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15. A system comprising: data processing apparatus; and a computer-readable medium storing instructions executable by the data processing apparatus to perform operations comprising: receiving, at a client computer system, a plurality of resources defined according to a first data structure, wherein each of the plurality of resources is a network-accessible data object or service identified by a respective identifier, and wherein each of the plurality of resources is associated with respective metadata which is received with the plurality of resources, the plurality of resources received from a server computer system connected to the client computer system through a data network in response to an Open Data Protocol (OData) request for the plurality of resources, and wherein each resource of the plurality of resources is associated with a boundary that specifies resource operations performable on the resource, the first data structure comprising a pre-defined eXtensible Markup Language (XML) structure, the first data structure defining each resource and an associated set of respective metadata and semantics specific to a particular resource, the respective metadata associated with at least one property corresponding to the particular resource; identifying, from the plurality of resources and for each resource, the boundary associated with each particular resource and defining the resource operations that are performable on that particular resource; translating, by the client computer system automatically in response to the identifying, the first data structure to a second data structure different from the first data structure, where, in the second data structure, each of the plurality of resources are editable to perform the resource operations on the corresponding resource, the second data structure comprising a tabular structure; and displaying, in a user interface associated with the client computer system, a subset of the plurality of resources according to the second data structure, and the resource operations that are performable on each particular resource, wherein the second data structure displays each resource of the subset of the plurality of resources in a table including multiple rows and at least one column, each row representing a respective resource of the subset of the plurality of resources from the first data structure and including in at least one column information from the first data structure indicating the resource operations available for the respective resource, wherein, in response to receiving a selection of a particular resource, the client computer system identifies the at least one property corresponding to the particular resource, translates information defining the at least one property into a visual format, and presents the translated information defining the at least one property in the user interface.

15. A system comprising: data processing apparatus; and a computer-readable medium storing instructions executable by the data processing apparatus to perform operations comprising: receiving, at a client computer system, a plurality of resources defined according to a first data structure, wherein each of the plurality of resources is a network-accessible data object or service identified by a respective identifier, and wherein each of the plurality of resources is associated with respective metadata which is received with the plurality of resources, the plurality of resources received from a server computer system connected to the client computer system through a data network in response to an Open Data Protocol (OData) request for the plurality of resources, and wherein each resource of the plurality of resources is associated with a boundary that specifies resource operations performable on the resource, the first data structure comprising a pre-defined eXtensible Markup Language (XML) structure, the first data structure defining each resource and an associated set of respective metadata and semantics specific to a particular resource, the respective metadata associated with at least one property corresponding to the particular resource; identifying, from the plurality of resources and for each resource, the boundary associated with each particular resource and defining the resource operations that are performable on that particular resource; translating, by the client computer system automatically in response to the identifying, the first data structure to a second data structure different from the first data structure, where, in the second data structure, each of the plurality of resources are editable to perform the resource operations on the corresponding resource, the second data structure comprising a tabular structure; and displaying, in a user interface associated with the client computer system, a subset of the plurality of resources according to the second data structure, and the resource operations that are performable on each particular resource, wherein the second data structure displays each resource of the subset of the plurality of resources in a table including multiple rows and at least one column, each row representing a respective resource of the subset of the plurality of resources from the first data structure and including in at least one column information from the first data structure indicating the resource operations available for the respective resource, wherein, in response to receiving a selection of a particular resource, the client computer system identifies the at least one property corresponding to the particular resource, translates information defining the at least one property into a visual format, and presents the translated information defining the at least one property in the user interface. 19. The system of claim 15 , wherein receiving the plurality of resources defined according to the first data structure comprises: receiving, in the user interface, authentication information that authenticates a requestor of the plurality of resources and an identifier identifying a computer-readable medium storing the plurality of resources; determining that the requestor is authorized to access resources stored on the computer-readable medium based on the authentication information; displaying, in the user interface, a plurality of services, each service being associated with a respective plurality of resources, the plurality of services including a specified resource associated with the received plurality of resources; and receiving, in the user interface, a selection of the specified resource.

11. A data processor readable medium, said medium comprising program code stored therein, said medium not being a transitory signal, said program code configured to be executed by a processor of a data processing system to perform a method of facilitating interaction between users of an electronic community, said method comprising: reviewing a user activity log for each user in the electronic community, each user having an associated user profile stored in a user profile and relationship database; executing a natural language parser to extract noun phrases from the user activity log; updating the user profiles from the extracted noun phrases, a keyword being associated with each extracted noun phrase, said updating based on a usage frequency of the extracted noun phrases and an importance value of the keywords; storing the updated user profiles in the user profile and relationship database; executing a similarity based clustering algorithm to generate clusters of the updated user profiles, each cluster consisting of a group of users of the users in the electronic community, each cluster representing a relationship between the users in each group; and storing each cluster in the user profile and relationship database, wherein a digital hierarchical dictionary comprises synsets, each synset being a set of cognitive synonyms consisting of noun phrases, said synsets being interlinked into a semantic hierarchical tree within the digital hierarchical dictionary, wherein the keyword associated with each extracted noun phrase is in a synset within the semantic hierarchical tree, wherein the similarity based clustering algorithm comprises a member importance function and a member similarity function, wherein the member importance function ascertains an importance value of keywords as a depth of said keywords in the semantic hierarchical tree, wherein the member similarity function ascertains a similarity distance between keywords as a path distance between said keywords in the semantic hierarchical tree, and wherein said executing the similarity based clustering algorithm comprises: using the member importance function and the member similarity function to ascertain the clusters.

11. A data processor readable medium, said medium comprising program code stored therein, said medium not being a transitory signal, said program code configured to be executed by a processor of a data processing system to perform a method of facilitating interaction between users of an electronic community, said method comprising: reviewing a user activity log for each user in the electronic community, each user having an associated user profile stored in a user profile and relationship database; executing a natural language parser to extract noun phrases from the user activity log; updating the user profiles from the extracted noun phrases, a keyword being associated with each extracted noun phrase, said updating based on a usage frequency of the extracted noun phrases and an importance value of the keywords; storing the updated user profiles in the user profile and relationship database; executing a similarity based clustering algorithm to generate clusters of the updated user profiles, each cluster consisting of a group of users of the users in the electronic community, each cluster representing a relationship between the users in each group; and storing each cluster in the user profile and relationship database, wherein a digital hierarchical dictionary comprises synsets, each synset being a set of cognitive synonyms consisting of noun phrases, said synsets being interlinked into a semantic hierarchical tree within the digital hierarchical dictionary, wherein the keyword associated with each extracted noun phrase is in a synset within the semantic hierarchical tree, wherein the similarity based clustering algorithm comprises a member importance function and a member similarity function, wherein the member importance function ascertains an importance value of keywords as a depth of said keywords in the semantic hierarchical tree, wherein the member similarity function ascertains a similarity distance between keywords as a path distance between said keywords in the semantic hierarchical tree, and wherein said executing the similarity based clustering algorithm comprises: using the member importance function and the member similarity function to ascertain the clusters. 14. The medium of claim 11 , wherein said updating comprises: using the member importance function to ascertain a first importance value as a first depth in the semantic hierarchical tree of a first keyword associated with a first noun phrase of the extracted noun phrases; ascertaining, via use of the member importance function, a second importance value as a second depth in the semantic hierarchical tree of a second keyword whose depth in the semantic hierarchical tree exceeds the first depth and whose meaning is more specific and descriptive than is the meaning of the first keyword; and in response to ascertaining that the second depth exceeds the first depth, replacing the first noun phrase in a first user profile of the user profiles by a second noun phrase to which the second keyword is associated.

1. An electronic device, comprising: a display unit that displays a character input screen; a sensor that detects motion; an input unit that inputs a character to be displayed on the character input screen; and a control unit that displays, on the display unit, conversion candidates or predictive candidates for the character that has been input from the input unit, wherein the control unit displays names of applications or names of processing in the applications, and when any one of the names is selected, the control unit executes processing of an application corresponding to the selected name; wherein, if there are names of functions for setting states of the electronic device, the control unit displays the names of functions along with the names or a name of processing, and when any one of the names of functions is selected, the control unit executes setting of a state of the electronic device corresponding to the selected name, wherein the control unit accepts selection of any one of the names of the processing or the names of the functions according to type of the motion detected by the sensor, wherein the control unit alternately displays the names of the processing or the names of the functions as ranked high in order in response to the sensor detecting predetermined motion in a state where the names of the processing or the names of the functions are displayed as mixed with the predictive candidates, and displays the names of the processing or the names of the functions as mixed with the predictive candidates in response to the sensor detecting predetermined motion in a state where the names of the processing or the names of the functions are displayed as ranked high in order, the names of the processing and the names of the functions representing activity candidates, in response to selection of one of which the control unit executes processing corresponding to the selected activity candidate, the predictive candidates representing at least one character, in response to selection of which the control unit adds the at least one character to the character input screen.

1. An electronic device, comprising: a display unit that displays a character input screen; a sensor that detects motion; an input unit that inputs a character to be displayed on the character input screen; and a control unit that displays, on the display unit, conversion candidates or predictive candidates for the character that has been input from the input unit, wherein the control unit displays names of applications or names of processing in the applications, and when any one of the names is selected, the control unit executes processing of an application corresponding to the selected name; wherein, if there are names of functions for setting states of the electronic device, the control unit displays the names of functions along with the names or a name of processing, and when any one of the names of functions is selected, the control unit executes setting of a state of the electronic device corresponding to the selected name, wherein the control unit accepts selection of any one of the names of the processing or the names of the functions according to type of the motion detected by the sensor, wherein the control unit alternately displays the names of the processing or the names of the functions as ranked high in order in response to the sensor detecting predetermined motion in a state where the names of the processing or the names of the functions are displayed as mixed with the predictive candidates, and displays the names of the processing or the names of the functions as mixed with the predictive candidates in response to the sensor detecting predetermined motion in a state where the names of the processing or the names of the functions are displayed as ranked high in order, the names of the processing and the names of the functions representing activity candidates, in response to selection of one of which the control unit executes processing corresponding to the selected activity candidate, the predictive candidates representing at least one character, in response to selection of which the control unit adds the at least one character to the character input screen. 2. The electronic device according to claim 1 , wherein, when any one of the names is selected, the control unit executes processing of an application corresponding to the selected name, or executes processing of displaying the selected name as a character string on the display unit.

15. A non-transitory, tangible computer readable storage medium, encoded with processor readable instructions to perform a method for: optimizing, at a compiler, the source code of files of a computer program at link-time; receiving, at a linker, a customized linker script, wherein the customized linker script is provided by an embedded application developer and overrides a default linker script existing within the linker, the customized liker script defining output sections for files of an executable version of the files of the computer program; adding, by the compiler, to intermediate representation files having global or local symbols, metadata comprising default section assignment information for the symbols; recording, by the linker, for symbols in machine code files, an origin path and an output section; sending, from the linker to the compiler, detailed global scope and use information, then; parsing, by the compiler, based on a request and the detailed global scope and use information from the linker, the intermediate representation files; recording, by the compiler, the symbols and related symbol information comprising the default section assignment information and dependency information of the intermediate representation files, then; assigning output sections to the symbols based on both the default section assignment information and instructions from the customized linker script; and linking optimized code of the files of the computer program based on the assigned output sections.

15. A non-transitory, tangible computer readable storage medium, encoded with processor readable instructions to perform a method for: optimizing, at a compiler, the source code of files of a computer program at link-time; receiving, at a linker, a customized linker script, wherein the customized linker script is provided by an embedded application developer and overrides a default linker script existing within the linker, the customized liker script defining output sections for files of an executable version of the files of the computer program; adding, by the compiler, to intermediate representation files having global or local symbols, metadata comprising default section assignment information for the symbols; recording, by the linker, for symbols in machine code files, an origin path and an output section; sending, from the linker to the compiler, detailed global scope and use information, then; parsing, by the compiler, based on a request and the detailed global scope and use information from the linker, the intermediate representation files; recording, by the compiler, the symbols and related symbol information comprising the default section assignment information and dependency information of the intermediate representation files, then; assigning output sections to the symbols based on both the default section assignment information and instructions from the customized linker script; and linking optimized code of the files of the computer program based on the assigned output sections. 19. The non-transitory, tangible computer readable storage medium of claim 15 , wherein the method includes: localizing global symbols of a complete scope to a current module being compiled.

21. A non-transitory computer readable medium storing computer executable instructions, which, when executed by a processor, cause the processor to carry out a method for editing an electronic presentation, comprising: providing an electronic presentation editing interface for editing an electronic presentation, wherein the interface comprises: a digital canvas comprising a plurality of canvas objects in a plurality of canvas layers; a digital timeline comprising a plurality of timeline objects, a time axis, and a graphical indicia on the time axis that represents a pause in the electronic presentation, wherein: each canvas object in the plurality of canvas objects is linked to a respective timeline object; a position of a timeline object on the digital timeline is indicative of a time and a canvas layer that a linked canvas object is displayed on the digital canvas; the position of the timeline object includes a first time coordinate on the time axis indicative of when the linked canvas object appears in the digital canvas, a second time coordinate on the time axis indicative of when the linked canvas object disappears from the digital canvas, and a layer coordinate indicative of a canvas layer in which the linked canvas object appears in the digital canvas; the graphical indicia extends over all layer coordinates that are displayed in the digital timeline; and the digital timeline further comprises a marker on the digital timeline, wherein a position of the marker is indicative of a time corresponding to a current view of the digital canvas, and wherein when the position of the marker coincides with the graphical indicia on the time axis, each canvas object linked to a timeline object that coincides with the position of the marker is paused.

21. A non-transitory computer readable medium storing computer executable instructions, which, when executed by a processor, cause the processor to carry out a method for editing an electronic presentation, comprising: providing an electronic presentation editing interface for editing an electronic presentation, wherein the interface comprises: a digital canvas comprising a plurality of canvas objects in a plurality of canvas layers; a digital timeline comprising a plurality of timeline objects, a time axis, and a graphical indicia on the time axis that represents a pause in the electronic presentation, wherein: each canvas object in the plurality of canvas objects is linked to a respective timeline object; a position of a timeline object on the digital timeline is indicative of a time and a canvas layer that a linked canvas object is displayed on the digital canvas; the position of the timeline object includes a first time coordinate on the time axis indicative of when the linked canvas object appears in the digital canvas, a second time coordinate on the time axis indicative of when the linked canvas object disappears from the digital canvas, and a layer coordinate indicative of a canvas layer in which the linked canvas object appears in the digital canvas; the graphical indicia extends over all layer coordinates that are displayed in the digital timeline; and the digital timeline further comprises a marker on the digital timeline, wherein a position of the marker is indicative of a time corresponding to a current view of the digital canvas, and wherein when the position of the marker coincides with the graphical indicia on the time axis, each canvas object linked to a timeline object that coincides with the position of the marker is paused. 27. The non-transitory computer readable medium of claim 21 , wherein the interface is configured to enable a user to modify the layer coordinate of the timeline object in the digital timeline to modify the canvas layer in which the linked canvas object appears in the digital canvas.

13. The mobile body track identification method according to claim 12 further comprising: storing a probability map defining detection probabilities of identifications of mobile bodies in response to positional coordinates of mobile bodies in the tracking area in advance; specifying a position of a mobile body on tracks indicated by each track-coupling candidate at each identification detection time for each track-coupling candidate/identification pair ascribed to each of the selected hypotheses; reading a probability value of detecting the mobile body at the position from the probability map; calculating an identification likelihood based on the probability value; further integrating identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood regarding each of the selected hypotheses; and estimating the most-probable hypothesis based on identification likelihoods of hypotheses.

13. The mobile body track identification method according to claim 12 further comprising: storing a probability map defining detection probabilities of identifications of mobile bodies in response to positional coordinates of mobile bodies in the tracking area in advance; specifying a position of a mobile body on tracks indicated by each track-coupling candidate at each identification detection time for each track-coupling candidate/identification pair ascribed to each of the selected hypotheses; reading a probability value of detecting the mobile body at the position from the probability map; calculating an identification likelihood based on the probability value; further integrating identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood regarding each of the selected hypotheses; and estimating the most-probable hypothesis based on identification likelihoods of hypotheses. 17. The mobile body track identification method according to claim 13 further comprising: correlating mobility values of mobile bodies detected in the predetermined time in the past to track-coupling candidates so as to generate sets of track-coupling candidate/identification pairs combining track-coupling candidates correlated to mobility values and identifications of mobile bodies, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying the predetermined condition; calculating a mobility likelihood representing a likelihood of indicating the same mobile body with mobility values correlated to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses; integrating mobility likelihoods for each track-coupling candidate/identification pair, thus calculating a mobility likelihood for each of the selected hypotheses; and estimating the most-probable hypothesis based on identification likelihoods and mobility likelihoods of hypotheses.

5. A system according to claim 2 , further comprising: a word locater to locate at least one significant word situated relative to one such matched word within the at least one phrase, and to place the at least one significant word into the summarized text subject to space restrictions.

5. A system according to claim 2 , further comprising: a word locater to locate at least one significant word situated relative to one such matched word within the at least one phrase, and to place the at least one significant word into the summarized text subject to space restrictions. 6. A system according to claim 5 , further comprising: a word marker to mark one or more unplaced words situated relative to one or more matched words and the at least one significant word within the at least one phrase, and to place one or more marked words or matched words into the summarized text subject to space restrictions.

1. A machine-implemented method, comprising: at an electronic device with one or more processors and memory: obtaining a document including text; receiving, from an automatic language identifier service, a first language identification for the document; in response to receiving the first language identification, automatically invoking a modifying operation; performing the modifying operation on the document in accordance with the first language identification; determining, based at least in part on results from the modifying operation, whether the first language identification for the document is correct, wherein the results from the modifying operation include at least one of the amount of errors or the nature of the errors associated with the modifying operation; in accordance with a determination that the first language identification is correct, providing the first language identification to a user application; in accordance with a determination that the first language identification is incorrect, determining a second language identification of the document, and performing a modifying function on the document in accordance with one or more alternate languages different from the first language, wherein the second language identification of the document is determined based at least in part on the results from performing the modifying function on the document in accordance with the one or more alternate languages.

1. A machine-implemented method, comprising: at an electronic device with one or more processors and memory: obtaining a document including text; receiving, from an automatic language identifier service, a first language identification for the document; in response to receiving the first language identification, automatically invoking a modifying operation; performing the modifying operation on the document in accordance with the first language identification; determining, based at least in part on results from the modifying operation, whether the first language identification for the document is correct, wherein the results from the modifying operation include at least one of the amount of errors or the nature of the errors associated with the modifying operation; in accordance with a determination that the first language identification is correct, providing the first language identification to a user application; in accordance with a determination that the first language identification is incorrect, determining a second language identification of the document, and performing a modifying function on the document in accordance with one or more alternate languages different from the first language, wherein the second language identification of the document is determined based at least in part on the results from performing the modifying function on the document in accordance with the one or more alternate languages. 3. The method as in claim 1 , further comprising, receiving an accuracy confidence ranking associated with the first language identification; and wherein the determining is based on the results from the modifying operation and the accuracy confidence ranking.

14. A computer program product, tangibly embodied on a non-transitory computer readable medium, for regular expression learning, the computer program product including instructions for causing a computer to execute a method, comprising: receiving an initial regular expression from a user; executing the initial regular expression over a database; receiving labeled positive matches and negative matches from a user, wherein the positive matches and the negative matches are results of executing the initial regular expression; inputting the initial regular expression and the labeled positive and negative matches in a transformation process, wherein the transformation process comprises: executing a plurality of restrictions on the initial regular expression to transform the initial regular expression into a pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of restrictions on the initial regular expression until each of the plurality of restrictions is executed; selecting a candidate regular expression from the pool of candidate regular expressions, where the selected candidate regular expression has a best F-Measure out of the pool of candidate regular expressions; wherein executing the plurality of restrictions for the transformation process comprises: executing a plurality of character class restrictions on the initial regular expression to transform the initial regular expression into the pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of character class restrictions on the initial regular expression until each of the plurality of character class restrictions are executed; executing a plurality of quantifier restrictions on the initial regular expression to transform the initial regular expression into the pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of quantifier restrictions on the initial regular expression until each of the plurality of quantifier restrictions are executed; and executing a plurality of negative lookaheads on the initial regular expression to transform the initial regular expression into the pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of negative lookaheads on the initial regular expression until each of the plurality of negative lookaheads are executed; wherein the transformation process executes, one at a time, the plurality of character class restrictions, the plurality of quantifier restrictions, the plurality of negative lookaheads.

14. A computer program product, tangibly embodied on a non-transitory computer readable medium, for regular expression learning, the computer program product including instructions for causing a computer to execute a method, comprising: receiving an initial regular expression from a user; executing the initial regular expression over a database; receiving labeled positive matches and negative matches from a user, wherein the positive matches and the negative matches are results of executing the initial regular expression; inputting the initial regular expression and the labeled positive and negative matches in a transformation process, wherein the transformation process comprises: executing a plurality of restrictions on the initial regular expression to transform the initial regular expression into a pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of restrictions on the initial regular expression until each of the plurality of restrictions is executed; selecting a candidate regular expression from the pool of candidate regular expressions, where the selected candidate regular expression has a best F-Measure out of the pool of candidate regular expressions; wherein executing the plurality of restrictions for the transformation process comprises: executing a plurality of character class restrictions on the initial regular expression to transform the initial regular expression into the pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of character class restrictions on the initial regular expression until each of the plurality of character class restrictions are executed; executing a plurality of quantifier restrictions on the initial regular expression to transform the initial regular expression into the pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of quantifier restrictions on the initial regular expression until each of the plurality of quantifier restrictions are executed; and executing a plurality of negative lookaheads on the initial regular expression to transform the initial regular expression into the pool of candidate regular expressions, wherein the transformation process singularly executes each one of the plurality of negative lookaheads on the initial regular expression until each of the plurality of negative lookaheads are executed; wherein the transformation process executes, one at a time, the plurality of character class restrictions, the plurality of quantifier restrictions, the plurality of negative lookaheads. 16. The computer program product of claim 14 , wherein an F-Measure is determined for the entire pool of the pool of candidate regular expressions.

48. A combination as defined in claim 47, wherein the interrogation processor indicates the data set as being not suitable for compression if the scanning limit value is reached without satisfying the entropy threshold value.

48. A combination as defined in claim 47, wherein the interrogation processor indicates the data set as being not suitable for compression if the scanning limit value is reached without satisfying the entropy threshold value. 49. A combination as defined in claim 48, wherein each new record block is twice the size of the prior record block and includes the prior record block.

27. A system comprising: one or more computers; a computer-readable medium coupled to the one or more computers having commands stored thereon which, when executed by the one or more computers, causes the one or more computers to perform operations comprising: receiving multiple context files from one or more third-party content providers, wherein each context file contains one or more commands for controlling an operation of the search engine in processing a search query input and in presenting search results, each context file is one of a plurality of predefined context files; receiving in a search engine the search query input, the search query input received from an interface provided by one of the third party content providers; aggregating the commands in the multiple context files into a set of aggregated commands; using the aggregated commands to control an organization and a presentation of the search results resulting from the processing of the search query input, including: processing the search query input using the aggregated commands to produce a context processed search query; generating context processed search results responsive to the context processed search query; and providing the context processed search results in accordance with the aggregated commands.

27. A system comprising: one or more computers; a computer-readable medium coupled to the one or more computers having commands stored thereon which, when executed by the one or more computers, causes the one or more computers to perform operations comprising: receiving multiple context files from one or more third-party content providers, wherein each context file contains one or more commands for controlling an operation of the search engine in processing a search query input and in presenting search results, each context file is one of a plurality of predefined context files; receiving in a search engine the search query input, the search query input received from an interface provided by one of the third party content providers; aggregating the commands in the multiple context files into a set of aggregated commands; using the aggregated commands to control an organization and a presentation of the search results resulting from the processing of the search query input, including: processing the search query input using the aggregated commands to produce a context processed search query; generating context processed search results responsive to the context processed search query; and providing the context processed search results in accordance with the aggregated commands. 45. The system of claim 27 , wherein the operations further comprise: providing a general search engine page comprising a search field and a search control; outputting, as the search engine query, search terms entered in the search field by the user at a time when the search control is selected.

35. A media distribution system as claimed in claim 31 wherein the mark-up language document further comprises a tag for accessing a two-way telecommunications link.

35. A media distribution system as claimed in claim 31 wherein the mark-up language document further comprises a tag for accessing a two-way telecommunications link. 36. A media distribution system as claimed in claim 35 wherein the mark-up language document further comprises a tag for requesting a further mark-up language or media file document over the telecommunications link.

11. An apparatus for providing primitive visual knowledge, comprising: a user interface input unit; a user interface output unit; a network interface; a memory configured to store instructions; and a processor configured to execute the instructions, wherein the instructions perform a primitive visual knowledge providing method, the method comprising: receiving an image video in a form of a digital image sequence; dividing the received image video into scenes; extracting a representative shot from each of the scenes; extracting objects from frames which compose the representative shot; extracting action verbs based on a mutual relationship among the extracted objects; selecting a frame best expressing the mutual relationship with the objects, which are the basis for the extracting of the action verbs, as a key frame; generating the primitive visual knowledge based on the selected key frame; storing the generated primitive visual knowledge in a database; and visualizing the primitive visual knowledge stored in the database to provide the primitive visual knowledge to a manager, wherein the extracting of the representative shot includes: calculating an entropy in a section while moving along sections of separate scenes; and extracting a section having the highest entropy as the representative shot.

11. An apparatus for providing primitive visual knowledge, comprising: a user interface input unit; a user interface output unit; a network interface; a memory configured to store instructions; and a processor configured to execute the instructions, wherein the instructions perform a primitive visual knowledge providing method, the method comprising: receiving an image video in a form of a digital image sequence; dividing the received image video into scenes; extracting a representative shot from each of the scenes; extracting objects from frames which compose the representative shot; extracting action verbs based on a mutual relationship among the extracted objects; selecting a frame best expressing the mutual relationship with the objects, which are the basis for the extracting of the action verbs, as a key frame; generating the primitive visual knowledge based on the selected key frame; storing the generated primitive visual knowledge in a database; and visualizing the primitive visual knowledge stored in the database to provide the primitive visual knowledge to a manager, wherein the extracting of the representative shot includes: calculating an entropy in a section while moving along sections of separate scenes; and extracting a section having the highest entropy as the representative shot. 18. The apparatus of claim 11 , wherein the visualizing includes: loading the primitive visual knowledge stored in the database into an interface for visualizing the primitive visual knowledge based on input constraints of a manager; and displaying and navigating the loaded primitive visual knowledge through the interface for visualizing the primitive visual knowledge.

8. A computer program product for providing search results for a search query, the computer program product comprising a non-transitory computer readable storage medium having instructions embodied therewith, the program instructions executable by a computer to cause the computer to: receive, by the computer, a search query including an entity and entity type; parse, by the computer, the entity into semantic components; generate, by the computer, variants based on input from auxiliary information and user configuration information for each of the semantic components; recompose, by the computer, the entity in different morphological forms from different variants of the semantic components; and present, by the computer, at least one morphological form for the entity as a search result.

8. A computer program product for providing search results for a search query, the computer program product comprising a non-transitory computer readable storage medium having instructions embodied therewith, the program instructions executable by a computer to cause the computer to: receive, by the computer, a search query including an entity and entity type; parse, by the computer, the entity into semantic components; generate, by the computer, variants based on input from auxiliary information and user configuration information for each of the semantic components; recompose, by the computer, the entity in different morphological forms from different variants of the semantic components; and present, by the computer, at least one morphological form for the entity as a search result. 13. The computer program product of claim 8 , further comprising program instructions executable by the computer to cause the computer to: recompose, by the computer, the entity by selectively excluding different semantic components for combining the different variants of the semantic components.

7. A transmitter/receiver as claimed in claim 6, further comprising an aerial tuning unit coupling said transmitter/receiver aerial to said non-reciprocal junction device.

7. A transmitter/receiver as claimed in claim 6, further comprising an aerial tuning unit coupling said transmitter/receiver aerial to said non-reciprocal junction device. 8. A transmitter/receiver as claimed in claim 7, further comprising a power amplifier coupling said oscillator to said non-reciprocal junction device.

1. A method for automatic recommendations of literature to users comprising: Introducing, by a computer, a first level into a first electronic hierarchical model in computer memory representing a first electronic text, wherein the first level comprises semantic relationships extracted from within the first electronic text, wherein each semantic relationship comprises a triple store in computer memory, each triple store having a pair of digital information items and an indication of an ontological relationship between the pair of digital information items, wherein each pair is selected from the group consisting of a pair of two characters, a pair of two plot details, and a pair of a character and a plot detail; abstracting, by a computer, using deep semantic analysis, a plurality of plot details and character relationships from the first level of the first electronic hierarchical model in computer memory; introducing, by a computer, a second level of semantic relationships to the first electronic hierarchical model in computer memory, wherein the second level comprises the plot details and character relationships abstracted from the first level such that the second level in the hierarchical model in computer memory is more abstract and less detailed than the first level; comparing, by a computer, at least the second level in the first electronic model to at least one level of a second hierarchical electronic model in computer memory, wherein the second hierarchical electronic model represents a second electronic text, and wherein the second electronic text has an associated user preference indicator; responsive to the comparing finding a match above a pre-determined degree of similarity between the two electronic models in computer memory, recommending, by a computer, to a user via a user interface device the first electronic text to have an expected user preference indicator as the user preference indicator associated with the second electronic text.

1. A method for automatic recommendations of literature to users comprising: Introducing, by a computer, a first level into a first electronic hierarchical model in computer memory representing a first electronic text, wherein the first level comprises semantic relationships extracted from within the first electronic text, wherein each semantic relationship comprises a triple store in computer memory, each triple store having a pair of digital information items and an indication of an ontological relationship between the pair of digital information items, wherein each pair is selected from the group consisting of a pair of two characters, a pair of two plot details, and a pair of a character and a plot detail; abstracting, by a computer, using deep semantic analysis, a plurality of plot details and character relationships from the first level of the first electronic hierarchical model in computer memory; introducing, by a computer, a second level of semantic relationships to the first electronic hierarchical model in computer memory, wherein the second level comprises the plot details and character relationships abstracted from the first level such that the second level in the hierarchical model in computer memory is more abstract and less detailed than the first level; comparing, by a computer, at least the second level in the first electronic model to at least one level of a second hierarchical electronic model in computer memory, wherein the second hierarchical electronic model represents a second electronic text, and wherein the second electronic text has an associated user preference indicator; responsive to the comparing finding a match above a pre-determined degree of similarity between the two electronic models in computer memory, recommending, by a computer, to a user via a user interface device the first electronic text to have an expected user preference indicator as the user preference indicator associated with the second electronic text. 2. The method as set forth in claim 1 wherein a highest abstraction level of at least one of the electronic models comprises a literary element category selected from the group consisting of genre, setting, central storyline event, and mood.

7. One or more computer-readable hardware storage devices storing instructions that, when executed by a processor, configure the processor to perform acts that generate a combined display of presented content and contextually-related search results, comprising: detecting a single search indication comprising an action performed by a user on a user device while media content is being presented via one or more of a webpage or a television program on the user device, wherein the single search indication A) captures, on the user device, media content being presented on the user device, B) defines a search scope corresponding to only a portion of the media content being presented, wherein the portion of the media content being presented comprises a region of content defined by the single search indication, C) includes a specific item of interest within the content being presented, and D) initiates a search related to the media content being presented; in response to detecting the single search indication, automatically obtaining contextual information related to the media content at or near detection of the single search indication; automatically communicating the contextual information at least to a remote search service that formulates a query based on the contextual information; and receiving one or more search results related to the obtained contextual information related to the media content; and modifying a display of the presented content on the user device, such that the one or more search results are presented on the user device with the content.

7. One or more computer-readable hardware storage devices storing instructions that, when executed by a processor, configure the processor to perform acts that generate a combined display of presented content and contextually-related search results, comprising: detecting a single search indication comprising an action performed by a user on a user device while media content is being presented via one or more of a webpage or a television program on the user device, wherein the single search indication A) captures, on the user device, media content being presented on the user device, B) defines a search scope corresponding to only a portion of the media content being presented, wherein the portion of the media content being presented comprises a region of content defined by the single search indication, C) includes a specific item of interest within the content being presented, and D) initiates a search related to the media content being presented; in response to detecting the single search indication, automatically obtaining contextual information related to the media content at or near detection of the single search indication; automatically communicating the contextual information at least to a remote search service that formulates a query based on the contextual information; and receiving one or more search results related to the obtained contextual information related to the media content; and modifying a display of the presented content on the user device, such that the one or more search results are presented on the user device with the content. 12. The devices of claim 7 , wherein the contextual information comprises at least user activity associated with a remote user device.

37. A machine-implemented method comprising: receiving search input from a user; determining, based on substantive characteristics of the search input, whether the first search input triggers an automatic submission of a query to an Internet search engine; determining, based on substantive characteristics of the first search input, whether to delay the trigger for automatic submission, wherein the query is automatically submitted to the search engine if the search input satisfies a temporal trigger, wherein the temporal trigger is based upon a connection speed to the search engine; automatically submitting the query to the search engine, the query based on the received search input; and displaying results of the query.

37. A machine-implemented method comprising: receiving search input from a user; determining, based on substantive characteristics of the search input, whether the first search input triggers an automatic submission of a query to an Internet search engine; determining, based on substantive characteristics of the first search input, whether to delay the trigger for automatic submission, wherein the query is automatically submitted to the search engine if the search input satisfies a temporal trigger, wherein the temporal trigger is based upon a connection speed to the search engine; automatically submitting the query to the search engine, the query based on the received search input; and displaying results of the query. 38. The method of claim 37 , wherein determining whether to automatically submit the query to the Internet search engine further comprises: determining whether the search input satisfies a substantive trigger.

5. The method of claim 1 , wherein said generating a dependency graph from the first syntax tree with each node of the dependency graph corresponding to a code statement and having an assigned weight comprises: generating a dependency graph by following on variables resulting from each code statement of the first syntax tree; and assigning weights to the nodes of the dependency graph.

5. The method of claim 1 , wherein said generating a dependency graph from the first syntax tree with each node of the dependency graph corresponding to a code statement and having an assigned weight comprises: generating a dependency graph by following on variables resulting from each code statement of the first syntax tree; and assigning weights to the nodes of the dependency graph. 6. The method of claim 5 , wherein said assigning weights to the nodes of the dependency graph comprises: assigning a first weight to nodes of the dependency graph corresponding to code statements that start asynchronous operations; assigning a second weight to nodes of the dependency graph corresponding to code statements that await; and assigning a third weight to nodes of the dependency graph not assigned the first weight and not assigned the second weight.

1. A computer implemented method comprising: receiving an indication of activation of a response request rule configured to create a response request for requesting information related to an identified information update from one or more recipients, the indication of activation being associated with a data object in an on-demand database, the response request rule being automatically activated by a computer language process operating on the data object; creating the response request based on activation of the response request rule; and storing the response request on a storage medium in association with the identified information update, the stored response request configured to be accessed and presented with the information update in an information feed configured to be displayed on a display device.

1. A computer implemented method comprising: receiving an indication of activation of a response request rule configured to create a response request for requesting information related to an identified information update from one or more recipients, the indication of activation being associated with a data object in an on-demand database, the response request rule being automatically activated by a computer language process operating on the data object; creating the response request based on activation of the response request rule; and storing the response request on a storage medium in association with the identified information update, the stored response request configured to be accessed and presented with the information update in an information feed configured to be displayed on a display device. 9. The computer implemented method recited in claim 1 , wherein the response request rule comprises an instruction for processing one or more responses to the response request.

7. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, receive data specifying a reference point-of-interest specified by a user and location data of a search region; retrieve a reference vector specifying a plurality of features associated with the reference point-of-interest; determine a plurality of candidates for similar points-of-interest based, at least in part, on the search region; retrieve candidate feature vectors specifying a plurality of features associated with respective candidates by at least real-time extracting semantic topics from one or more text descriptions and one or more user reviews for each of the candidates using a language model assigned a probability to each word thereof, and generating entries of the semantic topics in the feature vectors for each of the candidates, wherein the user reviews include one or more reviews by the user; determine a similarity score for each of the candidates via comparing the plurality of features of respective candidates to the plurality of features of the reference point-of-interest to determine a number of common features (n) shared between the plurality of features of the respective candidates and the plurality of features of the reference point-of-interest, and calculating the similarity score using an equation including a weighting vector (w), the reference feature vector (r), a respective candidate feature vector (p): similarity ⁢ ⁢ ( r , p ) = ∑ i = 1 -> n ⁢ w i ⁢ r i ⁢ p i where i=1 to the number of common features shared between the reference vector and the candidate feature vectors; and generate a list of one or more similar points-of-interest based on the similarity scores.

7. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, receive data specifying a reference point-of-interest specified by a user and location data of a search region; retrieve a reference vector specifying a plurality of features associated with the reference point-of-interest; determine a plurality of candidates for similar points-of-interest based, at least in part, on the search region; retrieve candidate feature vectors specifying a plurality of features associated with respective candidates by at least real-time extracting semantic topics from one or more text descriptions and one or more user reviews for each of the candidates using a language model assigned a probability to each word thereof, and generating entries of the semantic topics in the feature vectors for each of the candidates, wherein the user reviews include one or more reviews by the user; determine a similarity score for each of the candidates via comparing the plurality of features of respective candidates to the plurality of features of the reference point-of-interest to determine a number of common features (n) shared between the plurality of features of the respective candidates and the plurality of features of the reference point-of-interest, and calculating the similarity score using an equation including a weighting vector (w), the reference feature vector (r), a respective candidate feature vector (p): similarity ⁢ ⁢ ( r , p ) = ∑ i = 1 -> n ⁢ w i ⁢ r i ⁢ p i where i=1 to the number of common features shared between the reference vector and the candidate feature vectors; and generate a list of one or more similar points-of-interest based on the similarity scores. 10. The apparatus of claim 7 , wherein the features comprise classification features, tag features, price features, ratings features, the semantic topics, or a combination thereof.

17. The system of claim 16 , wherein selecting the set of knowledge modules comprises: determining, for two or more content categories, a preferred module type indicative of a type of knowledge module in which presentation of the content items of the two or more content categories is preferred; determining that a particular module type is the preferred module type for two of the content categories; and determining that content items for only a first of the two content categories will be presented in the particular module type, the determination being based, in part, on panel constraints that specify a maximum number of the knowledge modules of the particular module type that are allowed to be included in the knowledge panel.

17. The system of claim 16 , wherein selecting the set of knowledge modules comprises: determining, for two or more content categories, a preferred module type indicative of a type of knowledge module in which presentation of the content items of the two or more content categories is preferred; determining that a particular module type is the preferred module type for two of the content categories; and determining that content items for only a first of the two content categories will be presented in the particular module type, the determination being based, in part, on panel constraints that specify a maximum number of the knowledge modules of the particular module type that are allowed to be included in the knowledge panel. 18. The system of claim 17 , wherein determining that content items for only a first of the two identified content categories will be presented in the particular module type comprises: determining that presenting both of the of the two identified content categories in the particular module type will cause a number of the knowledge modules of the particular module type to exceed a maximum number of knowledge modules that are allowed to be of the particular module type; and determining that the content items in the first content category have a higher rank score than the rank score for the content items in the second content category.

51. The mobile telephone of claim 50 wherein the interactive feedback mechanism comprises an animated hand together with one or more motion indications.

51. The mobile telephone of claim 50 wherein the interactive feedback mechanism comprises an animated hand together with one or more motion indications. 52. The mobile telephone of claim 51 wherein the interactive feedback mechanism has superimposed thereon an animated display of a gesture so that a difference between the gesture currently being performed and an ideal gesture can be perceived.

26. A computer program product tangibly embodied on a computer readable storage device, the computer program product comprising instructions for causing a processor to: display a sequence of words on a user interface rendered on a display device; apply in response to a user-based selection of a first portion of words in the sequence of words, a first indicium to the user-selected first portion of words in the sequence of words; associate a first character having an associated first voice model to the first portion of words in the sequence of words; and associate a second character having an associated second, different voice model to a second, different portion of words in the sequence of words, the second portion of the words in the sequence of words being different from the first portion of words in the sequence of words.

26. A computer program product tangibly embodied on a computer readable storage device, the computer program product comprising instructions for causing a processor to: display a sequence of words on a user interface rendered on a display device; apply in response to a user-based selection of a first portion of words in the sequence of words, a first indicium to the user-selected first portion of words in the sequence of words; associate a first character having an associated first voice model to the first portion of words in the sequence of words; and associate a second character having an associated second, different voice model to a second, different portion of words in the sequence of words, the second portion of the words in the sequence of words being different from the first portion of words in the sequence of words. 35. The computer program product of claim 26 wherein the first indicium comprises a visual indicium selected from the group consisting of a semi-transparent color overlaid on portions of the text, a highlighting, a different color for the text, a different font for the text, underlining of the text, italicizing of the text.

7. A system for ingesting data stored in a relational database into a delimited column qualifier database, the system comprising: a communication module to communicate data; a non-relational delimited column qualifier database; a memory adapted to store non-transitory computer executable instructions; and one or more processors adapted to interface with the communication module and the delimited column qualifier database, wherein the one or more processors are configured to execute the non-transitory computer executable instructions to cause the one or more processors to: receive, via the communication module, a request to transform the data stored in the relational database into a format consistent with a delimited column qualifier database, the request including an indication of a set of the data stored in the relational database to be transformed; identify, by the one or more processors, a reference table, wherein the reference table defines the data format of the delimited column qualifier database; transform, by the one or more processors, the indicated set of the data stored in the relational database to the delimited column qualifier format defined within the reference table, wherein transforming the indicated set of data includes transforming an Entity Type associated with the indicated set of data into a translated entity code that is represented by an ASCII hexadecimal entity code using a base 62 conversion; generate, by the one or more processors, a column qualifier for each data value within the indicated set of data, the column qualifier including: (i) the translated entity code corresponding to a relational data table of a plurality of relational data tables in which the data value is stored in the relational database, (ii) a sequence count indexing data values for a particular parameter associated with the relational data table in which the data value is stored in the relational database, and (iii) a second delimiter character between the translated entity code and the sequence count, (i) the translated entity code corresponding to a relational data table of the plurality of relational data tables in which every parent of the data value is stored in the relational database, (ii) a sequence count indexing data values for a particular parameter associated with the relational data table in which every parent of the data value is stored in the relational database, and (iii) the second delimiter character between the respective translated entity code and the sequence count corresponding to every parent of the data value, and a first delimiter character between each respective set of translated entity codes, second delimiter characters, and sequence counts; and store, by the one or more processors, the transformed data, including the column qualifier for each data value, in the delimited column qualifier database, wherein the delimited column qualifier database requires less storage than the relational database to store the same data.

7. A system for ingesting data stored in a relational database into a delimited column qualifier database, the system comprising: a communication module to communicate data; a non-relational delimited column qualifier database; a memory adapted to store non-transitory computer executable instructions; and one or more processors adapted to interface with the communication module and the delimited column qualifier database, wherein the one or more processors are configured to execute the non-transitory computer executable instructions to cause the one or more processors to: receive, via the communication module, a request to transform the data stored in the relational database into a format consistent with a delimited column qualifier database, the request including an indication of a set of the data stored in the relational database to be transformed; identify, by the one or more processors, a reference table, wherein the reference table defines the data format of the delimited column qualifier database; transform, by the one or more processors, the indicated set of the data stored in the relational database to the delimited column qualifier format defined within the reference table, wherein transforming the indicated set of data includes transforming an Entity Type associated with the indicated set of data into a translated entity code that is represented by an ASCII hexadecimal entity code using a base 62 conversion; generate, by the one or more processors, a column qualifier for each data value within the indicated set of data, the column qualifier including: (i) the translated entity code corresponding to a relational data table of a plurality of relational data tables in which the data value is stored in the relational database, (ii) a sequence count indexing data values for a particular parameter associated with the relational data table in which the data value is stored in the relational database, and (iii) a second delimiter character between the translated entity code and the sequence count, (i) the translated entity code corresponding to a relational data table of the plurality of relational data tables in which every parent of the data value is stored in the relational database, (ii) a sequence count indexing data values for a particular parameter associated with the relational data table in which every parent of the data value is stored in the relational database, and (iii) the second delimiter character between the respective translated entity code and the sequence count corresponding to every parent of the data value, and a first delimiter character between each respective set of translated entity codes, second delimiter characters, and sequence counts; and store, by the one or more processors, the transformed data, including the column qualifier for each data value, in the delimited column qualifier database, wherein the delimited column qualifier database requires less storage than the relational database to store the same data. 8. The system of claim 7 , wherein the one or more processors are further configured to execute the non-transitory computer executable instructions to cause the one or more processors to: correspond, by the one or more processors, each column of the delimited column qualifier database to a unique name.

1. A computer-implemented method for initiating instant messaging within a social networking website, the method comprising: providing, using one or more computing devices, for display of a post within the social networking website, the post being associated with a first user and a second user; providing, using the one or more computing devices, for display of a graphical component within the social networking website, wherein the graphical component provides an interface for requesting communication related to the post by instant messaging; receiving, using the one or more computing devices and via the graphical component, a request for communication related to the post by instant messaging; providing, using the one or more computing devices and in response to the received request, for display of a separate window or a separate menu for allowing for selection of users for the communication related to the post by instant messaging; receiving, using the one or more computing devices and via the separate window or separate menu, a selection of users for the communication related to the post by instant messaging; and initiating, using the one or more computing devices and in response to the received selection, instant messaging between the first user and the selected users in response to the received request for communication related to the post.

1. A computer-implemented method for initiating instant messaging within a social networking website, the method comprising: providing, using one or more computing devices, for display of a post within the social networking website, the post being associated with a first user and a second user; providing, using the one or more computing devices, for display of a graphical component within the social networking website, wherein the graphical component provides an interface for requesting communication related to the post by instant messaging; receiving, using the one or more computing devices and via the graphical component, a request for communication related to the post by instant messaging; providing, using the one or more computing devices and in response to the received request, for display of a separate window or a separate menu for allowing for selection of users for the communication related to the post by instant messaging; receiving, using the one or more computing devices and via the separate window or separate menu, a selection of users for the communication related to the post by instant messaging; and initiating, using the one or more computing devices and in response to the received selection, instant messaging between the first user and the selected users in response to the received request for communication related to the post. 9. The method of claim 1 , wherein the first user initiates instant messaging between a poster of the post and one or more commenters to the post or between one or more commenters to the post.

1. A method to execute one or more functions of a new service module on a document personalization production system, the method comprising: establishing communication between an application framework of the document personalization production system and the new service module, wherein the new service module is located on a server and the application framework is configured to integrate the new service module in the document personalization production system without reprogramming a production manager of the document personalization production system; registering a machine of the document personalization production system to the application framework by communicating a name, a capability, a control system, and a metadata to the application framework in order to execute the new service module and determining the machine's operating parameters to execute the one or more functions of the new service module; the application framework providing one or more interfaces to enable the production manager to issue instructions and data transmission for executing the one or more functions of the new service module without reprogramming the production manager; and the application framework providing one or more plugins to execute the one or more functions of the new service module in the document personalization production system.

1. A method to execute one or more functions of a new service module on a document personalization production system, the method comprising: establishing communication between an application framework of the document personalization production system and the new service module, wherein the new service module is located on a server and the application framework is configured to integrate the new service module in the document personalization production system without reprogramming a production manager of the document personalization production system; registering a machine of the document personalization production system to the application framework by communicating a name, a capability, a control system, and a metadata to the application framework in order to execute the new service module and determining the machine's operating parameters to execute the one or more functions of the new service module; the application framework providing one or more interfaces to enable the production manager to issue instructions and data transmission for executing the one or more functions of the new service module without reprogramming the production manager; and the application framework providing one or more plugins to execute the one or more functions of the new service module in the document personalization production system. 3. The method to execute a new service module on a document personalization production system, as in claim 1 , wherein the new service module converts data from a first data format to a second data format, either or both of the first and second data formats not previously implemented in the document personalization production system.

39. The improvement of claim 37 , wherein the closure has a first top surface with a first drive feature and the set screw has a second central portion with a second drive feature.

39. The improvement of claim 37 , wherein the closure has a first top surface with a first drive feature and the set screw has a second central portion with a second drive feature. 41. The improvement of claim 39 , wherein the second drive feature is multi-lobular.

9. The non-transitory machine readable medium of claim 8 , wherein the set of instructions for calculating the angle comprises a set of instructions for determining a slope between at least two points of the character.

9. The non-transitory machine readable medium of claim 8 , wherein the set of instructions for calculating the angle comprises a set of instructions for determining a slope between at least two points of the character. 10. The non-transitory machine readable medium of claim 9 , wherein the set of instructions for determining the slope comprises sets of instructions for: identifying a position, in a coordinate system associated with the character, of a first point of the character; identifying a position, in the coordinate system, of a second point of the character; and computing the slope based on the positions of the first and second points.

7. A non-transitory computer-readable medium tangibly embodying computer-executable instructions for: generating a propositional formula representing an assertion in the specification using Boolean propositions, each Boolean proposition being associated with an atomic assertion in the assertion; generating a test case designed to assess a behavior of a system with respect to the assertion; generating configuration data in response to a simulation of the test case on the system; converting the configuration data into propositional symbols; generating a run of the system using the propositional symbols; converting the run of the system to propositional symbols so that the propositional symbols hold data from the configuration data and from the run of the system; and verifying the assertion using the propositional symbols and the propositional formula.

7. A non-transitory computer-readable medium tangibly embodying computer-executable instructions for: generating a propositional formula representing an assertion in the specification using Boolean propositions, each Boolean proposition being associated with an atomic assertion in the assertion; generating a test case designed to assess a behavior of a system with respect to the assertion; generating configuration data in response to a simulation of the test case on the system; converting the configuration data into propositional symbols; generating a run of the system using the propositional symbols; converting the run of the system to propositional symbols so that the propositional symbols hold data from the configuration data and from the run of the system; and verifying the assertion using the propositional symbols and the propositional formula. 8. The computer-readable medium of claim 7 , wherein the configuration data includes a test case input sequence, state variables with current values and output variables with current values.

10. The server of claim 9 , wherein the stored instructions further comprise instructions for modifying the search query in a first manner for identifying the first set of search listings; and modifying the search query in a second manner for identifying the second set of search listings.

10. The server of claim 9 , wherein the stored instructions further comprise instructions for modifying the search query in a first manner for identifying the first set of search listings; and modifying the search query in a second manner for identifying the second set of search listings. 11. The server of claim 10 , wherein each listing from the first set of search listings is associated with a first set of attributes, each attribute of the first set of attributes associated with an attribute weight, and wherein the confidence score is determined by weighting at least one attribute for each listing with the associated attribute weight.

1. A method programmed in a non-transitory memory of a device comprising: a. automatically generating narrative information by analyzing at least one first data source including extracting a first set of data from the at least one first data source and organizing the first set of data from the at least one first data source into a narrative, wherein the first set of data includes a plurality of different perspectives, wherein the plurality of different perspectives are based on a plurality of different languages, further wherein a first perspective is a first content in a first language, and a second perspective is a second content in a second language, wherein the first language and the second language are different; b. automatically generating aggregate information by aggregating a second set of data from at least one second data source or the at least one first data source; c. automatically analyzing target information, wherein the target information includes the narrative information, the aggregate information and additional information; d. automatically summarizing the target information to generate a summary of the target information; e. automatically fact checking, with the device, the summary of the target information by comparing the summary of the target information with source information to generate a result, wherein the summary of the target information includes a first summary content in the first language and a second summary content in the second language, wherein fact checking the first summary content comprises fact checking using sources in the second language, and fact checking the second summary content comprises fact checking using sources in the first language; and f. automatically providing a status of the target information in real-time based on the result of the comparison of the summary of the target information with the source information.

1. A method programmed in a non-transitory memory of a device comprising: a. automatically generating narrative information by analyzing at least one first data source including extracting a first set of data from the at least one first data source and organizing the first set of data from the at least one first data source into a narrative, wherein the first set of data includes a plurality of different perspectives, wherein the plurality of different perspectives are based on a plurality of different languages, further wherein a first perspective is a first content in a first language, and a second perspective is a second content in a second language, wherein the first language and the second language are different; b. automatically generating aggregate information by aggregating a second set of data from at least one second data source or the at least one first data source; c. automatically analyzing target information, wherein the target information includes the narrative information, the aggregate information and additional information; d. automatically summarizing the target information to generate a summary of the target information; e. automatically fact checking, with the device, the summary of the target information by comparing the summary of the target information with source information to generate a result, wherein the summary of the target information includes a first summary content in the first language and a second summary content in the second language, wherein fact checking the first summary content comprises fact checking using sources in the second language, and fact checking the second summary content comprises fact checking using sources in the first language; and f. automatically providing a status of the target information in real-time based on the result of the comparison of the summary of the target information with the source information. 10. The method of claim 1 wherein generating the narrative information includes analyzing a validity rating of an entity, wherein the validity rating is based on factual accuracy of content provided by the entity, and only extracting data as the first set of data from the content provided by the entity when the validity rating of the entity is above a validity rating threshold, wherein generating the aggregate information includes analyzing the validity rating of the entity, wherein the validity rating is based on the factual accuracy of content provided by the entity, and only aggregating the data from the content provided by the entity when the validity rating of the entity is above the validity rating threshold.

29. The system as recited in claim 27 , wherein the transaction includes a transfer of control of an agreed upon element from a party other than the transaction phrase token holder to the transaction phrase token holder.

29. The system as recited in claim 27 , wherein the transaction includes a transfer of control of an agreed upon element from a party other than the transaction phrase token holder to the transaction phrase token holder. 30. The system as recited in claim 29 , wherein the transaction phrase token processing service facilitates the transfer of control of the agreed element from a seller to the transaction phrase token holder.

1. A method for segmenting a stream of text into segments using a plurality of language models, the stream of text including a sequence of blocks of text, the method comprising: scoring the blocks of text against the language models to generate language model scores for the blocks of text, the language model score for a block of text against a language model indicating a correlation between the block of text and the language model; generating language model sequence scores for different sequences of language models to which a sequence of blocks of text may correspond, a language model sequence score being a function of the scores of a sequence of blocks of text against a sequence of language models; selecting a sequence of language models that satisfies a predetermined condition; and identifying segment boundaries in the stream of text that correspond to language model transitions in the selected sequence of language models.

1. A method for segmenting a stream of text into segments using a plurality of language models, the stream of text including a sequence of blocks of text, the method comprising: scoring the blocks of text against the language models to generate language model scores for the blocks of text, the language model score for a block of text against a language model indicating a correlation between the block of text and the language model; generating language model sequence scores for different sequences of language models to which a sequence of blocks of text may correspond, a language model sequence score being a function of the scores of a sequence of blocks of text against a sequence of language models; selecting a sequence of language models that satisfies a predetermined condition; and identifying segment boundaries in the stream of text that correspond to language model transitions in the selected sequence of language models. 10. The method of claim 1, wherein a block of text comprises a sentence.

25. A computer program product for use in a computer system that executes program steps recorded in a non-transitory computer-readable media to perform a method for processing a search query result, the program product comprising: a non-transitory recordable media: a program of computer-readable instructions recorded in the media and executable by the computer system to perform operations comprising: identifying a plurality of result pages in response to a search query submitted from a computing device that is associated with a subscriber, wherein the search query is directed to a collection of pages; determining a relevancy ranking for each of the result pages in accordance with a parameter set that includes metrics relating to the search query itself and also includes metrics unique to the subscriber associated with the computing device, and includes metrics related to the computing device from which the search query was submitted, wherein each of the parameter set metrics, when applied to the result pages, provides a re-ordering of the result pages, wherein the parameter set metrics are applied according to a tunable priority received from an administrative console input, and the determined relevancy ranking comprises a single merged ordering of the respective re-orderings; and providing the result pages in accordance with t determined relevancy ranking; wherein the administrative console input specifies parameter set processing for a plurality of ranking operations, and the administrative console input is used to select or deselect each of the ranking operations and specify an order of operation for the selected ranking operations, wherein determining a relevancy ranking includes: associating each result page with a relevancy ranking value; and ordering the plurality of result pages in accordance with the associated relevancy ranking values of the result pa yes: and wherein the relevancy ranking value is adjusted with a tunable parameter value, wherein the relevancy ranking value AR is calculated according to ∀ u ⁢ AR k ⁡ ( u ) = c ⁡ [ α ⁢ ∑ i ⁢ ( tf i * log ⁡ ( D df ) ) + β ⁢ ∑ v ∈ ⋃ u ⁢ P ⁡ ( v ) + λ ⁢ ∑ v ∈ ⋃ u ⁢ DSI ⁡ ( v ) + γ ⁡ ( 1 - ( u x - P ⁡ ( V x ) ) 2 + ( u y - P ⁡ ( V y ) ) 2 ) ] + ( 1 - c ) where the terms are defined by the following: AR=Active Rank matrix (sorted order of vectors) u=set of total search results v=set of metadata attributes associated with each search result item k=a given Active Rank row (value for a specific search result) c=normalization coefficient ≦1 α=bias to weight keyword matching effects (0 ≦α≦1) β=bias to weight Personalization profiles (0 ≦β≦1) λ=bias to weight Device Specificity effects (0 ≦λ≦1) γ=bias to weight LBS geometric distance effects (0 ≦γ≦1) tf i =term frequency (keyword counts) or number of times a term i occurs in a search result page df i =document frequency or number of pages in the search result pages containing term i D=number of documents in the database P=Personalization profile vector DSI=Device Specific Index vector (u x , u y )=each search result's geocoded location, if any (v x , v y )=cellular user's actual physical location (stored in vector P) provided by the cellular network.

25. A computer program product for use in a computer system that executes program steps recorded in a non-transitory computer-readable media to perform a method for processing a search query result, the program product comprising: a non-transitory recordable media: a program of computer-readable instructions recorded in the media and executable by the computer system to perform operations comprising: identifying a plurality of result pages in response to a search query submitted from a computing device that is associated with a subscriber, wherein the search query is directed to a collection of pages; determining a relevancy ranking for each of the result pages in accordance with a parameter set that includes metrics relating to the search query itself and also includes metrics unique to the subscriber associated with the computing device, and includes metrics related to the computing device from which the search query was submitted, wherein each of the parameter set metrics, when applied to the result pages, provides a re-ordering of the result pages, wherein the parameter set metrics are applied according to a tunable priority received from an administrative console input, and the determined relevancy ranking comprises a single merged ordering of the respective re-orderings; and providing the result pages in accordance with t determined relevancy ranking; wherein the administrative console input specifies parameter set processing for a plurality of ranking operations, and the administrative console input is used to select or deselect each of the ranking operations and specify an order of operation for the selected ranking operations, wherein determining a relevancy ranking includes: associating each result page with a relevancy ranking value; and ordering the plurality of result pages in accordance with the associated relevancy ranking values of the result pa yes: and wherein the relevancy ranking value is adjusted with a tunable parameter value, wherein the relevancy ranking value AR is calculated according to ∀ u ⁢ AR k ⁡ ( u ) = c ⁡ [ α ⁢ ∑ i ⁢ ( tf i * log ⁡ ( D df ) ) + β ⁢ ∑ v ∈ ⋃ u ⁢ P ⁡ ( v ) + λ ⁢ ∑ v ∈ ⋃ u ⁢ DSI ⁡ ( v ) + γ ⁡ ( 1 - ( u x - P ⁡ ( V x ) ) 2 + ( u y - P ⁡ ( V y ) ) 2 ) ] + ( 1 - c ) where the terms are defined by the following: AR=Active Rank matrix (sorted order of vectors) u=set of total search results v=set of metadata attributes associated with each search result item k=a given Active Rank row (value for a specific search result) c=normalization coefficient ≦1 α=bias to weight keyword matching effects (0 ≦α≦1) β=bias to weight Personalization profiles (0 ≦β≦1) λ=bias to weight Device Specificity effects (0 ≦λ≦1) γ=bias to weight LBS geometric distance effects (0 ≦γ≦1) tf i =term frequency (keyword counts) or number of times a term i occurs in a search result page df i =document frequency or number of pages in the search result pages containing term i D=number of documents in the database P=Personalization profile vector DSI=Device Specific Index vector (u x , u y )=each search result's geocoded location, if any (v x , v y )=cellular user's actual physical location (stored in vector P) provided by the cellular network. 35. A program product as defined in claim 25 , wherein the parameter set metrics include at least two dimensions selected from among the set of query keywords, popularity, subscriber, personalization, device, device-specific, location, and location-specific metrics.

1. A computer-implemented method for transparent data loss prevention classifications, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: identifying a document that received a classification by a machine learning classifier for data loss prevention; identifying at least one linguistic constituent within the document that contributed to the classification; identifying a relevant passage of the document that contextualizes the linguistic constituent; displaying a user interface comprising the linguistic constituent in context of the relevant passage; receiving user input via the user interface indicating a type of mistake, selected via the user interface from a plurality of types of mistakes, that potentially caused the machine learning classifier to misclassify the document, wherein indicating the type of mistake that potentially caused the machine learning classifier to misclassify the document comprises indicating a basis of classification relied upon by the machine learning classifier that resulted in the machine learning classifier misclassifying the document.

1. A computer-implemented method for transparent data loss prevention classifications, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: identifying a document that received a classification by a machine learning classifier for data loss prevention; identifying at least one linguistic constituent within the document that contributed to the classification; identifying a relevant passage of the document that contextualizes the linguistic constituent; displaying a user interface comprising the linguistic constituent in context of the relevant passage; receiving user input via the user interface indicating a type of mistake, selected via the user interface from a plurality of types of mistakes, that potentially caused the machine learning classifier to misclassify the document, wherein indicating the type of mistake that potentially caused the machine learning classifier to misclassify the document comprises indicating a basis of classification relied upon by the machine learning classifier that resulted in the machine learning classifier misclassifying the document. 7. The computer-implemented method of claim 1 , wherein identifying the linguistic constituent within the document that contributed to the classification comprises: identifying a plurality of linguistic constituents within the document that contributed to the classification; identifying a candidate passage comprising the plurality of linguistic constituents; selecting the linguistic constituent based at least in part on a degree to which the candidate passage contributed to the classification.

1. A computer-implemented method for determining an impact on a simulation model in creating a human resource allocation report in a modeling environment comprising a plurality of simulation models, said method including: acquiring, by a computer, performance data relating to said plurality of simulation models within a timeframe parameter and a presentation parameter, wherein said performance data includes dependency data and a simulation model score, wherein said plurality of simulation models simulate at least one of outcomes, effectiveness, penetration, utilization, or distribution of marketing strategies based upon at least one of historic, current or probability data of said marketing strategies; analyzing, by said computer, allocation data and said dependency data relating to said simulation model, having a model identifier; determining, by said computer, individual variables within a first subset of said plurality of simulation models impacted by said allocation data and dependency data, wherein said dependency data includes bidirectional pointers in a tree of said plurality of simulation models, said plurality of simulation models represented by nodes on said tree, and said dependency data provides interdependencies among said plurality of simulation models, wherein said variables are across said plurality of simulation models, wherein said dependency data depends upon and includes records having an identifier that is based upon said model identifier and that depend at least one of directly or indirectly from said simulation model, and wherein said allocation data and said dependency data relates to a transfer of information exchanged between said simulation model and at least one of said first subset of said plurality of simulation models or a second subset of said plurality of simulation models, wherein said information includes accuracy of said information, an amount of said information, a transfer rate of said information, and a processing rate of said information; analyzing, by said computer, said dependency data relating to said first subset of said plurality of simulation models; determining, by said computer, individual of said variables within said second subset of said plurality of simulation models impacted by said dependency data, wherein said second subset of said plurality of simulation models is dependent upon said first subset of said plurality of simulation models; propagating, by said computer, and based on said variables and said inter-dependencies, a change to a first variable to a plurality of variables among different branches across said plurality of simulation models, wherein said different branches are within at least one of said first subset of said plurality of variables or within said second subset of said plurality of variables; determining, by said computer, an impact on said first subset of said plurality of simulation models, and said second subset of said plurality of simulation models in response to said analysis of said allocation data and said dependency data; modifying, by said computer, said simulation model score based on said dependency data to create a modified simulation model score; and at least one of: modifying said simulation model or decommissioning said simulation model based on said modified simulation model score; and formatting, by said computer, said allocation data to transform said allocation data into said allocation report, according to said presentation parameter.

1. A computer-implemented method for determining an impact on a simulation model in creating a human resource allocation report in a modeling environment comprising a plurality of simulation models, said method including: acquiring, by a computer, performance data relating to said plurality of simulation models within a timeframe parameter and a presentation parameter, wherein said performance data includes dependency data and a simulation model score, wherein said plurality of simulation models simulate at least one of outcomes, effectiveness, penetration, utilization, or distribution of marketing strategies based upon at least one of historic, current or probability data of said marketing strategies; analyzing, by said computer, allocation data and said dependency data relating to said simulation model, having a model identifier; determining, by said computer, individual variables within a first subset of said plurality of simulation models impacted by said allocation data and dependency data, wherein said dependency data includes bidirectional pointers in a tree of said plurality of simulation models, said plurality of simulation models represented by nodes on said tree, and said dependency data provides interdependencies among said plurality of simulation models, wherein said variables are across said plurality of simulation models, wherein said dependency data depends upon and includes records having an identifier that is based upon said model identifier and that depend at least one of directly or indirectly from said simulation model, and wherein said allocation data and said dependency data relates to a transfer of information exchanged between said simulation model and at least one of said first subset of said plurality of simulation models or a second subset of said plurality of simulation models, wherein said information includes accuracy of said information, an amount of said information, a transfer rate of said information, and a processing rate of said information; analyzing, by said computer, said dependency data relating to said first subset of said plurality of simulation models; determining, by said computer, individual of said variables within said second subset of said plurality of simulation models impacted by said dependency data, wherein said second subset of said plurality of simulation models is dependent upon said first subset of said plurality of simulation models; propagating, by said computer, and based on said variables and said inter-dependencies, a change to a first variable to a plurality of variables among different branches across said plurality of simulation models, wherein said different branches are within at least one of said first subset of said plurality of variables or within said second subset of said plurality of variables; determining, by said computer, an impact on said first subset of said plurality of simulation models, and said second subset of said plurality of simulation models in response to said analysis of said allocation data and said dependency data; modifying, by said computer, said simulation model score based on said dependency data to create a modified simulation model score; and at least one of: modifying said simulation model or decommissioning said simulation model based on said modified simulation model score; and formatting, by said computer, said allocation data to transform said allocation data into said allocation report, according to said presentation parameter. 12. The method of claim 1 , further including reading a request to decommission a variable corresponding to said allocation data, wherein said request includes at least one of: decommission date, authorization request, model dependency data, variable dependency data, or model owner identifier.

1. A computer implemented method comprising: receiving a document that is based on a markup language, the document including language identifiers that are arranged based on the markup language; presenting a plurality of data entry fields based on the document, the data entry fields being associated with the language identifiers, respectively; receiving user input selecting a data entry field from among the plurality of data entry fields; detecting a language identifier associated with the selected data entry field, wherein detecting the language identifier comprises accessing a tag included in the document, the language identifier identifying a language associated with the selected data entry field; determining a key mapping corresponding to the language identified by the detected language identifier; configuring a virtual input device in accordance with the key mapping, wherein the virtual input device includes one or more controls, and wherein the key mapping specifies a character corresponding to at least one of the one or more controls; and presenting the virtual input device to a user.

1. A computer implemented method comprising: receiving a document that is based on a markup language, the document including language identifiers that are arranged based on the markup language; presenting a plurality of data entry fields based on the document, the data entry fields being associated with the language identifiers, respectively; receiving user input selecting a data entry field from among the plurality of data entry fields; detecting a language identifier associated with the selected data entry field, wherein detecting the language identifier comprises accessing a tag included in the document, the language identifier identifying a language associated with the selected data entry field; determining a key mapping corresponding to the language identified by the detected language identifier; configuring a virtual input device in accordance with the key mapping, wherein the virtual input device includes one or more controls, and wherein the key mapping specifies a character corresponding to at least one of the one or more controls; and presenting the virtual input device to a user. 3. The method of claim 1 , wherein the language identifier comprises the tag, wherein the markup language comprises one of an Extensible Markup Language or a Hypertext Markup Language.

8. A method for generating answers to questions, comprising: receiving an input query; obtaining, from an unstructured data source, a plurality of candidate answers to the input query; performing context independent answer processing to produce a first score for each of the candidate answers; computing specified information about each of the candidate answers during the context independent answer processing; sending the candidate answers to a model selection module; using the model selection module to use the specified information computed about the candidate answers during the context independent answer processing, to select one of a plurality of scoring models; sending each of the candidate answers to the selected one of the scoring models; using the selected one of the scoring models for weighting the first scores for the candidate answers to determine an answer score for each of the candidate answers; and generating at least one answer to the input query based on the answer scores.

8. A method for generating answers to questions, comprising: receiving an input query; obtaining, from an unstructured data source, a plurality of candidate answers to the input query; performing context independent answer processing to produce a first score for each of the candidate answers; computing specified information about each of the candidate answers during the context independent answer processing; sending the candidate answers to a model selection module; using the model selection module to use the specified information computed about the candidate answers during the context independent answer processing, to select one of a plurality of scoring models; sending each of the candidate answers to the selected one of the scoring models; using the selected one of the scoring models for weighting the first scores for the candidate answers to determine an answer score for each of the candidate answers; and generating at least one answer to the input query based on the answer scores. 12. The method according to claim 8 , wherein the computed specified information is a length of the candidate answers.

6. A system for enabling sighted persons who cannot read a standard alphanumeric text to pictorially communicate and interact with an environment via computer sensory perception, comprising: data entering means for acquiring alphanumeric text; encoding means for converting said alphanumeric text into a machine-readable set of informationally corresponding data; decoding means for correlating said informational content data set with one or more hieroglyphs that are representative of the actions, physical objects and the relationship between said actions and said physical objects presented by said informationally corresponding data set and arranging said one or more hieroglyphs syntactically to pictorially represent said informationally corresponding data set for said sighted persons thereby to inform said sighted person of said actions, objects and the relationship between said actions and said objects as was originally presented in said standard alphanumeric text; computer sensory perception means for enabling said sighted person to interact with an environment and act on the information conveyed by the hieroglyphs; and for recording inputs enabling said sighted person to record said sighted person's interaction with the environment; and said input analyzed via a computer processing means and added to the informational content data set, the computer processing means isolating those inputs not already present in the informational content data set and assigning additional hieroglyphs that are representative of the new actions, new physical objects and new relationships between said actions and physical objects presented by said informationally corresponding data set, the additional hieroglyphs used to allow the informational content data set to adapt to new environments and provide cognitive reasoning support and learning to the sighted person.

6. A system for enabling sighted persons who cannot read a standard alphanumeric text to pictorially communicate and interact with an environment via computer sensory perception, comprising: data entering means for acquiring alphanumeric text; encoding means for converting said alphanumeric text into a machine-readable set of informationally corresponding data; decoding means for correlating said informational content data set with one or more hieroglyphs that are representative of the actions, physical objects and the relationship between said actions and said physical objects presented by said informationally corresponding data set and arranging said one or more hieroglyphs syntactically to pictorially represent said informationally corresponding data set for said sighted persons thereby to inform said sighted person of said actions, objects and the relationship between said actions and said objects as was originally presented in said standard alphanumeric text; computer sensory perception means for enabling said sighted person to interact with an environment and act on the information conveyed by the hieroglyphs; and for recording inputs enabling said sighted person to record said sighted person's interaction with the environment; and said input analyzed via a computer processing means and added to the informational content data set, the computer processing means isolating those inputs not already present in the informational content data set and assigning additional hieroglyphs that are representative of the new actions, new physical objects and new relationships between said actions and physical objects presented by said informationally corresponding data set, the additional hieroglyphs used to allow the informational content data set to adapt to new environments and provide cognitive reasoning support and learning to the sighted person. 7. The system according to claim 6 wherein said machine-readable set of informationally corresponding data comprises a flat Braille symbology.

7. The method of claim 1 , wherein extracting a first fact from a first document comprises: extracting a first plurality of facts from the first document, each fact having an attribute and a value.

7. The method of claim 1 , wherein extracting a first fact from a first document comprises: extracting a first plurality of facts from the first document, each fact having an attribute and a value. 10. The method of claim 7 , wherein said first plurality of facts includes a third fact, the method further comprising: determining if the third fact is organized in the second document according to the contextual pattern.

16. An apparatus according to claim 14 , wherein the memory and computer program code are further configured to, with the processor, cause the apparatus to select by defining the sub-feature units based on correlations within the feature.

16. An apparatus according to claim 14 , wherein the memory and computer program code are further configured to, with the processor, cause the apparatus to select by defining the sub-feature units based on correlations within the feature. 17. An apparatus according to claim 16 , wherein the memory and computer program code are further configured to, with the processor, cause the apparatus to be tuned based on iterative conversion and training operations.

3. The method of claim 2 , wherein the particular book and each of the other books are described by respective book metadata.

3. The method of claim 2 , wherein the particular book and each of the other books are described by respective book metadata. 4. The method of claim 3 , wherein the book metadata comprises a title, a subtitle, and an author of the given book.

35. The method of claim 29 , further comprising a generating step of generating a pseudo-random vector as a label of said first feature vector in accordance with said probability distribution.

35. The method of claim 29 , further comprising a generating step of generating a pseudo-random vector as a label of said first feature vector in accordance with said probability distribution. 36. The method of claim 35 , further comprising a feedforward step of including a plurality of components of a pseudorandom vector generated by said generating step as a label of said first feature vector as components in a fourth feature vector and processing said fourth feature vector by said expanding step and said estimating step.

2. The medium of claim 1 , wherein each of the characters is either a character of a first type or a character of a second type.

2. The medium of claim 1 , wherein each of the characters is either a character of a first type or a character of a second type. 9. The medium of claim 2 , wherein when the plurality of characters in the line does not include at least one character of the second type, calculating the adjusted box width further includes: calculating a first term equal to the total number of characters in the line; calculating a second term equal to the sum of the initial box widths of all of the display boxes in the line; calculating a unit segment by dividing the second term by the first term; and determining the adjusted box width to be the unit segment multiplied by the total number of characters in the first display box.

4. The method of claim 1 , further comprising prompting, using one or more computing devices, the first user to select one or more types of answer information to be displayed by the user interface application; wherein the user interface application is generated so that the user interface application is configured to determine the portion of the answer information to be displayed, from the answer information received in response to the computer-generated query, according to the one or more types of answer information selected by the first user, and cause the selected types of the answer information received in response to the computer-generated query to be displayed on the display device.

4. The method of claim 1 , further comprising prompting, using one or more computing devices, the first user to select one or more types of answer information to be displayed by the user interface application; wherein the user interface application is generated so that the user interface application is configured to determine the portion of the answer information to be displayed, from the answer information received in response to the computer-generated query, according to the one or more types of answer information selected by the first user, and cause the selected types of the answer information received in response to the computer-generated query to be displayed on the display device. 5. The method of claim 4 , wherein the answer information received in response to the computer-generated query is second answer information, the method further comprising: providing the NL query to the NL query answering system; receiving first answer information in response to the NL query; and using the first answer information received in response to the NL query to prompt the first user to select one or more types of answer information for display by the user interface application.

1. A method comprising: extracting, with a processing system, macroinstructions that are hard-coded into parser code of a command line interface (CLI) parser, wherein the macroinstructions define parse nodes utilized by the CLI parser to analyze whether one or more CLI commands input to a CLI prompt have a proper CLI syntax; generating a parse graph from the macroinstructions with the processing system, wherein the parse graph includes a representation of the parse nodes defined by the macroinstructions; hiding selected information within the parse nodes to create condensed parse nodes with the processing system, wherein the hiding prevents further processing of the selected information; simplifying selected complex patterns in the parse graph to create simplified parse graph patterns with the processing system; creating, with the processing system, branches on an AND/OR command tree from the parse nodes, the condensed parse nodes, and the simplified parse graph patterns; and creating an exportable representation of the AND/OR command tree with the processing system.

1. A method comprising: extracting, with a processing system, macroinstructions that are hard-coded into parser code of a command line interface (CLI) parser, wherein the macroinstructions define parse nodes utilized by the CLI parser to analyze whether one or more CLI commands input to a CLI prompt have a proper CLI syntax; generating a parse graph from the macroinstructions with the processing system, wherein the parse graph includes a representation of the parse nodes defined by the macroinstructions; hiding selected information within the parse nodes to create condensed parse nodes with the processing system, wherein the hiding prevents further processing of the selected information; simplifying selected complex patterns in the parse graph to create simplified parse graph patterns with the processing system; creating, with the processing system, branches on an AND/OR command tree from the parse nodes, the condensed parse nodes, and the simplified parse graph patterns; and creating an exportable representation of the AND/OR command tree with the processing system. 3. The method of claim 1 further comprising merging selected cases in the AND/OR command tree that have common end of line terminations.

17. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, when executed by one or more processors, cause the one or more processors to: receive, from a first user device, a first pronunciation for a first named entity; receive, from a second user device, a second pronunciation for the first named entity; store the first and second pronunciations for the first named entity in a shared pronunciation lexicon receive, from a third user device, audio data representing user speech and a context of the third user device, the user speech including the first named entity in spoken form; and perform speech-to-text conversion on the audio data to generate a textual representation of the user speech, the speech-to-text conversion comprising selecting, for comparison with the audio data, the stored first pronunciation or the stored second pronunciation for the first named entity based on the context.

17. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, when executed by one or more processors, cause the one or more processors to: receive, from a first user device, a first pronunciation for a first named entity; receive, from a second user device, a second pronunciation for the first named entity; store the first and second pronunciations for the first named entity in a shared pronunciation lexicon receive, from a third user device, audio data representing user speech and a context of the third user device, the user speech including the first named entity in spoken form; and perform speech-to-text conversion on the audio data to generate a textual representation of the user speech, the speech-to-text conversion comprising selecting, for comparison with the audio data, the stored first pronunciation or the stored second pronunciation for the first named entity based on the context. 29. The computer-readable storage medium of claim 17 , wherein the first pronunciation for the first named entity comprises an audio recording of a user associated with the first user device speaking the first named entity, and wherein storing the first pronunciation for the first-named entity in the shared pronunciation lexicon comprises: generating an acoustic model representing the first pronunciation for the first named entity based on the audio recording, and storing the acoustic model in the shared pronunciation lexicon.

1. A method for user verification, the method comprising: receiving, from a user, a selection of a category from a subset of a set of categories; presenting, to the user, a base set of questions, wherein the base set of questions presents a plurality of social situations to the user and provides a plurality of answers to each question to allow the user to choose an answer most applicable to the user in a particular social situation of the plurality of social situations, and wherein the base set of questions is related to the category selected by the user; analyzing a base set of answers received from the user, the base set of answers corresponding to the base set of questions; computing, using a processor and a memory, a base score using the base set of answers, the base score being indicative of a behavioral characteristic related to an ingrained psychological nature of the user in social situations, wherein the behavioral characteristic is indicative of the user being either an introvert or an extrovert in social situations; saving the base score in a psychological profile associated with the user; presenting, to the user, a set of questions, wherein the user has not previously been asked the set of questions, wherein the set of questions is also related to the category, and wherein questions in the base set of questions are distinct from questions in the set of questions; analyzing a set of answers received from the user, the set of answers corresponding to the set of questions, wherein the answers in the set of answers are distinct from answers in the base set of answers; computing, using the processor and the memory, a score using the set of answers; determining whether the score matches, within a tolerance value, the base score in the psychological profile of the user, the behavioral characteristic causing the user to respond to the set of questions such that the score matches the base score within the tolerance value; and concluding one of (i) responsive to the score matching the base score within the tolerance value, that an identity of the user has been verified, and (ii) responsive to the score not matching the base score within the tolerance value, matching with a different base score a different score that is computed using a different set of answers corresponding to a different set of questions, and concluding that the identity of the user is not verified responsive to the different score not matching the different base score within a different tolerance value.

1. A method for user verification, the method comprising: receiving, from a user, a selection of a category from a subset of a set of categories; presenting, to the user, a base set of questions, wherein the base set of questions presents a plurality of social situations to the user and provides a plurality of answers to each question to allow the user to choose an answer most applicable to the user in a particular social situation of the plurality of social situations, and wherein the base set of questions is related to the category selected by the user; analyzing a base set of answers received from the user, the base set of answers corresponding to the base set of questions; computing, using a processor and a memory, a base score using the base set of answers, the base score being indicative of a behavioral characteristic related to an ingrained psychological nature of the user in social situations, wherein the behavioral characteristic is indicative of the user being either an introvert or an extrovert in social situations; saving the base score in a psychological profile associated with the user; presenting, to the user, a set of questions, wherein the user has not previously been asked the set of questions, wherein the set of questions is also related to the category, and wherein questions in the base set of questions are distinct from questions in the set of questions; analyzing a set of answers received from the user, the set of answers corresponding to the set of questions, wherein the answers in the set of answers are distinct from answers in the base set of answers; computing, using the processor and the memory, a score using the set of answers; determining whether the score matches, within a tolerance value, the base score in the psychological profile of the user, the behavioral characteristic causing the user to respond to the set of questions such that the score matches the base score within the tolerance value; and concluding one of (i) responsive to the score matching the base score within the tolerance value, that an identity of the user has been verified, and (ii) responsive to the score not matching the base score within the tolerance value, matching with a different base score a different score that is computed using a different set of answers corresponding to a different set of questions, and concluding that the identity of the user is not verified responsive to the different score not matching the different base score within a different tolerance value. 6. The method of claim 1 , further comprising: presenting, to the user, the different set of questions after the set of answers has been received from the user.

1. A speech recognition system, comprising: an elementary recognizer for classifying the elementary segments of an observed speech pattern as they are received, said elementary recognizer including correlation means for producing at an output node of said elementary recognizer a score of correlation of said elementary segments with stored spectral speech patterns; and a plurality of local decision modules each connected to said output node for receiving said score of correlation; said plurality of local decision modules being connected at node points in a network wherein different network paths through the nodes and their corresponding local decisions modules represent an accumulation of speech segments constituting different pronunciations of said speech pattern, the input of each said local decision module connected to said correlation means to receive the measures of correlation; each local decision module specializing in a particular network node and including, means for determining the probability of how well the input segment of speech matches the particular sound segments associated with a given node, means for receiving from the other local decision modules the prior correlation scores of all preceding sound segments, means for selecting the locally optimum time warping of each segment of speech which are input from other local decision modules, and accumulator memory means for providing an accumulated correlation score for any one path in the network of local decision modules, said path representing an accumulation of segments or parts of a word or sound; whereby the accumulated correlation score represents the most probable pronunciation of said speech pattern and the best recognition match derived from all the possible paths in the network of local decision modules.

1. A speech recognition system, comprising: an elementary recognizer for classifying the elementary segments of an observed speech pattern as they are received, said elementary recognizer including correlation means for producing at an output node of said elementary recognizer a score of correlation of said elementary segments with stored spectral speech patterns; and a plurality of local decision modules each connected to said output node for receiving said score of correlation; said plurality of local decision modules being connected at node points in a network wherein different network paths through the nodes and their corresponding local decisions modules represent an accumulation of speech segments constituting different pronunciations of said speech pattern, the input of each said local decision module connected to said correlation means to receive the measures of correlation; each local decision module specializing in a particular network node and including, means for determining the probability of how well the input segment of speech matches the particular sound segments associated with a given node, means for receiving from the other local decision modules the prior correlation scores of all preceding sound segments, means for selecting the locally optimum time warping of each segment of speech which are input from other local decision modules, and accumulator memory means for providing an accumulated correlation score for any one path in the network of local decision modules, said path representing an accumulation of segments or parts of a word or sound; whereby the accumulated correlation score represents the most probable pronunciation of said speech pattern and the best recognition match derived from all the possible paths in the network of local decision modules. 2. A system as recited in claim 1, wherein said means for selecting the locally optimum time warping includes a transition likelihood memory which provides the logarithmic probabilities of the current observation for the particular speech segment prototype at a given node.

5. A computer-implemented method, comprising: under the control of one or more computer systems configured with executable instructions, acquiring, using a first camera of a computing device, at least one first image; acquiring, using a second camera of the computing device, information corresponding to an environment of the computing device, wherein the second camera faces a different direction than the first camera; determine one or more conditions of the environment using the information acquired by the second camera of the computing device; determining at least one parameter associated with the one or more conditions; performing at least one preprocessing operation associated with the at least one first image, wherein the at least one preprocessing operation includes binarizing at least a portion of each of the at least one first image based upon the one or more conditions; and causing the at least one first image to be processed using an optical character recognition (OCR) engine in electronic communication with at least one of the one or more computer systems, wherein (i) the at least one parameter is used when performing the preprocessing operation or (ii) the at least one parameter is used by the OCR engine.

5. A computer-implemented method, comprising: under the control of one or more computer systems configured with executable instructions, acquiring, using a first camera of a computing device, at least one first image; acquiring, using a second camera of the computing device, information corresponding to an environment of the computing device, wherein the second camera faces a different direction than the first camera; determine one or more conditions of the environment using the information acquired by the second camera of the computing device; determining at least one parameter associated with the one or more conditions; performing at least one preprocessing operation associated with the at least one first image, wherein the at least one preprocessing operation includes binarizing at least a portion of each of the at least one first image based upon the one or more conditions; and causing the at least one first image to be processed using an optical character recognition (OCR) engine in electronic communication with at least one of the one or more computer systems, wherein (i) the at least one parameter is used when performing the preprocessing operation or (ii) the at least one parameter is used by the OCR engine. 15. The computer-implemented method of claim 5 , further comprising: analyzing the information corresponding to the environment to identify text captured by the second camera; and analyzing the text captured by the second camera to identify words corresponding to the environment.

10. The method of claim 9 wherein said interested object is an object registered with said object graph manager as an object to be informed of any changes to said first data bearing object.

10. The method of claim 9 wherein said interested object is an object registered with said object graph manager as an object to be informed of any changes to said first data bearing object. 11. The method of claim 10 wherein said step of transmitting a message indicating that said first data bearing object expects to undergo a change comprises the steps of: registering said object graph manager as an observer with said first data bearing object; and broadcasting said message indicating that said first data bearing object expects to undergo a change from said first data bearing object to registered observers of said data bearing object.

61. The computer program product of claim 15 , wherein the computer program product is operable such that the computer program product is capable of cooperating with at least one mobile application adapted for accessing at least one of the plurality of different online applications utilizing a mobile device.

61. The computer program product of claim 15 , wherein the computer program product is operable such that the computer program product is capable of cooperating with at least one mobile application adapted for accessing at least one of the plurality of different online applications utilizing a mobile device. 62. The computer program product of claim 61 , wherein the computer program product is operable such that the computer program product is capable of allowing the at least one mobile application to provide at least a portion of a functionality of the at least one of the plurality of different online applications.

1. A method comprising: identifying state dependent content in an electronic document, wherein the state dependent content is renderable to have a plurality of appearances; receiving an attestation corresponding to the state dependent content; associating the attestation with the electronic document; and digitally signing the electronic document to generate a signed electronic document including a digital signature corresponding to a user associated with the attestation.

1. A method comprising: identifying state dependent content in an electronic document, wherein the state dependent content is renderable to have a plurality of appearances; receiving an attestation corresponding to the state dependent content; associating the attestation with the electronic document; and digitally signing the electronic document to generate a signed electronic document including a digital signature corresponding to a user associated with the attestation. 6. The method of claim 1 , wherein identifying state dependent content further comprises: presenting a disclosure of the identified state dependent content to the user.

1. A computer-implemented method comprising: receiving, by an electronic document reader, an electronic document that includes (a) a plurality of content items, (b) a set of rules that defines a set of allowed operations which are authorized to be performed on the plurality of content items included in the electronic document, and (c) a first selective digest generated by a document author; identifying, by a processor, one or more invariant content items from amongst the plurality of content items, wherein the invariant content items remain unchanged when the set of allowed operations are performed on the plurality of content items included in the electronic document; generating, by the processor, a second selective digest of the one or more invariant content items; performing, by the processor, a comparison of the first selective digest generated by the document author and the second selective digest generated by the processor, wherein the comparison results in validation information; disabling, by the electronic document reader, the allowed operations when the comparison of the first and second selective digests indicates that operations have been performed on the electronic document which are not allowed under the set of rules; and saving, on a computer readable storage device, a version of the electronic document that includes the set of rules and the validation information.

1. A computer-implemented method comprising: receiving, by an electronic document reader, an electronic document that includes (a) a plurality of content items, (b) a set of rules that defines a set of allowed operations which are authorized to be performed on the plurality of content items included in the electronic document, and (c) a first selective digest generated by a document author; identifying, by a processor, one or more invariant content items from amongst the plurality of content items, wherein the invariant content items remain unchanged when the set of allowed operations are performed on the plurality of content items included in the electronic document; generating, by the processor, a second selective digest of the one or more invariant content items; performing, by the processor, a comparison of the first selective digest generated by the document author and the second selective digest generated by the processor, wherein the comparison results in validation information; disabling, by the electronic document reader, the allowed operations when the comparison of the first and second selective digests indicates that operations have been performed on the electronic document which are not allowed under the set of rules; and saving, on a computer readable storage device, a version of the electronic document that includes the set of rules and the validation information. 8. The method of claim 1 , wherein the one or more invariant content items includes a content item selected from the group consisting of an annotation region, a text label, an annotation flag, and an appearance state.

9. A non-transitory computer readable medium adapted to control a computer and comprising a plurality of code segments for aggregating data associated with a plurality of interactions between at least one customer and at least one agent for generating business process analytics, the non-transitory computer readable medium comprising: a code segment for identifying at least two of a plurality of interactions between at least one customer and at least one agent; a code segment for receiving voice data associated with each of the identified interactions, the code segment for receiving voice data comprising: a code segment for separating the voice data into customer voice data and agent voice data; a code segment for mining the separated agent voice data and the separated customer voice data and analyzing the separated agent voice data and the separated customer voice data by applying a linguistic-based psychological behavioral model to the separated customer voice data and the separated agent voice data; and a code segment for generating assessment data corresponding to the separated agent voice data and the separated customer voice data, the assessment data comprising behavioral assessment data; a code segment for receiving agent call activity data associated with each of the identified interactions, the agent call activity data comprising at least one of agent on-call activity data, agent after-call activity data, agent screen analytics and agent desktop recording data; a code segment for receiving customer call activity data associated with each of the identified interactions; a code segment for receiving customer history data corresponding to each customer of each of the identified interactions; a code segment for analyzing the assessment data, the agent call activity data, the customer call activity data, and the customer history data associated with each of the identified interactions; a code segment for generating dissatisfaction data for each of the identified interactions based on the analyzed voice data, the agent call activity data, the customer call activity data, and the customer history data; and a code segment for generating business process analytics for the identified interactions, the business process analytics comprising dissatisfaction data.

9. A non-transitory computer readable medium adapted to control a computer and comprising a plurality of code segments for aggregating data associated with a plurality of interactions between at least one customer and at least one agent for generating business process analytics, the non-transitory computer readable medium comprising: a code segment for identifying at least two of a plurality of interactions between at least one customer and at least one agent; a code segment for receiving voice data associated with each of the identified interactions, the code segment for receiving voice data comprising: a code segment for separating the voice data into customer voice data and agent voice data; a code segment for mining the separated agent voice data and the separated customer voice data and analyzing the separated agent voice data and the separated customer voice data by applying a linguistic-based psychological behavioral model to the separated customer voice data and the separated agent voice data; and a code segment for generating assessment data corresponding to the separated agent voice data and the separated customer voice data, the assessment data comprising behavioral assessment data; a code segment for receiving agent call activity data associated with each of the identified interactions, the agent call activity data comprising at least one of agent on-call activity data, agent after-call activity data, agent screen analytics and agent desktop recording data; a code segment for receiving customer call activity data associated with each of the identified interactions; a code segment for receiving customer history data corresponding to each customer of each of the identified interactions; a code segment for analyzing the assessment data, the agent call activity data, the customer call activity data, and the customer history data associated with each of the identified interactions; a code segment for generating dissatisfaction data for each of the identified interactions based on the analyzed voice data, the agent call activity data, the customer call activity data, and the customer history data; and a code segment for generating business process analytics for the identified interactions, the business process analytics comprising dissatisfaction data. 13. The non-transitory computer readable medium of claim 9 , wherein the dissatisfaction data for each of the identified interactions is defined by at least one distress event, the code segment for generating dissatisfaction data further comprising: a code segment for measuring each distress event occurring during each of the identified interactions according to a predetermined distress event scale; a code segment for generating a score for each of the identified interactions based on each of the measured distress events; a code segment for comparing the score for each of the identified interactions against a threshold score; and a code segment for identifying an interaction as a dissatisfactory interaction when the score of the interaction exceeds the threshold score.

1. An auditory relational robotic controller (RRC)-humanoid robot comprising: a human-like mechanical robotic system comprising a human-like tactile recording monitor, a human-like visual recording monitor, and a human-like robotic body comprising a set of bipedal limbs, a set of arms, a set of hands, a set of fingers, an energy power source, and sets of motors and gears used to move the body, limbs, arms, hands, and fingers; an auditory human-like recording monitor sensitive to an auditory frequency range of 1-20,000 cycles per second (cps), said auditory human-like recording monitor comprising a set of linear pickup microphones and a set of spectrum analyzers that convert incoming sound into electronic phonetic (a-f-t)-signal output characterized by an amplitude, frequency, time (a-f-t) chart showing the amplitude and frequency of the incoming sound as a function of time; a relational robotic controller (RRC) that satisfies a set of specification requirements for relational robotic controllers; a verbal-phoneme sound generator that generates sequences of phoneme sounds that are controlled by the RRC; an interface circuit positioned between the auditory human-like recording monitor and the RRC, said interface circuit configured to decompose an electronic phonetic (a-f-t)-signal output of each spectrum analyzer into collective modalities tuned to the characteristics of verbal speech, generate a q-magnitude and p-direction p-phoneme vector that is a suitable input to a multi-dimensional function space Nodal Map Module (NMM), standardize the p-phoneme vector to operate in a Task Selector Module (TSM), the NMM, a Sequence Stepper Module (SSM) of the RRC, and successfully activate the verbal-phoneme sound generator, and develop a speech processing methodology for obtaining a one-to-one mapping of the acoustic signals onto a phonetic structure free of segmentation errors; and a programming methodology defined by a Declarative Hierarchical Task Diagram (DHTD) specification that provides the robot a human-like, high IQ form of verbal artificial intelligence (AI).

1. An auditory relational robotic controller (RRC)-humanoid robot comprising: a human-like mechanical robotic system comprising a human-like tactile recording monitor, a human-like visual recording monitor, and a human-like robotic body comprising a set of bipedal limbs, a set of arms, a set of hands, a set of fingers, an energy power source, and sets of motors and gears used to move the body, limbs, arms, hands, and fingers; an auditory human-like recording monitor sensitive to an auditory frequency range of 1-20,000 cycles per second (cps), said auditory human-like recording monitor comprising a set of linear pickup microphones and a set of spectrum analyzers that convert incoming sound into electronic phonetic (a-f-t)-signal output characterized by an amplitude, frequency, time (a-f-t) chart showing the amplitude and frequency of the incoming sound as a function of time; a relational robotic controller (RRC) that satisfies a set of specification requirements for relational robotic controllers; a verbal-phoneme sound generator that generates sequences of phoneme sounds that are controlled by the RRC; an interface circuit positioned between the auditory human-like recording monitor and the RRC, said interface circuit configured to decompose an electronic phonetic (a-f-t)-signal output of each spectrum analyzer into collective modalities tuned to the characteristics of verbal speech, generate a q-magnitude and p-direction p-phoneme vector that is a suitable input to a multi-dimensional function space Nodal Map Module (NMM), standardize the p-phoneme vector to operate in a Task Selector Module (TSM), the NMM, a Sequence Stepper Module (SSM) of the RRC, and successfully activate the verbal-phoneme sound generator, and develop a speech processing methodology for obtaining a one-to-one mapping of the acoustic signals onto a phonetic structure free of segmentation errors; and a programming methodology defined by a Declarative Hierarchical Task Diagram (DHTD) specification that provides the robot a human-like, high IQ form of verbal artificial intelligence (AI). 7. The auditory RRC-humanoid robot of claim 1 , wherein the DHTD specification comprises a programming methodology that is used to program each auditory TSM.

44. The system according to claim 43 , wherein the resource store is further configured to store one or more resources received from a user interface of a learner computer.

44. The system according to claim 43 , wherein the resource store is further configured to store one or more resources received from a user interface of a learner computer. 47. The method according to claim 44 , wherein the resource store further stores a resource having a video track associated with one or more timestamps, wherein a timestamp is metadata identifying a segment of the video track related to a keyword in the keyword store.

18. A method, comprising: (a) receiving an input stream including an interpersonal communication; determining, by a meta-parser that performs a preliminary parse on one or more portions of the input stream, a structure of the input stream for either subsequent selection of a parser or subsequent creation of a parser, the meta-parser outputting the input stream and an indication of whether the meta-parser was able to successfully parse the input stream, the input stream output from the meta-parser being passed to the parser; (b) lexically analyzing, by a processor, the input stream to provide a tokenized equivalent; (c) syntactically parsing, by the parser, the tokenized equivalent to provide a parser output; and (d) semantically analyzing, by the processor, the parser output to provide an indication of at least one of a personality classification, type, and function associated with the input stream.

18. A method, comprising: (a) receiving an input stream including an interpersonal communication; determining, by a meta-parser that performs a preliminary parse on one or more portions of the input stream, a structure of the input stream for either subsequent selection of a parser or subsequent creation of a parser, the meta-parser outputting the input stream and an indication of whether the meta-parser was able to successfully parse the input stream, the input stream output from the meta-parser being passed to the parser; (b) lexically analyzing, by a processor, the input stream to provide a tokenized equivalent; (c) syntactically parsing, by the parser, the tokenized equivalent to provide a parser output; and (d) semantically analyzing, by the processor, the parser output to provide an indication of at least one of a personality classification, type, and function associated with the input stream. 25. The method of claim 18 , wherein step (d) outputs a personality dynamic associated with a source of the interpersonal communication and further comprising: (e) selecting a set of user interface transformation rules; and (f) applying the personality dynamic to the set of user interface transformation rules to select a set of commands to provide to a user interface.

5. The method of claim 1 , wherein the obtained sets of annotations are clustered into annotation clusters based on the features within the piece of source data identified by the annotations using the distributed data annotation server system.

5. The method of claim 1 , wherein the obtained sets of annotations are clustered into annotation clusters based on the features within the piece of source data identified by the annotations using the distributed data annotation server system. 7. The method of claim 5 , wherein the annotation clusters comprise annotations that are within a distance threshold from the ground truth for the feature identified by the annotations.

8. A speech-based speaker recognition system comprising: a passphrase recognition module to determine whether a test passphrase spoken as test speech input matches a reference passphrase spoken as reference speech input; a voice feature recognition module to determine whether a pitch as a function of a time period T of a speaker of the test passphrase matches a pitch as a function of the time period T of a speaker of the reference passphrase; and a recording storage to store a reference speech recording accessible by the passphrase recognition module and the voice feature recognition module, the reference speech recording comprising the reference passphrase.

8. A speech-based speaker recognition system comprising: a passphrase recognition module to determine whether a test passphrase spoken as test speech input matches a reference passphrase spoken as reference speech input; a voice feature recognition module to determine whether a pitch as a function of a time period T of a speaker of the test passphrase matches a pitch as a function of the time period T of a speaker of the reference passphrase; and a recording storage to store a reference speech recording accessible by the passphrase recognition module and the voice feature recognition module, the reference speech recording comprising the reference passphrase. 11. The speech-based speaker recognition system of claim 8 , wherein the passphrase recognition module comprises: a feature vector module for determining a test set of feature vectors for the test passphrase and for determining a reference set of feature vectors for the reference passphrase; and a dynamic time warping module to correlate the reference set of feature vectors with the test set of feature vectors over a time dimension; wherein the reference set of feature vectors comprises Mel Cepstrum feature vectors, and wherein the test set of feature vectors comprises Mel Cepstrum feature vectors.

26. A textual document authoring system, comprising: means for storing in storage a library of textual components and a database of marks identifying each of the components that is part of a first textual document, means for displaying in a word processor window on a display responsive to the storage the first textual document including a first plurality of the textual components, and a user interface for responding to user input by a first user to an interface of the word processor to edit the first textual document while the document is displayed by the means for displaying in the word processor window; for responding to user input by a second user to the interface of the word processor to edit a second textual document including a second plurality of the textual components, the first plurality including one or more textual components from the second plurality; for detecting when one of the textual components that belongs to both the first plurality and the second plurality is updated in one of the first and second textual documents; for prompting the user of the other of the first and second textual documents to accept or reject changes made to the one of the textual components, in response to updating of the one of the textual components; for responding to user input to the interface of the word processor to generate a second version of the first textual document that includes updated textual components; and means for responding to user input to the interface of the word processor to generate a second version of the second textual document that includes updated textual components.

26. A textual document authoring system, comprising: means for storing in storage a library of textual components and a database of marks identifying each of the components that is part of a first textual document, means for displaying in a word processor window on a display responsive to the storage the first textual document including a first plurality of the textual components, and a user interface for responding to user input by a first user to an interface of the word processor to edit the first textual document while the document is displayed by the means for displaying in the word processor window; for responding to user input by a second user to the interface of the word processor to edit a second textual document including a second plurality of the textual components, the first plurality including one or more textual components from the second plurality; for detecting when one of the textual components that belongs to both the first plurality and the second plurality is updated in one of the first and second textual documents; for prompting the user of the other of the first and second textual documents to accept or reject changes made to the one of the textual components, in response to updating of the one of the textual components; for responding to user input to the interface of the word processor to generate a second version of the first textual document that includes updated textual components; and means for responding to user input to the interface of the word processor to generate a second version of the second textual document that includes updated textual components. 38. The apparatus of claim 26 further including means for maintaining a classification field, and further including menus for identifying to the user new components that may be relevant to one of the documents based on the classification field.

1. A method of speaker dependent speech recognition, comprising: building a universal background model (UBM) for a user responsive to unknown speech from only the user; adapting the universal background model (UBM) to build a single Gaussian Mixture Model (GMM) responsive to a registration code phrase spoken by the user and using the single GMM to generate a code-phrase model (CPM) for the user; generating a longest common sequence (LCS) template for the user from the code-phrase model (CPM), wherein the LCS template is represented by a sequence of an index of a best performing component of the single GMM; utilizing the universal background model (UBM), code-phrase model (CPM) and longest common sequence (LCS) template to determine whether the user is an authorized user and has spoken the registration code phrase, the utilizing including, capturing a test code phrase; accepting the test code phrase as being spoken by the authorized user when the log likelihood ratio between the code-phrase model (CPM) and the universal background model (UBM) is positive; and comparing the Longest Common Sequence (LCS) template with a test code-phrase template for the captured test code phrase.

1. A method of speaker dependent speech recognition, comprising: building a universal background model (UBM) for a user responsive to unknown speech from only the user; adapting the universal background model (UBM) to build a single Gaussian Mixture Model (GMM) responsive to a registration code phrase spoken by the user and using the single GMM to generate a code-phrase model (CPM) for the user; generating a longest common sequence (LCS) template for the user from the code-phrase model (CPM), wherein the LCS template is represented by a sequence of an index of a best performing component of the single GMM; utilizing the universal background model (UBM), code-phrase model (CPM) and longest common sequence (LCS) template to determine whether the user is an authorized user and has spoken the registration code phrase, the utilizing including, capturing a test code phrase; accepting the test code phrase as being spoken by the authorized user when the log likelihood ratio between the code-phrase model (CPM) and the universal background model (UBM) is positive; and comparing the Longest Common Sequence (LCS) template with a test code-phrase template for the captured test code phrase. 9. The method of claim 1 wherein building a universal background model (UBM) for the user responsive to unknown speech from the user comprises using an electronic device as a phone and capturing the unknown speed from the user while the user is using the electronic device as a phone.

8. One or more devices comprising: one or more memories to store instructions; and one or more processors to execute the instructions to: store, in a memory associated with the one or more devices, feature data associated with links to a plurality of documents, the feature data, associated with the links, identifying: words in anchor text associated with the links, a quantity of the words in the anchor text, and context relating to one or more words before or after the links, the feature data associated with the links including feature data associated with one or more links that were selected and feature data associated with one or more other links that were not selected, generate a rank for a particular document, when generating the rank, the one or more processors are to: determine particular feature data associated with a link to the particular document, the particular feature data identifying one or more attributes of the link, determine a weight associated with the link, the weight indicating a probability of the link being selected, the weight being determined based on the stored feature data associated with the one or more links that were selected, the stored feature data associated with the one or more other links that were not selected, the particular feature data, and selection data, the selection data identifying user behavior relating to the links, the weight indicating a higher probability of the link being selected when the particular feature data corresponds to the stored feature data associated with the one or more links than when the particular feature data corresponds to the stored feature data associated with the one or more other links, the rank being generated based on the weight, identify documents associated with a search query, the documents including the particular document, and provide information associated with the particular document based on: the generated rank, and the search query.

8. One or more devices comprising: one or more memories to store instructions; and one or more processors to execute the instructions to: store, in a memory associated with the one or more devices, feature data associated with links to a plurality of documents, the feature data, associated with the links, identifying: words in anchor text associated with the links, a quantity of the words in the anchor text, and context relating to one or more words before or after the links, the feature data associated with the links including feature data associated with one or more links that were selected and feature data associated with one or more other links that were not selected, generate a rank for a particular document, when generating the rank, the one or more processors are to: determine particular feature data associated with a link to the particular document, the particular feature data identifying one or more attributes of the link, determine a weight associated with the link, the weight indicating a probability of the link being selected, the weight being determined based on the stored feature data associated with the one or more links that were selected, the stored feature data associated with the one or more other links that were not selected, the particular feature data, and selection data, the selection data identifying user behavior relating to the links, the weight indicating a higher probability of the link being selected when the particular feature data corresponds to the stored feature data associated with the one or more links than when the particular feature data corresponds to the stored feature data associated with the one or more other links, the rank being generated based on the weight, identify documents associated with a search query, the documents including the particular document, and provide information associated with the particular document based on: the generated rank, and the search query. 9. The one or more devices of claim 8 , where the one or more processors are further to: generate rules for a model based on information relating to links associated with certain documents, the links associated with the certain documents including the links to the plurality of documents, where the rank is generated using the model.

1. A method of identifying potential confidential information in a data item, the data item associated with a source, comprising: obtaining, from each of a set of alternative sources, a data item of a same type and format as the data item; comparing, using a hardware element, the data item to the data item(s) obtained from the set of alternative sources to identify occurrences of particular pieces of information in the data item, wherein multiple occurrences of a particular piece of information within a data item from each alternative source are treated as a single occurrence; and based on the occurrences of particular pieces of information in the data item and a given sensitivity criteria, and without knowledge that the particular pieces of information are considered by the source to be confidential, segmenting one or more pieces of information in the data item as representing the potential confidential information.

1. A method of identifying potential confidential information in a data item, the data item associated with a source, comprising: obtaining, from each of a set of alternative sources, a data item of a same type and format as the data item; comparing, using a hardware element, the data item to the data item(s) obtained from the set of alternative sources to identify occurrences of particular pieces of information in the data item, wherein multiple occurrences of a particular piece of information within a data item from each alternative source are treated as a single occurrence; and based on the occurrences of particular pieces of information in the data item and a given sensitivity criteria, and without knowledge that the particular pieces of information are considered by the source to be confidential, segmenting one or more pieces of information in the data item as representing the potential confidential information. 2. The method as described in claim 1 further including highlighting the one or more pieces of information.

1. A method of capturing information using a mobile handheld device with a camera comprising the steps of: providing a user with a capability to select a type of professional information to be captured; capturing information identified by the user, wherein software associated with the mobile handheld device adjusts the step of capturing information for the selected type of professional information; pre-processing information so as to obtain pre-processed information utilizing software, wherein the step of pre-processing the information is adjusted for the selected type of professional information; and providing the pre-processed information to a server for post-processing in a message that includes data identifying the type of professional information.

1. A method of capturing information using a mobile handheld device with a camera comprising the steps of: providing a user with a capability to select a type of professional information to be captured; capturing information identified by the user, wherein software associated with the mobile handheld device adjusts the step of capturing information for the selected type of professional information; pre-processing information so as to obtain pre-processed information utilizing software, wherein the step of pre-processing the information is adjusted for the selected type of professional information; and providing the pre-processed information to a server for post-processing in a message that includes data identifying the type of professional information. 3. The method of claim 1 , wherein the step of pre-processing the information further includes the step of: removing background information from the captured information.

1. An energy harvesting communication device configured with on chip signal booster apparatus; comprising: at least a communication apparatus; at least an antenna apparatus in association with an input and/or output device; at least a processor in communication with at least one of: said at least a communication apparatus; said at least an antenna apparatus; a charge platform in association with at least charging circuit; at least an interactive media in association with at least an interactive interface; an output device; and at least a sensor apparatus embedded in silicon substrate and etched and/or fused in nano-fiber and/or microfiber material, said at least a sensor apparatus in association with said at least a communication apparatus to provide at least one of an effective energy harvesting medium, communication clarity, communication medium, detection selectivity medium, and detection platform.

1. An energy harvesting communication device configured with on chip signal booster apparatus; comprising: at least a communication apparatus; at least an antenna apparatus in association with an input and/or output device; at least a processor in communication with at least one of: said at least a communication apparatus; said at least an antenna apparatus; a charge platform in association with at least charging circuit; at least an interactive media in association with at least an interactive interface; an output device; and at least a sensor apparatus embedded in silicon substrate and etched and/or fused in nano-fiber and/or microfiber material, said at least a sensor apparatus in association with said at least a communication apparatus to provide at least one of an effective energy harvesting medium, communication clarity, communication medium, detection selectivity medium, and detection platform. 60. The energy harvesting communication device of claim 1 , wherein said communication apparatus further comprises an audio device being configured for at least audio and/or visual application operable for at least one of: inputting communications; outputting communications; wherein said at least one audio and/or visual application further comprises at least an audio and/or visual device comprising at least one of: a touch screen input and/or output device; at least a speaker device; at least a microphone device; at least a voice enabled communications device, a device for multimedia communication including Voice Over Internet Protocol “VOIP.

83. A method performed by one or more server devices, the method comprising: receiving a query, at one or more processors of the one or more server devices, inquiring about a document in a set of ranked documents, where documents in the set of ranked documents that are well known are ranked higher than documents that are not well known; determining, by one or more processors of the one or more server devices, a fraud score for the document based on one or more factors, where the one or more factors include a ranking of the document relative to other documents in the set of ranked documents; designating, by one or more processors of the one or more server devices, the document as trustworthy when the fraud score does not pass a first threshold; designating, by one or more processors of the one or more server devices, the document as untrustworthy when the fraud score passes a second different threshold; obtaining, by one or more processors of the one or more server devices, a designation of trustworthiness from a user when the fraud score is between the first threshold and the second different threshold; and storing, in a memory associated with the one or more server devices, an indication of the trustworthiness of the document, determined based on designating the document as trustworthy, designating the document as untrustworthy, or obtaining a designation of trustworthiness, with the fraud score and an identifier for the document.

83. A method performed by one or more server devices, the method comprising: receiving a query, at one or more processors of the one or more server devices, inquiring about a document in a set of ranked documents, where documents in the set of ranked documents that are well known are ranked higher than documents that are not well known; determining, by one or more processors of the one or more server devices, a fraud score for the document based on one or more factors, where the one or more factors include a ranking of the document relative to other documents in the set of ranked documents; designating, by one or more processors of the one or more server devices, the document as trustworthy when the fraud score does not pass a first threshold; designating, by one or more processors of the one or more server devices, the document as untrustworthy when the fraud score passes a second different threshold; obtaining, by one or more processors of the one or more server devices, a designation of trustworthiness from a user when the fraud score is between the first threshold and the second different threshold; and storing, in a memory associated with the one or more server devices, an indication of the trustworthiness of the document, determined based on designating the document as trustworthy, designating the document as untrustworthy, or obtaining a designation of trustworthiness, with the fraud score and an identifier for the document. 86. The method of claim 83 , where the query is sent in response to receipt of an e-mail that includes a reference to the document.

13. The method of claim 1 , further comprising recommending, by the media content trend analysis system, the media program clip for access by a user of a media content processing device.

13. The method of claim 1 , further comprising recommending, by the media content trend analysis system, the media program clip for access by a user of a media content processing device. 14. The method of claim 13 , wherein the recommending of the media program clip comprises providing, for display by the media content processing device, a graphical user interface including a display element representative of the media content clip.

11. A computer program product comprising: non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method comprising: scanning an input image via an image input device; compressing the scanned image using an image compression tool by performing OCR (Optical Character Recognition) on the scanned image to generate OCR results and then performing tokenization on the scanned image using the OCR results, wherein the following rules are applied during the tokenization process: the symbols with different primary OCR results are not clustered into the same group; and for symbols with the same primary OCR result: if both symbols have high confidence levels for primary results and low confidence levels for secondary results, use a loose matching criteria to allow large shape variation; if both symbols have high confidence levels for primary results and at least one has a high confidence levels for its secondary results, use a tight matching criteria to avoid misclassification; if at least one symbol has a low confidence level for its primary result, use a moderate to tight matching criteria; and storing the compressed image in a storage device or printing the compressed image via an image output device after it has been decoded.

11. A computer program product comprising: non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method comprising: scanning an input image via an image input device; compressing the scanned image using an image compression tool by performing OCR (Optical Character Recognition) on the scanned image to generate OCR results and then performing tokenization on the scanned image using the OCR results, wherein the following rules are applied during the tokenization process: the symbols with different primary OCR results are not clustered into the same group; and for symbols with the same primary OCR result: if both symbols have high confidence levels for primary results and low confidence levels for secondary results, use a loose matching criteria to allow large shape variation; if both symbols have high confidence levels for primary results and at least one has a high confidence levels for its secondary results, use a tight matching criteria to avoid misclassification; if at least one symbol has a low confidence level for its primary result, use a moderate to tight matching criteria; and storing the compressed image in a storage device or printing the compressed image via an image output device after it has been decoded. 15. The computer program product defined in claim 11 , wherein the method further comprises: finding a text string with OCR; and comparing the text string before and after any lossy image processing operations such as pre-filtering or JBIG2 compression to determine whether the same text string is generated by the OCR process.

1. An electronic device for disambiguation of stroke input, the device comprising: a microprocessor for controlling the operation of the device; an input device coupled to the microprocessor for accepting a stroke input; a display device for showing a graphical user interface, the display device being coupled to the microprocessor for communicating an output; and a memory for storing instructions executable by the microprocessor; the device being configured to: receive a sequence of input strokes; apply one or more stroke disambiguation rules to the received sequence of input strokes to generate an updated sequence of input strokes, wherein the stroke disambiguation rules include a rule to correct for an incorrect stroke in the received sequence of input strokes based on similarity between the incorrect stroke and a correct stroke; and transmit a signal representing the updated sequence of input strokes; wherein the device is further configured to automatically apply the one or more stroke disambiguation rules to the received sequence of input strokes if less than a required minimum number of candidates is predicted for the received sequence of input strokes.

1. An electronic device for disambiguation of stroke input, the device comprising: a microprocessor for controlling the operation of the device; an input device coupled to the microprocessor for accepting a stroke input; a display device for showing a graphical user interface, the display device being coupled to the microprocessor for communicating an output; and a memory for storing instructions executable by the microprocessor; the device being configured to: receive a sequence of input strokes; apply one or more stroke disambiguation rules to the received sequence of input strokes to generate an updated sequence of input strokes, wherein the stroke disambiguation rules include a rule to correct for an incorrect stroke in the received sequence of input strokes based on similarity between the incorrect stroke and a correct stroke; and transmit a signal representing the updated sequence of input strokes; wherein the device is further configured to automatically apply the one or more stroke disambiguation rules to the received sequence of input strokes if less than a required minimum number of candidates is predicted for the received sequence of input strokes. 2. The device of claim 1 wherein the one or more stroke disambiguation rules include a rule to correct for one or more omitted strokes in a stroke sequence.

16. A computer program embodied on a computer-readable medium, the computer program comprising: a code segment operative to tokenize a target service description of a Service Oriented Architecture (SOA) into at least one token; a code segment operative to query a repository of source code elements using said tokens to identify a set of relevant matches in said repository and their locations within said source code; a code segment operative to combine any of said matches that are within a predefined distance from each other within a file containing said source code; a code segment operative to rank any of said combined matches in accordance with at least one predefined heuristic; a code segment operative to aggregate said ranked matches by procedure within said source code; a code segment operative to combine said rankings of said aggregated matches for any of said procedures into a score that represents a rank for said procedure for said query; a code segment operative to compare interface definitions defined for said service description to entry and exit points of any of said procedures to identify candidate procedures having input and output parameters similar to those of said interface definitions; and a code segment operative to present said candidate procedures together with said score.

16. A computer program embodied on a computer-readable medium, the computer program comprising: a code segment operative to tokenize a target service description of a Service Oriented Architecture (SOA) into at least one token; a code segment operative to query a repository of source code elements using said tokens to identify a set of relevant matches in said repository and their locations within said source code; a code segment operative to combine any of said matches that are within a predefined distance from each other within a file containing said source code; a code segment operative to rank any of said combined matches in accordance with at least one predefined heuristic; a code segment operative to aggregate said ranked matches by procedure within said source code; a code segment operative to combine said rankings of said aggregated matches for any of said procedures into a score that represents a rank for said procedure for said query; a code segment operative to compare interface definitions defined for said service description to entry and exit points of any of said procedures to identify candidate procedures having input and output parameters similar to those of said interface definitions; and a code segment operative to present said candidate procedures together with said score. 18. A computer program according to claim 16 wherein said code segment operative to tokenize is operative to query is operative to employ stemming techniques, predefined thesauri, and predefined abbreviation lists to find inexact matches.

102. The method of claim 101 , wherein the storing of the parsed resume further comprises: storing each said at least one skill or experience-related phrase having an identical term of experience as an element defined by a markup language, the element comprising a start tag, content data, and an end tag, wherein the start tag and the end tag include the identical term of experience, and wherein the content data includes each said at least one skill or experience-related phrase having the identical term of experience.

102. The method of claim 101 , wherein the storing of the parsed resume further comprises: storing each said at least one skill or experience-related phrase having an identical term of experience as an element defined by a markup language, the element comprising a start tag, content data, and an end tag, wherein the start tag and the end tag include the identical term of experience, and wherein the content data includes each said at least one skill or experience-related phrase having the identical term of experience. 105. The method of claim 102 , wherein the storing of the element is to a file.

1. A method for translating stenographic strokes, the method comprising: receiving a series of stenographic strokes on a stenographic keyboard; creating a table of translations of one or more strokes within the series of strokes; sequentially assigning a score to each of the one or more strokes; determining at least one alternate translation to at least one of the translations in the table of translations by; determining a number of possible ways a single key could be one of added to and taken away from a beginning of the series of stenographic strokes, each of the possible ways corresponding to a dictionary entry in a steno-to-text dictionary having a plurality of dictionary entries, each dictionary entry having an index associated therewith; locating in the steno-to-text dictionary each dictionary entry corresponding with each of the determined possible ways; creating a list of the indices associated with the located dictionary entries; including an index of an untouched entry in the list of the indices; setting a maximum physical key distance N between the indices of the located dictionary entries to be examined; examining at least one index in the list of indices and discarding each of the indices in the list of indices that is within the distance N of the at least one index; for each of the non-discarded indices of the list of indices, locating dictionary entries in the steno-to-text dictionary having an index within N distance of the non-discarded index; evaluating the dictionary entries associated with the non-discarded indices and evaluating the located dictionary entries associated with the indices within the distance N of the non-discarded indices; creating a sequence of ranges that contain potential mis-strokes for the dictionary entry being evaluated; and determining an appropriateness of a match to a word in the steno-to-text dictionary by counting a degree of error; ranking the translations and alternate translations based on an accumulation of the score of the strokes within; and selecting at least one of: one of the ranked translations; and one of the ranked alternate translations, based on a best score.

1. A method for translating stenographic strokes, the method comprising: receiving a series of stenographic strokes on a stenographic keyboard; creating a table of translations of one or more strokes within the series of strokes; sequentially assigning a score to each of the one or more strokes; determining at least one alternate translation to at least one of the translations in the table of translations by; determining a number of possible ways a single key could be one of added to and taken away from a beginning of the series of stenographic strokes, each of the possible ways corresponding to a dictionary entry in a steno-to-text dictionary having a plurality of dictionary entries, each dictionary entry having an index associated therewith; locating in the steno-to-text dictionary each dictionary entry corresponding with each of the determined possible ways; creating a list of the indices associated with the located dictionary entries; including an index of an untouched entry in the list of the indices; setting a maximum physical key distance N between the indices of the located dictionary entries to be examined; examining at least one index in the list of indices and discarding each of the indices in the list of indices that is within the distance N of the at least one index; for each of the non-discarded indices of the list of indices, locating dictionary entries in the steno-to-text dictionary having an index within N distance of the non-discarded index; evaluating the dictionary entries associated with the non-discarded indices and evaluating the located dictionary entries associated with the indices within the distance N of the non-discarded indices; creating a sequence of ranges that contain potential mis-strokes for the dictionary entry being evaluated; and determining an appropriateness of a match to a word in the steno-to-text dictionary by counting a degree of error; ranking the translations and alternate translations based on an accumulation of the score of the strokes within; and selecting at least one of: one of the ranked translations; and one of the ranked alternate translations, based on a best score. 4. The method according to claim 1 , wherein the determining alternate translation comprises: determining at least one phonetic profile of the series of strokes; and locating at least one word in a steno-to-text dictionary that corresponds to the at least one phonetic profile.

12. The apparatus of claim 9 , wherein the composite features are selected so as to differentiate training data.

12. The apparatus of claim 9 , wherein the composite features are selected so as to differentiate training data. 13. The apparatus of claim 12 , wherein the composite features are selected such that feature discrimination and class labels are considered.

5. A method in accordance with claim 2, wherein said receiving step further comprises: transmitting the complete handwritten new character in the handwriting capture widget to a handwriting recognition device; when a recognized new character has been detected in the transmitted complete handwritten new character by the handwriting recognition device, replacing the visual representation of the complete handwritten new character in the handwriting capture widget with the recognized new character.

5. A method in accordance with claim 2, wherein said receiving step further comprises: transmitting the complete handwritten new character in the handwriting capture widget to a handwriting recognition device; when a recognized new character has been detected in the transmitted complete handwritten new character by the handwriting recognition device, replacing the visual representation of the complete handwritten new character in the handwriting capture widget with the recognized new character. 6. A method in accordance with claim 5, wherein said receiving step further comprises the step of displaying the recognized new character in the current text entry widget.

8. The method of claim 1 , wherein the list of one or more suggested character strings is displayed in a ranked order.

8. The method of claim 1 , wherein the list of one or more suggested character strings is displayed in a ranked order. 9. The method of claim 8 , wherein the character string is a highest ranked character string from the list of one or more suggested character strings that are ranked in the ranked order.

1. A computer implemented method for semantic version control of source code, the computer implemented method comprising: scanning, by a computer, a source code file for semantic relationships between source code sections within the source code file; generating dependencies, by a dependency generator, between the source code sections using the semantic relationships of the source code sections; constructing a semantic graph by the computer using the dependencies and associated metadata describing a respective section of the source code sections individually, wherein the dependencies indicate the semantic relationships between the source code sections and wherein the metadata includes semantic graph relationships and developer comments entered by a set of developers, wherein the set of developers comprises at least one appropriate developer to receive a notification, wherein an appropriate developer is one who wants to receive notice of a change to the semantic graph including a developer whose workflow is affected by the source code sections and, wherein the semantic graph comprises vertices and graph edges, wherein the vertices describe the source code sections and include the metadata applicable to each respective source code section, wherein the vertices are a geometric shape and the graph edges are directional paths between the vertices that track a dependency between the vertices; creating a blank semantic graph, by the computer, for projects that do not have an existing source code file in the source code repository and storing the blank semantic graph in the source code repository; responsive to receiving dependency changes for the semantic graph through a user interface of a semantic graph editor, modifying the semantic graph with the dependency changes to add or delete one or more vertices and graph edges of the semantic graph to form a modified semantic graph, wherein the modifying the semantic graph includes making the dependency changes on the semantic graph through a user interface of a semantic graph editor and modifying the blank semantic graph by adding dependencies to the blank semantic graph as source code classes are created for the projects that do not have the existing source code file in the source code repository; storing the modified semantic graph by the computer in the source code repository; and notifying, by the computer, the set of developers of the modified semantic graph.

1. A computer implemented method for semantic version control of source code, the computer implemented method comprising: scanning, by a computer, a source code file for semantic relationships between source code sections within the source code file; generating dependencies, by a dependency generator, between the source code sections using the semantic relationships of the source code sections; constructing a semantic graph by the computer using the dependencies and associated metadata describing a respective section of the source code sections individually, wherein the dependencies indicate the semantic relationships between the source code sections and wherein the metadata includes semantic graph relationships and developer comments entered by a set of developers, wherein the set of developers comprises at least one appropriate developer to receive a notification, wherein an appropriate developer is one who wants to receive notice of a change to the semantic graph including a developer whose workflow is affected by the source code sections and, wherein the semantic graph comprises vertices and graph edges, wherein the vertices describe the source code sections and include the metadata applicable to each respective source code section, wherein the vertices are a geometric shape and the graph edges are directional paths between the vertices that track a dependency between the vertices; creating a blank semantic graph, by the computer, for projects that do not have an existing source code file in the source code repository and storing the blank semantic graph in the source code repository; responsive to receiving dependency changes for the semantic graph through a user interface of a semantic graph editor, modifying the semantic graph with the dependency changes to add or delete one or more vertices and graph edges of the semantic graph to form a modified semantic graph, wherein the modifying the semantic graph includes making the dependency changes on the semantic graph through a user interface of a semantic graph editor and modifying the blank semantic graph by adding dependencies to the blank semantic graph as source code classes are created for the projects that do not have the existing source code file in the source code repository; storing the modified semantic graph by the computer in the source code repository; and notifying, by the computer, the set of developers of the modified semantic graph. 6. The computer implemented method of claim 1 , further comprising: determining whether the source code file is located; responsive to scanning a source code repository and not locating the source code file, creating a blank semantic graph; storing the blank semantic graph in the source code repository; adding the dependencies and the metadata to the blank semantic graph as the source code sections are created for a new source code file; responsive to scanning the source code repository and locating the source code file without an associated semantic graph creating the blank semantic graph; and associating the blank semantic graph with the source code file located.

1. In an electronic word processing system for creating and editing a document, the document comprising a plurality of sentences, a method for verifying the accuracy of spelling and grammatical composition of the plurality of sentences in the document, the method comprising the steps of: performing a first sequence comprising the steps of: extracting one of the plurality of sentences from the document, the sentence comprising a plurality of words; checking the spelling of each word in the sentence for a misspelled word in a spell checker program module; displaying the sentence and each misspelled word within a first instance of a combined spelling and grammar dialog box; displaying a plurality of common command buttons operative for correcting the spelling errors; and performing a second sequence, subsequent to the first sequence, comprising the steps of: checking the grammatical composition of the sentence in a grammar checker program module; displaying the sentence and the grammatical errors within a second instance of the combined spelling and grammar dialog box; and displaying the plurality of common command buttons operative for correcting the grammatical errors.

1. In an electronic word processing system for creating and editing a document, the document comprising a plurality of sentences, a method for verifying the accuracy of spelling and grammatical composition of the plurality of sentences in the document, the method comprising the steps of: performing a first sequence comprising the steps of: extracting one of the plurality of sentences from the document, the sentence comprising a plurality of words; checking the spelling of each word in the sentence for a misspelled word in a spell checker program module; displaying the sentence and each misspelled word within a first instance of a combined spelling and grammar dialog box; displaying a plurality of common command buttons operative for correcting the spelling errors; and performing a second sequence, subsequent to the first sequence, comprising the steps of: checking the grammatical composition of the sentence in a grammar checker program module; displaying the sentence and the grammatical errors within a second instance of the combined spelling and grammar dialog box; and displaying the plurality of common command buttons operative for correcting the grammatical errors. 4. The method recited in claim 1, wherein the step of extracting one sentence of the plurality of sentences from the document comprises the steps of: calling a grammar checker program module; transferring a buffer of text to the grammar checker program module, the buffer of text comprising at least one of the plurality of sentences; and receiving sentence indices from the grammar checker program module, the sentence indices indicative of a beginning and an end of the sentence.

9. The system of claim 8 , wherein the digitized user speech is recognized using a sub-grammar based on a sub-component of the digitized user speech.

9. The system of claim 8 , wherein the digitized user speech is recognized using a sub-grammar based on a sub-component of the digitized user speech. 10. The system of claim 9 , wherein the sub-grammar is associated with a task.

14. The one or more storage media of claim 13 , wherein the instructions, when executed by the one or more processors, further cause: storing order data that indicates an order among the first plurality of threads, wherein a first thread of the first plurality of threads is ordered before a second thread of the first plurality of threads; wherein, for the second thread, copying the one or more node identities from the thread-local data structure to the first data structure comprises: determining a number of node identities that are stored in a thread-local data structure of the first thread, and based on the number, identifying a position, in the first data structure, in which to store the one or more node identities identified by the second thread.

14. The one or more storage media of claim 13 , wherein the instructions, when executed by the one or more processors, further cause: storing order data that indicates an order among the first plurality of threads, wherein a first thread of the first plurality of threads is ordered before a second thread of the first plurality of threads; wherein, for the second thread, copying the one or more node identities from the thread-local data structure to the first data structure comprises: determining a number of node identities that are stored in a thread-local data structure of the first thread, and based on the number, identifying a position, in the first data structure, in which to store the one or more node identities identified by the second thread. 15. The one or more storage media of claim 14 , wherein identifying the position in the first data structure comprises identifying the position in the first data structure prior to the first thread storing, in the first data structure, the one or more node identities identified by the first thread.

13. The system of claim 11 , wherein the data structure stores the unstructured data and the structured data associated with an email.

13. The system of claim 11 , wherein the data structure stores the unstructured data and the structured data associated with an email. 16. The system of claim 13 , wherein the data structure stores the unstructured data associated with an email attachment.

16. The system of claim 11 , wherein the one or more contexts are received from a user device.

16. The system of claim 11 , wherein the one or more contexts are received from a user device. 17. The system of claim 16 , wherein the one or more contexts include data including a user's geographic location, a user's search history, user's interests, or a user's activity.

10. The method of claim 1 , further comprising: determining that a document link is to be found; determining at least one document descriptor associated with a document link; and finding a document link based on the determined document descriptor.

10. The method of claim 1 , further comprising: determining that a document link is to be found; determining at least one document descriptor associated with a document link; and finding a document link based on the determined document descriptor. 11. The method of claim 10 , wherein determining at least one document descriptor associated with a document link comprises identifying at least one document descriptor for a currently presented document.

1. A method for constructing a search database using an ontology, the method comprising: via at least one processor: obtaining a list of items, each item corresponding to a potential search result; obtaining a list of terms from an ontology that describes all of the items, each term being an attribute of a corresponding item; dividing the terms into user-selectable search queries and non-selectable terms; activating the items and the terms as nodes in a network, wherein: the nodes are connected based on semantic relationships specified by the ontology, the network has three layers, a first layer including a node for each of the items, a second layer including a node for each search query, and a third layer including a node for each non-selectable term, the second layer nodes correspond to user-observed states of their respective terms, and the third layer nodes correspond to hidden states of the terms represented in the second layer; and configuring the network such that a probability of nodes in the first layer is determined by updating states of nodes in the third layer: deterministically based on the semantic relationships, and probabilistically based on at least one of (ii) frequency information indicating a frequency with which a term of a node in the third layer is known to occur when an associated item of a node in the first layer also occurs and (ii) a predefined false positive or false negative rate.

1. A method for constructing a search database using an ontology, the method comprising: via at least one processor: obtaining a list of items, each item corresponding to a potential search result; obtaining a list of terms from an ontology that describes all of the items, each term being an attribute of a corresponding item; dividing the terms into user-selectable search queries and non-selectable terms; activating the items and the terms as nodes in a network, wherein: the nodes are connected based on semantic relationships specified by the ontology, the network has three layers, a first layer including a node for each of the items, a second layer including a node for each search query, and a third layer including a node for each non-selectable term, the second layer nodes correspond to user-observed states of their respective terms, and the third layer nodes correspond to hidden states of the terms represented in the second layer; and configuring the network such that a probability of nodes in the first layer is determined by updating states of nodes in the third layer: deterministically based on the semantic relationships, and probabilistically based on at least one of (ii) frequency information indicating a frequency with which a term of a node in the third layer is known to occur when an associated item of a node in the first layer also occurs and (ii) a predefined false positive or false negative rate. 3. The method of claim 1 , further comprising: adding two additional nodes to the network, wherein a first one of the additional nodes influences a local probability distribution of the nodes in the third layer in accordance with the false positive rate and a second one of the additional nodes influences the local probability distribution of the nodes in the third layer in accordance with the false negative rate.

255. A system for using a computer to improve a precision ratio when searching a resume database, comprising: a memory device resident in the computer; and a processor disposed in communication with the memory device, the processor configured to: receive a resume; parse the resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range; store the resume in the resume database; associate at least one of said at least one skill or experience-related phrase located in the resume with at least one implied skill or experience-related phrase, wherein a term of experience for each said at least one implied skill or experience-related phrase is the term of experience computed for said at least one of said at least one skill or experience-related phrase, and wherein said at least one skill or experience-related phrase and said at least one implied skill or experience-related phrase are searchable phrases in the resume; create a parsed resume based on the resume, the parsed resume including each searchable phrase in the resume, the term of experience for each searchable phrase, and a relationship between the term of experience and each searchable phrase; store the parsed resume in the resume database; send a database query to the resume database, the database query including a job description that includes at least one job requirement, each said at least one job requirement including a required skill or experience-related phrase, and a required term of experience; and receive a result set in response to the database query, wherein the result set includes the resume when the parsed resume satisfies the job description.

255. A system for using a computer to improve a precision ratio when searching a resume database, comprising: a memory device resident in the computer; and a processor disposed in communication with the memory device, the processor configured to: receive a resume; parse the resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range; store the resume in the resume database; associate at least one of said at least one skill or experience-related phrase located in the resume with at least one implied skill or experience-related phrase, wherein a term of experience for each said at least one implied skill or experience-related phrase is the term of experience computed for said at least one of said at least one skill or experience-related phrase, and wherein said at least one skill or experience-related phrase and said at least one implied skill or experience-related phrase are searchable phrases in the resume; create a parsed resume based on the resume, the parsed resume including each searchable phrase in the resume, the term of experience for each searchable phrase, and a relationship between the term of experience and each searchable phrase; store the parsed resume in the resume database; send a database query to the resume database, the database query including a job description that includes at least one job requirement, each said at least one job requirement including a required skill or experience-related phrase, and a required term of experience; and receive a result set in response to the database query, wherein the result set includes the resume when the parsed resume satisfies the job description. 264. The system of claim 255 , wherein the resume includes at least one word, and wherein said at least one skill or experience-related phrase comprises said at least one word.

4. The automated computer based method as recited in claim 2 wherein a pre-determined sequence of color-codes are incorporated into the method of displaying the resultant category-based error analysis and evaluation whereby error relationships and frequency are easily discernible by the writer who is then able to make appropriate decisions allocating available practice time to the most critical areas for his or her personal performance characteristics.

4. The automated computer based method as recited in claim 2 wherein a pre-determined sequence of color-codes are incorporated into the method of displaying the resultant category-based error analysis and evaluation whereby error relationships and frequency are easily discernible by the writer who is then able to make appropriate decisions allocating available practice time to the most critical areas for his or her personal performance characteristics. 7. The automated computer based method as recited in claim 4 wherein the evaluation analysis with errors identified and color coded according to the category based analysis performed may be displayed as a single continuous results file whereby users may easily identify patterns of categories of errors as they occur throughout the writing.

1. A speech query recognition system comprising: a speech recognition engine for generating recognized words taken from an articulated speech utterance; and a natural language engine configured for processing said recognized words to generate at least two different types of search predicates for said articulated speech utterance; wherein said search predicates correspond to logical operators to be satisfied by a potential recognition match; a query formulation engine adapted to convert said recognized words and said search predicates into a structured query suitable for locating a set of one or more corresponding recognized matches for said articulated speech utterance; and said natural language engine further being configured for processing said set of one or more corresponding recognized matches to determine a final match for said articulated speech utterance using both semantic decoding and statistical based processing performed on said recognized words; wherein said semantic decoding is performed on entire word sentences contained in said articulated speech utterance to determine semantic variants of said word sentences in said one or more corresponding recognized matches, said semantic decoding being based on a term frequency calculation, which term frequency calculation is based on calculating a lexical distance between each word in said recognized words with each word of one or more topic query entries using a lexical dictionary.

1. A speech query recognition system comprising: a speech recognition engine for generating recognized words taken from an articulated speech utterance; and a natural language engine configured for processing said recognized words to generate at least two different types of search predicates for said articulated speech utterance; wherein said search predicates correspond to logical operators to be satisfied by a potential recognition match; a query formulation engine adapted to convert said recognized words and said search predicates into a structured query suitable for locating a set of one or more corresponding recognized matches for said articulated speech utterance; and said natural language engine further being configured for processing said set of one or more corresponding recognized matches to determine a final match for said articulated speech utterance using both semantic decoding and statistical based processing performed on said recognized words; wherein said semantic decoding is performed on entire word sentences contained in said articulated speech utterance to determine semantic variants of said word sentences in said one or more corresponding recognized matches, said semantic decoding being based on a term frequency calculation, which term frequency calculation is based on calculating a lexical distance between each word in said recognized words with each word of one or more topic query entries using a lexical dictionary. 2. The system of claim 1 , wherein said query formulation engine generates a first level query to a set of electronic records using said recognized words alone, and further customizes said first level query using said search predicates to generate a second level query to said set of electronic records.

1. A computer-implemented method of clustering items, each item having at least one associated feature, the method comprising: storing a data structure in memory the data structure holding a plurality of clusters; for each item, one or more associated features; for each cluster, at least one cluster membership parameter related to a prior probability distribution representing belief about whether any one of the items is a member of that cluster; for each cluster and feature combination, at least one feature parameter related to a prior probability distribution representing belief about whether any one of the items in that cluster is associated with that feature; receiving and storing an input comprising an observed item having observed associated features; updating the parameters in the data structure on a basis of the received input and using a Bayesian update process; identifying features that have a similar feature parameter across all clusters and using a same default value for those feature parameters; and iterating the receiving and updating for a plurality of such inputs.

1. A computer-implemented method of clustering items, each item having at least one associated feature, the method comprising: storing a data structure in memory the data structure holding a plurality of clusters; for each item, one or more associated features; for each cluster, at least one cluster membership parameter related to a prior probability distribution representing belief about whether any one of the items is a member of that cluster; for each cluster and feature combination, at least one feature parameter related to a prior probability distribution representing belief about whether any one of the items in that cluster is associated with that feature; receiving and storing an input comprising an observed item having observed associated features; updating the parameters in the data structure on a basis of the received input and using a Bayesian update process; identifying features that have a similar feature parameter across all clusters and using a same default value for those feature parameters; and iterating the receiving and updating for a plurality of such inputs. 6. A method as claimed in claim 1 wherein the data structure is stored such that each cluster membership parameter is related to a Dirichlet distribution.

1. A method performed by a first computing device for determining a layout of a structural document, the method comprising: receiving a plurality of images from a second computing device; determining a design associated with a structural document, the structural document comprising one or more facets, each facet representing one or more exterior surfaces of the structural document; determining a number of facets associated with the structural document based on the determined design; determining an image area associated with the structural document based on the received plurality of images and the number of facets; determining an image aspect ratio associated with each of the plurality of received images; determining a layout associated with the structural document based on the determined image area by: determining one or more areas of each facet in the number of facets; for each area of each facet in the number of facets: determining an area aspect ratio associated with each of the one or more areas; assigning a received image to the area, and determining a fit ratio comprising a ratio of the determined aspect ratio associated with the area and the determined aspect ratio associated with the image assigned to the area; evaluating the layout by: for each facet in the number of facets, determining an average fit ratio of aspect ratios associated with the facet, and determining whether the average fit ratio of aspect ratios exceeds a threshold value; when the average fit ratio of aspect ratios exceeds a threshold value for one or more of the facets in the number of facets, determining a new layout associated with the structural document and evaluating the new layout; and when the average fit ratio of aspect ratios for all facets associated with the evaluated layout does not exceed the threshold value, causing a graphical representation of the structural document to be displayed at the second computing device, wherein each received image is displayed on its assigned area on the graphical representation.

1. A method performed by a first computing device for determining a layout of a structural document, the method comprising: receiving a plurality of images from a second computing device; determining a design associated with a structural document, the structural document comprising one or more facets, each facet representing one or more exterior surfaces of the structural document; determining a number of facets associated with the structural document based on the determined design; determining an image area associated with the structural document based on the received plurality of images and the number of facets; determining an image aspect ratio associated with each of the plurality of received images; determining a layout associated with the structural document based on the determined image area by: determining one or more areas of each facet in the number of facets; for each area of each facet in the number of facets: determining an area aspect ratio associated with each of the one or more areas; assigning a received image to the area, and determining a fit ratio comprising a ratio of the determined aspect ratio associated with the area and the determined aspect ratio associated with the image assigned to the area; evaluating the layout by: for each facet in the number of facets, determining an average fit ratio of aspect ratios associated with the facet, and determining whether the average fit ratio of aspect ratios exceeds a threshold value; when the average fit ratio of aspect ratios exceeds a threshold value for one or more of the facets in the number of facets, determining a new layout associated with the structural document and evaluating the new layout; and when the average fit ratio of aspect ratios for all facets associated with the evaluated layout does not exceed the threshold value, causing a graphical representation of the structural document to be displayed at the second computing device, wherein each received image is displayed on its assigned area on the graphical representation. 7. The method of claim 1 , further comprising: receiving one or more changes to the displayed graphical representation of the structural document from a user of the second computing device; determining a new design associated with the structural document that incorporates the one or more changes; determining an updated number of facets associated with the structural document based on the new design; determining an updated image area associated with the structural document based on the received plurality of images and the updated number of facets; determining an updated layout associated with the structural document based on the determined image area; and evaluating the updated layout by: for each facet in the updated number of facets, determining an average fit ratio of aspect ratios associated with the facet, and determining whether the average fit ratio of aspect ratios exceeds a threshold value.

1. A method comprising: detecting, at a mobile device, a change in geographic location of the mobile device to yield a new context of a speech dialog; identifying a plurality of speech models on a remote server; calculating a likelihood score for each speech model in the plurality of speech models, wherein the likelihood score indicates a compatibility of each speech model to recognize speech in the speech dialog in the new context to yield likelihood scores; selecting a speech model from the plurality of speech models according to the likelihood scores; determining that a local storage on the mobile device has insufficient space to store the speech model; calculating local likelihood scores of local speech models stored in the local storage, wherein the local likelihood scores indicate a probability of each local speech model to recognize the speech in the speech dialog with the new context to yield a respective scored local speech model; and removing the respective scored local speech model having a lowest likelihood score of the local likelihood scores until a sufficient space for the speech model is made available on the mobile device.

1. A method comprising: detecting, at a mobile device, a change in geographic location of the mobile device to yield a new context of a speech dialog; identifying a plurality of speech models on a remote server; calculating a likelihood score for each speech model in the plurality of speech models, wherein the likelihood score indicates a compatibility of each speech model to recognize speech in the speech dialog in the new context to yield likelihood scores; selecting a speech model from the plurality of speech models according to the likelihood scores; determining that a local storage on the mobile device has insufficient space to store the speech model; calculating local likelihood scores of local speech models stored in the local storage, wherein the local likelihood scores indicate a probability of each local speech model to recognize the speech in the speech dialog with the new context to yield a respective scored local speech model; and removing the respective scored local speech model having a lowest likelihood score of the local likelihood scores until a sufficient space for the speech model is made available on the mobile device. 4. The method of claim 1 , wherein the new context is further based on one of language translation, speech in a different language, user language settings, available local storage, installing an application on the mobile device, and removing the application on the mobile device.

26. A computer-readable storage medium having stored thereon instructions that, when executed by one or more processors of a computer system, cause the computer system to at least: obtain a plurality of character strings forming at least part of queries previously submitted by corresponding searchers; obtain behavioral information associated with the character strings, the behavioral information associated with each character string indicative of one or more actions taken by one or more of the corresponding searchers in connection with the character string; identify, based at least in part on the obtained behavioral information, a plurality of pairs, each pair comprising a single character string from the obtained character strings and one or more substrings of the character string, the single character string being a connected combination of the one or more substrings; provide the identified plurality of pairs to a segmentation data store for use in processing subsequently received queries, the segmentation data store including a plurality of pairs each including a first member composed of one or more substrings of a character string and a second member composed of a single connected combination of at least a subset of the one or more substrings of the character string of the first member; upon receiving a search query, compare the search query against the plurality of pairs in the segmentation data store; upon identifying a corresponding pair for the search query in the segmentation data store, substitute the search query with the corresponding pair; and process the search query using the corresponding pair.

26. A computer-readable storage medium having stored thereon instructions that, when executed by one or more processors of a computer system, cause the computer system to at least: obtain a plurality of character strings forming at least part of queries previously submitted by corresponding searchers; obtain behavioral information associated with the character strings, the behavioral information associated with each character string indicative of one or more actions taken by one or more of the corresponding searchers in connection with the character string; identify, based at least in part on the obtained behavioral information, a plurality of pairs, each pair comprising a single character string from the obtained character strings and one or more substrings of the character string, the single character string being a connected combination of the one or more substrings; provide the identified plurality of pairs to a segmentation data store for use in processing subsequently received queries, the segmentation data store including a plurality of pairs each including a first member composed of one or more substrings of a character string and a second member composed of a single connected combination of at least a subset of the one or more substrings of the character string of the first member; upon receiving a search query, compare the search query against the plurality of pairs in the segmentation data store; upon identifying a corresponding pair for the search query in the segmentation data store, substitute the search query with the corresponding pair; and process the search query using the corresponding pair. 28. The computer-readable storage medium of claim 26 , further comprising updating a search index by at least associating a document with a character string and substring of at least one pair, and wherein using the identified pairs to process search queries includes using the updated search index.

1. A computer system for troubleshooting executing application programs, the computer system comprising: a display; a logical processor; a memory in operable communication with the logical processor; an application program residing at least partially in the memory and being executed by the logical processor; a markup loaded in the memory by the application program; a markup annotation which preserves a pre-evaluation version of an expression in the markup that was subsequently replaced by an evaluated version of the expression during execution of the application program; an application visual which is rendered on the display and includes at least one visual construct defined by the markup; a live authoring diagnostics round trip module (“LADRT module”) having the following functionality codes which upon execution by the logical processor provide the indicated functionality without pausing execution of the application program: (a) a visual construct property inspection functionality code which upon execution inspects at least one property of at least one visual construct; (b) a visual construct property modification functionality code which upon execution modifies at least one property of at least one visual construct; (c) a source code identification functionality code which upon execution identifies an application program source code that pertains to a selected visual construct, wherein a source code pertains to the visual construct when the source code specifies at least one of the following: creation of the visual construct, modification of the visual construct; and (d) a source code alteration functionality code which upon execution alters application program source code that is identified as pertaining to the visual construct.

1. A computer system for troubleshooting executing application programs, the computer system comprising: a display; a logical processor; a memory in operable communication with the logical processor; an application program residing at least partially in the memory and being executed by the logical processor; a markup loaded in the memory by the application program; a markup annotation which preserves a pre-evaluation version of an expression in the markup that was subsequently replaced by an evaluated version of the expression during execution of the application program; an application visual which is rendered on the display and includes at least one visual construct defined by the markup; a live authoring diagnostics round trip module (“LADRT module”) having the following functionality codes which upon execution by the logical processor provide the indicated functionality without pausing execution of the application program: (a) a visual construct property inspection functionality code which upon execution inspects at least one property of at least one visual construct; (b) a visual construct property modification functionality code which upon execution modifies at least one property of at least one visual construct; (c) a source code identification functionality code which upon execution identifies an application program source code that pertains to a selected visual construct, wherein a source code pertains to the visual construct when the source code specifies at least one of the following: creation of the visual construct, modification of the visual construct; and (d) a source code alteration functionality code which upon execution alters application program source code that is identified as pertaining to the visual construct. 3. The computer system of claim 1 , further comprising an integrated development environment (IDE), wherein the LADRT module comprises code which upon execution makes an alteration to the application visual within the application program within the IDE while the application program is executing, and wherein the change is reflected in real time in the executing application program.

16. The system of claim 12 , wherein the data quality model builder generates the at least one model by: using a first data mining algorithm to generate at least one model to validate values in at least one column in the records of the first data set; using a second data mining algorithm to generate at least one model to validate values in at least one column in the records of the first data set; and wherein the data quality validator applies each model in the data quality model to the records in the second data set by: determining a metric for each result of applying the models generated by the first and second data mining algorithms for each record in the second data set; and generating a summary metric for each record in the second data set that is a function of the metrics resulting from applying the models to the record.

16. The system of claim 12 , wherein the data quality model builder generates the at least one model by: using a first data mining algorithm to generate at least one model to validate values in at least one column in the records of the first data set; using a second data mining algorithm to generate at least one model to validate values in at least one column in the records of the first data set; and wherein the data quality validator applies each model in the data quality model to the records in the second data set by: determining a metric for each result of applying the models generated by the first and second data mining algorithms for each record in the second data set; and generating a summary metric for each record in the second data set that is a function of the metrics resulting from applying the models to the record. 18. The system of claim 16 , wherein the models generated by the first data mining algorithm comprise a plurality of association rule models, wherein each association rule model includes at least one association rule to predict a value in one column based on at least one value in at least one predictor column, and wherein the models generated by the second data mining algorithm comprise a predictive model predicting values in a user selected column; wherein the data quality validator determines the metrics for each record in the second data set by: determining for each association model association rules not satisfied by the data in the record; for each association rule model having association rules not satisfied by data in the record, determining an association rule model metric as a function of statistical values of the association rules in the association rule model not satisfied; determining from the predictive model a confidence level of a value in each column predicted by one predictive model; determining a predictive model metric for each predictive model as a function of the determined confidence level for each predictive model applied to the record.

7. The node of claim 5 , wherein the connectivity matrix updater is configured to determine the transition based on an activity variable representing probability of receiving a co-occurrence at the first time and previous co-occurrences before the first time according to the temporal relationships represented by the connectivity matrix.

7. The node of claim 5 , wherein the connectivity matrix updater is configured to determine the transition based on an activity variable representing probability of receiving a co-occurrence at the first time and previous co-occurrences before the first time according to the temporal relationships represented by the connectivity matrix. 9. The node of claim 7 , wherein the temporal pooler comprises: a mapper configured to map spatial co-occurrences of the first input patterns at a second time to sub-occurrences based on spatial co-occurrences of the first input patterns at a first time preceding the second time; a connectivity matrix counter for adding counts to a connectivity matrix, the connectivity matrix configured to store counts or frequency of the transition from a first co-occurrence or a first sub-occurrence to a second co-occurrence or a second sub-occurrence; a splitter configured to expand the connectivity matrix counter by selectively splitting a co-occurrence in the connectivity matrix, the splitter configured to update the mapper based on the splitting of the co-occurrence; and a sequence grouper configured to group sequences of co-occurrences based on the connectivity matrix.

13. A computer program product, comprising: a hardware storage device having program code stored thereon for managing a cloud computing environment adapted to host a virtual machine, generating, responsive to a determination that a predicate of a rule matches a given node in the set of nodes, a compound result and an interim mutated output document by applying a function of the rule to the given node; determining, responsive to a determination that the compound result contains a next node, whether the next node is compared with a predicate of a next rule; applying, responsive to a determination that the predicate of the next rule matches the next node, a function of an identified rule to next node to generate a second compound result and interim mutated output document; and generating a final mutated output document using the interim mutated output document as input, wherein the compound result includes a node to process next and an instruction indicating continue or break.

13. A computer program product, comprising: a hardware storage device having program code stored thereon for managing a cloud computing environment adapted to host a virtual machine, generating, responsive to a determination that a predicate of a rule matches a given node in the set of nodes, a compound result and an interim mutated output document by applying a function of the rule to the given node; determining, responsive to a determination that the compound result contains a next node, whether the next node is compared with a predicate of a next rule; applying, responsive to a determination that the predicate of the next rule matches the next node, a function of an identified rule to next node to generate a second compound result and interim mutated output document; and generating a final mutated output document using the interim mutated output document as input, wherein the compound result includes a node to process next and an instruction indicating continue or break. 18. The computer program product of claim 13 , wherein responsive to a determination that no rules in the set of rules matches a given node, the next node in document order is selected; and the next node is processed commencing with a first rule of the set of rules.

1. A computer-implemented method, comprising: outputting, for display, a plurality of open document representations in a carousel view, wherein each of the plurality of representations includes content from a corresponding open document and a document viewport portion is displayed in the carousel view, the document viewport portion corresponding to content from the corresponding open document that would be output for display in a full view of the corresponding open document; upon receiving an indication of a first gesture associated with a selected representation of the plurality of representations, outputting, for display, the full view of the document viewport portion of the open document corresponding to the selected representation; adjusting content of the open document included in the document viewport portion based upon a user navigation to a next position in the open document; and upon receiving an indication of a second gesture: closing the full view of the document viewport portion; and outputting, for display, the plurality of open document representations in the carousel view, wherein the document viewport portion for the selected representation is displayed in the carousel view corresponding to the adjusted content of the corresponding open document, and a remaining portion of the content from the corresponding open document is displayed outside of the document viewport portion for the selected representation.

1. A computer-implemented method, comprising: outputting, for display, a plurality of open document representations in a carousel view, wherein each of the plurality of representations includes content from a corresponding open document and a document viewport portion is displayed in the carousel view, the document viewport portion corresponding to content from the corresponding open document that would be output for display in a full view of the corresponding open document; upon receiving an indication of a first gesture associated with a selected representation of the plurality of representations, outputting, for display, the full view of the document viewport portion of the open document corresponding to the selected representation; adjusting content of the open document included in the document viewport portion based upon a user navigation to a next position in the open document; and upon receiving an indication of a second gesture: closing the full view of the document viewport portion; and outputting, for display, the plurality of open document representations in the carousel view, wherein the document viewport portion for the selected representation is displayed in the carousel view corresponding to the adjusted content of the corresponding open document, and a remaining portion of the content from the corresponding open document is displayed outside of the document viewport portion for the selected representation. 8. The method of claim 1 , wherein two or more of the plurality of representations represent open web browser tabs.

76. The system of claim 75 wherein the flesh-out component is configured to insert function words in the ALR.

76. The system of claim 75 wherein the flesh-out component is configured to insert function words in the ALR. 78. The system of claim 76 wherein the flesh-out component identifies case of noun phrases in the ALR.

1. A method for use of a medical ontology for computer assisted clinical decision support, the method comprising: identifying, with a processor, a plurality of associated terms from a medical ontology, the associated terms including associated drugs; generating, with the processor, a domain-knowledge base from the associated terms, including the associated drugs; and mining a medical record as a function of the domain-knowledge base, the mining including mining for the associated drugs.

1. A method for use of a medical ontology for computer assisted clinical decision support, the method comprising: identifying, with a processor, a plurality of associated terms from a medical ontology, the associated terms including associated drugs; generating, with the processor, a domain-knowledge base from the associated terms, including the associated drugs; and mining a medical record as a function of the domain-knowledge base, the mining including mining for the associated drugs. 11. The method of claim 1 further comprising: receiving an update of the medical ontology; and repeating the identifying and generating for the update.

18. Process according to claim 9 , wherein when none of the text requests are selected, the operation of processing the request by the voice recognition device ( 40 ) is repeated while eliminating the unselected text requests from a list of expressions that the voice recognition device ( 40 ) may recognize.

18. Process according to claim 9 , wherein when none of the text requests are selected, the operation of processing the request by the voice recognition device ( 40 ) is repeated while eliminating the unselected text requests from a list of expressions that the voice recognition device ( 40 ) may recognize. 19. Process according to claim 18 , wherein having recorded the voice request beforehand, the operation of processing the request by the voice recognition device is carried out on the basis of the initial recorded voice request.