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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.


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Patent
03 Apr 2009
TL;DR: In this paper, a point-of-interest mapping search system that combines inexact string searches with a proximity search to provide an extremely high probability of return of a set of search results in an initial search response that are useful to the user is presented.
Abstract: A point-of-interest mapping search system that combines inexact string searches with a proximity search to provide an extremely high probability of return of a set of search results in an initial search response that are useful to the user. Relevance of any particular point-of-interest item in a combined inexact string/proximity is dependent on both (1) a quality of the name match; and (2) a proximity to the starting location (or other relevant search center point) of the POI search. The inexact string name/proximity search is performed efficiently by iteratively expanding a search radius around a given location, searching concentric circles of proximity until a specified target number of relevant results have been found. It is the combination of the use of a combined inexact string match together with a proximity search performed against a database of geo-referenced business names that provides advantageous results.

63 citations

Journal ArticleDOI
TL;DR: The ability of three Neotropical parrot species to spontaneously solve a novel physical problem: the string-pulling test is examined, highlighting the potential of this taxonomic group for the understanding of the underpinnings of cognition in evolutionarily distant groups such as birds and primates.
Abstract: Although parrots share with corvids and primates many of the traits believed to be associated with advanced cognitive processing, knowledge of parrot cognition is still limited to a few species, none of which are Neotropical. Here we examine the ability of three Neotropical parrot species (Blue-Fronted Amazons, Hyacinth and Lear’s macaws) to spontaneously solve a novel physical problem: the string-pulling test. The ability to pull up a string to obtain out-of-reach food has been often considered a cognitively complex task, as it requires the use of a sequence of actions never previously assembled, along with the ability to continuously monitor string, food and certain body movements. We presented subjects with pulling tasks where we varied the spatial relationship between the strings, the presence of a reward and the physical contact between the string and reward to determine whether (1) string-pulling is goal-oriented in these parrots, (2) whether the string is recognized as a means to obtain the reward and (3) whether subjects can visually determine the continuity between the string and the reward, selecting only those strings for which no physical gaps between string and reward were present. Our results show that some individuals of all species were able to use the string as a means to reach a specific goal, in this case, the retrieval of the food treat. Also, subjects from both macaw species were able to visually determine the presence of physical continuity between the string and reward, making their choices consistently with the recognition that no gaps should be present between the string and the reward. Our findings highlight the potential of this taxonomic group for the understanding of the underpinnings of cognition in evolutionarily distant groups such as birds and primates.

63 citations

Journal ArticleDOI
TL;DR: An FL-system is presented, an extension of an L- system that allows the instantiation of generic objects during the course of the rewriting process as well as their initialization, and is able to simulate all of the existing solutions proposed by classical L-systems but is also able to generate VRML97 scene graphs and geometry on the fly.
Abstract: In this paper, we present an FL-system, an extension of an L-system that allows us to generate any kind of object hierarchy and mesh on the fly. This has been made possible thanks to a modification of the classical L-system rewriting mechanism that produces a string of symbols interpreted afterwards. In our system, terminal symbols are not characters, but functions that can be executed at any step of the rewriting process. Thanks to this extension, our system allows the instantiation of generic objects during the course of the rewriting process as well as their initialization. Therefore, we are able to simulate all of the existing solutions proposed by classical L-systems, but we are also able to generate VRML97 scene graphs and geometry on the fly, since VRML97 nodes are handled as generic objects. As an example, we will show in the second part of this paper how to use our extension to describe building styles that are utilized to generate large sets of different building models. We also present some models of urban features (street lamps, etc.) and plants modeled and generated using FL-systems.

63 citations

Journal ArticleDOI
TL;DR: An off-line system for the recognition of handwritten numeral strings based on a cascade of two recognition methods that compares favorably to other published methods.

63 citations

Patent
04 Dec 2008
TL;DR: In this paper, a computer-based method for character string matching of a candidate character string with a plurality of character string records stored in a database is described, which includes a set of reference character strings in the database.
Abstract: A computer-based method for character string matching of a candidate character string with a plurality of character string records stored in a database is described. The method includes a) identifying a set of reference character strings in the database, the reference character strings identified utilizing an optimization search for a set of dissimilar character strings, b) generating an n-gram representation for one of the reference character strings in the set of reference character strings, c) generating an n-gram representation for the candidate character string, d) determining a similarity between the n-gram representations, e) repeating steps b) and d) for the remaining reference character strings in the set of identified reference character strings, and f) indexing the candidate character string within the database based on the determined similarities between the n-gram representation of the candidate character string and the reference character strings in the identified set.

63 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
2021491
2020704
2019759
2018816
2017806