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.

Cells as Computation

Abstract: Although we are successfully consolidating our knowledge of the’ sequence’ and ‘structure’ branches of molecular cell biology in an accessible manner, the mountains of knowledge about the function, activity and interaction of molecular systems in cells remain fragmented. Sequence and structure research use computers and computerized databases to share, compare, criticize and correct scientific knowledge, to reach a consensus quickly and effectively. Why can’t the study of biomolecular systems make a similar computational leap? Both sequence and structure research have adopted good abstractions: ‘DNA-as-string’ (a mathematical string is a finite sequence of symbols) and ‘protein-as-threedimensional- labelled-graph’, respectively. Biomolecular systems research has yet to find a similarly successful one.

System and method for referencing object instances and invoking methods on those object instances from within a speech recognition grammar

Abstract: A system and method for referencing object instances of an application program, and invoking methods on those object instances from within a recognition grammar. A mapping is maintained between at least one string formed using characters in the character set of the recognition grammar and instances of objects in the application program. During operation of the disclosed system, when either the application program or script within a recognition grammar creates an application object instance, a reference to the object instance is added to the mapping table, together with an associated unique string. The unique string may then be used within scripting language in tags of the rule grammar, in order to refer to the object instance that has been “registered” by the application program in this way. A tags parser program may be used to interpret such object instance names while interpreting the scripting language contained in tags included in a recognition result object. The tags parser program calls the methods on such object instances directly, eliminating the need for logic in the application program to make such calls in response to the result tag information.

Method and apparatus for similarity matching of handwritten data objects

Abstract: Apparatus for determining a distance between two handwritten strings in a database. A processor extracts global features from each string. The processor divides the string into strokes, and identifies a plurality of bounding boxes. Each box contains a different stroke. The processor extracts global features from the suing, including: (1) a number of points; (2) a maximum angle between a first point in the string and a corner of the tallest bounding box; (3) a number of positive inversions; and (4) a number of negative inversions. The apparatus calculates the distance between the strings based on all of the numbers of points, maximum angles, numbers of positive inversions and numbers of negative inversions. A fixed query tree index may be formed. The tree has leaves and internal nodes belonging to multiple levels. A different key is associated with each level. Each key is a handwritten string. Each string is associated with one of the leaves, such that each child of each internal node in any of the levels between the one leaf and the root node is a root of a respective subtree. Each string associated with any leaf in the subtree which includes the one leaf is equally distant from the key associated with the one level, based on the global features. The tree is queried to search for a subset of the strings, such that each string in the subset is within a threshold distance of an input string, according to the distance function.

S3: A Symbolic String Solver for Vulnerability Detection in Web Applications

Abstract: Motivated by the vulnerability analysis of web programs which work on string inputs, we present S3, a new symbolic string solver. Our solver employs a new algorithm for a constraint language that is expressive enough for widespread applicability. Specifically, our language covers all the main string operations, such as those in JavaScript. The algorithm first makes use of a symbolic representation so that membership in a set defined by a regular expression can be encoded as string equations. Secondly, there is a constraint-based generation of instances from these symbolic expressions so that the total number of instances can be limited. We evaluate S3 on a well-known set of practical benchmarks, demonstrating both its robustness (more definitive answers) and its efficiency (about 20 times faster) against the state-of-the-art.

A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance

Abstract: The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.