String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

System and method for secure credit and debit card transactions

Abstract: There is disclosed a method and system for conducting secure credit and debit card transactions between a customer and a merchant. The customer is issued with a pseudorandom security string by a host computer, the security string being sent to the customer's mobile telephone. A cryptographic algorithm running in a SIM card of the mobile telephone performs a hash on the security string or the One Time Code extracted from the security string, a customer PIN and a transaction amount, these last two items being entered by way of a keypad of the mobile telephone. A three-digit response code is generated by the algorithm and then passed to the merchant. The merchant then transmits the response code, transaction amount and a customer account number (card number) to the host computer, where the pseudorandom security string and PIN are retrieved from memory. The host computer then applies the same algorithm to the security string, PIN and transaction amount so as to generate a check code, and if the check code matches the response code transmitted by the merchant, the transaction is authorised. Embodiments of the present invention make use of existing CVV2 security infrastructure, but provide a significantly greater degree of security. Embodiments of the present invention may be used with ordinary face-to-face or telephone transactions, and also in e-commerce (web-based) and m-commerce (mobile telephone-based) transactions.

String Similarity Metrics for Ontology Alignment

Abstract: Ontology alignment is an important part of enabling the semantic web to reach its full potential. The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention. In this work we evaluate a wide range of such metrics, along with string pre-processing strategies such as removing stop words and considering synonyms, on different types of ontologies. We also present a set of guidelines on when to use which metric. We furthermore show that if optimal string similarity metrics are chosen, those alone can produce alignments that are competitive with the state of the art in ontology alignment systems. Finally, we examine the improvements possible to an existing ontology alignment system using an automated string metric selection strategy based upon the characteristics of the ontologies to be aligned.

Error-tolerant finite-state recognition with applications to morphological analysis and spelling correction

Abstract: This paper presents the notion of error-tolerant recognition with finite-state recognizers along with results from some applications. Error-tolerant recognition enables the recognition of strings that deviate mildly from any string in the regular set recognized by the underlying finite-state recognizer. Such recognition has applications to error-tolerant morphological processing, spelling correction, and approximate string matching in information retrieval. After a description of the concepts and algorithms involved, we give examples from two applications: in the context of morphological analysis, error-tolerant recognition allows misspelled input word forms to be corrected and morphologically analyzed concurrently. We present an application of this to error-tolerant analysis of the agglutinative morphology of Turkish words. The algorithm can be applied to morphological analysis of any language whose morphology has been fully captured by a single (and possibly very large) finite-state transducer, regardless of the word formation processes and morphographemic phenomena involved. In the context of spelling correction, error-tolerant recognition can be used to enumerate candidate correct forms from a given misspelled string within a certain edit distance. Error-tolerant recognition can be applied to spelling correction for any language, if (a) it has a word list comprising all inflected forms, or (b) its morphology has been fully described by a finite-state transducer. We present experimental results for spelling correction for a number of languages. These results indicate that such recognition works very efficiently for candidate generation in spelling correction for many European languages (English, Dutch, French, German, and Italian, among others) with very large word lists of root and inflected forms (some containing well over 200,000 forms), generating all candidate solutions within 10 to 45 milliseconds (with an edit distance of 1) on a SPARCStation 10/41. For spelling correction in Turkish, error-tolerant recognition operating with a (circular) recognizer of Turkish words (with about 29,000 states and 119,000 transitions) can generate all candidate words in less than 20 milliseconds, with an edit distance of 1.

Time and area efficient pattern matching on FPGAs

Abstract: Pattern matching for network security and intrusion detection demands exceptionally high performance. Much work has been done in this field, and yet there is still significant room for improvement in efficiency, flexibility, and throughput. We develop a novel linear-array string matching architecture using a buffered, two-comparator variation on the Knuth-Morris-Pratt(KMP) algorithm. For small (16 or fewer characters) patterns, it competes favorably with the state-of-the-art while providing better scalability and reconfiguration, and more efficient hardware utilization. The area efficiency compared to other approaches improves further still as the pattern size increases because only the tables increase in size.KMP is a well-known, efficient string matching technique using a single comparator and a precomputed transition table. We add a second comparator and an input buffer, allowing the system to accept at least one character in each cycle and terminate after a number of clock cycles at maximum equal to the length of the input string plus the size of the buffer. The system also provides a clean, modular route to reconfiguring the patterns on-the-fly and scaling the system to support more units, using several rows of linear array elements. In this paper, we prove the bound on the buffer size and running time, and provide performance comparisons against other approaches.

Polynomial Time Inference of Extended Regular Pattern Languages

Abstract: A pattern is a string of constant symbols and variable symbols. The language of a pattern p is the set of all strings obtained by substituting any non-empty constant string for each variable symbol in p. A regular pattern has at most one occurrence of each variable symbol. In this paper, we consider polynomial time inference from positive data for the class of extended regular pattern languages which are sets of all strings obtained by substituting any (possibly empty) constant string, instead of non-empty string. Our inference machine uses MINL calculation which finds a minimal language containing a given finite set of strings. The relation between MINL calculation for the class of extended regular pattern languages and the longest common subsequence problem is also discussed.