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.

Computationally Binding Quantum Commitments

Abstract: We present a new definition of computationally binding commitment schemes in the quantum setting, which we call "collapse-binding". The definition applies to string commitments, composes in parallel, and works well with rewinding-based proofs. We give simple constructions of collapse-binding commitments in the random oracle model, giving evidence that they can be realized from hash functions like SHA-3. We evidence the usefulness of our definition by constructing three-round statistical zero-knowledge quantum arguments of knowledge for all NP languages.

A parallel decision tree-based method for user authentication based on keystroke patterns

Abstract: We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to complement the 387 vectors of raw data. The training set, including raw and simulated data, is split into four subsets. For each subset, wavelet transforms are performed to obtain a total of eight training subsets for each user. Eight DTs are thus trained using the eight subsets. A parallel DT is constructed for each user, which contains all eight DTs with a criterion for its output that it authenticates the user if at least three DTs do so; otherwise it rejects the user. Training and testing data were collected from 43 users who typed the exact same string of length 37 nine consecutive times to provide data for training purposes. The users typed the same string at various times over a period from November through December 2002 to provide test data. The average false reject rate was 9.62% and the average false accept rate was 0.88%.

Method and apparatus for an improved language recognition system

Abstract: Methods and apparatus for a language model and language recognition systems are disclosed. The method utilizes a plurality of probabilistic finite state machines having the ability to recognize a pair of sequences, one sequence scanned leftwards, the other scanned rightwards. Each word in the lexicon of the language model is associated with one or more such machines which model the semantic relations between the word and other words. Machine transitions create phrases from a set of word string hypotheses, and incrementally calculate costs related to the probability that such phrases represent the language to be recognized. The cascading lexical head machines utilized in the methods and apparatus capture the structural associations implicit in the hierachical organization of a sentence, resulting in a language model and language recognition systems that combine the lexical sensitivity of N-gram models with the structural properties of dependency grammar.

A comparison of approximate string matching algorithms

Abstract: Experimental comparisons of the running time of approximate string matching algorithms for the k differences problem are presented. Given a pattern string, a text string, and an integer k, the task is to find all approximate occurrences of the pattern in the text with at most k differences (insertions, deletions, changes). We consider seven algorithms based on different approaches including dynamic programming, Boyer-Moore string matching, suffix automata, and the distribution of characters. It turns out that none of the algorithms is the best for all values of the problem parameters, and the speed differences between the methods can be considerable.

Method and apparatus for universal parsing of language

Abstract: A method and apparatus for natural language parsing are described. The invention includes the steps of retrieving an input string, and performing a dictionary look-up for each word in the input string to form a correspondence between each word and a dictionary entry. The dictionary entry provides lexical features of the word. The invention includes the additional step of processing the words in the input string beginning with a last word in the input string and continuing toward the first word in the input string. This step includes the step of associating a selected word in the input string with a word located to the left of the selected word in the input string to form a word phrase. The associating step is performed according to predetermined selection restriction rules. The steps of processing the words and associating a selected word are repeated until all words of the input string have been processed.