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1. A method for constructing an automaton for automated analysis of an agglutinative language, the method comprising: constructing, using a processor of a computer, an affix automaton for each of a plurality of affix types of the agglutinative language, wherein each of said affix types is associated with one or more affixes associated with a morphological concept; combining, using the processor of the computer, any of said affix automatons to form a plurality of template automatons, where each of said template automatons is patterned after any of a plurality of agglutination templates of any of said affix types for said agglutinative language; combining, using the processor of the computer, said template automatons into a master automaton; receiving, by the processor of the computer, a word in the agglutinative language as an input for analysis; executing the master automaton to perform a morphological analysis of the received word, using the processor of the computer; and responsive to the executing, producing an output that indicates an expected part of speech for the word based on which of said template automatons were traversed within said master automaton during the executing. | 1. A method for constructing an automaton for automated analysis of an agglutinative language, the method comprising: constructing, using a processor of a computer, an affix automaton for each of a plurality of affix types of the agglutinative language, wherein each of said affix types is associated with one or more affixes associated with a morphological concept; combining, using the processor of the computer, any of said affix automatons to form a plurality of template automatons, where each of said template automatons is patterned after any of a plurality of agglutination templates of any of said affix types for said agglutinative language; combining, using the processor of the computer, said template automatons into a master automaton; receiving, by the processor of the computer, a word in the agglutinative language as an input for analysis; executing the master automaton to perform a morphological analysis of the received word, using the processor of the computer; and responsive to the executing, producing an output that indicates an expected part of speech for the word based on which of said template automatons were traversed within said master automaton during the executing. 3. The method according to claim 1 , wherein said constructing an affix automaton comprises constructing any of said affix automatons by starting with a last letter of each of said affixes and continuing towards a beginning letter of each of said affixes. | 0.617248 |
23. A method of executing a query in a HADOOP™ distributed computing cluster having multiple data nodes forming a peer-to-peer network for the query, each data node functioning as a peer in the peer-to-peer network and being capable of interacting with components of HADOOP™ cluster, each peer having an instance of a query engine running in memory, each instance of the query engine is configured to perform; the method comprising: receiving, by a one data node in the distributed computing cluster, a query; designating the one data node that receives the query as a coordinating data node; obtaining, by the coordinating data node and through a state store and a name node, (1) membership information regarding all query engine instances that are running in the cluster, and (2) location information regarding where data blocks relevant to the query are distributed among the plurality of data nodes, wherein the state store is separate from the data nodes; parsing the query to create fragments of the query based on data obtained from the state store and the name node; constructing a query plan based on the data obtained from the state store; distributing, by the coordinating data node and according to the query plan, the fragments of the query to data nodes in the distributed computing cluster that have data relevant to the query; receiving, from the data nodes having data relevant to the query, intermediate results corresponding to execution of the fragments of the query; and generating a final result based on the intermediate results for a client. | 23. A method of executing a query in a HADOOP™ distributed computing cluster having multiple data nodes forming a peer-to-peer network for the query, each data node functioning as a peer in the peer-to-peer network and being capable of interacting with components of HADOOP™ cluster, each peer having an instance of a query engine running in memory, each instance of the query engine is configured to perform; the method comprising: receiving, by a one data node in the distributed computing cluster, a query; designating the one data node that receives the query as a coordinating data node; obtaining, by the coordinating data node and through a state store and a name node, (1) membership information regarding all query engine instances that are running in the cluster, and (2) location information regarding where data blocks relevant to the query are distributed among the plurality of data nodes, wherein the state store is separate from the data nodes; parsing the query to create fragments of the query based on data obtained from the state store and the name node; constructing a query plan based on the data obtained from the state store; distributing, by the coordinating data node and according to the query plan, the fragments of the query to data nodes in the distributed computing cluster that have data relevant to the query; receiving, from the data nodes having data relevant to the query, intermediate results corresponding to execution of the fragments of the query; and generating a final result based on the intermediate results for a client. 39. The method of claim 23 , further comprising: upon determining that the state store has failed, continuing to operate based on last information received from the state store. | 0.557692 |
11. A method for recommending a keyword, the method comprising: determining whether the keyword is a direct click keyword by determining, referring to a click log, whether the keyword is associated with a first universal resource locator (URL) of an advertisement of an advertiser; determining whether the keyword is an indirect click keyword by determining that the keyword does not locate the first URL but locates a second URL having an attribute similar to the first URL; recommending the determined indirect click keyword; and computing selection probabilities that the first URL is selected using the direct click keyword and the indirect click keyword, wherein the recommending selects at least one indirect click keyword having a corresponding one of the selection probabilities greater than zero from among the plurality of indirect click keywords and then provides the selected at least one indirect click keyword to a user, and the computing includes, computing the selection probabilities based on selection rates of the first URL and the second URL with respect to the direct click keyword and the indirect click keyword, and a similarity probability between the first URL and the second URL, and updating the similarity probability based on the selection probabilities. | 11. A method for recommending a keyword, the method comprising: determining whether the keyword is a direct click keyword by determining, referring to a click log, whether the keyword is associated with a first universal resource locator (URL) of an advertisement of an advertiser; determining whether the keyword is an indirect click keyword by determining that the keyword does not locate the first URL but locates a second URL having an attribute similar to the first URL; recommending the determined indirect click keyword; and computing selection probabilities that the first URL is selected using the direct click keyword and the indirect click keyword, wherein the recommending selects at least one indirect click keyword having a corresponding one of the selection probabilities greater than zero from among the plurality of indirect click keywords and then provides the selected at least one indirect click keyword to a user, and the computing includes, computing the selection probabilities based on selection rates of the first URL and the second URL with respect to the direct click keyword and the indirect click keyword, and a similarity probability between the first URL and the second URL, and updating the similarity probability based on the selection probabilities. 14. The method of claim 11 , further comprising: selecting a reference number of indirect click keywords; and providing the selected number of indirect click keywords in a descending order of selection probabilities. | 0.611401 |
1. A computer implemented method for benchmarking a brand based on social media strength of said brand, comprising: providing a brand monitoring platform comprising at least one processor configured to monitor said brand in a virtual social media environment; acquiring input information on said brand by said brand monitoring platform; identifying industries related to said brand and competing brands in said identified industries using said acquired input information on said brand by said brand monitoring platform; acquiring social media information related to said brand and said competing brands in said identified industries from a plurality of social media sources in said virtual social media environment by said brand monitoring platform via a network; dynamically generating categories in one or more hierarchical levels in each of said identified industries by said brand monitoring platform based on an independent analysis of said acquired social media information related to said brand and said competing brands from each of said social media sources; sorting said acquired social media information related to said brand and said competing brands in said each of said identified industries into one or more of said dynamically generated categories in said one or more hierarchical levels by said brand monitoring platform using a sorting interface provided by said brand monitoring platform; determining an audience score for said brand and each of said competing brands by measuring an aggregate reach of said brand and said each of said competing brands in said virtual social media environment by said brand monitoring platform based on one or more of a plurality of weighted audience score metric parameters using said sorted social media information; determining an engagement score for said brand and said each of said competing brands by measuring interaction between said brand and said each of said competing brands and their followers by said brand monitoring platform based on one or more of a plurality of weighted engagement score metric parameters using said sorted social media information; generating an aggregate score for said brand and said each of said competing brands by said brand monitoring platform using said determined audience score and said determined engagement score; and determining social media strength of said brand in comparison with said competing brands in said virtual social media environment by assigning a rank to said brand and said each of said competing brands by said brand monitoring platform based on said aggregate score; whereby said brand is benchmarked in comparison with said competing brands in said virtual social media environment based on said social media strength of said brand in comparison with said competing brands in said virtual social media environment. | 1. A computer implemented method for benchmarking a brand based on social media strength of said brand, comprising: providing a brand monitoring platform comprising at least one processor configured to monitor said brand in a virtual social media environment; acquiring input information on said brand by said brand monitoring platform; identifying industries related to said brand and competing brands in said identified industries using said acquired input information on said brand by said brand monitoring platform; acquiring social media information related to said brand and said competing brands in said identified industries from a plurality of social media sources in said virtual social media environment by said brand monitoring platform via a network; dynamically generating categories in one or more hierarchical levels in each of said identified industries by said brand monitoring platform based on an independent analysis of said acquired social media information related to said brand and said competing brands from each of said social media sources; sorting said acquired social media information related to said brand and said competing brands in said each of said identified industries into one or more of said dynamically generated categories in said one or more hierarchical levels by said brand monitoring platform using a sorting interface provided by said brand monitoring platform; determining an audience score for said brand and each of said competing brands by measuring an aggregate reach of said brand and said each of said competing brands in said virtual social media environment by said brand monitoring platform based on one or more of a plurality of weighted audience score metric parameters using said sorted social media information; determining an engagement score for said brand and said each of said competing brands by measuring interaction between said brand and said each of said competing brands and their followers by said brand monitoring platform based on one or more of a plurality of weighted engagement score metric parameters using said sorted social media information; generating an aggregate score for said brand and said each of said competing brands by said brand monitoring platform using said determined audience score and said determined engagement score; and determining social media strength of said brand in comparison with said competing brands in said virtual social media environment by assigning a rank to said brand and said each of said competing brands by said brand monitoring platform based on said aggregate score; whereby said brand is benchmarked in comparison with said competing brands in said virtual social media environment based on said social media strength of said brand in comparison with said competing brands in said virtual social media environment. 2. The computer implemented method of claim 1 , wherein said dynamically generated categories comprise a location of each of said identified industries related to said brand and said each of said competing brands, a location of each of a plurality of authors of said social media information, types of said social media sources utilized by said brand and said each of said competing brands, and marketing elements. | 0.785419 |
1. A computer implemented method for managing a flow model simulation, the computer implemented method comprising: responsive to receiving a source model created in a non-native modeler, associating annotated simulation settings with the source model to form an annotated source model, wherein the annotated simulation settings are derived from at least one of a set of user-defined simulation settings and default simulation settings; transforming the annotated source model into an internal domain model using a set of links, wherein the set of links are generated using a set of link rules that comprise instructions that govern the creation of the set of links, the set of link rules customizable for the source model, and wherein the set of links maps a set of source model elements to a set of internal domain model elements of the internal domain model; mapping results from a simulation of the internal domain model back to the source model to identify a context for the results, wherein the simulation is governed by the annotated simulation settings; and generating a target view model from the internal domain model, wherein the target view model comprises the results presented in the context of the source model. | 1. A computer implemented method for managing a flow model simulation, the computer implemented method comprising: responsive to receiving a source model created in a non-native modeler, associating annotated simulation settings with the source model to form an annotated source model, wherein the annotated simulation settings are derived from at least one of a set of user-defined simulation settings and default simulation settings; transforming the annotated source model into an internal domain model using a set of links, wherein the set of links are generated using a set of link rules that comprise instructions that govern the creation of the set of links, the set of link rules customizable for the source model, and wherein the set of links maps a set of source model elements to a set of internal domain model elements of the internal domain model; mapping results from a simulation of the internal domain model back to the source model to identify a context for the results, wherein the simulation is governed by the annotated simulation settings; and generating a target view model from the internal domain model, wherein the target view model comprises the results presented in the context of the source model. 8. The computer implemented method of claim 1 , wherein the set of links comprise one or more pointers corresponding to one or more elements in the set of source model elements and one or more pointers corresponding to one or more elements in the set of internal domain model elements. | 0.605572 |
1. A method, comprising: a computer system processing each text sample in a training set, wherein each text sample in the training set corresponds to one of a plurality of classifications, and wherein processing each text sample includes: generating a respective set of features from that text sample; populating an entry in a data structure based on results of one or more dimension reduction operations performed on ones of the respective set of features for that text sample; the computer system using a non-linear classifier on entries in the data structure to establish criteria usable to classify an unknown text sample into one of the plurality of classifications; wherein each text sample in the training set is made up of characters within a text character set having C characters, wherein each feature in the respective set of features for a given text sample in the training set has N characters, and wherein populating an entry in the data structure for the given text sample includes a dimension reduction operation on individual ones of the respective set of features such that a corresponding value for each of those features is reduced from C N possible values to not greater than C N-1 possible values; wherein N is an integer greater than or equal to 3 and C is an integer greater than or equal to 20. | 1. A method, comprising: a computer system processing each text sample in a training set, wherein each text sample in the training set corresponds to one of a plurality of classifications, and wherein processing each text sample includes: generating a respective set of features from that text sample; populating an entry in a data structure based on results of one or more dimension reduction operations performed on ones of the respective set of features for that text sample; the computer system using a non-linear classifier on entries in the data structure to establish criteria usable to classify an unknown text sample into one of the plurality of classifications; wherein each text sample in the training set is made up of characters within a text character set having C characters, wherein each feature in the respective set of features for a given text sample in the training set has N characters, and wherein populating an entry in the data structure for the given text sample includes a dimension reduction operation on individual ones of the respective set of features such that a corresponding value for each of those features is reduced from C N possible values to not greater than C N-1 possible values; wherein N is an integer greater than or equal to 3 and C is an integer greater than or equal to 20. 3. The method of claim 1 , wherein features in a respective set of features for a given text sample in the training set include n-grams of different lengths. | 0.701422 |
12. A computer-readable storage medium having computer-executable instructions for performing steps comprising: allowing, at a computing device, a user to select a document style from document styles of a document in a first column portion and a corresponding style from styles of a web page in a second column portion on a same interface which the first and second column portions are presented; allowing the user to choose between styles that are used to transform the document, the styles including a first style that approximates formatting of the document, a second style that maps the document to particular style of the web page, and a third style that transforms an extensible markup language (XML) document to the web page; allowing the user to select between different layout templates to define how the web page is rendered; allowing the user to choose to create and store the web page in a current publishing site or to select a publishing site; mapping the document styles in a document to the styles of the web page; extracting resources from the document; storing the extracted resources at a location defined by a content type; assigning each of the extracted resources a name to uniquely identify each resource; converting contents of the document into hypertext markup language based on the mapping; rendering the web page based on the hypertext markup language; and outputting the web page. | 12. A computer-readable storage medium having computer-executable instructions for performing steps comprising: allowing, at a computing device, a user to select a document style from document styles of a document in a first column portion and a corresponding style from styles of a web page in a second column portion on a same interface which the first and second column portions are presented; allowing the user to choose between styles that are used to transform the document, the styles including a first style that approximates formatting of the document, a second style that maps the document to particular style of the web page, and a third style that transforms an extensible markup language (XML) document to the web page; allowing the user to select between different layout templates to define how the web page is rendered; allowing the user to choose to create and store the web page in a current publishing site or to select a publishing site; mapping the document styles in a document to the styles of the web page; extracting resources from the document; storing the extracted resources at a location defined by a content type; assigning each of the extracted resources a name to uniquely identify each resource; converting contents of the document into hypertext markup language based on the mapping; rendering the web page based on the hypertext markup language; and outputting the web page. 15. The computer-readable storage medium of claim 12 , further comprising storing the extracted resources to a particular location on a server. | 0.675101 |
8. A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program when executed on a processor of a computer causes the computer to perform steps comprising: receiving an image as a search query, the image being a first event; identifying search parameters in the search query that include first event attributes associated with the first event; identifying a second event and second event attributes for the second event; identifying contextual information associated with a single activity; associating the first event and the second event as being associated with the single activity based on contextual information describing relatedness of the first and second event attributes; generating a task associated with completing the single activity for a user; and updating the single activity based on a status change of the task. | 8. A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program when executed on a processor of a computer causes the computer to perform steps comprising: receiving an image as a search query, the image being a first event; identifying search parameters in the search query that include first event attributes associated with the first event; identifying a second event and second event attributes for the second event; identifying contextual information associated with a single activity; associating the first event and the second event as being associated with the single activity based on contextual information describing relatedness of the first and second event attributes; generating a task associated with completing the single activity for a user; and updating the single activity based on a status change of the task. 9. The computer program product of claim 8 , wherein the contextual information includes one from the group of a time, a location, a date, and a similar situation. | 0.59311 |
14. A computer-implemented method for facilitating a knowledge management system in a distributed computing network have a server in communication with client computers, and memory storing a first database related to work-product documents of a law firm, and a second database related to non-work-product legal precedence, wherein the method comprises the steps of: (a) receiving a query at the server from an agent of a law firm operating one of the client computers, (b) providing, to the agent for display on a graphical user interface, a taxonomy of legal topics for selection by the agent, with selection of one or more of the legal topics indicative of the query being received; (c) searching the first and second databases for content related to the query; (d) retrieving a first set of work-product documents of the law firm from the first database based upon the searching step; (e) storing the first set of work-product documents in a third database in one of the client computers; (f) converting the first set of work product documents into a markup language and subsequently indexing the first set based on legal citations and text to permit the first set of work product documents to be searched, wherein the work-product documents are internal law-firm content; (g) retrieving a second set of non-work-product legal precedence from the second database based upon the searching step; and (h) providing at least a portion of the work-product documents and the non-work-product legal precedence from the first and second sets including citations within the work-product documents and the non-work-product legal precedence, wherein at least one of the citations is associated with an indicator of current reliability of a corresponding document as a legal authority, and each provided work-product document is associated with a depth-of-treatment indicator indicating a degree to which the provided work-product document treats a legal case and one or more of the provided work-product documents are associated with a feedback indicator selected to view one or more user comments on the one or more listed work-product documents. | 14. A computer-implemented method for facilitating a knowledge management system in a distributed computing network have a server in communication with client computers, and memory storing a first database related to work-product documents of a law firm, and a second database related to non-work-product legal precedence, wherein the method comprises the steps of: (a) receiving a query at the server from an agent of a law firm operating one of the client computers, (b) providing, to the agent for display on a graphical user interface, a taxonomy of legal topics for selection by the agent, with selection of one or more of the legal topics indicative of the query being received; (c) searching the first and second databases for content related to the query; (d) retrieving a first set of work-product documents of the law firm from the first database based upon the searching step; (e) storing the first set of work-product documents in a third database in one of the client computers; (f) converting the first set of work product documents into a markup language and subsequently indexing the first set based on legal citations and text to permit the first set of work product documents to be searched, wherein the work-product documents are internal law-firm content; (g) retrieving a second set of non-work-product legal precedence from the second database based upon the searching step; and (h) providing at least a portion of the work-product documents and the non-work-product legal precedence from the first and second sets including citations within the work-product documents and the non-work-product legal precedence, wherein at least one of the citations is associated with an indicator of current reliability of a corresponding document as a legal authority, and each provided work-product document is associated with a depth-of-treatment indicator indicating a degree to which the provided work-product document treats a legal case and one or more of the provided work-product documents are associated with a feedback indicator selected to view one or more user comments on the one or more listed work-product documents. 16. The computer-implemented method of claim 14 , further comprising the steps of: providing, to the agent for display on a graphical user interface, at least a portion of each document found by the search step; and providing, to the agent for display on a graphical user interface, a displayable table of authorities listing documents cited within a work-product document selected from within the first set. | 0.773533 |
28. A non-transitory computer-readable medium having instructions stored thereon that, in response to execution by at least one processor, cause the at least one processor to perform operations comprising: receiving, from at least two different sources, a group of input listings based at least in part on a search query, each input listing comprising different item listing variants corresponding to a particular item; identifying at least one common attribute representing the item among at least a portion of the group of input listings; assigning a weight to each of the at least two different sources based at least in part on a correlation of sample input listings from each of the at least two different sources to input listings from a trusted source or based at least in part on a correlation of the input listings from the at least the portion of the group of input listings to an existing consolidated listing; selecting an item listing variant from the group of input listings as a consolidated listing based at least in part on the at least one common attribute and the weight assigned to each of the at least two different sources; mapping each input listing of the at least the portion of the group of input listings to the consolidated listing; and providing the consolidated listing to a client device for display instead of displaying the group of input listings. | 28. A non-transitory computer-readable medium having instructions stored thereon that, in response to execution by at least one processor, cause the at least one processor to perform operations comprising: receiving, from at least two different sources, a group of input listings based at least in part on a search query, each input listing comprising different item listing variants corresponding to a particular item; identifying at least one common attribute representing the item among at least a portion of the group of input listings; assigning a weight to each of the at least two different sources based at least in part on a correlation of sample input listings from each of the at least two different sources to input listings from a trusted source or based at least in part on a correlation of the input listings from the at least the portion of the group of input listings to an existing consolidated listing; selecting an item listing variant from the group of input listings as a consolidated listing based at least in part on the at least one common attribute and the weight assigned to each of the at least two different sources; mapping each input listing of the at least the portion of the group of input listings to the consolidated listing; and providing the consolidated listing to a client device for display instead of displaying the group of input listings. 35. The non-transitory computer-readable medium of claim 28 , wherein the group of input listings further comprises different input listings that are unassociated with the at least one common attribute value. | 0.598321 |
13. The stored program computer of claim 12 wherein: the editing styles tailorable by said applications programmer further comprise extended column definitions of display characteristics for data of corresponding columns of said database table beyond those characteristics inherent in a data dictionary of said database table. | 13. The stored program computer of claim 12 wherein: the editing styles tailorable by said applications programmer further comprise extended column definitions of display characteristics for data of corresponding columns of said database table beyond those characteristics inherent in a data dictionary of said database table. 14. The stored program computer of claim 13 wherein: said extended column definitions comprise a label for labelling the display of data of a column of said database table, said label being different than the column name maintained by said database manager program. | 0.931119 |
10. A computer system, comprising: a first computer simulator configured to run a first simulation of a system based on a model of said system, said model having a plurality of state variables; a first user interface for receiving input data representative of user interaction with said first computer simulation to change values of one or more of said state variables in a manner consistent with an interaction with the simulated system; a first data output device delivering output from said first computer simulator; a second computer simulator configured to run, contemporaneously with said first computer simulation, a second computer simulation of said system based on the same model as said first simulation, said second simulation being accelerated relative to said first simulation so as to be running at further progression than said first simulation at a current time under the assumption of no further user interaction than those represented by the input data received from the first user input interface, said second computer simulation being used to generate information representing expected future events in said first simulation; a second user input interface for outputting said information representing expected future events, and for receiving input data while said first simulation is run and said information representing expected future events is outputted, said input data received by the second user input interface being used to adjust the extent to which a condition is present in said first simulation; a second data output device delivering as output from said second computer simulator the information representing expected future events in said first simulation; and a module configured to, while said first simulation is running, translate said input from said second user input interface to values for one or more state variables in said first computer simulation consistent with a description of said condition in terms of rules embodied in the model. | 10. A computer system, comprising: a first computer simulator configured to run a first simulation of a system based on a model of said system, said model having a plurality of state variables; a first user interface for receiving input data representative of user interaction with said first computer simulation to change values of one or more of said state variables in a manner consistent with an interaction with the simulated system; a first data output device delivering output from said first computer simulator; a second computer simulator configured to run, contemporaneously with said first computer simulation, a second computer simulation of said system based on the same model as said first simulation, said second simulation being accelerated relative to said first simulation so as to be running at further progression than said first simulation at a current time under the assumption of no further user interaction than those represented by the input data received from the first user input interface, said second computer simulation being used to generate information representing expected future events in said first simulation; a second user input interface for outputting said information representing expected future events, and for receiving input data while said first simulation is run and said information representing expected future events is outputted, said input data received by the second user input interface being used to adjust the extent to which a condition is present in said first simulation; a second data output device delivering as output from said second computer simulator the information representing expected future events in said first simulation; and a module configured to, while said first simulation is running, translate said input from said second user input interface to values for one or more state variables in said first computer simulation consistent with a description of said condition in terms of rules embodied in the model. 17. The computer system of claim 10 , wherein said pathological state is chosen from the group consisting of: hypovolemia, anaphylaxis, opoid poisoning, and severity of bleeding. | 0.517992 |
8. A non-transitory computer-readable storage medium, having instructions stored therein, which when executed by a processing device, cause the processing device to: track search queries of a first user and a second user of a search engine to generate a first search query history of the first user and a second search query history of the second user, wherein the first search query history comprises a plurality of first search queries and the second search query history comprises a plurality of second search queries; compare the first search query history with the second search query history to identify a plurality of similar search queries between the first search queries and the second search queries; determine that the second search queries comprise a next sequential search query after the similar search queries in the second search queries in response to the identification of the similar search queries; and responsive to determining that the second search queries comprise the next sequential search query after the similar ones of the second search queries, generate, by the processing device, a predicted search query for the first user comprising the next sequential search query of the second user that the first user is predicted to use to perform a next search in relation to other possible searches in view of the comparing. | 8. A non-transitory computer-readable storage medium, having instructions stored therein, which when executed by a processing device, cause the processing device to: track search queries of a first user and a second user of a search engine to generate a first search query history of the first user and a second search query history of the second user, wherein the first search query history comprises a plurality of first search queries and the second search query history comprises a plurality of second search queries; compare the first search query history with the second search query history to identify a plurality of similar search queries between the first search queries and the second search queries; determine that the second search queries comprise a next sequential search query after the similar search queries in the second search queries in response to the identification of the similar search queries; and responsive to determining that the second search queries comprise the next sequential search query after the similar ones of the second search queries, generate, by the processing device, a predicted search query for the first user comprising the next sequential search query of the second user that the first user is predicted to use to perform a next search in relation to other possible searches in view of the comparing. 13. The non-transitory computer-readable storage medium of claim 8 , wherein the processing device is further to: execute the predicted search query prior to selection of the predicted search query by the first user; and cache the search results of the predicted search query prior to selection of the predicted search query by the first user. | 0.836742 |
1. A computer-implemented method comprising: accessing a document stored in one or more tangible media, the document comprising a plurality of text units, a text unit comprising a plurality of words, the plurality of words comprising a plurality of keywords; performing the following for each text unit using a processor: ranking the plurality of words of the each text unit according to a ranking technique; selecting one or more highly ranked words as the keywords of the each text unit; establishing relatedness among the keywords of each text unit; and selecting one or more keywords according to the established relatedness as one or more candidate tags to yield a candidate tag set for the each text unit; using the processor, determining relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets; and using the processor, assigning at least one candidate tag to the document according to the determined relatedness. | 1. A computer-implemented method comprising: accessing a document stored in one or more tangible media, the document comprising a plurality of text units, a text unit comprising a plurality of words, the plurality of words comprising a plurality of keywords; performing the following for each text unit using a processor: ranking the plurality of words of the each text unit according to a ranking technique; selecting one or more highly ranked words as the keywords of the each text unit; establishing relatedness among the keywords of each text unit; and selecting one or more keywords according to the established relatedness as one or more candidate tags to yield a candidate tag set for the each text unit; using the processor, determining relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets; and using the processor, assigning at least one candidate tag to the document according to the determined relatedness. 6. The method of claim 1 , the determining relatedness between the candidate tags of the each candidate tag set and the candidate tags of the other candidate tag sets further comprising generating a profile for a candidate tag of the each candidate tag set by: determining the number of candidate tag sets that include the candidate tag; and generating the profile from the number. | 0.73961 |
3. A method of regulating operation of a computer using computer program instructions conforming to a structure characterized by: (a) an invocation string having (i) a string identifying an operation, (ii) an invocation destination list identifying, for each operand sourced externally to the invocation string, a place from which the operand is sourced, and (iii) an invocation source list identifying a place for a result of the operation; and (b) a definition string associated with the invocation string and having (i) a resolution string defining the operation, (ii) a definition destination list identifying, for each operand sourced external to the definition string, a place from which the operand is sourced, and (iii) a definition source list identifying a place for a result of the operation; Where the method comprises: (1) instantiating an operation of a first invocation string upon completeness of necessary operands identified by a invocation destination list of the first invocation string, where an element in a invocation destination list of a second invocation string identifies an element in a invocation source list of the first invocation string; and (2) resolving the instantiated operation of the first invocation string according to a definition string associated with the first invocation string including a step of coordinating a content flow with an operation of the second invocation string that has a dependency relationship with the instantiated operation of the first invocation string. | 3. A method of regulating operation of a computer using computer program instructions conforming to a structure characterized by: (a) an invocation string having (i) a string identifying an operation, (ii) an invocation destination list identifying, for each operand sourced externally to the invocation string, a place from which the operand is sourced, and (iii) an invocation source list identifying a place for a result of the operation; and (b) a definition string associated with the invocation string and having (i) a resolution string defining the operation, (ii) a definition destination list identifying, for each operand sourced external to the definition string, a place from which the operand is sourced, and (iii) a definition source list identifying a place for a result of the operation; Where the method comprises: (1) instantiating an operation of a first invocation string upon completeness of necessary operands identified by a invocation destination list of the first invocation string, where an element in a invocation destination list of a second invocation string identifies an element in a invocation source list of the first invocation string; and (2) resolving the instantiated operation of the first invocation string according to a definition string associated with the first invocation string including a step of coordinating a content flow with an operation of the second invocation string that has a dependency relationship with the instantiated operation of the first invocation string. 7. The method of claim 3 further includes a step of arbitrating sources for an invocation destination list. | 0.684211 |
10. A method comprising: receiving a first plurality of images at a first image resolution and a second plurality of images at a second image resolution, wherein the first image resolution is lower than the second image resolution; for each image of the first plurality of images: transforming the image into a transformed image of the second image resolution; identifying a plurality of first image pixel sets comprising a plurality of pixels of the transformed image; identifying, for each first image pixel set, a corresponding second image pixel set of the second plurality of images; storing each first image pixel set and each corresponding second image pixel set in a database repository of corresponding image pixel sets; based on the first image pixel sets in the database repository of corresponding image pixel sets, computing a plurality of first image pixel set features, wherein each first image pixel set may be computed as a linear combination of a subset of the plurality of first image pixel set features; based on the first image pixel set features for each first image pixel set and corresponding second image pixel set, computing a corresponding plurality of second image pixel set features, wherein each first image pixel feature corresponds to a second image pixel set feature of the corresponding plurality of second image pixel set features, and wherein each second image pixel set may be computed as a linear combination of a subset of the corresponding plurality of second image pixel set features and each second image pixel feature of the subset corresponds to a first image pixel feature in the subset of the plurality of first image pixel set features for the first image pixel set that corresponds to the second image pixel set; storing, in a data record, each first image pixel feature and each corresponding second image pixel feature; receiving a first particular image at the first image resolution; transforming the first particular image from the first image resolution to the second image resolution; identifying a plurality of first particular image pixel sets of the transformed particular image; for each of the plurality of first particular image pixel sets: identifying a particular subset of the plurality of first image pixel set features such that the first particular image pixel set may be computed as a linear combination of the particular subset of the plurality of first image pixel set features; for each first image pixel set feature of the particular subset, identifying a corresponding second image pixel set feature; computing a second particular image pixel set as a linear combination of the identified corresponding second image pixel set features; generating and displaying, on a computing device, a second particular image from the second particular image sets. | 10. A method comprising: receiving a first plurality of images at a first image resolution and a second plurality of images at a second image resolution, wherein the first image resolution is lower than the second image resolution; for each image of the first plurality of images: transforming the image into a transformed image of the second image resolution; identifying a plurality of first image pixel sets comprising a plurality of pixels of the transformed image; identifying, for each first image pixel set, a corresponding second image pixel set of the second plurality of images; storing each first image pixel set and each corresponding second image pixel set in a database repository of corresponding image pixel sets; based on the first image pixel sets in the database repository of corresponding image pixel sets, computing a plurality of first image pixel set features, wherein each first image pixel set may be computed as a linear combination of a subset of the plurality of first image pixel set features; based on the first image pixel set features for each first image pixel set and corresponding second image pixel set, computing a corresponding plurality of second image pixel set features, wherein each first image pixel feature corresponds to a second image pixel set feature of the corresponding plurality of second image pixel set features, and wherein each second image pixel set may be computed as a linear combination of a subset of the corresponding plurality of second image pixel set features and each second image pixel feature of the subset corresponds to a first image pixel feature in the subset of the plurality of first image pixel set features for the first image pixel set that corresponds to the second image pixel set; storing, in a data record, each first image pixel feature and each corresponding second image pixel feature; receiving a first particular image at the first image resolution; transforming the first particular image from the first image resolution to the second image resolution; identifying a plurality of first particular image pixel sets of the transformed particular image; for each of the plurality of first particular image pixel sets: identifying a particular subset of the plurality of first image pixel set features such that the first particular image pixel set may be computed as a linear combination of the particular subset of the plurality of first image pixel set features; for each first image pixel set feature of the particular subset, identifying a corresponding second image pixel set feature; computing a second particular image pixel set as a linear combination of the identified corresponding second image pixel set features; generating and displaying, on a computing device, a second particular image from the second particular image sets. 16. The method of claim 10 , wherein, for each image of the first plurality of images, transforming the image into a transformed image of the second image resolution comprises interpolating each pixel of the image to a high resolution grid using bicubic interpolation. | 0.636763 |
11. A method comprising: determining at least one spammer targeted keyword relating to a common keyword used in commerce search queries, the determining being based in part on a popularity of the at least one spammer targeted keyword amongst advertisers, wherein the popularity of the at least one spammer targeted keyword amongst the advertisers is based in part on a number of bids provided by the advertisers, and wherein the at least one spammer targeted keyword is associated with a syndication business, the syndication business including at least a publisher, an advertiser, and a syndicator; inputting the at least one spammer targeted keyword to a search engine to generate search results including a plurality of uniform resource locators (URLs); accessing, by one or more processors, one or more URLs of the plurality of URLs; recording the one or more URLs, wherein the recording comprises redirection tracking that intercepts redirection traffic; grouping the one or more recorded URLs using similarity-based grouping; verifying that at least one of the one or more URLs comprises a spam URL; and determining that the spam URL is associated with a spam syndication program, the spam syndication program including at least a spam publisher associated with a doorway page for redirecting a browser to a redirection domain associated with the spam publisher. | 11. A method comprising: determining at least one spammer targeted keyword relating to a common keyword used in commerce search queries, the determining being based in part on a popularity of the at least one spammer targeted keyword amongst advertisers, wherein the popularity of the at least one spammer targeted keyword amongst the advertisers is based in part on a number of bids provided by the advertisers, and wherein the at least one spammer targeted keyword is associated with a syndication business, the syndication business including at least a publisher, an advertiser, and a syndicator; inputting the at least one spammer targeted keyword to a search engine to generate search results including a plurality of uniform resource locators (URLs); accessing, by one or more processors, one or more URLs of the plurality of URLs; recording the one or more URLs, wherein the recording comprises redirection tracking that intercepts redirection traffic; grouping the one or more recorded URLs using similarity-based grouping; verifying that at least one of the one or more URLs comprises a spam URL; and determining that the spam URL is associated with a spam syndication program, the spam syndication program including at least a spam publisher associated with a doorway page for redirecting a browser to a redirection domain associated with the spam publisher. 13. The method of claim 11 , wherein the similarity-based grouping comprises grouping based in part on doorway-domain similarity. | 0.677934 |
19. A device formed by the process comprising: generating a hardware description language (HDL) implementation of a first circuit design; receiving an HDL or netlist implementation of a public portion of a second circuit design, the second circuit design also including a secret portion not included with the public portion; receiving an HDL or netlist implementation of an interface between the public portion and the secret portion, the interface including one or more boundary locations between the secret portion and the public portion; generating an HDL implementation of an integrated design formed by integrating the HDL implementation of the first circuit design with the public portion of the second design and the interface; receiving an exclusion list of resources to be reserved for a secret portion of the second circuit design; generating a programming file for the integrated design including programming bits for configuring the integrated design into the device; programming the programming bits in the programming file for the integrated design into the device; receiving programming bits for the secret portion of the second circuit design; and programming the programming bits for the secret portion of the second circuit design into the device after the programming bits for the integrated circuit design have been programmed into the device. | 19. A device formed by the process comprising: generating a hardware description language (HDL) implementation of a first circuit design; receiving an HDL or netlist implementation of a public portion of a second circuit design, the second circuit design also including a secret portion not included with the public portion; receiving an HDL or netlist implementation of an interface between the public portion and the secret portion, the interface including one or more boundary locations between the secret portion and the public portion; generating an HDL implementation of an integrated design formed by integrating the HDL implementation of the first circuit design with the public portion of the second design and the interface; receiving an exclusion list of resources to be reserved for a secret portion of the second circuit design; generating a programming file for the integrated design including programming bits for configuring the integrated design into the device; programming the programming bits in the programming file for the integrated design into the device; receiving programming bits for the secret portion of the second circuit design; and programming the programming bits for the secret portion of the second circuit design into the device after the programming bits for the integrated circuit design have been programmed into the device. 21. The device of claim 19 , wherein the interface includes one or more boundary lookup tables (LUTs) for mapping signals from the secret portion to the public portion and from the public portion to the secret portion. | 0.5 |
4. The computer program product recited in claim 3 , wherein the computer usable program code to exclude the identified at least one multiple-clause rule from evaluation with the plurality of rules on the basis of the first clause in a context of the current state of the first characteristic, further comprises: computer usable program code to evaluate the current state of the of the first characteristic with the first clause of the identified at least one multiple-clause rule; computer usable program code to realize an evaluation outcome of FALSE; and computer usable program code to set the identified at least one multiple-clause rule as unavailable for evaluation. | 4. The computer program product recited in claim 3 , wherein the computer usable program code to exclude the identified at least one multiple-clause rule from evaluation with the plurality of rules on the basis of the first clause in a context of the current state of the first characteristic, further comprises: computer usable program code to evaluate the current state of the of the first characteristic with the first clause of the identified at least one multiple-clause rule; computer usable program code to realize an evaluation outcome of FALSE; and computer usable program code to set the identified at least one multiple-clause rule as unavailable for evaluation. 5. The computer program product recited in claim 4 , wherein the computer usable program code to set the identified at least one multiple-clause rule as unavailable for evaluation, further comprises computer usable program code to mark the identified at least one multiple-clause rule as inactive. | 0.744112 |
1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive a plurality of words; map each of the plurality of words; associate the mapped words to provide a plurality of phrases, each of the plurality of phrases having a representation of a first type; encode each of the plurality of phrases to provide a respective plurality of encoded phrases, each of the plurality of encoded phrases having a representation of a second type different than the first type; determine a value of each of the plurality of encoded phrases; and identify one or more phrases of the plurality of encoded phrases having a value exceeding a threshold. | 1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive a plurality of words; map each of the plurality of words; associate the mapped words to provide a plurality of phrases, each of the plurality of phrases having a representation of a first type; encode each of the plurality of phrases to provide a respective plurality of encoded phrases, each of the plurality of encoded phrases having a representation of a second type different than the first type; determine a value of each of the plurality of encoded phrases; and identify one or more phrases of the plurality of encoded phrases having a value exceeding a threshold. 3. The non-transitory computer-readable storage medium of claim 1 , wherein each of the plurality of phrases includes a same number of mapped words. | 0.639282 |
11. Non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to create a social query response application comprising: a) a software module monitoring queries from users, each query submitted to a vendor via an interactive chat feature of an external electronic communication platform, monitoring human responses to the queries, and monitoring subsequent communications conducted via the electronic communication platform until each query is resolved; b) a software module applying a first machine learning algorithm to the monitored communications to identify one or more queries susceptible to response automation; and c) a software module applying a second machine learning algorithm to the queries susceptible to response automation to identify one or more responses likely to resolve the query. | 11. Non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to create a social query response application comprising: a) a software module monitoring queries from users, each query submitted to a vendor via an interactive chat feature of an external electronic communication platform, monitoring human responses to the queries, and monitoring subsequent communications conducted via the electronic communication platform until each query is resolved; b) a software module applying a first machine learning algorithm to the monitored communications to identify one or more queries susceptible to response automation; and c) a software module applying a second machine learning algorithm to the queries susceptible to response automation to identify one or more responses likely to resolve the query. 17. The media of claim 11 , wherein the application further comprises a software module notifying a human to respond to a query susceptible to response automation with one or more responses likely to resolve the query. | 0.69458 |
12. The personal service support system according to claim 11 , wherein: said dialog storage unit stores said structured information on a content and situation of said dialogs as information on said nodes; and said dialog search unit searches said dialogs by using said certain structured information as a search condition by said user or said agent. | 12. The personal service support system according to claim 11 , wherein: said dialog storage unit stores said structured information on a content and situation of said dialogs as information on said nodes; and said dialog search unit searches said dialogs by using said certain structured information as a search condition by said user or said agent. 14. The personal service support system according to claim 12 , wherein said dialog creating unit inserts a second dialog between two consecutive nodes of a first dialog to create a new dialog in said branch tree structure, upon determining that said two consecutive nodes of said first dialog respectively contain a same structured information as start and end nodes of said second dialog. | 0.8251 |
1. A system comprising at least one computer processor for analyzing requirements data, comprising: a requirements database storing partially-structured data related to a subject matter domain; and an analytic tool, comprising: a user interface configured to receive a query from a user; and an analyzer configured to: parse the query into a plurality of search terms; identify a first requirement from the requirements database based on a degree of relatedness between the plurality of search terms and the textual content of the first requirement; identify a plurality of soft links between the first requirement and a plurality of related requirements, wherein each of the plurality of soft links represents a relatedness score between the first requirement and a related requirement from the plurality of related requirements, and each of the relatedness scores exceeds a threshold; rank the plurality of related requirements based on their relatedness scores; and provide the highest ranked requirement to the user. | 1. A system comprising at least one computer processor for analyzing requirements data, comprising: a requirements database storing partially-structured data related to a subject matter domain; and an analytic tool, comprising: a user interface configured to receive a query from a user; and an analyzer configured to: parse the query into a plurality of search terms; identify a first requirement from the requirements database based on a degree of relatedness between the plurality of search terms and the textual content of the first requirement; identify a plurality of soft links between the first requirement and a plurality of related requirements, wherein each of the plurality of soft links represents a relatedness score between the first requirement and a related requirement from the plurality of related requirements, and each of the relatedness scores exceeds a threshold; rank the plurality of related requirements based on their relatedness scores; and provide the highest ranked requirement to the user. 4. The system of claim 1 , wherein the partially-structured data comprises requirements related to the subject matter domain. | 0.645296 |
12. A computer-readable storage medium with instructions stored thereon for providing multistage speech recognition, the instructions comprising: receiving a first utterance from a speaker; performing a first pass speech recognition on the first utterance employing a plurality of algorithms including a gender detection algorithm and a feature MLLR (fMLLR) algorithm executed sequentially; creating an index of allowed words and an associated time point based on a result of the first pass speech recognition; adapting the plurality of algorithms by computing a set of allowed states in a phonetic network associated with the algorithms based on the allowed words and reducing a search space for subsequent passes based on the allowed states; performing the subsequent passes of speech recognition on the first utterance employing the adapted algorithms, wherein the algorithms are further adapted in a progressive manner by reducing the search space at each subsequent pass based on a result of a preceding pass of speech recognition by constraining at least one of an acoustic model and a language model associated with the algorithms, and wherein constraining at least one of the acoustic model and the language model associated with the algorithms includes reducing a search space for the algorithm; employing the progressively adapted algorithms for performing speech recognition on subsequent utterances from the same speaker; and performing a Viterbi search within the search space based on the progressively adapted algorithms. | 12. A computer-readable storage medium with instructions stored thereon for providing multistage speech recognition, the instructions comprising: receiving a first utterance from a speaker; performing a first pass speech recognition on the first utterance employing a plurality of algorithms including a gender detection algorithm and a feature MLLR (fMLLR) algorithm executed sequentially; creating an index of allowed words and an associated time point based on a result of the first pass speech recognition; adapting the plurality of algorithms by computing a set of allowed states in a phonetic network associated with the algorithms based on the allowed words and reducing a search space for subsequent passes based on the allowed states; performing the subsequent passes of speech recognition on the first utterance employing the adapted algorithms, wherein the algorithms are further adapted in a progressive manner by reducing the search space at each subsequent pass based on a result of a preceding pass of speech recognition by constraining at least one of an acoustic model and a language model associated with the algorithms, and wherein constraining at least one of the acoustic model and the language model associated with the algorithms includes reducing a search space for the algorithm; employing the progressively adapted algorithms for performing speech recognition on subsequent utterances from the same speaker; and performing a Viterbi search within the search space based on the progressively adapted algorithms. 16. The computer-readable medium of claim 12 , wherein the search space is reduced to a portion of the first utterance determined to be unclear during the first pass speech recognition. | 0.509898 |
14. A method for knowledge-based interpretable predictive modeling of patients, the method comprising: training, with machine training using training data for a plurality of previous lung cancer patients, a graphic model to predict survivability of lung cancer based on relationships between variables from a lung cancer expert, the training data including previous patient values for the variables, the variables including the survivability; applying, with a processor, current patient values of the variables for a current lung cancer patient to the graphic model, the graphic model configured to predict even with one of the variables not having a current patient value as a function of the relationships; displaying a representation of the graphic model, the representation showing the variables and the relationships remaining after training; and displaying the survivability for the current lung cancer patient predicted by the graphic model, wherein the variables comprise tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and the survivability. | 14. A method for knowledge-based interpretable predictive modeling of patients, the method comprising: training, with machine training using training data for a plurality of previous lung cancer patients, a graphic model to predict survivability of lung cancer based on relationships between variables from a lung cancer expert, the training data including previous patient values for the variables, the variables including the survivability; applying, with a processor, current patient values of the variables for a current lung cancer patient to the graphic model, the graphic model configured to predict even with one of the variables not having a current patient value as a function of the relationships; displaying a representation of the graphic model, the representation showing the variables and the relationships remaining after training; and displaying the survivability for the current lung cancer patient predicted by the graphic model, wherein the variables comprise tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and the survivability. 18. The method of claim 14 wherein the graphic model configured to predict even with one of the variables not having a current patient value comprises using a substitute value for the one of the variables, the substitute value selected from a distribution learned from the training data. | 0.5 |
1. A system, comprising: a server; a probable measure of meaning (PMM) database stored on the server, the PMM database storing PMMs for a plurality of tokens; an input port to receive a content; a token determination unit on the server operative to determine a token in said content; a relative measure of meaning (RMM) calculation unit on the server operative to calculate a RMM for said token, factoring in a first probable measure of meaning (PMM) from the PMM database for said token and a plurality of second PMMs from the PMM database for a plurality of second tokens in said content using a proximity function (PF) according to R M M ( token n ) = P M M ( token n ) ⨯ Mean d i = - d ( P M M ( token n - i ) * P F ( token n , token n - i , i ) ) ; and an output port to output a report including the RMM of said token. | 1. A system, comprising: a server; a probable measure of meaning (PMM) database stored on the server, the PMM database storing PMMs for a plurality of tokens; an input port to receive a content; a token determination unit on the server operative to determine a token in said content; a relative measure of meaning (RMM) calculation unit on the server operative to calculate a RMM for said token, factoring in a first probable measure of meaning (PMM) from the PMM database for said token and a plurality of second PMMs from the PMM database for a plurality of second tokens in said content using a proximity function (PF) according to R M M ( token n ) = P M M ( token n ) ⨯ Mean d i = - d ( P M M ( token n - i ) * P F ( token n , token n - i , i ) ) ; and an output port to output a report including the RMM of said token. 2. A system according to claim 1 , wherein the PMM database is stored on a second server, said second server and the server connected via a network. | 0.780793 |
1. A system for extracting information on biological entities from natural-language text data, comprising: (i) a computer processing apparatus configured to parse the natural-language text data; (ii) a computer processing apparatus configured to regularize the parsed text data to form structured word terms; and (iii) a computer processing apparatus configured to extract interactions between the biological entities from the natural-language text data, wherein the biological entities include genes and/or proteins. | 1. A system for extracting information on biological entities from natural-language text data, comprising: (i) a computer processing apparatus configured to parse the natural-language text data; (ii) a computer processing apparatus configured to regularize the parsed text data to form structured word terms; and (iii) a computer processing apparatus configured to extract interactions between the biological entities from the natural-language text data, wherein the biological entities include genes and/or proteins. 2. The system according to claim 1 , further comprising a computer processing apparatus configured to preprocess the data prior to parsing comprising identifying biological entities. | 0.587838 |
7. A data processing system for retrieving one or more data values stored in a searchable archive, the system comprising: a data store storing one or more compacted files; a processor coupled to the data store; and a memory coupled to the processor, the memory having stored therein program instructions executable by the processor and which cause the processor to: select a compacted file from one or more compacted files associated with a data archive, the selected compacted file including one or more compressed segments of tokenized data represented as bit vectors and a metadata file containing bit vector segment metadata, wherein the tokenized data comprises one or more token values corresponding to one or more data values stored in the data archive; enable access of the metadata file within the selected compacted file; select one or more of the bit vectors corresponding to one or more data values being searched for from the selected compacted file based on the bit vector segment metadata stored in the metadata file; perform a Boolean operation on the bit vectors included in the selected compacted file to determine if the one or more token values corresponding to the one or more data values being searched for are contained within the selected compacted file; compile search results from each compacted file which match the token value being searched for and return the search results. | 7. A data processing system for retrieving one or more data values stored in a searchable archive, the system comprising: a data store storing one or more compacted files; a processor coupled to the data store; and a memory coupled to the processor, the memory having stored therein program instructions executable by the processor and which cause the processor to: select a compacted file from one or more compacted files associated with a data archive, the selected compacted file including one or more compressed segments of tokenized data represented as bit vectors and a metadata file containing bit vector segment metadata, wherein the tokenized data comprises one or more token values corresponding to one or more data values stored in the data archive; enable access of the metadata file within the selected compacted file; select one or more of the bit vectors corresponding to one or more data values being searched for from the selected compacted file based on the bit vector segment metadata stored in the metadata file; perform a Boolean operation on the bit vectors included in the selected compacted file to determine if the one or more token values corresponding to the one or more data values being searched for are contained within the selected compacted file; compile search results from each compacted file which match the token value being searched for and return the search results. 26. The data processing system of claim 7 , wherein the metadata file includes values identifying a token value range index for selecting the compacted file. | 0.578338 |
7. An input apparatus inputting a sequence of characters by sequentially selecting each character by using a pointer manipulated by a user, wherein only one of the characters, selected from a plurality of two-dimensional tables, each of the two-dimensional tables including a plurality of two-dimensionally arranged candidate characters, is displayed on a display apparatus at one time and the plurality of candidate characters are arranged in a form of a user-recognized character array, comprising: a pointer movement detection part detecting movement of the pointer manipulated by the user; a scoped character replacement part replacing a currently displayed character with another character of the plurality of candidate characters; and a scoped character determination part determining a scoped character as an input character by conducting a coordinate determination operation based on combinations of whether vertical and/or horizontal shift amounts between a start position and an end position of the pointer detected by the pointer movement detection part are non-negative or non-positive, wherein the coordinate determination operation determines a movement direction of the position and one of the plurality of two-dimensional tables based on a first combination of whether the vertical and/or horizontal shift amounts are non-negative or non-positive and further determines a manipulation stage of the user based on a second combination of whether the vertical and/or horizontal shift amounts are non-negative or non-positive, and wherein the manipulation stage includes a manipulation continuation phase, a manipulation completion phase and an error phase, and wherein if the coordinate determination operation determines that the manipulation stage is in the manipulation continuation phase, the coordinate determination operation stores coordinate data of the end position, and if the coordinate determination operation determines that the manipulation stage is in the manipulation completion phase or the error phase, the coordinate determination operation clears the stored coordinate data of the end position. | 7. An input apparatus inputting a sequence of characters by sequentially selecting each character by using a pointer manipulated by a user, wherein only one of the characters, selected from a plurality of two-dimensional tables, each of the two-dimensional tables including a plurality of two-dimensionally arranged candidate characters, is displayed on a display apparatus at one time and the plurality of candidate characters are arranged in a form of a user-recognized character array, comprising: a pointer movement detection part detecting movement of the pointer manipulated by the user; a scoped character replacement part replacing a currently displayed character with another character of the plurality of candidate characters; and a scoped character determination part determining a scoped character as an input character by conducting a coordinate determination operation based on combinations of whether vertical and/or horizontal shift amounts between a start position and an end position of the pointer detected by the pointer movement detection part are non-negative or non-positive, wherein the coordinate determination operation determines a movement direction of the position and one of the plurality of two-dimensional tables based on a first combination of whether the vertical and/or horizontal shift amounts are non-negative or non-positive and further determines a manipulation stage of the user based on a second combination of whether the vertical and/or horizontal shift amounts are non-negative or non-positive, and wherein the manipulation stage includes a manipulation continuation phase, a manipulation completion phase and an error phase, and wherein if the coordinate determination operation determines that the manipulation stage is in the manipulation continuation phase, the coordinate determination operation stores coordinate data of the end position, and if the coordinate determination operation determines that the manipulation stage is in the manipulation completion phase or the error phase, the coordinate determination operation clears the stored coordinate data of the end position. 8. The input apparatus as claimed in claim 7 , wherein the display apparatus further displays a number of additional marks for punctuation and derivative marks at one time. | 0.5 |
2. The method of claim 1 , wherein the generating of the theme vector comprises: extracting feature candidates from the music title; selecting a feature from the extracted feature candidates; and generating the theme vector by assigning a feature value of the selected feature. | 2. The method of claim 1 , wherein the generating of the theme vector comprises: extracting feature candidates from the music title; selecting a feature from the extracted feature candidates; and generating the theme vector by assigning a feature value of the selected feature. 7. The method of claim 2 , wherein the selecting of the feature from the extracted feature candidates comprises selecting the feature from the extracted feature candidates based on predetermined chi square statistics. | 0.763689 |
16. A system comprising a computer-readable storage device that stores program code, which, when executed by a processor, performs operations comprising: receiving a string of characters that comprises a plurality of characters with no token-delineating breaks; segmenting the string of characters into a first segmented result that comprises a first plurality of tokens and at least one break, wherein the first plurality of tokens includes all of the plurality of characters; segmenting the string of characters into a second segmented result that comprises a second plurality of tokens and at least one break, wherein the second plurality of tokens includes all the plurality of characters, wherein the second plurality of tokens is different than the first plurality of tokens; determining a first frequency of occurrence for the first segmented result in a corpus and a second frequency of occurrence for the second segmented result in the corpus by providing the first segmented result and second segmented result to a search engine and receiving in response from the search engine the first frequency of occurrence for the first segmented result and the second frequency of occurrence for the second segmented result; comparing the first frequency of occurrence for the first result to the second frequency of occurrence for the second segmented result; selecting the first segmented result as an operable segmented result for the received string of characters when the first frequency of occurrence for the first request is determined to exceed a determined value relative to the second frequency of occurrence for the second result; and providing the operable segmented result for further processing. | 16. A system comprising a computer-readable storage device that stores program code, which, when executed by a processor, performs operations comprising: receiving a string of characters that comprises a plurality of characters with no token-delineating breaks; segmenting the string of characters into a first segmented result that comprises a first plurality of tokens and at least one break, wherein the first plurality of tokens includes all of the plurality of characters; segmenting the string of characters into a second segmented result that comprises a second plurality of tokens and at least one break, wherein the second plurality of tokens includes all the plurality of characters, wherein the second plurality of tokens is different than the first plurality of tokens; determining a first frequency of occurrence for the first segmented result in a corpus and a second frequency of occurrence for the second segmented result in the corpus by providing the first segmented result and second segmented result to a search engine and receiving in response from the search engine the first frequency of occurrence for the first segmented result and the second frequency of occurrence for the second segmented result; comparing the first frequency of occurrence for the first result to the second frequency of occurrence for the second segmented result; selecting the first segmented result as an operable segmented result for the received string of characters when the first frequency of occurrence for the first request is determined to exceed a determined value relative to the second frequency of occurrence for the second result; and providing the operable segmented result for further processing. 22. The system of claim 16 , wherein the further processing comprises selecting an article based at least in pan on the operable segmented result. | 0.64375 |
1. A computerized system for sale of non pre-catalogued products having non pre-catalogued parameters in a multi-lingual environment wherein a buyer and a seller each communicate in a different language, the system comprising: a non pre-catalogued product database including: a multiple language product parameter table including selectable pre-translated parameters; and a multiple language product parameter value table including selectable pre-translated values corresponding to said pre-translated parameters, said multiple language value table being displayed together with said multiple language parameter table; at least one server computer connected to said non pre-catalogued product database, said at least one server including: a non pre-catalogued product listing engine configured to prompt, in a first language, a listing seller to select in a structured manner at least one of said parameters appearing in said first language relating to a product to be listed and to select at least one of said values appearing in said first language and corresponding to said selected parameters and to store selected ones of said parameters and selected ones of said values corresponding to said selected parameters in said non pre-catalogued product database to create a listed product in said first language; and a non pre-catalogued multiple language product description engine configured to provide for each said listed product, human-readable descriptions of said selected pre-translated, non pre-catalogued parameters and corresponding selected pre-translated, non pre-catalogued values in at least one language different from said first language, said human readable descriptions corresponding to selections of said non pre-catalogued parameters and values made by said listing seller using said first language; and a display providing to a customer, different from the listing seller, said human readable descriptions of said listed product in said at least one language different from said first language in response to a customer query. | 1. A computerized system for sale of non pre-catalogued products having non pre-catalogued parameters in a multi-lingual environment wherein a buyer and a seller each communicate in a different language, the system comprising: a non pre-catalogued product database including: a multiple language product parameter table including selectable pre-translated parameters; and a multiple language product parameter value table including selectable pre-translated values corresponding to said pre-translated parameters, said multiple language value table being displayed together with said multiple language parameter table; at least one server computer connected to said non pre-catalogued product database, said at least one server including: a non pre-catalogued product listing engine configured to prompt, in a first language, a listing seller to select in a structured manner at least one of said parameters appearing in said first language relating to a product to be listed and to select at least one of said values appearing in said first language and corresponding to said selected parameters and to store selected ones of said parameters and selected ones of said values corresponding to said selected parameters in said non pre-catalogued product database to create a listed product in said first language; and a non pre-catalogued multiple language product description engine configured to provide for each said listed product, human-readable descriptions of said selected pre-translated, non pre-catalogued parameters and corresponding selected pre-translated, non pre-catalogued values in at least one language different from said first language, said human readable descriptions corresponding to selections of said non pre-catalogued parameters and values made by said listing seller using said first language; and a display providing to a customer, different from the listing seller, said human readable descriptions of said listed product in said at least one language different from said first language in response to a customer query. 2. A computerized system for sale of non pre-catalogued products according to claim 1 and wherein said at least one server also includes: a question and/or answer parameter entry engine configured to prompt, in a first language, at least one of a buyer and a seller to enter multiple parameters relating to a product being listed; and a question and/or answer generating engine which is operative to provide human-readable questions and/or answers based on said multiple parameters in at least one language other than said first language. | 0.5 |
10. The method according to claim 8 , wherein selecting the member engine according to the meta index of each member engine, the search request and the user interest model comprises: obtaining a first similarity of a first document in a certain database to a search request vector Q, wherein the first document satisfies a condition that the similarity of the first document to the search request vector Q is the highest; the search request vector Q=(q 1 , q 2 , . . . qi . . . qk), and qi is a weight of a term ti in the search request; obtaining a second similarity of a second document in the database to a user interest model R, wherein the second document satisfies a condition that the similarity of the second document to the user interest model vector R is the highest while a matching degree with a vector Q′ (q 1 ′, q 2 ′ . . . qm′) meets a specified threshold value, the vector Q′ is a conversion form of the search request vector Q specific to the user interest model vector R, the user interest model vector R=(r 1 , r 2 , . . . ri . . . , rn), and ri is a weight score of the i th dimension of the user interest model; selecting a higher value from the first similarity and the second similarity as a similarity of a combination of the search request and the user interest model to the database; repeating the above method, so as to obtain the similarity of the combination of the search request and the user interest model to each database, wherein each database is corresponding to a member engine; and ranking each database according to the similarity of the combination of the search request and the user interest model to each database, and selecting member engines corresponding to one or more databases having larger similarities ranked top. | 10. The method according to claim 8 , wherein selecting the member engine according to the meta index of each member engine, the search request and the user interest model comprises: obtaining a first similarity of a first document in a certain database to a search request vector Q, wherein the first document satisfies a condition that the similarity of the first document to the search request vector Q is the highest; the search request vector Q=(q 1 , q 2 , . . . qi . . . qk), and qi is a weight of a term ti in the search request; obtaining a second similarity of a second document in the database to a user interest model R, wherein the second document satisfies a condition that the similarity of the second document to the user interest model vector R is the highest while a matching degree with a vector Q′ (q 1 ′, q 2 ′ . . . qm′) meets a specified threshold value, the vector Q′ is a conversion form of the search request vector Q specific to the user interest model vector R, the user interest model vector R=(r 1 , r 2 , . . . ri . . . , rn), and ri is a weight score of the i th dimension of the user interest model; selecting a higher value from the first similarity and the second similarity as a similarity of a combination of the search request and the user interest model to the database; repeating the above method, so as to obtain the similarity of the combination of the search request and the user interest model to each database, wherein each database is corresponding to a member engine; and ranking each database according to the similarity of the combination of the search request and the user interest model to each database, and selecting member engines corresponding to one or more databases having larger similarities ranked top. 13. The method according to claim 10 , wherein obtaining the first similarity comprises: calculating a value of Max 1 ≤ i ≤ k ( ( qi * gidfi * mnwi + ∑ j = 1 , j ≠ i k qj * gidfj * anwj ) / Q + ∑ j = 1 n rj * anvj * IM_gidfj / R ) as the first similarity; obtaining the second similarity comprises: calculating a value of Max 1 ≤ i ≤ n ( if ( sim ( V ( mnvi , anvj ( j ≠ i , 1 ≤ j ≤ n ) ) , Q ′ ) > T ) then ( ( ri * mnvi * IM_gidfi + ∑ j = 1 , j ≠ i n rj * anvj * IM_gidfj ) / R + ∑ i = 1 k qi * gidfi * anwi ) / Q ) as the second similarity; wherein, |Q| is a norm of the search request vector Q; |R| is a norm of the user interest model R; Q′ is calculated by mapping a value of qi into a weight of a certain dimension in the user interest model if the term ti belongs to the scope of the dimension in the user interest model, adding the weights of the same dimension to obtain qi', and then normalizing; V is a vector formed with mnvi and anvj(j≠i,1≦j≦n); sim(V(mnvi,anvj(j≠i,1≦j≦n)), Q′) is a cosine similarity of the vector V and the vector Q′; T is a threshold value, and 0≦T≦1; and i, k, j, and n are natural numbers. | 0.510491 |
15. The method of claim 13 , wherein the communication event is receipt of a message from a second terminal. | 15. The method of claim 13 , wherein the communication event is receipt of a message from a second terminal. 17. The method of claim 15 , further comprising: transmitting a reply message to the second terminal, the reply message comprising a text corresponding to the symbol. | 0.932334 |
2. The method of claim 1 , further comprising: generating, by the first device, a first cryptographic nonce; and generating the query to include the first cryptographic nonce. | 2. The method of claim 1 , further comprising: generating, by the first device, a first cryptographic nonce; and generating the query to include the first cryptographic nonce. 3. The method of claim 2 , wherein the response from the second device further comprises a second cryptographic nonce, and wherein the first device generates the encryption key further-based on the first cryptographic nonce and the second cryptographic nonce. | 0.914698 |
9. A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving unstructured textual data; parsing the unstructured textual data into a plurality of sections including a first section and a second section that is a different section in the unstructured textual data than the first section; for each section in the plurality of sections: identifying one or more keywords in data for the section in the plurality of sections; determining one or more patterns that match the section using the identified one or more keywords; and identifying one or more intelligence types that correspond to the section using the determined one or more patterns; associating, for a first intelligence type from the identified one or more intelligence types for the first section, the data for the first section from the unstructured textual data with the first intelligence type; associating, for a second intelligence type from the identified one or more intelligence types for the second section, the data for the second section from the unstructured textual data with the second intelligence type, wherein the second intelligence type is a different intelligence type than the first intelligence type; determining a rule for a third party that indicates that the third party should receive data associated with a particular intelligence type of the one or more intelligence types; determining that the first intelligence type is the particular intelligence type; and providing the data for the first section to a system of the third party. | 9. A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving unstructured textual data; parsing the unstructured textual data into a plurality of sections including a first section and a second section that is a different section in the unstructured textual data than the first section; for each section in the plurality of sections: identifying one or more keywords in data for the section in the plurality of sections; determining one or more patterns that match the section using the identified one or more keywords; and identifying one or more intelligence types that correspond to the section using the determined one or more patterns; associating, for a first intelligence type from the identified one or more intelligence types for the first section, the data for the first section from the unstructured textual data with the first intelligence type; associating, for a second intelligence type from the identified one or more intelligence types for the second section, the data for the second section from the unstructured textual data with the second intelligence type, wherein the second intelligence type is a different intelligence type than the first intelligence type; determining a rule for a third party that indicates that the third party should receive data associated with a particular intelligence type of the one or more intelligence types; determining that the first intelligence type is the particular intelligence type; and providing the data for the first section to a system of the third party. 14. The computer storage medium of claim 9 , wherein each of the plurality of sections comprises a paragraph. | 0.613906 |
1. A computer-readable medium having computer-executable instructions for causing a computer to perform a method for identifying handwriting recognition decisions that are suspect comprising: storing one or more samples of handwriting of a user; receiving a handwritten input from the user; performing a recognition operation on the handwritten input to produce an initial recognition result; and identifying a possible incorrect recognition within at least part of the initial recognition result using a self-consistency process and one or more of the stored handwriting samples, the self-consistency process identifying the possible incorrect recognition using a Kullback-Leibler distance measure operation. | 1. A computer-readable medium having computer-executable instructions for causing a computer to perform a method for identifying handwriting recognition decisions that are suspect comprising: storing one or more samples of handwriting of a user; receiving a handwritten input from the user; performing a recognition operation on the handwritten input to produce an initial recognition result; and identifying a possible incorrect recognition within at least part of the initial recognition result using a self-consistency process and one or more of the stored handwriting samples, the self-consistency process identifying the possible incorrect recognition using a Kullback-Leibler distance measure operation. 4. The computer-readable medium of claim 1 , wherein the Kullback-Leibler distance measure operation comprises
KL( s,k )=Σ c p k ( c|I k )log( p k ( c|I k )/ p s ( c|I s )). | 0.574341 |
8. A non-transitory machine-readable medium storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising: accessing a first attribute of a first item retrieved from the first query; accessing a second attribute of a second item retrieved from the second query; identifying a further relationship between the first attribute and the second attribute; relating the first query to the second query based on the identifying of the further relationship between the first attribute and the second attribute; and assigning a weight to the relationship between the first query and the second query that is derived from a weight assigned to the first attribute and a weight assigned to the second attribute. | 8. A non-transitory machine-readable medium storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising: accessing a first attribute of a first item retrieved from the first query; accessing a second attribute of a second item retrieved from the second query; identifying a further relationship between the first attribute and the second attribute; relating the first query to the second query based on the identifying of the further relationship between the first attribute and the second attribute; and assigning a weight to the relationship between the first query and the second query that is derived from a weight assigned to the first attribute and a weight assigned to the second attribute. 18. The non-transitory machine-readable medium of claim 8 , wherein the first attribute is a brand associated with the first item. | 0.843806 |
11. A computer-readable medium, comprising volatile or non-volatile memory, carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform steps comprising: for each sequence of a plurality of attribute determination sequences in a training set, receiving a subset of attribute determinations in the attribute determination sequence that are likely to be a false positive; receiving input text, wherein at least a subset of tokens within the input text are a set of attribute tokens with corresponding attribute determinations; identifying at least one attribute determination corresponding to an attribute token of the set of attribute tokens as a false positive based on the training set; producing a set of filtered attribute tokens by filtering from said set of attribute tokens, all attribute tokens corresponding to attribute determinations that have been identified as false positives. | 11. A computer-readable medium, comprising volatile or non-volatile memory, carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform steps comprising: for each sequence of a plurality of attribute determination sequences in a training set, receiving a subset of attribute determinations in the attribute determination sequence that are likely to be a false positive; receiving input text, wherein at least a subset of tokens within the input text are a set of attribute tokens with corresponding attribute determinations; identifying at least one attribute determination corresponding to an attribute token of the set of attribute tokens as a false positive based on the training set; producing a set of filtered attribute tokens by filtering from said set of attribute tokens, all attribute tokens corresponding to attribute determinations that have been identified as false positives. 12. The computer-readable medium of claim 11 , wherein identifying at least one attribute determination corresponding to an attribute token of the set of attribute tokens as a false positive comprises generating a skeleton token in which: each token within the input text is represented by a corresponding character; and the character that corresponds to a given token of the input text indicates which attribute, if any, the given token was determined to be. | 0.56617 |
1. In a system having a plurality of controllable functions, apparatus comprising: means for storing text data representing text information for guiding a user in controlling said functions; said text information including a first string of text and a second string of text identical to said first string of text except for a change of format; said second string of text represented by a format change code word associated with text data representing said first string of text; means for retrieving said text data from said storing means, converting the text data representing said first string of text to text data representing said second string of text when said format change code word is associated with said text data representing said first string of text; and means for generating text representative signals capable of causing the display of said text information by a display device in response to said text data provided by said retrieving means. | 1. In a system having a plurality of controllable functions, apparatus comprising: means for storing text data representing text information for guiding a user in controlling said functions; said text information including a first string of text and a second string of text identical to said first string of text except for a change of format; said second string of text represented by a format change code word associated with text data representing said first string of text; means for retrieving said text data from said storing means, converting the text data representing said first string of text to text data representing said second string of text when said format change code word is associated with said text data representing said first string of text; and means for generating text representative signals capable of causing the display of said text information by a display device in response to said text data provided by said retrieving means. 2. The apparatus recited in claim 1, wherein: said format change code word indicates a change of one of case, underlining, italicization, style or size. | 0.662324 |
1. A method for visually displaying search results comprising: receiving, by a processor from a first search engine, first search result data; determining, by the processor, search query information from said first search result data; transmitting said search query information to a second search engine; receiving, from said second search engine, second search result data identifying a plurality of webpages; determining network address information for a webpage of said plurality of webpages from the second search result data; retrieving, at a first location, said webpage using said network address information determined from the second search result data; creating, at said first location, a reduced size representation of said webpage as it would currently appear if presently displayed; and displaying, at said first location, said representation of said webpage from the second search result data and said first search result data. | 1. A method for visually displaying search results comprising: receiving, by a processor from a first search engine, first search result data; determining, by the processor, search query information from said first search result data; transmitting said search query information to a second search engine; receiving, from said second search engine, second search result data identifying a plurality of webpages; determining network address information for a webpage of said plurality of webpages from the second search result data; retrieving, at a first location, said webpage using said network address information determined from the second search result data; creating, at said first location, a reduced size representation of said webpage as it would currently appear if presently displayed; and displaying, at said first location, said representation of said webpage from the second search result data and said first search result data. 2. The method of claim 1 wherein said representation is displayed in a browser window with said first search result data. | 0.639773 |
1. A computer-implemented method comprising: causing a context engine comprising an in-memory database engine to collect data from a first source comprising a first gamification platform regarding a first event comprising an action taken in an enterprise by an actor; causing the context engine to collect first context data over an asynchronous message broker from a second source comprising a machine-to-machine stack including a hygroscopic sensor from a wearable of the actor; causing the context engine to collect second context data over the asynchronous message broker from a third source comprising a second gamification platform regarding a second event involving the actor; causing the context engine to perform a first aggregation of the first context data from the second source, and then to perform a second aggregation to process the data and aggregated first context data to create context enriched data by calculating a defined trust metric from the data and the second context data; causing the context engine to store the context enriched data in an in-memory database; causing the context engine to provide the context enriched data in a view within the in-memory database; determining from the context enriched data that the actor has achieved a predetermined goal; based upon achievement of the predetermined goal, triggering the asynchronous message broker to communicate a message to assign an additional role to the actor. | 1. A computer-implemented method comprising: causing a context engine comprising an in-memory database engine to collect data from a first source comprising a first gamification platform regarding a first event comprising an action taken in an enterprise by an actor; causing the context engine to collect first context data over an asynchronous message broker from a second source comprising a machine-to-machine stack including a hygroscopic sensor from a wearable of the actor; causing the context engine to collect second context data over the asynchronous message broker from a third source comprising a second gamification platform regarding a second event involving the actor; causing the context engine to perform a first aggregation of the first context data from the second source, and then to perform a second aggregation to process the data and aggregated first context data to create context enriched data by calculating a defined trust metric from the data and the second context data; causing the context engine to store the context enriched data in an in-memory database; causing the context engine to provide the context enriched data in a view within the in-memory database; determining from the context enriched data that the actor has achieved a predetermined goal; based upon achievement of the predetermined goal, triggering the asynchronous message broker to communicate a message to assign an additional role to the actor. 5. The method of claim 1 wherein the context engine processes the data and the first context data by filtering. | 0.884058 |
29. A tangible computer readable medium having stored thereon, computer-executable instructions that, upon execution by a computing device, cause the computing device to perform operations comprising: defining a displayed interface for interacting with objects having associated resource locators; receiving an input and sending a search communication based at least in part on the input to a network element for objects from a set of indexed objects; receiving from the network element at least respective resource locators of objects corresponding to the input; displaying a hierarchal object comprising at least three levels of a taxonomic hierarchy each containing at least one of the respective resource locators of the objects received from the network element corresponding to the input, and at least one level having at least two of the objects, the objects being taxonomically organized based on a content associated with a respective object; and presenting within the taxonomic hierarchy a set of separate objects, separate from the objects received from the network element corresponding to the input, wherein the levels of the taxonomic hierarchy contain at least one separate object corresponding to the input, the set of separate objects providing a basis for a third party subsidy. | 29. A tangible computer readable medium having stored thereon, computer-executable instructions that, upon execution by a computing device, cause the computing device to perform operations comprising: defining a displayed interface for interacting with objects having associated resource locators; receiving an input and sending a search communication based at least in part on the input to a network element for objects from a set of indexed objects; receiving from the network element at least respective resource locators of objects corresponding to the input; displaying a hierarchal object comprising at least three levels of a taxonomic hierarchy each containing at least one of the respective resource locators of the objects received from the network element corresponding to the input, and at least one level having at least two of the objects, the objects being taxonomically organized based on a content associated with a respective object; and presenting within the taxonomic hierarchy a set of separate objects, separate from the objects received from the network element corresponding to the input, wherein the levels of the taxonomic hierarchy contain at least one separate object corresponding to the input, the set of separate objects providing a basis for a third party subsidy. 38. The tangible computer readable medium of claim 29 , wherein the operations further comprise selectively sending information to charge a third party subsidy based on a value of a commercial transaction predicated on a display of the object. | 0.537105 |
30. The one or more tangible computer-readable media of claim 1 , further having encoded thereon a data structure for defining the user interface surface having elements to be selectively shown according to the current context, wherein the data structure is loadable into a user interface system, the data structure comprising: a definition of one or more user interface zones for the user interface surface; at least two specifications of context factors to be included in the current context when filtering user interface elements to be shown at runtime of the user interface system, wherein at least one of the context factors is a problem status of a managed item; and at least two sets of user interface element definitions associated with the context factors, wherein one of the sets of user interface element definitions comprises trouble ticket functionality for the managed item, and wherein only the user interface element definitions that occur in each of the sets of user interface element definitions are selected to be shown according to the current context and using one or more management element definitions comprising logic for retrieving data to be displayed on the user interface surface. | 30. The one or more tangible computer-readable media of claim 1 , further having encoded thereon a data structure for defining the user interface surface having elements to be selectively shown according to the current context, wherein the data structure is loadable into a user interface system, the data structure comprising: a definition of one or more user interface zones for the user interface surface; at least two specifications of context factors to be included in the current context when filtering user interface elements to be shown at runtime of the user interface system, wherein at least one of the context factors is a problem status of a managed item; and at least two sets of user interface element definitions associated with the context factors, wherein one of the sets of user interface element definitions comprises trouble ticket functionality for the managed item, and wherein only the user interface element definitions that occur in each of the sets of user interface element definitions are selected to be shown according to the current context and using one or more management element definitions comprising logic for retrieving data to be displayed on the user interface surface. 32. The one or more tangible computer-readable media of claim 30 wherein at least one of the context factors is a management item type of at least one of following: a network server, a network workstation, a network user, and a network share. | 0.779506 |
3. A computer-implemented method of providing cues to a user while capturing an utterance, the computer-implemented method comprising: capturing, by an electronic communication device, a user utterance; and providing, by the electronic communication device to the user in at least near real-time, one or more cues associated with the user utterance, wherein said providing includes, for each portion of a plurality of portions of the user utterance: communicating, from the electronic communication device, data representative of the respective portion of the user utterance to a remote electronic device, in response to the communication of data representative of the respective portion of the user utterance, receiving, at the electronic communication device, data representative of at least one parameter associated with the respective portion of the user utterance, and providing, by the electronic communication device to the user, at least one cue based at least in part on the at least one parameter associated with the respective portion of the user utterance; wherein at least one cue of the one or more cues is provided by the electronic communication device to the user prior to completion of capturing the user utterance. | 3. A computer-implemented method of providing cues to a user while capturing an utterance, the computer-implemented method comprising: capturing, by an electronic communication device, a user utterance; and providing, by the electronic communication device to the user in at least near real-time, one or more cues associated with the user utterance, wherein said providing includes, for each portion of a plurality of portions of the user utterance: communicating, from the electronic communication device, data representative of the respective portion of the user utterance to a remote electronic device, in response to the communication of data representative of the respective portion of the user utterance, receiving, at the electronic communication device, data representative of at least one parameter associated with the respective portion of the user utterance, and providing, by the electronic communication device to the user, at least one cue based at least in part on the at least one parameter associated with the respective portion of the user utterance; wherein at least one cue of the one or more cues is provided by the electronic communication device to the user prior to completion of capturing the user utterance. 7. The computer-implemented method of claim 3 , wherein each respective portion of the user utterance comprises a word, each respective portion of the user utterance comprises a syllable, each respective portion of the user utterance comprises a phrase, or each respective portion of the user utterance comprises a sentence. | 0.814856 |
3. The voice server of claim 1 , wherein the processor is further configured to provide a speech synthesized prompt to the access device. | 3. The voice server of claim 1 , wherein the processor is further configured to provide a speech synthesized prompt to the access device. 4. The voice server of claim 3 , wherein the prompt requests that the user select an operation associated with the plurality of legacy systems. | 0.947678 |
12. A method of informing a user that sufficient data has been scanned from a document to identify the document, the method comprising: scanning the document, via a portable device, to acquire a scanned image; executing program instructions, stored in memory of the portable device, to detect user-readable words within the scanned image and to count a number of words the portable device detected within the scanned image; comparing the number of words the portable device detected within the scanned image to a threshold number for identifying a document; and indicating, via a user interface of the portable device, that a sufficient number of user-readable words have been detected within the scanned image to identify the document. | 12. A method of informing a user that sufficient data has been scanned from a document to identify the document, the method comprising: scanning the document, via a portable device, to acquire a scanned image; executing program instructions, stored in memory of the portable device, to detect user-readable words within the scanned image and to count a number of words the portable device detected within the scanned image; comparing the number of words the portable device detected within the scanned image to a threshold number for identifying a document; and indicating, via a user interface of the portable device, that a sufficient number of user-readable words have been detected within the scanned image to identify the document. 14. The method of claim 12 in which the threshold number for identifying a document is within the range of four to ten words. | 0.648132 |
17. A data processing system, comprising: a query analyzer, in response to a first structured query language (SQL) query statement for accessing a relational database, to generate a syntax tree representing semantic information of the first SQL query statement, the first SQL query statement being received from a client application programmed in an object-oriented programming language, wherein the first SQL query statement is represented by a data object associated with the object-oriented programming language which is incompatible with a format of the relational database, and wherein the first SQL query statement includes a wildcard parameter that is not recognizable by the relational database; a data type predictor coupled to the query analyzer to predict a data type of the wildcard parameter based on the semantic information obtained from the syntax tree in view of a structure representing the syntax tree; an object-relational mapping (ORM) unit coupled to the query analyzer and the data type predictor to generate a second SQL query statement that is tailored to the format of the relational database, the second SQL query statement including a parameter representing the wildcard parameter of the first SQL query statement, wherein a data type of the parameter of the second SQL query statement is specified using the predicted data type, and wherein the second SQL query statement is used to access the relational database. | 17. A data processing system, comprising: a query analyzer, in response to a first structured query language (SQL) query statement for accessing a relational database, to generate a syntax tree representing semantic information of the first SQL query statement, the first SQL query statement being received from a client application programmed in an object-oriented programming language, wherein the first SQL query statement is represented by a data object associated with the object-oriented programming language which is incompatible with a format of the relational database, and wherein the first SQL query statement includes a wildcard parameter that is not recognizable by the relational database; a data type predictor coupled to the query analyzer to predict a data type of the wildcard parameter based on the semantic information obtained from the syntax tree in view of a structure representing the syntax tree; an object-relational mapping (ORM) unit coupled to the query analyzer and the data type predictor to generate a second SQL query statement that is tailored to the format of the relational database, the second SQL query statement including a parameter representing the wildcard parameter of the first SQL query statement, wherein a data type of the parameter of the second SQL query statement is specified using the predicted data type, and wherein the second SQL query statement is used to access the relational database. 18. The system of claim 17 , wherein the ORM unit is configured to map the first SQL query statement with the predicted data type of the wildcard parameter to a corresponding data entry of the relational database. | 0.599474 |
3. The method of claim 1 further comprising: receiving and processing register transfer level (RTL) code representing the circuit design is performed separately from parsing the behavioral representation of the circuit design to identify signal dependencies within the circuit design. | 3. The method of claim 1 further comprising: receiving and processing register transfer level (RTL) code representing the circuit design is performed separately from parsing the behavioral representation of the circuit design to identify signal dependencies within the circuit design. 4. The method of claim 3 , further comprising: accepting a hardware description language input model and operating on a register-transfer-level (RTL) or network of nodes; receiving analog, digital, or mixed analog-and-digital RTL representations; accepting combinational, sequential, or mixed combinational-and-sequential networks; operating on a synchronous, asynchronous, or mixed synchronous-and-asynchronous networks; accepting a single or a plurality of HDL files containing RTL circuit design information; receiving RTL circuit design information from a character scanner; and operating on the RTL circuit design information provided by dedicated input files or from a database. | 0.809086 |
10. A computer-implemented method executed by one or more processors of a social video search system, the method comprising: obtaining, by the one or more processors, social context information about interactions of a plurality of users from a social graph, the social context information including social video content sources; generating, by the one or more processors and based on the social context information, social attachment information, the social attachment information for generation of annotations that indicate associations with social video content and a relation to one or more of the plurality of users; and storing, by the one or more processors, the social attachment information in association with a video search index. | 10. A computer-implemented method executed by one or more processors of a social video search system, the method comprising: obtaining, by the one or more processors, social context information about interactions of a plurality of users from a social graph, the social context information including social video content sources; generating, by the one or more processors and based on the social context information, social attachment information, the social attachment information for generation of annotations that indicate associations with social video content and a relation to one or more of the plurality of users; and storing, by the one or more processors, the social attachment information in association with a video search index. 11. The method of claim 10 wherein the social attachment information enables identification of a video from the video search index that matches a search query and is socially relevant to a user that entered the search query. | 0.576571 |
6. A computer-readable storage medium having stored thereon an index entry data structure, comprising: a first portion indicative of said index entry being indicative of a range of values, wherein the minimum value and maximum value of said range of values are different; a second portion indicative of said range of values, said second portion comprising: a first part indicative of a predetermined value and a second part indicative of a range of values; a populated index with a set of index entries comprising a minimum number of index entries that includes an entire range of numerical values, in which each index entry provides an indication of a respective portion of said range of values associated with the index entry, wherein a value representable by the range corresponds to only one entry in the index; a data structure of a formatted query value comprises a number of digit positions, wherein each digit position comprises a digit value; a data structure of a first search term of said plurality of search terms comprises a don't care indicator in each digit position of said first search term, wherein said don't care indicator indicates that any value can be placed at said digit position; and a data structure for each subsequent search term of said plurality of search terms comprises: a replica of a previously constructed search term with a don't care indicator replaced with a digit value in a corresponding digit position of said formatted query value. | 6. A computer-readable storage medium having stored thereon an index entry data structure, comprising: a first portion indicative of said index entry being indicative of a range of values, wherein the minimum value and maximum value of said range of values are different; a second portion indicative of said range of values, said second portion comprising: a first part indicative of a predetermined value and a second part indicative of a range of values; a populated index with a set of index entries comprising a minimum number of index entries that includes an entire range of numerical values, in which each index entry provides an indication of a respective portion of said range of values associated with the index entry, wherein a value representable by the range corresponds to only one entry in the index; a data structure of a formatted query value comprises a number of digit positions, wherein each digit position comprises a digit value; a data structure of a first search term of said plurality of search terms comprises a don't care indicator in each digit position of said first search term, wherein said don't care indicator indicates that any value can be placed at said digit position; and a data structure for each subsequent search term of said plurality of search terms comprises: a replica of a previously constructed search term with a don't care indicator replaced with a digit value in a corresponding digit position of said formatted query value. 7. A computer-readable storage medium in accordance with claim 6 , wherein: a minimum value of said second portion is indicative of a minimum value of said range of values; and a maximum value of said second portion is indicative of a maximum value of said range of values. | 0.838634 |
1. A method of indexing a stimulus comprising: measuring a plurality of facial expressions in an audience of the stimulus; interpreting measurements of the facial expressions using a processor, wherein the processor interpreting the measurements produces a result estimating a mood for a time period of the stimulus, and wherein interpreting measurements of the facial expressions comprises: classifying each of the facial expressions as corresponding to an emotion from a set of emotions; and statistically analyzing the respective emotions corresponding to the plurality of facial expressions to estimate the mood; generating an annotation of the stimulus, wherein the annotation is generated based on the mood estimated; repeating the measuring, interpreting, and generating steps to produce a plurality of the annotations that respectively correspond to different time periods of the stimulus; and indexing the annotations according to the respective moods, wherein the indexing provides a two-way linkage between the annotations and the time periods of the stimulus. | 1. A method of indexing a stimulus comprising: measuring a plurality of facial expressions in an audience of the stimulus; interpreting measurements of the facial expressions using a processor, wherein the processor interpreting the measurements produces a result estimating a mood for a time period of the stimulus, and wherein interpreting measurements of the facial expressions comprises: classifying each of the facial expressions as corresponding to an emotion from a set of emotions; and statistically analyzing the respective emotions corresponding to the plurality of facial expressions to estimate the mood; generating an annotation of the stimulus, wherein the annotation is generated based on the mood estimated; repeating the measuring, interpreting, and generating steps to produce a plurality of the annotations that respectively correspond to different time periods of the stimulus; and indexing the annotations according to the respective moods, wherein the indexing provides a two-way linkage between the annotations and the time periods of the stimulus. 2. The method of claim 1 , wherein the stimulus comprises an audio-video stimulus. | 0.873089 |
1. An apparatus, comprising: a device including at least one input device, at least one display, and memory in communication with at least one hardware processor; and a browser installed on the memory of the device for allowing access, utilizing the at least one input device and the at least one hardware processor, to a system including a hardware server, the system configured for: identifying at least parts of a plurality of original documents including a plurality of original values, the plurality of original documents including a first document including first values and a second document including second values; processing at least a part of the first document and at least a part of the second document, resulting in at least one data structure including at least one of the plurality of original values of at least one of the plurality of original documents; receiving one or more indications for one or more of the original values for adding, in connection with at least one computer-readable Extensible Markup Language (XML)-compliant data document, a corresponding one or more computer-readable semantic tags in association with the one or more original values; associating the one or more computer-readable semantic tags with the one or more original values such that the one or more computer-readable semantic tags are each computer-readably associated with the one or more original values; causing output of a presentation that is based on at least a portion of the at least one data structure, the presentation capable of including at least a portion of the original values including the at least one original value, where the system is configured such that, based on the at least one data structure, a change to the at least one original value results in a corresponding change in a subsequent output of the presentation; causing output of a report that is based on at least a portion of the at least one data structure, the report capable of including at least a portion of the original values including the at least one original value, where the system is configured such that, based on the at least one data structure, a change to the at least one original value results in a corresponding change in a subsequent output of the report; and causing output of the computer-readable XML-compliant data document that is based on at least a portion of at least one data structure, the at least one computer-readable XML-compliant data document capable of including a plurality of line items at least one of which utilizes at least a portion of the original values including the at least one original value and at least some of the one or more computer-readable semantic tags, where the system is configured such that, based on the at least one data structure, a change to the at least one original value results in a corresponding change in a subsequent output of the at least one computer-readable XML-compliant data document; said apparatus configured for: receiving user input utilizing the browser, and displaying the at least one computer-readable XML-compliant data document utilizing the browser, after the user input. | 1. An apparatus, comprising: a device including at least one input device, at least one display, and memory in communication with at least one hardware processor; and a browser installed on the memory of the device for allowing access, utilizing the at least one input device and the at least one hardware processor, to a system including a hardware server, the system configured for: identifying at least parts of a plurality of original documents including a plurality of original values, the plurality of original documents including a first document including first values and a second document including second values; processing at least a part of the first document and at least a part of the second document, resulting in at least one data structure including at least one of the plurality of original values of at least one of the plurality of original documents; receiving one or more indications for one or more of the original values for adding, in connection with at least one computer-readable Extensible Markup Language (XML)-compliant data document, a corresponding one or more computer-readable semantic tags in association with the one or more original values; associating the one or more computer-readable semantic tags with the one or more original values such that the one or more computer-readable semantic tags are each computer-readably associated with the one or more original values; causing output of a presentation that is based on at least a portion of the at least one data structure, the presentation capable of including at least a portion of the original values including the at least one original value, where the system is configured such that, based on the at least one data structure, a change to the at least one original value results in a corresponding change in a subsequent output of the presentation; causing output of a report that is based on at least a portion of the at least one data structure, the report capable of including at least a portion of the original values including the at least one original value, where the system is configured such that, based on the at least one data structure, a change to the at least one original value results in a corresponding change in a subsequent output of the report; and causing output of the computer-readable XML-compliant data document that is based on at least a portion of at least one data structure, the at least one computer-readable XML-compliant data document capable of including a plurality of line items at least one of which utilizes at least a portion of the original values including the at least one original value and at least some of the one or more computer-readable semantic tags, where the system is configured such that, based on the at least one data structure, a change to the at least one original value results in a corresponding change in a subsequent output of the at least one computer-readable XML-compliant data document; said apparatus configured for: receiving user input utilizing the browser, and displaying the at least one computer-readable XML-compliant data document utilizing the browser, after the user input. 39. The apparatus of claim 1 , wherein the system is configured such that the change to the at least one original value that results in the corresponding change in the subsequent output of the report, is a change to the at least one original value in connection with the one or more of the plurality of original documents prior to the subsequent output of the report. | 0.754334 |
1. A method for incrementally and multi-dimensionally adjusting prose style comprising: creating a database of sets of phrase synonyms within a data processing environment wherein a phrase is defined as a character string including one or more words, numbers, abbreviations or combinations thereof, and wherein set of phrase synonyms is defined as a set of phrases in which there is at least one phrase in the set for which all other phrases in the set can be substituted in prose usage without causing significant changes in the meaning of the prose or grammatical errors in the prose; assigning rankings and/or values to phrases in the database for each phrase's ranking and/or value with respect to a dimension of prose style, for at least two different dimensions of prose style; receiving input prose selected by a user via an interface between the user and a computer selected from a group consisting of: direct entry of prose by means of a physical or virtual keyboard, keypad, touchpad, or touch screen; importing prose from a file, document, or website; highlighting or identifying a portion of prose using a cursor; voice, speech, or gesture recognition; and selection of a file, document, or website in response to a search request; receiving a style adjustment preference from a user for multiple dimensions of prose style through a multi-dimensional style-adjusting interface having multiple dimensions of prose style each assigned to an incrementally adjustable control between the user and the computer selected from one or more control elements in a group consisting of: virtual or physical slider bar; virtual or physical buttons, keyboard, keypad, or touch screen; virtual or physical dials or knobs; popup menu, drop down menu, or other virtual menu; data entry box, line, or space; mouse and/or cursor movement; voice or speech recognition; and gesture or posture recognition; and making adjustments to the style of the input prose within a data processing environment according to the style adjustment preference specified by the user through the multi-dimensional style-adjusting interface, wherein style adjustments are done (a) searching for phrases in the input prose that are in the database of phrase synonyms and (b) replacing the phrases in the input prose with intra-set phrase synonyms from the database with higher rankings and/or values in a dimension of style for which the user has indicated an increased preference through the multi-dimensional style-adjusting interface and/or replacing the phrases in the input prose with intra-set phrase synonyms from the database with lower rankings and/or values in a dimension of style for which the user has indicated a decreased preference through the multi-dimensional style-adjusting interface. | 1. A method for incrementally and multi-dimensionally adjusting prose style comprising: creating a database of sets of phrase synonyms within a data processing environment wherein a phrase is defined as a character string including one or more words, numbers, abbreviations or combinations thereof, and wherein set of phrase synonyms is defined as a set of phrases in which there is at least one phrase in the set for which all other phrases in the set can be substituted in prose usage without causing significant changes in the meaning of the prose or grammatical errors in the prose; assigning rankings and/or values to phrases in the database for each phrase's ranking and/or value with respect to a dimension of prose style, for at least two different dimensions of prose style; receiving input prose selected by a user via an interface between the user and a computer selected from a group consisting of: direct entry of prose by means of a physical or virtual keyboard, keypad, touchpad, or touch screen; importing prose from a file, document, or website; highlighting or identifying a portion of prose using a cursor; voice, speech, or gesture recognition; and selection of a file, document, or website in response to a search request; receiving a style adjustment preference from a user for multiple dimensions of prose style through a multi-dimensional style-adjusting interface having multiple dimensions of prose style each assigned to an incrementally adjustable control between the user and the computer selected from one or more control elements in a group consisting of: virtual or physical slider bar; virtual or physical buttons, keyboard, keypad, or touch screen; virtual or physical dials or knobs; popup menu, drop down menu, or other virtual menu; data entry box, line, or space; mouse and/or cursor movement; voice or speech recognition; and gesture or posture recognition; and making adjustments to the style of the input prose within a data processing environment according to the style adjustment preference specified by the user through the multi-dimensional style-adjusting interface, wherein style adjustments are done (a) searching for phrases in the input prose that are in the database of phrase synonyms and (b) replacing the phrases in the input prose with intra-set phrase synonyms from the database with higher rankings and/or values in a dimension of style for which the user has indicated an increased preference through the multi-dimensional style-adjusting interface and/or replacing the phrases in the input prose with intra-set phrase synonyms from the database with lower rankings and/or values in a dimension of style for which the user has indicated a decreased preference through the multi-dimensional style-adjusting interface. 8. The method of claim 1 wherein at least one dimension of prose style is selected from the group consisting of: level of use of national-specific and/or regional-specific expressions; and geographic specification of national-specific and/or regional-specific expressions. | 0.549505 |
1. A computer program product embodied in a non-transitory computer readable medium that, when executing on one or more computers, helps a user make a decision through the use of a computing facility by performing the steps of: inferring a profile for the user based upon a similarity to one or more other users; receiving an initial question at the computing facility from the user through a social networking application; providing the user with a dialogue consisting of questions from the computing facility and answers provided by the user, wherein at least one of the questions from the computing facility is selected based upon the profile; continuing the dialogue until the computing facility develops a predetermined confidence level in a reduced set of decisions; and presenting one of the reduced set of decisions having a highest confidence level to the user from the computing facility, wherein the one of the reduced set of decisions is a single answer to the initial question from the user based on the dialogue and the profile. | 1. A computer program product embodied in a non-transitory computer readable medium that, when executing on one or more computers, helps a user make a decision through the use of a computing facility by performing the steps of: inferring a profile for the user based upon a similarity to one or more other users; receiving an initial question at the computing facility from the user through a social networking application; providing the user with a dialogue consisting of questions from the computing facility and answers provided by the user, wherein at least one of the questions from the computing facility is selected based upon the profile; continuing the dialogue until the computing facility develops a predetermined confidence level in a reduced set of decisions; and presenting one of the reduced set of decisions having a highest confidence level to the user from the computing facility, wherein the one of the reduced set of decisions is a single answer to the initial question from the user based on the dialogue and the profile. 5. The computer program product of claim 1 , wherein the single answer is further based on expert training. | 0.817568 |
1. A phonetic E-mail reader comprising: a speech synthesizer for converting text data delivered thereto into vocal data corresponding to said text data; quotation code storing means for storing quotation codes used for indicating a quotation line in an E-mail text, said quotation codes being found at a top of said quotation line, said E-mail text including communication lines not having quotation codes; and quotation code elimination means for detecting and eliminating at least one of said quotation codes inserted at tops of quotation lines in E-mail texts referring to said quotation code storing means, and for passing on communication lines and quotation lines with said quotation codes eliminated to said speech synthesizer to be converted into vocal data corresponding thereto. | 1. A phonetic E-mail reader comprising: a speech synthesizer for converting text data delivered thereto into vocal data corresponding to said text data; quotation code storing means for storing quotation codes used for indicating a quotation line in an E-mail text, said quotation codes being found at a top of said quotation line, said E-mail text including communication lines not having quotation codes; and quotation code elimination means for detecting and eliminating at least one of said quotation codes inserted at tops of quotation lines in E-mail texts referring to said quotation code storing means, and for passing on communication lines and quotation lines with said quotation codes eliminated to said speech synthesizer to be converted into vocal data corresponding thereto. 2. A phonetic E-mail reader as recited in claim 1, wherein: said quotation code eliminating means eliminates said at least one of said quotation codes, together with at least one blank code following said at least one of said quotation codes. | 0.847842 |
15. A system supporting speech recognition comprising: two or more clients, each client comprising the capability to receive audio speech from a user, store the audio speech in one or more buffers, each buffer comprising a portion of the received audio speech, encode a buffer of the received audio speech before all of the audio speech is received, package the encoded buffer to receive audio speech into one or more packets to be transmitted over the Internet before all of the audio speech is received, and transmit a packet of encoded audio speech over the Internet before all of the audio speech is received; and a server, the server comprising the capability to receive packets of encoded audio speech from at least two clients, decode each of the packets of audio speech and store the resultant raw speech into one or more buffers for the respective client, and evaluate the resultant raw speech received from each of the at least two clients, wherein the server further comprises two or more stored text format files, and the server selects a stored text format file to transmit to a client of the two or more clients as a result of the server's evaluation of the resultant raw speech received from the client, the server further comprises the capability to partition a stored text format file into two or more packets for the transmission over the Internet, and to transmit each packet over the Internet to a client, a client further comprises an audio output device, and the capability to receive the packets of text format, convert the packets of text format to audio data and play the audio data to a user, and a processing time used to evaluate the resultant raw speech will vary based on a value communicated to the server from a client. | 15. A system supporting speech recognition comprising: two or more clients, each client comprising the capability to receive audio speech from a user, store the audio speech in one or more buffers, each buffer comprising a portion of the received audio speech, encode a buffer of the received audio speech before all of the audio speech is received, package the encoded buffer to receive audio speech into one or more packets to be transmitted over the Internet before all of the audio speech is received, and transmit a packet of encoded audio speech over the Internet before all of the audio speech is received; and a server, the server comprising the capability to receive packets of encoded audio speech from at least two clients, decode each of the packets of audio speech and store the resultant raw speech into one or more buffers for the respective client, and evaluate the resultant raw speech received from each of the at least two clients, wherein the server further comprises two or more stored text format files, and the server selects a stored text format file to transmit to a client of the two or more clients as a result of the server's evaluation of the resultant raw speech received from the client, the server further comprises the capability to partition a stored text format file into two or more packets for the transmission over the Internet, and to transmit each packet over the Internet to a client, a client further comprises an audio output device, and the capability to receive the packets of text format, convert the packets of text format to audio data and play the audio data to a user, and a processing time used to evaluate the resultant raw speech will vary based on a value communicated to the server from a client. 18. The system of claim 15 wherein the encoded audio speech is in a compressed format. | 0.793307 |
1. A computer-implemented method comprising: obtaining a set of language indicators that comprises an indication of a language used by a second user that is socially connected to a first user, each language indicator related to a language potentially preferred by the first user; by a processor, applying a set of rules to the language indicators to obtain a set of preferred languages for the first user, the set of preferred languages being ordered according to a determined likelihood that each language is the first user's primary preferred language; and localizing a first application based upon the highest-order preferred language. | 1. A computer-implemented method comprising: obtaining a set of language indicators that comprises an indication of a language used by a second user that is socially connected to a first user, each language indicator related to a language potentially preferred by the first user; by a processor, applying a set of rules to the language indicators to obtain a set of preferred languages for the first user, the set of preferred languages being ordered according to a determined likelihood that each language is the first user's primary preferred language; and localizing a first application based upon the highest-order preferred language. 10. The method of claim 1 , further comprising the step of modifying the set of rules based upon a comparison of the set of preferred languages obtained for the first user to a primary preferred language identified by the first user. | 0.751064 |
15. A system for protecting a programming code through watermarking, the system comprising: a memory storage; and a processing unit coupled to the memory storage, wherein the processing unit is configured to: determine at least one portion of a programming code to be watermarked; determine a watermarking mechanism to be applied to the at least one portion of the programming code; determine a size of repeated binary format patterns associated with the watermarking mechanism, the watermarking mechanism being repeatedly encoded throughout the programming code; and encode the watermarking mechanism to the at least one portion of the programming code by modifying a text stream of the programming code with a repeated binary format pattern. | 15. A system for protecting a programming code through watermarking, the system comprising: a memory storage; and a processing unit coupled to the memory storage, wherein the processing unit is configured to: determine at least one portion of a programming code to be watermarked; determine a watermarking mechanism to be applied to the at least one portion of the programming code; determine a size of repeated binary format patterns associated with the watermarking mechanism, the watermarking mechanism being repeatedly encoded throughout the programming code; and encode the watermarking mechanism to the at least one portion of the programming code by modifying a text stream of the programming code with a repeated binary format pattern. 20. The system of claim 15 , wherein the processing unit being configured to determine the watermarking mechanism comprises the processing unit being configured to employ more than one watermarking mechanism in encoding the programming code. | 0.757384 |
3. The system of claim 1 , comprising a configuration station configured to enable an operator to configure the multi-Boolean function block for a particular automation process. | 3. The system of claim 1 , comprising a configuration station configured to enable an operator to configure the multi-Boolean function block for a particular automation process. 5. The system of claim 3 , wherein the configuration station is configured to download the configured multi-Boolean function block into the distributed automation control device. | 0.933009 |
1. A method for inferring problem data from bug repositories, the method comprising; identifying a plurality of phrases that are repeated in a bug report; selecting a first phrase from the plurality of phrases to keep and a second phrase from the plurality of phrases to drop based on a meaning of the first phrase being greater in significance in the bug report than a meaning of the second phrase; mapping the first phrase to one or more of a plurality of classes of an ontology model associated with the bug report, the ontology model defining valid interactions between the plurality of classes; determining whether the first phrase corresponds to a valid interaction defined by the ontology model; and based on determining the first phrase corresponds to a valid interaction in the ontology model, generating an output corresponding to the mapping for use in analyzing the bug report. | 1. A method for inferring problem data from bug repositories, the method comprising; identifying a plurality of phrases that are repeated in a bug report; selecting a first phrase from the plurality of phrases to keep and a second phrase from the plurality of phrases to drop based on a meaning of the first phrase being greater in significance in the bug report than a meaning of the second phrase; mapping the first phrase to one or more of a plurality of classes of an ontology model associated with the bug report, the ontology model defining valid interactions between the plurality of classes; determining whether the first phrase corresponds to a valid interaction defined by the ontology model; and based on determining the first phrase corresponds to a valid interaction in the ontology model, generating an output corresponding to the mapping for use in analyzing the bug report. 9. The method of claim 1 further comprising, using incremental learning to one or more of build the knowledge base and update the knowledge base. | 0.691619 |
3. The method of claim 2 , further comprising determining whether the evidentiary attributes associated with an entity correspond to known attributes comprising a behavioral profile defined by a set of behavioral attributes, the determination being made by comparing the evidentiary attributes to the set of behavioral attributes. | 3. The method of claim 2 , further comprising determining whether the evidentiary attributes associated with an entity correspond to known attributes comprising a behavioral profile defined by a set of behavioral attributes, the determination being made by comparing the evidentiary attributes to the set of behavioral attributes. 10. The method of claim 3 , wherein the behavioral profile defines an entity that has a reputation for sending malicious communications including viruses. | 0.891026 |
1. A computer implemented method of modeling a reservoir property of subsurface reservoir structure by support vector machine processing in the computer of input data available from the reservoir to form measures of the reservoir property at regions of interest in the subsurface reservoir by regression analysis of the available input data, the method comprising the computer processing steps of: (a) receiving training input data about subsurface attributes from seismic survey data obtained from seismic surveys of the reservoir; (b) receiving training target data about formation rock characteristics from data obtained from wells in the reservoir; (c) partitioning the subsurface attributes training data and the formation rock characteristics training target data into a plurality of subsets; (d) selecting formation attribute parameters for support vector machine modeling by performing the steps of: (1) cross-validating the subsets of subsurface attributes training data each with the other subsets of the plurality of subsets for a radial based kernel function pair comprising a kernel parameter value and a penalty parameter pair value; (2) forming an error function for each of the cross-validated subsets; (3) repeating the steps of cross-validating the subsets of subsurface attributes training data and forming an error function for a plurality of different radial based kernel function pairs; (e) optimizing the selected formation attribute parameters by determining a minimum error function of the formed error functions for the plurality of different radial based kernel function pairs; (f) providing the training data, the selected formation attribute parameters, the cross-validated subsets of subsurface attributes training data, and the error functions for the plurality of radial based function kernel pairs as training inputs for support vector machine modeling; (g) performing support vector machine modeling by regression analysis to determine a minimum error function of the error functions of the provided training inputs; (h) predicting the reservoir property based on the support vector modeling of the training inputs; and (i) forming an output display of the predicted reservoir property. | 1. A computer implemented method of modeling a reservoir property of subsurface reservoir structure by support vector machine processing in the computer of input data available from the reservoir to form measures of the reservoir property at regions of interest in the subsurface reservoir by regression analysis of the available input data, the method comprising the computer processing steps of: (a) receiving training input data about subsurface attributes from seismic survey data obtained from seismic surveys of the reservoir; (b) receiving training target data about formation rock characteristics from data obtained from wells in the reservoir; (c) partitioning the subsurface attributes training data and the formation rock characteristics training target data into a plurality of subsets; (d) selecting formation attribute parameters for support vector machine modeling by performing the steps of: (1) cross-validating the subsets of subsurface attributes training data each with the other subsets of the plurality of subsets for a radial based kernel function pair comprising a kernel parameter value and a penalty parameter pair value; (2) forming an error function for each of the cross-validated subsets; (3) repeating the steps of cross-validating the subsets of subsurface attributes training data and forming an error function for a plurality of different radial based kernel function pairs; (e) optimizing the selected formation attribute parameters by determining a minimum error function of the formed error functions for the plurality of different radial based kernel function pairs; (f) providing the training data, the selected formation attribute parameters, the cross-validated subsets of subsurface attributes training data, and the error functions for the plurality of radial based function kernel pairs as training inputs for support vector machine modeling; (g) performing support vector machine modeling by regression analysis to determine a minimum error function of the error functions of the provided training inputs; (h) predicting the reservoir property based on the support vector modeling of the training inputs; and (i) forming an output display of the predicted reservoir property. 2. The computer implemented method of claim 1 , wherein the computer includes a graphical display device and further including: storing a record of the predicted reservoir property. | 0.80597 |
20. The method according to claim 16 and wherein said enabling comprises enabling focusing on interesting parts of a current portion of said query digital media, wherein said interesting parts are defined by said importance scores. | 20. The method according to claim 16 and wherein said enabling comprises enabling focusing on interesting parts of a current portion of said query digital media, wherein said interesting parts are defined by said importance scores. 21. The method according to claim 20 and wherein said enabling focusing comprises at least one of: cropping the uninteresting parts of said current portion, changing the temporal length of the video sequence, generating a shorter video clip, playing only said interesting parts of said current portion, omitting the uninteresting parts of said current portion and changing the display size to one of the following: the size of a mobile phone screen, a movie screen, a TV screen, an MP player screen, a portable screen, a thumbnail and an image preview. | 0.802481 |
1. A method in a content recommendation system, the method comprising: under control of a computing system, extracting quotations from a corpus of text documents; identifying one or more entities that are referenced by each of the extracted quotations, each of the identified entities being electronically represented by the content recommendation system; indexing, by the computing system, the extracted quotations, wherein indexing the extracted quotations includes storing a speaker-verb-quote triple in an inverted index managed by the content recommendation system; determining one or more of the extracted quotations that match a received quotation search request; and providing the determined one or more quotations. | 1. A method in a content recommendation system, the method comprising: under control of a computing system, extracting quotations from a corpus of text documents; identifying one or more entities that are referenced by each of the extracted quotations, each of the identified entities being electronically represented by the content recommendation system; indexing, by the computing system, the extracted quotations, wherein indexing the extracted quotations includes storing a speaker-verb-quote triple in an inverted index managed by the content recommendation system; determining one or more of the extracted quotations that match a received quotation search request; and providing the determined one or more quotations. 9. The method of claim 1 wherein identifying one or more entities includes assigning a type or facet to each of the one or more entities. | 0.600317 |
1. A method, comprising: at an electronic device comprising a processor and memory storing instructions for execution by the processor: while the device is operating with a first setting in a first state, detecting, at a first time, a change in settings of the device to change the first setting from the first state to a second state that is different from the first state; while the device is operating with the first setting in the second state, receiving, at a second time that is after the first time, a user input that corresponds to a pattern of user behavior, wherein the user input is a user voice input; in response to receiving the user input: comparing the pattern of user behavior to a plurality of predefined conditions that, when met, indicate that the user is having difficulty with operating the device, wherein a predefined condition of the plurality of predefined conditions includes the user voice input containing one or more predetermined words associated with user difficulty; in accordance with a determination, based on the comparison of the pattern of user behavior to the plurality of predefined conditions, that the device changed the first setting from the first state to the second state within a predetermined time period prior to receiving the user input, restoring the first setting to the first state; and in accordance with a determination, based on the comparison of the pattern of user behavior to the plurality of predefined conditions, that the user is not having difficulty with operating the device, maintaining the first setting in the second state. | 1. A method, comprising: at an electronic device comprising a processor and memory storing instructions for execution by the processor: while the device is operating with a first setting in a first state, detecting, at a first time, a change in settings of the device to change the first setting from the first state to a second state that is different from the first state; while the device is operating with the first setting in the second state, receiving, at a second time that is after the first time, a user input that corresponds to a pattern of user behavior, wherein the user input is a user voice input; in response to receiving the user input: comparing the pattern of user behavior to a plurality of predefined conditions that, when met, indicate that the user is having difficulty with operating the device, wherein a predefined condition of the plurality of predefined conditions includes the user voice input containing one or more predetermined words associated with user difficulty; in accordance with a determination, based on the comparison of the pattern of user behavior to the plurality of predefined conditions, that the device changed the first setting from the first state to the second state within a predetermined time period prior to receiving the user input, restoring the first setting to the first state; and in accordance with a determination, based on the comparison of the pattern of user behavior to the plurality of predefined conditions, that the user is not having difficulty with operating the device, maintaining the first setting in the second state. 8. The method of claim 1 , further comprising: prior to restoring the first setting to the first state, prompting the user to confirm that they are having difficulty with operating the device; and in response to receiving confirmation that the user is having difficulty with operating the device, restoring the first setting to the first state. | 0.581469 |
1. A method comprising: receiving an image file representing an image comprising text, the text comprising gridless text comprising a plurality of letters; performing optical character recognition (OCR) on the image file to generate a text file comprising a series of character codes representing the text, wherein performing OCR comprises: determining, for a first letter of the plurality of letters, a set of letter properties, wherein the set of letter properties includes letter shape properties that are independent of other letters of the plurality of letters, inter-letter properties that are dependent on an adjacent letter of the first letter, and letter-combination properties that are dependent on letters in a same word or line as the first letter; classifying the first letter into a first letterform class of a plurality of letterform classes based on the set of letter properties; determining a character code for the first letterform class; and generating the text file based on the character code. | 1. A method comprising: receiving an image file representing an image comprising text, the text comprising gridless text comprising a plurality of letters; performing optical character recognition (OCR) on the image file to generate a text file comprising a series of character codes representing the text, wherein performing OCR comprises: determining, for a first letter of the plurality of letters, a set of letter properties, wherein the set of letter properties includes letter shape properties that are independent of other letters of the plurality of letters, inter-letter properties that are dependent on an adjacent letter of the first letter, and letter-combination properties that are dependent on letters in a same word or line as the first letter; classifying the first letter into a first letterform class of a plurality of letterform classes based on the set of letter properties; determining a character code for the first letterform class; and generating the text file based on the character code. 3. The method of claim 1 , wherein the letter shape properties comprise a rectangular bounding box determined as the smallest rectangle that encompasses every pixel of the first letter and a slanted bounding box determined as the smallest parallelogram that encompasses every pixel of the letter. | 0.620546 |
13. A non-transitory computer-readable storage device encoded with a computer program product, the computer program product comprising instructions that when executed on one or more computers cause the one or more computers to perform operations comprising: receiving over a network a plurality of sets of predictive modeling training data from a first plurality of client computing systems; for each set of plurality of sets of predictive modeling training data: using the training data and a plurality of training functions to train a plurality of predictive models; generating a score for each of the plurality of trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model; and selecting a trained predictive model from among the plurality of trained predictive models based on the generated scores; wherein a plurality of trained predictive models are thereby generated and selected; storing the plurality of selected trained predictive models in a repository of trained predictive models; and providing access to the plurality of trained predictive models to a second plurality of client computing systems. | 13. A non-transitory computer-readable storage device encoded with a computer program product, the computer program product comprising instructions that when executed on one or more computers cause the one or more computers to perform operations comprising: receiving over a network a plurality of sets of predictive modeling training data from a first plurality of client computing systems; for each set of plurality of sets of predictive modeling training data: using the training data and a plurality of training functions to train a plurality of predictive models; generating a score for each of the plurality of trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model; and selecting a trained predictive model from among the plurality of trained predictive models based on the generated scores; wherein a plurality of trained predictive models are thereby generated and selected; storing the plurality of selected trained predictive models in a repository of trained predictive models; and providing access to the plurality of trained predictive models to a second plurality of client computing systems. 15. The computer-readable storage device of claim 13 , wherein providing access to the plurality of trained predictive models includes providing a web page accessible by the second plurality of client computing systems that is configured for the second plurality of client computing systems to browse the repository of trained predictive models that are available for use by the second plurality of client computing systems. | 0.775194 |
1. A system for predicting data, comprising: a processor configured to: obtain a name or title from a taste profile; index into a data set based on the name or the title, and retrieve a set of descriptive terms and corresponding term weights associated with the name or the title; construct a sparse vector based on the set of descriptive terms and term weights; identify a target brand or segment of interest; generate a first list of accounts who follow the brand or segment of interest by examining social media data, and a second list of additional entities followed by accounts in the first list; filter the second list through a space mapping that maps entities to names or titles from taste profiles, to generate a subset of test data having a correspondence to the target brand or segment of interest; input the sparse vector to a training model including target data, wherein the target data includes the subset of test data having a correspondence to the target brand or segment of interest and the training model is based on a machine learning from ground truths from a selection of the target data, and output a respective binary value and confidence level for each descriptive term above a threshold, corresponding to an association between the descriptive term and the target brand or segment of interest. | 1. A system for predicting data, comprising: a processor configured to: obtain a name or title from a taste profile; index into a data set based on the name or the title, and retrieve a set of descriptive terms and corresponding term weights associated with the name or the title; construct a sparse vector based on the set of descriptive terms and term weights; identify a target brand or segment of interest; generate a first list of accounts who follow the brand or segment of interest by examining social media data, and a second list of additional entities followed by accounts in the first list; filter the second list through a space mapping that maps entities to names or titles from taste profiles, to generate a subset of test data having a correspondence to the target brand or segment of interest; input the sparse vector to a training model including target data, wherein the target data includes the subset of test data having a correspondence to the target brand or segment of interest and the training model is based on a machine learning from ground truths from a selection of the target data, and output a respective binary value and confidence level for each descriptive term above a threshold, corresponding to an association between the descriptive term and the target brand or segment of interest. 6. The system according to claim 1 , wherein the processor is further configured to filter the output to only those terms with a confidence level above a set threshold. | 0.585985 |
10. A computer-based system for transmitting an electronic document, comprising: an intermediate computer that is remote from a mobile electronic device sending an email message, the intermediate computer including a memory storing instructions; and a processor configured to: execute the instructions to receive the email message having a delivery address from the mobile electronic device, execute the instructions to determine that the email message has an attached document, execute the instructions to determine that the attached document is to be cleansed of metadata according to a cleansing policy, execute the instructions to automatically remove metadata from the attached document, execute the instructions to create a cleansed version of the attached document at the intermediate computer, execute the instructions to replace in the email message the attached document with the cleansed version of the attached document, and execute the instructions to send the email message with the cleansed version of the attached document from the intermediate computer to the delivery address. | 10. A computer-based system for transmitting an electronic document, comprising: an intermediate computer that is remote from a mobile electronic device sending an email message, the intermediate computer including a memory storing instructions; and a processor configured to: execute the instructions to receive the email message having a delivery address from the mobile electronic device, execute the instructions to determine that the email message has an attached document, execute the instructions to determine that the attached document is to be cleansed of metadata according to a cleansing policy, execute the instructions to automatically remove metadata from the attached document, execute the instructions to create a cleansed version of the attached document at the intermediate computer, execute the instructions to replace in the email message the attached document with the cleansed version of the attached document, and execute the instructions to send the email message with the cleansed version of the attached document from the intermediate computer to the delivery address. 13. The system of claim 10 , wherein the cleansing policy specifies the attached-document is to be cleansed if the attached document comes from a specific user. | 0.572246 |
10. A computer system for customizing speech recognition for users with language accents, the computer system comprising: one or more computer processors, one or more computer-readable storage media, and program instructions stored on one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions, comprising: identifying a spoken language of a user; receiving an indicator of a speech accent language initiated by the user using a computer, the indicator identifying the speech accent language and defining an influence of the speech accent language on the spoken language, the indicator is provided on an interface of the computer, and the indicator is adjustable by the user to identify the influence of the speech accent language on the spoken language, wherein the indicator includes a value identifying the speech accent language influence on the spoken language; setting speech recognition characteristics according to the language and the indicator of the speech accent language; adjusting an automatic speech recognition (ASR) conversion based on the speech recognition characteristics; converting the spoken language of the user into text using the automatic speech recognition conversion; and receiving an adjustable value on a numbered scale as part of the indicator to identify the influence of the speech accent language on the spoken language, the adjustable value being set by the user and identifying an amount of influence of the accent language on the spoken language. | 10. A computer system for customizing speech recognition for users with language accents, the computer system comprising: one or more computer processors, one or more computer-readable storage media, and program instructions stored on one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions, comprising: identifying a spoken language of a user; receiving an indicator of a speech accent language initiated by the user using a computer, the indicator identifying the speech accent language and defining an influence of the speech accent language on the spoken language, the indicator is provided on an interface of the computer, and the indicator is adjustable by the user to identify the influence of the speech accent language on the spoken language, wherein the indicator includes a value identifying the speech accent language influence on the spoken language; setting speech recognition characteristics according to the language and the indicator of the speech accent language; adjusting an automatic speech recognition (ASR) conversion based on the speech recognition characteristics; converting the spoken language of the user into text using the automatic speech recognition conversion; and receiving an adjustable value on a numbered scale as part of the indicator to identify the influence of the speech accent language on the spoken language, the adjustable value being set by the user and identifying an amount of influence of the accent language on the spoken language. 11. The system of claim 10 , wherein the value is stored and used in subsequent ASR implementations for the user. | 0.540517 |
3. The computer implemented, data driven method of teaching a student to read according to claim 2, wherein the whole word recognition test; the partial word recognition test; and the word sequence recognition test represent three interactive process types respectively corresponding to choosing a target word from a list of displayed words by first communicating the target then choosing the target word from a list of subsequently displayed words; filling in letter blanks by first communicating a target word and then filling in letter blanks of a displayed, partial target word having blanked letters; and determining a correct sequence of words by individually communicating a plurality of words that includes the target word in a first sequence one word at a time, then simultaneously displaying the plurality of words including the target word in a second sequence, and then selecting words from the displayed plurality of words in the first sequence, said presenting step presenting the student with at least one of nine interactive processes wherein each interactive process type includes three interactive processes, the three interactive processes including a show only process which communicates the target word by displaying the target word for a show interval, a say only process which communicates the target word by audibly announcing the target word, and a show and say process that communicates the target word by displaying the target word for a show interval and by audibly announcing the target word. | 3. The computer implemented, data driven method of teaching a student to read according to claim 2, wherein the whole word recognition test; the partial word recognition test; and the word sequence recognition test represent three interactive process types respectively corresponding to choosing a target word from a list of displayed words by first communicating the target then choosing the target word from a list of subsequently displayed words; filling in letter blanks by first communicating a target word and then filling in letter blanks of a displayed, partial target word having blanked letters; and determining a correct sequence of words by individually communicating a plurality of words that includes the target word in a first sequence one word at a time, then simultaneously displaying the plurality of words including the target word in a second sequence, and then selecting words from the displayed plurality of words in the first sequence, said presenting step presenting the student with at least one of nine interactive processes wherein each interactive process type includes three interactive processes, the three interactive processes including a show only process which communicates the target word by displaying the target word for a show interval, a say only process which communicates the target word by audibly announcing the target word, and a show and say process that communicates the target word by displaying the target word for a show interval and by audibly announcing the target word. 4. The computer implemented, data driven method of teaching a student to read according to claim 3, further comprising the step of: a second iterating step iterating said presenting step, said inputting step, said measuring step, said determining step, and said providing step until the response input in said inputting step is the correct response as determined by said determining step; said calculating step calculating student performance based on a number of iterations performed by said second iterating step until the correct response was inputted in said inputting step, a difficulty level of the test presented in said presenting step, and the response time measured in said measuring step. | 0.584654 |
1. A method of generating an ontology, comprising: determining plural concepts from a data set by using a first predetermined pattern, said determinin plural concepts comprising generating a plurality of web service ontologies; using a second predetermined pattern to determine a relationship between said plural concepts, and between a concept and a concept token in said plural concepts; and generating, using a computer processor, said ontology based on said relationship. | 1. A method of generating an ontology, comprising: determining plural concepts from a data set by using a first predetermined pattern, said determinin plural concepts comprising generating a plurality of web service ontologies; using a second predetermined pattern to determine a relationship between said plural concepts, and between a concept and a concept token in said plural concepts; and generating, using a computer processor, said ontology based on said relationship. 4. The method of claim 1 , wherein said determining said plural concepts comprises generating a source ontology from a source web service collection, and a target ontology from a target web service collection. | 0.687571 |
14. The method of claim 11 , wherein detecting the one or more predetermined features in each sentence of the legal document comprises detecting the one or more predetermined features in each sentence of the legal document by referring to lexical, syntactic and semantic information stored in a knowledge repository. | 14. The method of claim 11 , wherein detecting the one or more predetermined features in each sentence of the legal document comprises detecting the one or more predetermined features in each sentence of the legal document by referring to lexical, syntactic and semantic information stored in a knowledge repository. 16. The method of claim 14 , wherein detecting the one or more predetermined features in each sentence of the legal document comprises detecting the one or more predetermined features using a list of words stored in a repository. | 0.895833 |
1. A method comprising: providing, by one or more processors associated with a device, an interface to facilitate composition of a message by a first user; obtaining, by the one or more processors, a search query, obtaining the search query including one or more of: generating, by the one or more processors and based on a content of the message, the search query, or receiving, via the interface, the search query from the first user, the search query not being generated by the one or more processors when the search query is received from the first user via the interface; obtaining, by the one or more processors, search results identifying documents that are relevant to the search query; refining, by the one or more processors, the search results based on a type of application associated with the composition of the message; providing, by the one or more processors, the refined search results for display to the first user, the refined search results being provided for display via a first region of the interface when the search query is generated by the one or more processors, and the refined search results being provided for display via a second region of the interface when the search query is received from the first user, the second region being different from the first region, and the first region including a button that, when selected by the first user, causes the first region to be removed from the interface, the search query not being generated by the one or more processors when the button is selected; receiving, by the one or more processors and from the first user, a selection of a particular one of the refined search results, the particular one of the refined search results being associated with a geographic location; incorporating, by the one or more processors and based on receiving the selection, data, associated with the particular one of the refined search results, into the message to form a modified message, the data associated with the particular one of the refined search results including: a link to a particular document that is associated with the particular one of the refined search results, and a snippet including a portion of text included in the particular document, the portion being selected from the text included in the particular document based on content of the message and a user profile associated with the first user, the user profile being determined based on at least one of: a prior search associated with the first user, or information provided by the first user; and causing, by the by the one or more processors, the modified message to be sent to a second user, the link, when selected by the second user, being associated with a web page presenting a map of the geographic location relative to a geographic location of the second user. | 1. A method comprising: providing, by one or more processors associated with a device, an interface to facilitate composition of a message by a first user; obtaining, by the one or more processors, a search query, obtaining the search query including one or more of: generating, by the one or more processors and based on a content of the message, the search query, or receiving, via the interface, the search query from the first user, the search query not being generated by the one or more processors when the search query is received from the first user via the interface; obtaining, by the one or more processors, search results identifying documents that are relevant to the search query; refining, by the one or more processors, the search results based on a type of application associated with the composition of the message; providing, by the one or more processors, the refined search results for display to the first user, the refined search results being provided for display via a first region of the interface when the search query is generated by the one or more processors, and the refined search results being provided for display via a second region of the interface when the search query is received from the first user, the second region being different from the first region, and the first region including a button that, when selected by the first user, causes the first region to be removed from the interface, the search query not being generated by the one or more processors when the button is selected; receiving, by the one or more processors and from the first user, a selection of a particular one of the refined search results, the particular one of the refined search results being associated with a geographic location; incorporating, by the one or more processors and based on receiving the selection, data, associated with the particular one of the refined search results, into the message to form a modified message, the data associated with the particular one of the refined search results including: a link to a particular document that is associated with the particular one of the refined search results, and a snippet including a portion of text included in the particular document, the portion being selected from the text included in the particular document based on content of the message and a user profile associated with the first user, the user profile being determined based on at least one of: a prior search associated with the first user, or information provided by the first user; and causing, by the by the one or more processors, the modified message to be sent to a second user, the link, when selected by the second user, being associated with a web page presenting a map of the geographic location relative to a geographic location of the second user. 7. The method of claim 1 , where the data associated with the particular one of the refined search results further includes information identifying the search query. | 0.583576 |
9. A method for focus navigation of a user interface in a computer system, comprising: receiving a single, discrete, directional input for changing the input focus from a current user interface object in the user interface, the single, discrete, directional input being received from an input device other than a mouse, wherein the single, discrete, directional input indicates a traversal direction selected by the user to indicate the direction of a user interface object desired by the user; selecting target candidates in the direction of travel indicated by the single discrete, directional input from among one or more user interface objects for receiving the input focus, and by: defining a selection region that includes at least an edge of the current user interface object in the direction of the single, discrete, directional input through a parallel edge of a display area; defining a baseline region within the selected region and which is a subset of the selection region; and selecting target candidates that overlap the selection region in the direction for the single, discrete, directional input and only when an edge opposite to the direction of the single, discrete, directional input overlaps the selection region; scoring the target candidates selected for receiving the input focus, wherein scoring the target candidates comprises: for all selected target candidates that at least partially overlap the baseline region, scoring them according to their perpendicular distance from an edge of the current user interface object, in a direction parallel to the direction of the single, discrete, directional input; and for all selected target candidates that do not overlap the baseline region, scoring them according to their radial distance from a point on a line partially defining one edge of the baseline region; and changing the input focus to a user interface object based upon the scoring. | 9. A method for focus navigation of a user interface in a computer system, comprising: receiving a single, discrete, directional input for changing the input focus from a current user interface object in the user interface, the single, discrete, directional input being received from an input device other than a mouse, wherein the single, discrete, directional input indicates a traversal direction selected by the user to indicate the direction of a user interface object desired by the user; selecting target candidates in the direction of travel indicated by the single discrete, directional input from among one or more user interface objects for receiving the input focus, and by: defining a selection region that includes at least an edge of the current user interface object in the direction of the single, discrete, directional input through a parallel edge of a display area; defining a baseline region within the selected region and which is a subset of the selection region; and selecting target candidates that overlap the selection region in the direction for the single, discrete, directional input and only when an edge opposite to the direction of the single, discrete, directional input overlaps the selection region; scoring the target candidates selected for receiving the input focus, wherein scoring the target candidates comprises: for all selected target candidates that at least partially overlap the baseline region, scoring them according to their perpendicular distance from an edge of the current user interface object, in a direction parallel to the direction of the single, discrete, directional input; and for all selected target candidates that do not overlap the baseline region, scoring them according to their radial distance from a point on a line partially defining one edge of the baseline region; and changing the input focus to a user interface object based upon the scoring. 17. The method of claim 9 wherein selecting target candidates in the direction of travel indicated by the single, discrete, directional input comprises selecting user interface object that overlap a region as target candidates. | 0.625601 |
1. A method, comprising: at an electronic device with one or more processors, memory, and a touch-sensitive display: receiving data relating to at least one of device movement and device proximity; receiving a touch input at a location on the touch-sensitive display; processing the received data relating to the at least one of device movement and device proximity to determine whether the electronic device is in one of a first or second state based on the received data relating to at least one of device movement and device proximity; if it is determined that the electronic device is in the first state, processing the touch input, including determining the location the touch input was received on the touch-sensitive display, and performing a function associated with the location of the processed touch input; and if it is determined that the electronic device is in the second state, processing the touch input, including determining the location the touch input was received on the touch-sensitive display, and forgoing the function associated with the location of the processed touch input. | 1. A method, comprising: at an electronic device with one or more processors, memory, and a touch-sensitive display: receiving data relating to at least one of device movement and device proximity; receiving a touch input at a location on the touch-sensitive display; processing the received data relating to the at least one of device movement and device proximity to determine whether the electronic device is in one of a first or second state based on the received data relating to at least one of device movement and device proximity; if it is determined that the electronic device is in the first state, processing the touch input, including determining the location the touch input was received on the touch-sensitive display, and performing a function associated with the location of the processed touch input; and if it is determined that the electronic device is in the second state, processing the touch input, including determining the location the touch input was received on the touch-sensitive display, and forgoing the function associated with the location of the processed touch input. 21. The method of claim 1 , wherein: a user interface item is located at the location on the touch-sensitive display, determining the location the touch input was received on the touch-sensitive display comprises determining the user interface item located at the location on the touch-sensitive display, and the function is associated with the user interface item. | 0.551574 |
11. A non-transitory computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to carry out the steps of: selecting a target word from a set of words from a plurality of documents; determining a first subset of words from the set comprising words that are most correlated with the target word; determining a set of values, wherein each value is associated with a correlation between a first word from the first subset and a second word from the first subset, wherein the first word and the second word co-occur with the target word in a document of a plurality of documents; and determining, based on the set of values, at least two clusters of words from the first subset; assigning weights to words in the first subset of words based on the at least two determined clusters; determining a second subset of words that occur in a particular document and correspond to words in the first subset of words, wherein the particular document has the target word; based on the weights of words in the first subset of words that correspond to the second subset of words, selecting, for the particular document, a cluster of the at least two clusters; refining the weights of words in the first subset of words based on the selected cluster. | 11. A non-transitory computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to carry out the steps of: selecting a target word from a set of words from a plurality of documents; determining a first subset of words from the set comprising words that are most correlated with the target word; determining a set of values, wherein each value is associated with a correlation between a first word from the first subset and a second word from the first subset, wherein the first word and the second word co-occur with the target word in a document of a plurality of documents; and determining, based on the set of values, at least two clusters of words from the first subset; assigning weights to words in the first subset of words based on the at least two determined clusters; determining a second subset of words that occur in a particular document and correspond to words in the first subset of words, wherein the particular document has the target word; based on the weights of words in the first subset of words that correspond to the second subset of words, selecting, for the particular document, a cluster of the at least two clusters; refining the weights of words in the first subset of words based on the selected cluster. 16. A non-transitory computer-readable storage medium as recited in claim 11 , wherein the one or more instructions, when executed, further causes the one or more processors to perform the step of: removing duplicate words from the set of words. | 0.543061 |
5. The method of claim 4 , wherein the copying step further comprises invoking a distributed component to flow to the target system. | 5. The method of claim 4 , wherein the copying step further comprises invoking a distributed component to flow to the target system. 6. The method of claim 5 , further comprising defining a query procedure deployment set as comprising the source code, the symbol table, the optimization results, and the deployed version of the internal representation of the procedural logic component; and wherein the copying step further comprises driving a bind package process for the query procedure deployment set. | 0.894334 |
1. A computer-implemented method of scoring non-native speech, comprising: receiving a speech sample spoken by a non-native speaker; performing automatic speech recognition on the speech sample to generate a transcript of the speech sample; processing the speech sample to generate a plurality of speech metrics associated with the speech sample; applying a plurality of non-scorable response filters to the plurality of speech metrics; determining whether the speech sample is scorable or non-scorable based upon the transcript and a collective application of said non-scorable response filters, wherein said determining is based on assessment of audio quality of the speech sample, an amount of speech of the speech sample, a degree to which the speech sample is off-topic, and whether the speech sample includes speech from an incorrect language; associating an indication of non-scorability with the speech sample when the sample is determined to be non-scorable; and providing the sample to a scoring model for scoring when the sample is determined to be scorable. | 1. A computer-implemented method of scoring non-native speech, comprising: receiving a speech sample spoken by a non-native speaker; performing automatic speech recognition on the speech sample to generate a transcript of the speech sample; processing the speech sample to generate a plurality of speech metrics associated with the speech sample; applying a plurality of non-scorable response filters to the plurality of speech metrics; determining whether the speech sample is scorable or non-scorable based upon the transcript and a collective application of said non-scorable response filters, wherein said determining is based on assessment of audio quality of the speech sample, an amount of speech of the speech sample, a degree to which the speech sample is off-topic, and whether the speech sample includes speech from an incorrect language; associating an indication of non-scorability with the speech sample when the sample is determined to be non-scorable; and providing the sample to a scoring model for scoring when the sample is determined to be scorable. 11. The method of claim 1 , further comprising determining whether the speech sample includes plagiarized material. | 0.588145 |
1. A computationally-implemented system, comprising: means for acquiring a first inference data indicative of an inferred mental state of a first authoring user in connection with a particular item of an electronic document, wherein said means for acquiring a first inference data indicative of an inferred mental state of a first authoring user in connection with a particular item of an electronic document comprises: means for receiving a first inference data indicative of an inferred mental state of the first authoring user in connection with the particular item, wherein said means for receiving a first inference data indicative of an inferred mental state of the first authoring user in connection with the particular item comprises: means for receiving a first inference data indicative of an inferred mental state of the first authoring user that was obtained based, at least in part, on one or more physical characteristics of the first authoring user sensed during or proximate to an action executed in connection with the particular item and performed, at least in part, by the first authoring user; means for acquiring a second inference data indicative of an inferred mental state of a second authoring user in connection with the particular item of the electronic document; means for comparing the first inference data with the second inference data; and means for presenting data indicative of an extent of congruity between the inferred mental state of the first authoring user and the inferred mental state of the second authoring user based, at least in part, on said comparing. | 1. A computationally-implemented system, comprising: means for acquiring a first inference data indicative of an inferred mental state of a first authoring user in connection with a particular item of an electronic document, wherein said means for acquiring a first inference data indicative of an inferred mental state of a first authoring user in connection with a particular item of an electronic document comprises: means for receiving a first inference data indicative of an inferred mental state of the first authoring user in connection with the particular item, wherein said means for receiving a first inference data indicative of an inferred mental state of the first authoring user in connection with the particular item comprises: means for receiving a first inference data indicative of an inferred mental state of the first authoring user that was obtained based, at least in part, on one or more physical characteristics of the first authoring user sensed during or proximate to an action executed in connection with the particular item and performed, at least in part, by the first authoring user; means for acquiring a second inference data indicative of an inferred mental state of a second authoring user in connection with the particular item of the electronic document; means for comparing the first inference data with the second inference data; and means for presenting data indicative of an extent of congruity between the inferred mental state of the first authoring user and the inferred mental state of the second authoring user based, at least in part, on said comparing. 27. The computationally-implemented system of claim 1 , wherein said means for presenting data indicative of an extent of congruity between the inferred mental state of the first authoring user and the inferred mental state of the second authoring user based, at least in part, on said comparing comprises: means for displaying data indicative of an extent of congruity between the inferred mental state of the first authoring user and the inferred mental state of the second authoring user to the first authoring user or the second authoring user. | 0.533617 |
1. A method for object detection in remotely-sensed images, the method comprising: identifying a first domain within a remotely-sensed image, wherein the first domain comprises textural features and spectral features; training a first classifier to detect a first object within the image based on a textural relationship between the textural features within the first domain; training a second classifier to detect the first object based on: the textural relationships within the first domain; and a spectral relationship between the spectral features within the first domain; learning a classifier relationship between the trained first classifier and the trained second classifier; training an object detector to detect at least one object based on the learned classifier relationship in the first domain; detecting the at least one object within a second domain using the trained object detector, wherein the second domain comprises textural and spectral features; comparing the detection of the at least one object in the first domain to the detection of the at least one object detected in the second domain; modifying the object detector based on the comparison between the detection of the at least one object in the first domain and the detection of the at least one object in the second domain; and detecting one or more objects in an additional domain with the modified optimal fast object detector. | 1. A method for object detection in remotely-sensed images, the method comprising: identifying a first domain within a remotely-sensed image, wherein the first domain comprises textural features and spectral features; training a first classifier to detect a first object within the image based on a textural relationship between the textural features within the first domain; training a second classifier to detect the first object based on: the textural relationships within the first domain; and a spectral relationship between the spectral features within the first domain; learning a classifier relationship between the trained first classifier and the trained second classifier; training an object detector to detect at least one object based on the learned classifier relationship in the first domain; detecting the at least one object within a second domain using the trained object detector, wherein the second domain comprises textural and spectral features; comparing the detection of the at least one object in the first domain to the detection of the at least one object detected in the second domain; modifying the object detector based on the comparison between the detection of the at least one object in the first domain and the detection of the at least one object in the second domain; and detecting one or more objects in an additional domain with the modified optimal fast object detector. 13. The method of claim 1 , wherein a gradient orientation origin angle is specified for the textural features. | 0.62493 |
1. A computer-implemented method comprising: obtaining a plurality of search results responsive to a search query submitted by a user, wherein each search result refers to a respective document that is associated with a respective plurality of click measures, each click measure relating to a different respective natural language and representing, at least, a measure of behavior of users associated with the respective language in regards to the document when the document was referred to in a search result previously provided in response to the search query; for each of a first plurality of the search results, reducing the click measure associated with the document referred to by the search result, wherein the click measure relates to a respective natural language that is incompatible with a natural language of the user; calculating a respective scoring factor for each of the first plurality of search results based on the respective click measures associated with the document referred to by the first search result; and ranking the search results based upon, at least, the calculated scoring factors. | 1. A computer-implemented method comprising: obtaining a plurality of search results responsive to a search query submitted by a user, wherein each search result refers to a respective document that is associated with a respective plurality of click measures, each click measure relating to a different respective natural language and representing, at least, a measure of behavior of users associated with the respective language in regards to the document when the document was referred to in a search result previously provided in response to the search query; for each of a first plurality of the search results, reducing the click measure associated with the document referred to by the search result, wherein the click measure relates to a respective natural language that is incompatible with a natural language of the user; calculating a respective scoring factor for each of the first plurality of search results based on the respective click measures associated with the document referred to by the first search result; and ranking the search results based upon, at least, the calculated scoring factors. 6. The method of claim 1 wherein calculating the scoring factor further comprises summing the respective click measures for the document. | 0.598596 |
1. A method of searching for content objects associated with a search term, the method being executed by a system comprising a processor and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to perform the method, the method comprising: performing a first search of a content database, wherein the first search identifies content information included in the content database that includes the first search term; analyzing the content information to generate a plurality of relationship vectors between objects included in the content information; identifying a plurality of sections in the content information; scoring the plurality of sections based on the plurality of relationship vectors; generating an object summary for at least one section of the plurality of sections selected based on a score of the at least one section relative to scores of the other sections of the plurality of sections; determining, based on the object summary, at least one additional search term; and performing a second search for content objects relating to the at least one additional search term. | 1. A method of searching for content objects associated with a search term, the method being executed by a system comprising a processor and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to perform the method, the method comprising: performing a first search of a content database, wherein the first search identifies content information included in the content database that includes the first search term; analyzing the content information to generate a plurality of relationship vectors between objects included in the content information; identifying a plurality of sections in the content information; scoring the plurality of sections based on the plurality of relationship vectors; generating an object summary for at least one section of the plurality of sections selected based on a score of the at least one section relative to scores of the other sections of the plurality of sections; determining, based on the object summary, at least one additional search term; and performing a second search for content objects relating to the at least one additional search term. 15. The method of claim 1 , wherein the sections of the plurality of sections comprise at least one of paragraphs, sentences, phrases, and a portion of the content information. | 0.533766 |
1. One or more computer-readable storage media comprising computer-executable instructions for providing linguistic services, the computer-executable instructions directed to steps comprising: receiving linguistic input in a first language from a user; generating a text-based linguistic input in the first language by recognizing the received linguistic input; utilizing machine translation to translate the text-based linguistic input from the first language into a second language differing from the first language; providing the translated text-based linguistic input, in the second language, to pre-existing components providing linguistic services that operate in the context of the second language; receiving, from the pre-existing components, output that is responsive to the linguistic input, the output being provided in the second language; utilizing the machine translation to translate the output in the second language into the first language; generating output that is receivable by the user in accordance with the translated output in the first language. | 1. One or more computer-readable storage media comprising computer-executable instructions for providing linguistic services, the computer-executable instructions directed to steps comprising: receiving linguistic input in a first language from a user; generating a text-based linguistic input in the first language by recognizing the received linguistic input; utilizing machine translation to translate the text-based linguistic input from the first language into a second language differing from the first language; providing the translated text-based linguistic input, in the second language, to pre-existing components providing linguistic services that operate in the context of the second language; receiving, from the pre-existing components, output that is responsive to the linguistic input, the output being provided in the second language; utilizing the machine translation to translate the output in the second language into the first language; generating output that is receivable by the user in accordance with the translated output in the first language. 8. The computer-readable storage media of claim 1 , wherein the machine translation is modified to account for linguistic elements specific to a functionality provided by the computer-executable instructions. | 0.74129 |
9. A non-transitory computer-readable medium storing instructions, the instructions comprising: a plurality of instructions which, when executed by one or more processors, cause the one or more processors to: provide a list of search results obtained based on a search query that includes one or more keywords, a particular search result, in the list of search results, including: a reference to a search result document, and a snippet of text obtained from content of the search result document; receive a request associated with detecting a cursor being placed over an area associated with the particular search result; provide, based on the received request, an expanded snippet of text, the expanded snippet of text comprising text, from the content of the search result document, that includes: at least one of the one or more keywords, and additional text, different than the snippet of text, from the content of the search result document, the expanded snippet of text comprising less than all of the content of the search result document, and the expanded snippet of text being provided for display, within one of an overlay or a frame, with the list of search results; and provide an option to remove the expanded snippet of text, the particular search result being presented without the expanded snippet of text based on receiving selection of the option to remove the expanded snippet of text. | 9. A non-transitory computer-readable medium storing instructions, the instructions comprising: a plurality of instructions which, when executed by one or more processors, cause the one or more processors to: provide a list of search results obtained based on a search query that includes one or more keywords, a particular search result, in the list of search results, including: a reference to a search result document, and a snippet of text obtained from content of the search result document; receive a request associated with detecting a cursor being placed over an area associated with the particular search result; provide, based on the received request, an expanded snippet of text, the expanded snippet of text comprising text, from the content of the search result document, that includes: at least one of the one or more keywords, and additional text, different than the snippet of text, from the content of the search result document, the expanded snippet of text comprising less than all of the content of the search result document, and the expanded snippet of text being provided for display, within one of an overlay or a frame, with the list of search results; and provide an option to remove the expanded snippet of text, the particular search result being presented without the expanded snippet of text based on receiving selection of the option to remove the expanded snippet of text. 15. The non-transitory computer-readable medium of claim 9 , where the one or more instructions to provide the expanded snippet of text include: one or more instructions to visually distinguish the at least one of the one or more keywords within the expanded snippet of text. | 0.590755 |
33. The computer-readable storage medium defined in claim 27 wherein the method further comprises: representing the spatial relationships using Delaunay triangulation; and transforming triplets for the Delaunay triangulation into a graph model. | 33. The computer-readable storage medium defined in claim 27 wherein the method further comprises: representing the spatial relationships using Delaunay triangulation; and transforming triplets for the Delaunay triangulation into a graph model. 36. The computer-readable storage medium defined in claim 33 wherein the representative point is a center of gravity. | 0.863029 |
15. A system for previewing hyperlinks while browsing the world wide web, said system connected to a user terminal which requests one or more web pages from the world wide web, said terminal including a browser, said browser functionally connecting said user terminal to the world wide web and displaying one or more hyperlinks, said system comprising: a proxy server, operatively connected to said user terminal, downloading said requested one or more web pages from said world wide web; a database operatively connected to said proxy server for storing said textual abstracts and associated hyperlinks, wherein proxy server determines whether said requested one or more web pages have been downloaded before, if so: determining if one or more hyperlinks are present in a downloaded web page; determining for each hyperlink whether a textual abstract is stored within said database; for each hyperlink having a stored textual abstract, automatically modifying said downloaded web page to include a reference to said stored textual abstract, and displaying said modified downloaded web page wherein said stored textual abstracts are rendered upon selecting associated hyperlinks in said downloaded web page at said remote terminal, else, creating a textual abstract of a specified requested web page and associated hyperlink, wherein said stored textual abstracts are rendered upon selecting such associated hyperlinks in requested web pages, wherein textual abstract of a given specified requested web page comprises elements of said given specified requested web page including at least the following: title and one or more keywords, said elements previously identified based on received inputs. | 15. A system for previewing hyperlinks while browsing the world wide web, said system connected to a user terminal which requests one or more web pages from the world wide web, said terminal including a browser, said browser functionally connecting said user terminal to the world wide web and displaying one or more hyperlinks, said system comprising: a proxy server, operatively connected to said user terminal, downloading said requested one or more web pages from said world wide web; a database operatively connected to said proxy server for storing said textual abstracts and associated hyperlinks, wherein proxy server determines whether said requested one or more web pages have been downloaded before, if so: determining if one or more hyperlinks are present in a downloaded web page; determining for each hyperlink whether a textual abstract is stored within said database; for each hyperlink having a stored textual abstract, automatically modifying said downloaded web page to include a reference to said stored textual abstract, and displaying said modified downloaded web page wherein said stored textual abstracts are rendered upon selecting associated hyperlinks in said downloaded web page at said remote terminal, else, creating a textual abstract of a specified requested web page and associated hyperlink, wherein said stored textual abstracts are rendered upon selecting such associated hyperlinks in requested web pages, wherein textual abstract of a given specified requested web page comprises elements of said given specified requested web page including at least the following: title and one or more keywords, said elements previously identified based on received inputs. 17. A system for previewing hyperlinks while browsing the world wide web, as per claim 15 , wherein said proxy server automatically generates said textual abstracts corresponding to said downloaded one or more web pages. | 0.511621 |
12. The non-transitory storage medium of claim 11 further comprising instructions for forming a plurality of regular expression rules, each rule being formed to correspond to a different predetermined set of non-standard features, and said processing comprising processing said distinct name using a selected regular expression rule tailored to the non-standard feature vector of said distinct name. | 12. The non-transitory storage medium of claim 11 further comprising instructions for forming a plurality of regular expression rules, each rule being formed to correspond to a different predetermined set of non-standard features, and said processing comprising processing said distinct name using a selected regular expression rule tailored to the non-standard feature vector of said distinct name. 14. The non-transitory storage medium of claim 12 , wherein said processing comprises removing from said distinct name all of the features in the feature vector to convert the distinct name to said standard name format. | 0.863598 |
4. The method of claim 1 , and further comprising recording environment information for each recorded waypoint, wherein the recorded environment information is selected from the group consisting of humidity, precipitation, temperature, barometric pressure, and combinations thereof, and wherein the PAP parameter is also based on the recorded environment information. | 4. The method of claim 1 , and further comprising recording environment information for each recorded waypoint, wherein the recorded environment information is selected from the group consisting of humidity, precipitation, temperature, barometric pressure, and combinations thereof, and wherein the PAP parameter is also based on the recorded environment information. 6. The method of claim 4 , wherein calculating the PAP parameter comprises: generating a quality tuple having the first weight value, the first confidence value, and the first error distance; and adjusting at least one of the first confidence value and the first error distance based on the environment information. | 0.86847 |
1. A computer-implemented method comprising: defining, in an automated speech recognizer in which audio data is processed by a signal conditioning stage followed by a noise suppression stage followed by a language modeling stage, the signal conditioning stage including quantity i processing alternatives, the noise suppression stage including quantity j processing alternatives, and the language modeling stage including quantity k processing alternatives, quantity (i*j*k) alternative paths for processing the audio data through the multiple stages of the automated speech recognizer, i, j, and k being greater than one; generating, for each of the quantity (i*j*k) alternative paths, a transcription of particular audio data based on processing the particular audio data through each of the stages of the automated speech recognizer according to the alternative path; and selecting a particular transcription from among the respective transcriptions that are generated for the quantity (i*j*k) alternative paths; and providing the particular transcription for output. | 1. A computer-implemented method comprising: defining, in an automated speech recognizer in which audio data is processed by a signal conditioning stage followed by a noise suppression stage followed by a language modeling stage, the signal conditioning stage including quantity i processing alternatives, the noise suppression stage including quantity j processing alternatives, and the language modeling stage including quantity k processing alternatives, quantity (i*j*k) alternative paths for processing the audio data through the multiple stages of the automated speech recognizer, i, j, and k being greater than one; generating, for each of the quantity (i*j*k) alternative paths, a transcription of particular audio data based on processing the particular audio data through each of the stages of the automated speech recognizer according to the alternative path; and selecting a particular transcription from among the respective transcriptions that are generated for the quantity (i*j*k) alternative paths; and providing the particular transcription for output. 3. The method of claim 1 , comprising: generating, for each of the transcriptions, a confidence score, wherein selecting the particular transcription is based on the confidence scores. | 0.800216 |
13. A method of operating a computing device, the method comprising: receiving a composite user input comprising a speech input and a non-speech input, one of the speech input and the non-speech input requesting an action and another of the speech input and the non-speech input providing an indefinite quantitative term related to the action requested; determining a definite quantity corresponding to the indefinite quantitative term; and applying the definite quantity to an action performed in response to the speech input. | 13. A method of operating a computing device, the method comprising: receiving a composite user input comprising a speech input and a non-speech input, one of the speech input and the non-speech input requesting an action and another of the speech input and the non-speech input providing an indefinite quantitative term related to the action requested; determining a definite quantity corresponding to the indefinite quantitative term; and applying the definite quantity to an action performed in response to the speech input. 18. The method of claim 13 , wherein the non-speech input comprises one or more of a gesture and a posture. | 0.677778 |
8. A method for sorting and routing e-mail messages, the method comprising: sorting e-mail messages by language by: determining a language in which a web-site that receives the e-mail messages is written, appending a meta-tag to each e-mail message that identifies the web-site language, and sorting the messages through reference to the language meta-tags; and subsequently sorting the e-mail messages by topic by: determining a topic to which each e-mail message applies, appending a meta-tag to each e-mail message that identifies the topic, and sorting the messages through reference to the topic meta-tags. | 8. A method for sorting and routing e-mail messages, the method comprising: sorting e-mail messages by language by: determining a language in which a web-site that receives the e-mail messages is written, appending a meta-tag to each e-mail message that identifies the web-site language, and sorting the messages through reference to the language meta-tags; and subsequently sorting the e-mail messages by topic by: determining a topic to which each e-mail message applies, appending a meta-tag to each e-mail message that identifies the topic, and sorting the messages through reference to the topic meta-tags. 9. The method of claim 8 , further comprising sending all messages generated at the web-site to a global mail box. | 0.628446 |
3. The method of claim 1 , further comprising deriving a document control policy for a parent node in the document control policy ontology based on a combination of multiple document control policies associated with child nodes of the parent node in the document control policy ontology, and associating the derived document control policy with the parent node in the document control policy ontology. | 3. The method of claim 1 , further comprising deriving a document control policy for a parent node in the document control policy ontology based on a combination of multiple document control policies associated with child nodes of the parent node in the document control policy ontology, and associating the derived document control policy with the parent node in the document control policy ontology. 6. The method of claim 3 , wherein the deriving comprises recursively deriving document control policies for parent nodes in the document control policy ontology up to a root node; and wherein the deriving and the associating are performed before deployment of the document control policy ontology. | 0.9383 |
1. A system for detecting dates in digital text written in Farsi, the system comprising: one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of: (g) receiving a string of digital text from a social media source; (h) searching for a date in the string of digital text by searching for one of the words yesterday, today and tomorrow in Farsi as follows: a. if one of the words is found, then providing temporal textCreationDate tag for the text based on the date the digital text was created and the found word and then stopping for the string of digital text; b. if one of the words is not found, then continuing; (i) performing a month search in the string of digital text by searching for a name of a month; a. if a name of the month is found, then performing a day of the month search; i. if a day of the month is found, then converting the day of the month to Gregorian calendar and logging the day of the mouth; ii. if a day of the month is not found, then stopping; b. if a name of the month is not found, then continuing; (j) performing a weekday name search by looking for weekday names within the string of text followed by a word in Farsi having a meaning of “next”; a. if a weekday name is found following by a word in Farsi having a meaning of “next,” then identifying a data for a first occurrence of name of a weekday after the date the digital text was created; b. if a weekday name is not found, then continuing; (k) searching for a year-month-date label in the string of digital text; a. if a year-month-date label is not found, then stopping; b. if a year-month-date label is found, then continuing; and (l) convening the year-month-date label to Gregorian calendar and logging the day of the month. | 1. A system for detecting dates in digital text written in Farsi, the system comprising: one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of: (g) receiving a string of digital text from a social media source; (h) searching for a date in the string of digital text by searching for one of the words yesterday, today and tomorrow in Farsi as follows: a. if one of the words is found, then providing temporal textCreationDate tag for the text based on the date the digital text was created and the found word and then stopping for the string of digital text; b. if one of the words is not found, then continuing; (i) performing a month search in the string of digital text by searching for a name of a month; a. if a name of the month is found, then performing a day of the month search; i. if a day of the month is found, then converting the day of the month to Gregorian calendar and logging the day of the mouth; ii. if a day of the month is not found, then stopping; b. if a name of the month is not found, then continuing; (j) performing a weekday name search by looking for weekday names within the string of text followed by a word in Farsi having a meaning of “next”; a. if a weekday name is found following by a word in Farsi having a meaning of “next,” then identifying a data for a first occurrence of name of a weekday after the date the digital text was created; b. if a weekday name is not found, then continuing; (k) searching for a year-month-date label in the string of digital text; a. if a year-month-date label is not found, then stopping; b. if a year-month-date label is found, then continuing; and (l) convening the year-month-date label to Gregorian calendar and logging the day of the month. 5. The system as set forth in claim 1 , wherein performing the month search further includes searching for names of the month from both Persian and Gregorian calendars and French pronunciations of Gregorian month names. | 0.507375 |
9. A method as claimed in claim 8 wherein said searching step comprises comparing the incoming character code to each standard character code in said portion of said set and ranking such standard character codes according to the closeness of match with the incoming character code. | 9. A method as claimed in claim 8 wherein said searching step comprises comparing the incoming character code to each standard character code in said portion of said set and ranking such standard character codes according to the closeness of match with the incoming character code. 10. A method as claimed in claim 9 wherein said selecting step comprises displaying the standard characters having the highest ranked standard character codes, and selecting either a correct character from amongst those displayed or an opportunity to enter a new incoming character. | 0.904665 |
14. The system of claim 13 , wherein the tree traverse module repeatedly, until a root node is reached: (i) moves up a level the tree to determine a parent node; (ii) determines whether the parent node is an AND operator; (iii) if the parent node is an AND operator, identifies each child expression of the parent node as an expression in the plurality of expressions that will be ANDed with results of the determined measure expression. | 14. The system of claim 13 , wherein the tree traverse module repeatedly, until a root node is reached: (i) moves up a level the tree to determine a parent node; (ii) determines whether the parent node is an AND operator; (iii) if the parent node is an AND operator, identifies each child expression of the parent node as an expression in the plurality of expressions that will be ANDed with results of the determined measure expression. 15. The system of claim 14 , wherein the tree traverse module also repeatedly, until a root node is reached: (iv) if the parent node is an AND operator, identifies each child operator of the parent node that is an AND operator; (v) for each child operator identified in (iv), traverses down the tree starting at the child operator to determine which expressions in the child operator's branch of the tree will be ANDed with results of the determined measure expression. | 0.79823 |
1. A computer implemented method, the method comprising: generating seed nodes from seed information, the seed nodes representing one or more web pages, and wherein the one or more web pages comprise one or more reference links; performing probabilistic percolation crawling from at least one of the seed nodes, wherein performing probabilistic percolation crawling comprises following the one or more reference links in and out of the one or more web pages to one or more neighboring nodes probabilistically, wherein performing percolation crawling further comprises randomly selecting reference links in and out of the web page and in and out of the one or more neighboring nodes, wherein selected reference out-links are added to a linked database when the link satisfies a first probability and selected reference in-links are added to the linked database when the link satisfies a second probability; generating a structural web community neighborhood based on the percolation crawling from the at least one of the seed nodes by iteratively partitioning the linked database into overlapping communities, the structured web community neighborhood comprising a plurality of communities of network nodes linked by edges around the one of the seed nodes, each of the plurality of communities comprising a set of network nodes that are more linked amongst themselves than to network nodes that are not included in the community; annotating each of the plurality of communities of network nodes in the structural web community with a concept; and storing the annotated structural web neighborhood. | 1. A computer implemented method, the method comprising: generating seed nodes from seed information, the seed nodes representing one or more web pages, and wherein the one or more web pages comprise one or more reference links; performing probabilistic percolation crawling from at least one of the seed nodes, wherein performing probabilistic percolation crawling comprises following the one or more reference links in and out of the one or more web pages to one or more neighboring nodes probabilistically, wherein performing percolation crawling further comprises randomly selecting reference links in and out of the web page and in and out of the one or more neighboring nodes, wherein selected reference out-links are added to a linked database when the link satisfies a first probability and selected reference in-links are added to the linked database when the link satisfies a second probability; generating a structural web community neighborhood based on the percolation crawling from the at least one of the seed nodes by iteratively partitioning the linked database into overlapping communities, the structured web community neighborhood comprising a plurality of communities of network nodes linked by edges around the one of the seed nodes, each of the plurality of communities comprising a set of network nodes that are more linked amongst themselves than to network nodes that are not included in the community; annotating each of the plurality of communities of network nodes in the structural web community with a concept; and storing the annotated structural web neighborhood. 15. The method of claim 1 wherein at least one of the links originates at one of the neighboring nodes. | 0.592413 |
25. The method of claim 24 , wherein the act (B) comprises displaying the icons using a carousel metaphor, each of the icons having an associated slot on the carousel. | 25. The method of claim 24 , wherein the act (B) comprises displaying the icons using a carousel metaphor, each of the icons having an associated slot on the carousel. 28. The method of claim 25 , wherein the act (B) comprises displaying slots in an order that is based at least in part on consideration received from an operator of at least one of the plurality of search engines. | 0.898381 |
6. The computer system of claim 1 , wherein a user specifies one or more columns from at least one database table that are to be added to the schema graph. | 6. The computer system of claim 1 , wherein a user specifies one or more columns from at least one database table that are to be added to the schema graph. 7. The computer system of claim 6 , wherein the specified columns are specified using a declarative language. | 0.969643 |
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