection systems (IDSs) have been widely deployed in network environments aiming to defend against different kinds of attacks. However, a large number of alarms, especially noncritical alarms could be generated during the detection, which can greatly lower the effectiveness of detection and increase the difficulty in analyzing the generated IDS alarms. The main reason is that the detection capability of a signature-based IDS heavily depends on its signatures, whereas current IDS signatures are short of information related to actual deployment (i.e., lacking of contextual information). In addition, the traditional signature matching is a key limiting factor for IDSs in which the processing burden is at least linear to the size of an input string. To mitigate these issues, in this paper, we propose a novel scheme of hash-based contextual signatures that combines the original intrusion detection signatures with contextual information and hash functions. By using hash functions, our scheme can be used to construct an adaptive hash-based non-critical alarm filter which can further improve the performance of existing contextual signatures in filtering out non-critical alarms. Some examples of contextual information matching are also provided. In the evaluation, we discuss how to choose appropriate hash functions and investigate the performance upon implementation of the scheme with a real dataset and in a real network environment. The experimental results are positive and indicate that our scheme is encouraging and effective in filtering out non-critical alarms.

Efficient edit distance with duplications and contractions

We propose three algorithms for string edit distance with duplications and contractions. These include an efficient general algorithm and two improvements which apply under certain constraints on the cost function. The new algorithms solve a more general problem variant and obtain better time complexities with respect to previous algorithms. Our general algorithm is based on min-plus multiplication of square matrices and has time and space complexities of O (|Σ|MP (n)) and O (|Σ|n2), respectively, where |Σ| is the alphabet size, n is the length of the strings, and MP (n) is the time bound for the computation of min-plus matrix multiplication of two n × n matrices (currently, due to an algorithm by Chan).For integer cost functions, the running time is further improved to . In addition, this variant of the algorithm is online, in the sense that the input strings may be given letter by letter, and its time complexity bounds the processing time of the first n given letters. This acceleration is based on our efficient matrix-vector min-plus multiplication algorithm, intended for matrices and vectors for which differences between adjacent entries are from a finite integer interval D. Choosing a constant , the algorithm preprocesses an n × n matrix in time and space. Then, it may multiply the matrix with any given n-length vector in time. Under some discreteness assumptions, this matrix-vector min-plus multiplication algorithm applies to several problems from the domains of context-free grammar parsing and RNA folding and, in particular, implies the asymptotically fastest time algorithm for single-strand RNA folding with discrete cost functions.Finally, assuming a different constraint on the cost function, we present another version of the algorithm that exploits the run-length encoding of the strings and runs in time and space, where is the length of the run-length encoding of the strings.

Irreversibility and dissipation in finite-state automata

Abstract Irreversibility and dissipation in finite-state automata (FSA) are considered from a physical-information-theoretic perspective. A quantitative measure for the computational irreversibility of finite automata is introduced, and a fundamental lower bound on the average energy dissipated per state transition is obtained and expressed in terms of FSA irreversibility. The irreversibility measure and energy bound are germane to any realization of a deterministic automaton that faithfully registers abstract FSA states in distinguishable states of a physical system coupled to a thermal environment, and that evolves via a sequence of interactions with an external system holding a physical instantiation of a random input string. The central result, which is shown to follow from quantum dynamics and entropic inequalities alone, can be regarded as a generalization of Landauerʼs Principle applicable to FSAs and tailorable to specified automata. Application to a simple FSA is illustrated.

Exact online two-dimensional pattern matching using multiple pattern matching algorithms

Baker and Bird and Baeza-Yates and Regnier are two of the most efficient and widely used algorithms for exact online two-dimensional pattern matching. Both use the automaton of the Aho-Corasick multiple pattern matching algorithm to locate all the occurrences of a two-dimensional pattern in a two-dimensional input string, a data structure that is considered by many as inefficient, especially when used to process long patterns or data using large alphabet sizes. This article presents variants of the Baker and Bird and the Baeza-Yates and Regnier algorithms that use the data structures of the Set Horspool, Wu-Manber, Set Backward Oracle Matching, and SOG multiple pattern matching algorithms in place of the automaton of Aho-Corasick and evaluates their performance experimentally in terms of preprocessing and searching time.

Algorithms for path-constrained sequence alignment

We define a novel variation on the constrained sequence alignment problem in which the constraint is given in the form of a regular expression. Given two sequences, an alphabet Γ describing pairwise sequence alignment operations, and a regular expression R over Γ, the problem is to compute the highest scoring sequence alignment A of the given sequences, such that A∈Γ⁎L(R)Γ⁎.Two algorithms are given for solving this problem. The first basic algorithm is general and solves the problem in O(nmrlog2r) time and O(min{n,m}r) space, where m and n are the lengths of the two sequences and r is the size of the NFA for R. The second algorithm is restricted to rigid patterns and exploits this restriction to reduce the NFA size factor r in the time complexity to a smaller factor corresponding to the length of the rigid pattern. A rigid pattern P is a regular expression of the form P=P1∪⋯∪Pk, where Pi does not contain the Kleene-closure star or union. |P| is compacted by supporting alignment patterns P that do not contain the Kleene-closure star, and exploits this constraint to reduce the NFA size factor r in the time complexity to a smaller factor |P|. |P| is compacted by supporting alignment patterns extended by meta-characters including general insertion, deletion and match operations, as well as some cases of substitutions. meta-characters used in P. {m,i}⁎ or P∈(Γ∪{m,d})⁎, the problem can be solved in time O(nm), while for a pattern P∈(Γ∪{m,i,d})⁎, the problem can be solved in time O(nmlog|P|). For a pattern P∈(Γ∪{m,s,i,d})⁎, the problem can be solved in time O(nmlog|P|) in some cases: one case is for scoring functions Score for which there exists Score′:Σ→R such that Score(ν,σ)=Score′(ν)+Score′(σ) for every ν≠σ, and the other is when occs(P)=O(log(max{n,m})). For a rigid pattern P=P1∪⋯∪Pk, these time bounds range from O(knm) to O(knmlog(max{|Pi|})), depending on the meta-characters used in P.An additional result obtained along the way is an extension of the algorithm of Fischer and Paterson for String Matching with Wildcards. Our extension allows the input strings to include “negation symbols” (that match all letters but a specific one) while retaining the original time complexity.We implemented both algorithms and applied them to data-mine new miRNA seeding patterns in C. elegans Clip-seq experimental data.

Parsing by matrix multiplication generalized to Boolean grammars

The well-known parsing algorithm for context-free grammars due to Valiant (1975) [25] is analyzed and extended to handle the more general Boolean grammars, which are context-free grammars augmented with conjunction and negation operators in the rules. The algorithm reduces construction of a parsing table to computing multiple products of Boolean matrices of various sizes. Its time complexity on an input string of length n is O(BMM(n)logn), where BMM(n) is the number of operations needed to multiply two Boolean matrices of size n×n, which is O(nω) with ω<2.373 as per the current knowledge. A parse tree can be constructed in time MM(n)logO(1)n (where MM(n) is the complexity of multiplying two integer matrices), by applying a known efficient procedure for determining witnesses for Boolean matrix multiplication. The algorithm has a succinct proof of correctness and is ready to be implemented.

Towards adaptive character frequency-based exclusive signature matching scheme and its applications in distributed intrusion detection

Network intrusion detection systems (NIDSs), especially signature-based NIDSs, are being widely deployed in a distributed network environment with the purpose of defending against a variety of network attacks. However, signature matching is a key limiting factor to limit and lower the performance of a signature-based NIDS in a large-scale network environment, in which the cost is at least linear to the size of an input string. The overhead network packets can greatly reduce the effectiveness of such detection systems and heavily consume computer resources. To mitigate this issue, a more efficient signature matching algorithm is desirable. In this paper, we therefore develop an adaptive character frequency-based exclusive signature matching scheme (named ACF-EX) that can improve the process of signature matching for a signature-based NIDS. In the experiment, we implemented the ACF-EX scheme in a distributed network environment, evaluated it by comparing with the performance of Snort. In addition, we further apply this scheme to constructing a packet filter that can filter out network packets by conducting exclusive signature matching for a signature-based NIDS, which can avoid implementation issues and improve the flexibility of the scheme. The experimental results demonstrate that, in the distributed network environment, the proposed ACF-EX scheme can positively reduce the time consumption of signature matching and that our scheme is promising in constructing a packet filter to reduce the burden of a signature-based NIDS.

A Speculative Parallel DFA Membership Test for Multicore, SIMD and Cloud Computing Environments

We present techniques to parallelize membership tests for Deterministic Finite Automata (DFAs). Our method searches arbitrary regular expressions by matching multiple bytes in parallel using speculation. We partition the input string into chunks, match chunks in parallel, and combine the matching results. Our parallel matching algorithm exploits structural DFA properties to minimize the speculative overhead. Unlike previous approaches, our speculation is failure-free, i.e., (1) sequential semantics are maintained, and (2) speed-downs are avoided altogether. On architectures with a SIMD gather-operation for indexed memory loads, our matching operation is fully vectorized. The proposed load-balancing scheme uses an off-line profiling step to determine the matching capacity of each par- ticipating processor. Based on matching capacities, DFA matches are load-balanced on inhomogeneous parallel architectures such as cloud computing environments. We evaluated our speculative DFA membership test for a representative set of benchmarks from the Perl-compatible Regular Expression (PCRE) library and the PROSITE protein database. Evaluation was conducted on a 4 CPU (40 cores) shared-memory node of the Intel Manycore Testing Lab (Intel MTL), on the Intel AVX2 SDE simulator for 8-way fully vectorized SIMD execution, and on a 20-node (288 cores) cluster on the Amazon EC2 computing cloud.

Irredundant tandem motifs

Eliminating the possible redundancy from a set of candidate motifs occurring in an input string is fundamental in many applications. The existing techniques proposed to extract irredundant motifs are not suitable when the motifs to search for are structured, i.e., they are made of two (or several) subwords that co-occur in a text string s of length n.The main effort of this work is studying and characterizing a compact class of tandem motifs, that is, pairs of substrings 〈m1,m2〉 occurring in tandem within a maximum distance of d symbols in s, where d is an integer constant given in input. To this aim, we first introduce the concept of maximality, related to four specific conditions that hold only for this class of motifs. Then, we eliminate the remaining redundancy by defining the notion of irredundancy for tandem motifs.We prove that the number of non-overlapping irredundant tandem motifs is O(d2n) which, considering d as a constant, leads to a linear number of tandems in the length of the input string. This is an order of magnitude less than previously developed compact indexes for tandem extraction.The notions and bounds provided for tandem motifs are generalized for the case r⩾2, if r is the number of subwords composing the motifs. Finally, we also provide an algorithm to extract irredundant tandem motifs.

Practical linear-time O (1)-workspace suffix sorting for constant alphabets

This article presents an O ( n )-time algorithm called SACA-K for sorting the suffixes of an input string T [0, n -1] over an alphabet A [0, K -1]. The problem of sorting the suffixes of T is also known as constructing the suffix array (SA) for T . The theoretical memory usage of SACA-K is n log K + n log n + K log n bits. Moreover, we also have a practical implementation for SACA-K that uses n bytes + ( n + 256) words and is suitable for strings over any alphabet up to full ASCII, where a word is log n bits. In our experiment, SACA-K outperforms SA-IS that was previously the most time- and space-efficient linear-time SA construction algorithm (SACA). SACA-K is around 33% faster and uses a smaller deterministic workspace of K words, where the workspace is the space needed beyond the input string and the output SA. Given K = O (1), SACA-K runs in linear time and O (1) workspace. To the best of our knowledge, such a result is the first reported in the literature with a practical source code publicly available.

Bounded repairability of word languages

What do you do if a computational object (e.g. program trace) fails a specification? An obvious approach is to perform a repair: modify the object minimally to get something that satisfies the constraints. This approach has been investigated in the database community, for integrity constraints, and in the AI community for propositional logics. Here we study how difficult it is to repair a document in the form of a string. Specifically, we consider number of edits that must be applied to an input string in order to satisfy a given target language. This number may be unbounded; our main contribution is to isolate the complexity of the bounded repair problem based on a characterization of the regular languages that admit bounded repairr. We consider the settings where the repair strategy is unconstrained and when the editing must be produced in a streaming way, i.e. by a letter-to-letter transducer.

The impact of number mismatch and passives on the real-time processing of relative clauses

Language processing plays a crucial role in language development, providing the ability to assign structural representations to input strings (e.g., Fodor, 1998). In this paper we aim at contributing to the study of children's processing routines, examining the operations underlying the auditory processing of relative clauses in children compared to adults. English-speaking children (6;0-8;11) and adults participated in the study, which employed a self-paced listening task with a final comprehension question. The aim was to determine (i) the role of number agreement in object relative clauses in which the subject and object NPs differ in terms of number properties, and (ii) the role of verb morphology (active vs. passive) in subject relative clauses. Even though children's off-line accuracy was not always comparable to that of adults, analyses of reaction times results support the view that children have the same structural processing reflexes observed in adults.

On approximating string selection problems with outliers

Many problems in bioinformatics are about finding strings that approximately represent a collection of given strings. We look at more general problems where some input strings can be classified as outliers. The Close to Most Strings problem is, given a set S of the same-length strings, and a parameter d, find a string x that maximizes the number of “non-outliers” within Hamming distance d of x. We prove that this problem has no polynomial-time approximation scheme (PTAS) unless NP has randomized polynomial-time algorithms, correcting a decade-old erroneous proof made previously in the literature. The Most Strings with Few Bad Columns problem is to find a maximum-size subset of input strings so that the number of non-identical positions is at most k; we show it has no PTAS unless P=NP. We also observe Closest to k Strings has no efficient PTAS (EPTAS) unless a parameterized complexity hierarchy collapses. In sum, outliers help model problems associated with using biological data, but we show the problem of finding an approximate solution is computationally difficult.

Adaptive blacklist-based packet filter with a statistic-based approach in network intrusion detection

Network intrusion detection systems (NIDS) are widely deployed in various network environments. Compared to an anomaly based NIDS, a signature-based NIDS is more popular in real-world applications, because of its relatively lower false alarm rate. However, the process of signature matching is a key limiting factor to impede the performance of a signature-based NIDS, in which the cost is at least linear to the size of an input string and the CPU occupancy rate can reach more than 80% in the worst case. In this paper, we develop an adaptive blacklist-based packet filter using a statistic-based approach aiming to improve the performance of a signature-based NIDS. The filter employs a blacklist technique to help filter out network packets based on IP confidence and the statistic-based approach allows the blacklist generation in an adaptive way, that is, the blacklist can be updated periodically. In the evaluation, we give a detailed analysis of how to select weight values in the statistic-based approach, and investigate the performance of the packet filter with a DARPA dataset, a real dataset and in a real network environment. Our evaluation results under various scenarios show that our proposed packet filter is encouraging and effective to reduce the burden of a signature-based NIDS without affecting network security.

Efficient repeat finding in sets of strings via suffix arrays

Discrete Algorithms We consider two repeat finding problems relative to sets of strings: (a) Find the largest substrings that occur in every string of a given set; (b) Find the maximal repeats in a given string that occur in no string of a given set. Our solutions are based on the suffix array construction, requiring O(m) memory, where m is the length of the longest input string, and O(n &amp;log;m) time, where n is the the whole input size (the sum of the length of each string in the input). The most expensive part of our algorithms is the computation of several suffix arrays. We give an implementation and experimental results that evidence the efficiency of our algorithms in practice, even for very large inputs.

PASQUAL: Parallel Techniques for Next Generation Genome Sequence Assembly

The study of genomes has been revolutionized by sequencing machines that output many short overlapping substrings (called reads). The task of sequence assembly in practice is to reconstruct long contiguous genome subsequences from the reads. With Next Generation Sequencing (NGS) technologies, assembly software needs to be more accurate, faster, and more memory-efficient due to the problem complexity and the size of the data sets. In this paper, we develop parallel algorithms and compressed data structures to address several computational challenges of NGS assembly. We demonstrate how commonly available multicore architectures can be efficiently utilized for sequence assembly. In all stages (indexing input strings, string graph construction and simplification, extraction of contiguous subsequences) of our software Pasqual, we use shared-memory parallelism to speed up the assembly process. In our experiments with data of up to 6.8 billion base pairs, we demonstrate that Pasqual generally delivers the best tradeoff between speed, memory consumption, and solution quality. On synthetic and real data sets Pasqual scales well on our test machine with 40 CPU cores with increasing number of threads. Given enough cores, Pasqual is fastest in our comparison.

The Moments Of The Profile In Random Binary Digital Trees

The purpose of this paper is to provide a precise analysis of the \(t\)-th moment of the profile in random binary digital trees. We assume that the \(n\) input strings are independent and follow a (binary) Bernoulli model. In tries, the main difference with the previous analysis is that we have to deal with an inhomogeneous part in the proper functional equation satisfied by the \(t\)-th moment and in digital search trees with an inhomogeneous part in a proper functional-differential equation. We show that \(t\)-th moment of the profile (\(t\geq 2\)) is asymptotically of the same order as the expected value (\(t=1\)). These results are derived by methods of analytic combinatorics.

String analysis by sliding positioning strategy

Discovering frequent factors from long strings is an important problem in many applications, such as biosequence mining. In classical approaches, the algorithms process a vast database of small strings. However, in this paper we analyze a small database of long strings. The main difference resides in the high number of patterns to analyze. To tackle the problem, we have developed a new algorithm for discovering frequent factors in long strings. We present an Apriori-like solution which exploits the fact that any super-pattern of a non-frequent pattern cannot be frequent. The SANSPOS algorithm does a multiple-pass, candidate generation and test approach. Multiple length patterns can be generated in a pass. This algorithm uses a new data structure to arrange nodes in a trie. A Positioning Matrix is defined as a new positioning strategy. By using Positioning Matrices, we can apply advanced prune heuristics in a trie with a minimal computational cost. The Positioning Matrices let us process strings including Short Tandem Repeats and calculate different interestingness measures efficiently. Furthermore, in our algorithm we apply parallelism to transverse different sections of the input strings concurrently, speeding up the resulting running time. The algorithm has been successfully used in natural language and biological sequence contexts.

Symbolic PathFinder: integrating symbolic execution with model checking for Java bytecode analysis

Symbolic PathFinder (SPF) is a software analysis tool that combines symbolic execution with model checking for automated test case generation and error detection in Java bytecode programs. In SPF, programs are executed on symbolic inputs representing multiple concrete inputs and the values of program variables are represented by expressions over those symbolic inputs. Constraints over these expressions are generated from the analysis of different paths through the program. The constraints are solved with off-the-shelf solvers to determine path feasibility and to generate test inputs. Model checking is used to explore different symbolic program executions, to systematically handle aliasing in the input data structures, and to analyze the multithreading present in the code. SPF incorporates techniques for handling input data structures, strings, and native calls to external libraries, as well as for solving complex mathematical constraints. We describe the tool and its application at NASA, in academia, and in industry.