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1. A method for an electronic device, comprising: receiving, in a text input region of a user interface displayed by the electronic device, a first handwritten input comprising at least one handwritten character; obtaining a plurality of candidate words, the candidate words being associated with the at least one handwritten character; generating a first instruction to display information identifying one or more of the candidate words in a text display region of the user interface displayed by the electronic device, the text display region being separated from the text input region; receiving, in the text input region, a second handwritten input comprising a handwritten geometric shape indicative of a selection of one of the displayed candidate words; and in response to receiving the second handwritten input comprising a handwritten geometric shape in the text input region, generating a second instruction to display the selected candidate word in the text display region of the user interface displayed by the electronic device.

10. The method of claim 1 , further comprising generating a third electronic instruction to display the information identifying a subset of the candidate words within a first portion of the display and along at least one of a longitudinal axis of the display or a transverse axis of the display.

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4. An individual verification apparatus comprising: a verification data file in which identification codes set by customers, speech reference data for the identification codes uttered by the customers and name speech reference data for names of the customers spoken by themselves are registered; speech input means for providing speech data including identification code data in response to an input speech from a customer; memory means coupled to said speech input means for storing identification code data uttered by the customer and provided by said speech input means; identification code recognition means coupled to said memory means for recognizing the identification code of the customer on the basis of the identification code data uttered by the customer and stored in said memory means through said speech input means; and speaker verifying means coupled to said verification data file, said speech input means and said memory means for verifying the customer by comparing the identification speech data stored in said memory means with the identification code speech reference data of customers having the identification code recognized by said speech recognition means and previously registered in said verification data file, said speaker verifying means being arranged to, when the identification code of the customer is recognized by said speech recognition means but the customer cannot be verified by the identification code speech data, verify the customer by comparing name speech data spoken by the customer and stored in said memory means through said speech input means with the name speech reference data of the customers having the identification code which has been recognized by said speech recognition means and previously registered in said verification data file.

5. An apparatus according to claim 4 further comprising speech responding means coupled to said speech recognition means and said speaker verification means for audibly indicating to the customer the identification code recognized by said speech recognition means and a result of the speaker verification performed by said speaker verification means.

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12. The system according to claim 8 , wherein said determined portion of the document data is image data and wherein said copy detection pattern data comprises said image data encrypted using said cryptographic key.

8. A system for printing a document having a printed copy detection pattern, comprising: a computing device; an intermediate electronic device operatively coupled to said computing device through a first communications channel, said intermediate electronic device storing a cryptographic key; a printing device operatively coupled to said intermediate electronic device through a second communications channel; wherein said computing device is adapted to generate printer control commands and send said printer control commands to said intermediate electronic device over said first communications channel, said printer control commands including: (i) commands for printing based on document data, and (ii) an identification of a determined portion of the document data that is to be used in generating said printed copy detection pattern; and wherein said intermediate electronic device is adapted to: (i) generate copy detection pattern data using said determined portion of the document data and said cryptographic key, (ii) generate modified printer control commands, said modified printer control commands including commands for printing a first document portion based on the document data and a second document portion including said printed copy detection pattern based on said copy detection pattern data, and (iii) send said modified printer control commands to said printing device over said second communications channel for printing said first document portion and said second document portion.

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3. A method of claim 1 , further comprising: associating one of the topics and subtopics of the hierarchy with one or more reference documents; and extracting a set of reference noun tokens from the reference documents, wherein the generating of the topology comprises, at least in part, matching the extracted noun tokens with the set of reference noun tokens and associating the extracted noun tokens with the one topic or subtopic based on the matching.

4. A method of claim 3 , further comprising: translating the topics and subtopics to another language; associating another one of the topics and subtopics of the hierarchy with one or more reference documents in the another language; and extracting another set of reference noun tokens specific to the another language; wherein the generating of the topology further comprises, at least in part, matching the extracted noun tokens with the another set of reference noun tokens in the another language.

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1. A method for classifying digital content, wherein the digital content includes two or more items, the method comprising the following acts performed by one or more hardware processors: identifying a grouping of the two or more items in the digital content; assigning a raw score to the identified items based on predetermined criteria; deriving an aggregate score for the digital content, wherein the aggregate score is derived from the raw scores; and using the aggregate score to output a classification of the digital content, wherein the aggregate score is used to derive a category score, and wherein the category score is derived for a user account.

21. The method of claim 1 , further comprising: deriving one or more of the raw scores by using information from multiple websites.

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2. The method of claim 1 , wherein the first detector and the second detector have substantially the same view of the target.

1. A method for detecting and tracking a target, comprising: detecting the target using a plurality of feature cues; fusing the plurality of feature cues to form a set of target hypotheses; tracking the target based on the set of target hypotheses and a scene context analysis; and updating the tracking of the target based on a target motion model; wherein the plurality of feature cues is generated by at least a first detector of a first type and a second detector of a second type, wherein the first and second types are different; wherein the first detector is a appearance-based detector and the second detector is a silhouette-based detector; and wherein the tracking is used to compensate for a motion of a radiation source tracked by a radiation imaging device.

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5. The method of claim 1 , wherein the visual representations presented to different ones of the plurality of audience members are filtered using criteria provided by a respective audience member.

1. A method of selectively visually representing speaker content generated in an audio conference, the method comprising: obtaining a profile for each of a plurality of audience members who participates in the audio conference through a respective communication device; monitoring, using a computer with a processor and memory, the speaker content spoken during the audio conference; classifying different words included in the speaker content to have different weights according to a parameter of the profile for each of the plurality of audience members; determining a relation of the speaker content to the profile for each of the plurality of audience members based on the classifying; and presenting, at a specific point in time, different visual representations of the speaker content to different ones of the plurality of audience members based on the determining, wherein the visual representations presented include a selective visual representation of the speaker content related to the parameter of the profile, such that the speaker content that is not related to the parameter of the profile is not visually represented.

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7. The method of claim 1 , further comprising using an IME and an API to identify an input scope class associated with a plurality of application input fields.

1. A method comprising: identifying an input identifier associated with an application input interface based in part on an input field associated with the application input interface and one or more input scope values for use when providing one or more candidates to a user; storing the input identifier including an associated string with the one or more input scope values associated with the input field; predicting potential candidates based in part on a prediction model and the one or more input scope values; and, displaying one or more suggested candidates using one or more of the potential candidates.

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22. Device according to claim 18 , comprising a cursor control component for controlling the display of a cursor based on user input received from the input device, the cursor being configured to allow selection of a group designator and/or a candidate word by being moved to the respective displayed item, the cursor highlighting the selected item when positioned accordingly.

18. Context sensitive text input device, comprising: a text prediction component for determining a list of candidate words for the present input context, a candidate word being a possible textual continuation of the present context; wherein the present context comprises at least one preceding word or delimiter; a candidate grouping component for arranging the candidate words in groups, each group having a group designator; a display device for displaying the group designators and candidate words; and an input device for receiving a user input; wherein the input device is configured to receive a group selection from the user and the display device is configured to respond by displaying candidate words of the selected group, the displayed candidate word being arranged according to their respective contextual relevance; wherein the input device is further configured to receive a candidate selection from the user and to accept the selected candidate word as text input; wherein the text prediction component is further configured to update the context based on the text input and determines an updated list of candidate words for the updated context.

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1. A computer system for providing annotations for revising a message, comprising: a processor; and a storage device connected to the processor, wherein the storage device has stored thereon a program, and wherein the processor is configured to execute instructions of the program to perform operations, wherein the operations comprise: receiving a message to be sent from a sender to a recipient; receiving selection of a dialect for the sender and a dialect for the recipient; receiving a level of misunderstanding that is acceptable to the recipient; selecting a meaning map associated with the sender based on the dialect for the sender to determine a first context of the message that indicates a first way in which the message is understood; selecting a meaning map associated with the recipient based on the dialect for the recipient to determine a second context of the message that indicates a second way in which the message is understood; parsing the message into sub-constructs; comparing the sub-constructs in the meaning map associated with the sender and the meaning map associated with the recipient to identify words and phrases where there are differences between perceptions of the sender and the recipient; and in response to the comparisons showing that the differences are greater than a threshold that is based on the level of misunderstanding that is acceptable, identifying alternative language for the sub-constructs in the message; and providing annotations for the message to the sender based on the alternative language before the message is sent from the sender to the recipient, wherein the annotations indicate the second context of the message.

4. The computer system of claim 1 , wherein the operations further comprise: receiving selection of a modality for each of multiple recipients who are to receive the message.

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14. A method of processing information comprising the steps of: receiving from a mobile handheld device that includes a camera a message including data representing an image of a document and an identifier of a type of the document; storing the data and the associated identifier; performing image enhancement based on the identifier so as to obtain an enhanced image; recognizing information in the enhanced image so as to obtain data representing the image in a non-image format, wherein a recognition technique is determined in accordance with the identifier; for specific documents, analyzing at least some of the data representing the image in a non-image format so as to obtain the meaning of information in said data and storing portions of the data in accordance with the meaning; creating an output message including the result of one of the step of recognizing and the step of analyzing depending on the value of the identifier having information formatted in accordance with the identifier; and providing said message to a user.

20. The method of claim 14 , wherein the step of analyzing at least some of the data includes using a dictionary and grammar rules selected on the basis of the identifier.

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1. A method of communications-network shopping by buyers of products and services for purchasing such from sellers, wherein buyers request an automatic reverse auctioneer or auction controller (RAC) to initiate a reverse auction in real time amongst willing sellers and to solicit from automated seller engines (SAEJ) of the sellers their automatic real-time iterative bidding price quotations for such products and services to be returned automatically over the network back to the controller without any manual intervention and under the iterative processing guidance of the controller to assure a best bid price quotation for the buyer; the method comprising, implementing price managing within a processor, for receiving price requests and outputting a price quotation, an automated seller engine (SAEJ) that is responsible for receiving requests from the controller (RAC) for one or more items that the buyer expresses interest in buying; in the light of received market data and historical prices, configuring the seller engine itself to enter the business objectives of that seller in specific terms of targets or goals that the seller enters; and, within the seller engine, automatically optimizing prices for at least two different seller goals and integrating the prices at the seller engine for realizing the business objectives of the seller as configured in said engine.

2. The method of claim 1 wherein the seller engine is enabled simultaneously to process in real-time, a number of multiple interacting variables including revenue, profit, market share, inventory, supplier-break, competitor events, and market demand changes, while automatically optimizing the iterative bidding in real-time on demand—all without the need for manually tracking of said variables or any other manual intervention from the seller, even in the presence of dynamically changing market conditions.

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2. The apparatus of claim 1 further comprising: wherein the deployment script is invoked by an external automation process; and wherein an entry of values into the property file is the only manual operation required to accomplish an installation or modification process on the application server by the automated application server administration framework.

1. An apparatus for automated application server administration framework comprising: an application server connected to a primary system by a network and having a plurality of configuration files and a scripting engine in a first memory; a computer connected to the application server by a network and having, in a second memory, a property file, a deployment script, a procedure script, a component script in a second memory and a property file containing a plurality of values needed by the component script; wherein the deployment script, responsive to identifying a change in the property file, invokes the procedure script to link the property file in which the change was detected, to the component script so that the change in the property file is populated to the component script; and wherein, responsive to the change in the property file being populated to the component script, the deployment script invokes the scripting engine to make a plurality of changes to a configuration file of the application server; wherein the deployment script invokes an error program that performs a plurality of environmental setting checks, monitors the installation or the modification process, and conducts a final error check, wherein the plurality of environmental setting checks determine whether a plurality of appropriate permissions have been granted, whether a plurality of appropriate paths have been properly defined, and a plurality of enterprise archive files properly deployed, and responsive to identifying a plurality of errors, the error program accesses a policy script to automatically fix the plurality of errors.

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13. The system of claim 12 , the operation further comprising: receiving a query specifying a first topic and a first speaker; retrieving at least one cluster group based on the set of properties of the at least one cluster group matching the first topic; retrieving a first speaker group based on the presenter of the first speaker group matching the first speaker; and returning the at least one cluster group and the first speaker group as responsive to the query.

12. The system of claim 11 , the operation further comprising: storing each time line series associatively with each corresponding topically related segment in the data storage.

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16. The method of claim 12 , wherein said production rules are a four tuple (H, M, C, F), where HεN is the head of the production, M ⊂ Σ∪N is a multiset of symbols, C is a boolean constraint defined on M and F is a constructor defined on M.

12. A method for extracting semantic information about a plurality of electronic documents autonomously created by different sources and being accessible via a computer network, comprising: accessing an electronic document via the computer network; generating a set of tokens by a computer, the tokens indicative of document object model (DOM) nodes associated with visual information in a displayed document image of the electronic document; deriving a non-prescribed visual grammar from the set of tokens by the computer, to represent a hidden syntax convention of visual presentation in the displayed document image; and applying said derived visual grammar by the computer to construct multiple parse trees that represent semantic structure of the electronic document and interpret a maximum subset of the set of tokens, wherein said non-prescribed visual grammar is derived from autonomous or heterogeneous Web documents to represent the hidden syntax convention of the visual presentation, and said derived non-prescribed visual grammar is a five tuple <Σ, N, s, Pd, Pf> where Σ is a set of terminal symbols, N is a set of nonterminal symbols, sεN is a start symbol, Pd is a set of production rules that represent visual patterns and Pf is a set of preference rules that represent pattern precedence.

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1. A method for detection of page numbers in a document comprising: identifying a plurality of text fragments associated with a plurality of pages of a single document; from the identified text fragments, identifying at least one sequence, each identified sequence comprising a plurality of terms, each term derived from a text fragment selected from the plurality text fragments, the terms of a sequence complying with at least one predefined numbering scheme which defines a form and an incremental state of the terms in a sequence, the at least one numbering scheme excluding terms from a sequence which do not comply with an incremental state in which terms on each two consecutive pages vary by a constant value, the identifying of the at least one sequence comprising, for each page of a plurality of pages of the document in sequence: identifying text fragments which comprise a term that complies with the form of the predefined numbering scheme; for each of the identified fragments, determining if the term of the identified fragment complies with an incremental state accepted by an existing sequence and if so, adding the term to that sequence, the existing sequence comprising at least one term derived from a text fragment of a previous page of the document; and for each of the terms which do not comply with an incremental state accepted by an existing sequence, considering the term as a potential start of a new sequence; with a computer processor, computing a subset of the identified sequences which cover at least some of the pages of the document, wherein the computing of the set of sequences comprises applying a Viterbi algorithm to the identified sequences to identify a subset of the identified sequences; and construing of at least some of the terms of the subset of the identified sequences as page numbers of pages of the document.

5. The method of claim 1 , wherein the computing subset of the identified sequences comprises: for each of the identified sequences, defining the identified sequence as a series of nodes, each node representing a state of the sequence for a page of a plurality of consecutive pages, each node comprising a term or a hole, wherein a hole identifies the page as lacking a term of the sequence; selecting a subset of identified sequences based on assigned scores of nodes of the subset of identified sequences which cover at best the entire document, the assigned score of each node of the selected sequences being a function of at least one of: whether the node comprises a hole or a term, a number of terms in the sequence, and a coverage of the sequence.

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1. A machine translating method for a source language PDF file in a machine translation device, the method comprising: extracting, by the machine translation device, source language text from the source language PDF file; analyzing, by the machine translation device, the source language text to classify the source language text into text paragraphs; classifying, by the machine translation device, the text paragraphs into body paragraphs and non-body paragraphs; selecting, by the machine translation device, a first body paragraph that is not contextually divided among the body paragraphs based on at least one of font information and paragraph information; testing, by the machine translation device, a paragraph of a second body paragraph other than the first body paragraph among the body paragraphs to extract start and end information of the paragraph; selecting, by the machine translation device, a contextually divided body paragraph based on the start and end information; restoring, by the machine translation device, the source language text by combining a contextually divided body paragraph in the source language text; and translating, by the machine translation device, the restored source language text into target language text.

5. The method of claim 1 , wherein the step of classifying the text paragraphs includes classifying a text paragraph corresponding to a preface, a postface, a footnote, or a caption from among the at least one text paragraph into the at least one non-body paragraph, and the step of classifying the text paragraph into at least one non-body paragraph includes: determining an upper text paragraph that digresses from the page layout of the source language PDF file as the preface; determining a lower text paragraph that digresses from the page layout of the source language PDF file as the postface; determining a text paragraph that is identified by a random line over the postface, which is provided at the bottom of the page, and in which a string start character is a subscript, to be the footnote; and determining a text paragraph that is provided below or above a figure or a table and that starts with a string for designating the figure or the table to be the caption.

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6. The method of claim 4 , comprising: creating the hash table, including writing topic names and the node identifiers for respective nodes of the topic tree.

4. The method of claim 1 , comprising: adding node identifiers to nodes of the topic tree.

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15. The method of providing the game as in claim 14 further comprising providing activities that provide a community inventory.

14. The method of providing the game as in claim 13 further comprising enabling a character object exchange arranged such that characters exchange PGO's, PGO enhancements, and character enhancements whereby the required object is obtained by exchange.

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4. The method according to claim 3 , wherein selecting at least one said graph comprises: selecting at least one said graph based on the total costs obtained for each graph.

3. The method according to claim 1 , wherein parsing the music symbol candidates comprises: calculating for each graph a total cost, taking into account each symbol cost assigned to the music symbol candidates of said graphs and each spatial cost associated with the at least one grammar rule applied in said graph.

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15. The apparatus of claim 9 , wherein entries in the QA cache comprise extracted features for previously processed questions with corresponding candidate answer information and confidence measures associated with the candidate answers.

16. The apparatus of claim 15 , wherein the entries in the QA cache further comprise at least one of ranking information for the candidate answer, evidence information indicating the evidence used to generate the candidate answer, a determined domain of a corresponding previously processed question, information about a submitter of the corresponding previously processed question, or time of receipt of the corresponding previously processed question.

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15. The system as described in claim 13 , wherein the positive relevancy of the patterns between respective said layers of the neural network is based on a probabilistic Winner-Take-All (WTA) approach.

13. In a digital medium environment supportive of image search, a system comprising: a neural network implemented at least partially using processing hardware, the neural network having a plurality of layers; an aggregation module implemented at least partially using processing hardware to cause the neural network to aggregate patterns of neurons by progressing through a sequence of layers, the patterns classifying an image as relating to a semantic class; a back propagation module implemented at least partially using processing hardware to cause the neural network to communicate relevancy of the patterns of the neurons as a positive and not negative relevancy to the semantic class by progressing backwards through the sequence of layers; a digital content generation module implemented at least partially using processing hardware to generate digital content that localizes the semantic class within the image based at least in part on the aggregated patterns of neurons and the relevancy of the patterns of the neurons to the semantic class; and an image search module implemented at least partially using processing hardware to perform a search using a plurality of items of said digital content to locate respective said images as corresponding to text of a search query.

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23. The apparatus of claim 14 , further comprising a document rank determining unit to determine a document rank of the second document based on the document rank score.

14. An apparatus to determine a document rank, the apparatus comprising: a processor, a term relationship score calculating unit, a term relationship score changing unit, a contribution score calculating unit, and a document rank score calculating unit, the processor to control the term relationship score calculating unit, the term relationship score changing unit, the contribution score calculating unit, and the document rank score calculating unit, wherein the term relationship score calculating unit is configured to calculate a first term relationship score of a first document and a second term relationship score, and wherein the contribution score calculating unit is configured to calculate a first contribution score based on a common keyword between the first document and a second document, the second document being linked by a link to the first document, wherein the term relationship score changing unit is configured to change the first term relationship score to the second term relationship score, wherein the document rank score calculating unit is configured to calculate a document rank score of the second document based on the first term relationship score and the first contribution score, and to update the document rank score of the second document based on the second term relationship score, wherein the first term relationship score is determined based on content of the first document and the link, and wherein the processor is further configured to determine whether each of a plurality of contribution scores is greater than a predetermined threshold value to update the document rank score, whereby if one of the contribution scores is less than or equal to the predetermined threshold value, that contribution score is set to a zero value.

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19. The computer-readable storage device of claim 17 , wherein the individual's voice is associated with an individual who is not the sender.

17. A computer-readable device having instructions stored, which, when executed by a computing device, cause the computing device to perform operations comprising: receiving, from a sender, a textual message generated by a spoken dialog system, the textual message having a fixed text portion and a variable text portion; selecting, based on voice characteristics of the sender and the sender speaking a particular set of lines, a speech template from a plurality of speech templates, the speech template comprising information representing characteristics of an individual's voice, wherein each speech template in the plurality of speech templates is personalized to the individual and in a distinct language from other speech templates in the plurality of speech templates; accessing pre-recorded speech from storage, the pre-recorded speech corresponding to the fixed text portion of the textual message; generating variable speech corresponding to the variable text portion of the textual message; and merging the pre-recorded speech and the variable speech in an order defined by the speech template.

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6. The computer-implemented method of claim 1 , wherein identifying the file hosted by the web site comprises: receiving, from a client by one or more computer processors, data identifying the web site and the file hosted by the web site.

1. A computer-implemented method of providing computer security, comprising: receiving, by one or more computer processors, from a plurality of clients, data describing client hygiene scores for the clients and describing a plurality of web sites visited by the clients, the client hygiene score for a client calculated responsive to an amount of malicious software detected at the client based on a scan of the client; determining, by one or more computer processors, a secondary hygiene score for a web site of the plurality of web sites visited by the clients based at least in part on client hygiene scores of clients that visited the web site; identifying, by one or more computer processors, a file hosted by the web site; and calculating and storing, by one or more computer processors, a reputation score for the file responsive to the secondary hygiene score of the web site that hosts the file.

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11. The method according to claim 1 , wherein the user interface mechanism comprises one of a bracket, a button, an icon, or a pull-down menu.

1. A method for presenting information, the method comprising the acts of: presenting an input expression on a workspace associated with a computational software application; presenting on the workspace a result that is an evaluation of the input expression by the computational software application, while presenting on the workspace the input expression; presenting on the workspace a first user interface mechanism associated with the input expression, while presenting on the workspace the result; hiding on the workspace the input expression, while presenting on the workspace the result, in response to an activation of the first user interface mechanism, wherein hiding the input expression reduces the size of the workspace as compared to when both the input expression and the result are presented on the workspace; presenting on the workspace a second user interface mechanism associated with the input expression; and presenting on the workspace the input expression in response to an activation of the second user interface mechanism, while presenting on the workspace the result.

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7. The method of claim 6 , wherein analyzing the website structure occurs off-line.

6. The method of claim 1 , further comprising analyzing a website structure associated with the form, where the prompt is associated with the website structure.

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1. An alphanumeric registration method for use in a system having a push button telephone with alphanumeric entry keys, comprising the steps of: classifying alphanumeric characters into respective sets of data each comprised of a group value and a party value, said alphanumeric characters comprising arabic numerals, upper case alphabetic characters, and lower case alphabetic characters; selecting a single alphanumeric character in response to two successive key strokes of said alphanumeric entry keys that respectively define said group value and said party value, said single alphanumeric character being generated by performing an alphanumeric registration subroutine using said group value and said party value; said single alphanumeric character generated being one of said arabic numerals when said group value is equal to a value represented by said one of said arabic numerals and said party value is equal to a first value; said single alphanumeric character generated being one of said upper case alphabetic characters when said group value is equal to a value indicated on said alphanumeric entry keys corresponding to said one of said upper case alphabetic characters and said party value is equal to one of a plurality of second values; said single alphanumeric character generated being one of said lower case alphabetic characters when said group value is equal to a value indicated on said alphanumeric entry keys corresponding to said one of said lower case alphabetic characters and said party value is equal to one of a plurality of third values; and storing said single alphanumeric character in storage means within said system by creating a physical characteristic distinctly representative of said single alphanumeric character within said storage means.

3. The alphanumeric registration method as claimed in claim 1, wherein said alphanumeric characters are classified using ten group values and seven party values.

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5. The method of claim 1 , wherein the normal key input is a character.

6. The method of claim 5 , wherein the character is an alphabet, number or punctuation.

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13. The computer system of claim 12 , wherein the at least one processor is further configured to: receive from the user a selection of one of the plurality of first language descriptor strings; and send a message indicating the language selected by the user to the first application.

15. The computer system of claim 13 , wherein the message indicating the language selected by the user includes at least one RFC 5646-compliant tag.

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12. The method of claim 11 , wherein transcription of the portions of the one or more verbal messages is carried out by one of the operators at the workbench station, and further comprising the step of displaying a graphical representation of an audio waveform for at least a part of the verbal message to the operator, with the portions to be transcribed visually indicated.

11. The method of claim 1 , wherein the operators at the workbench stations edit and control transcription of the verbal messages in a browsing program display, and wherein transcription of the whole verbal messages is selectively carried out in one of three modes, including: a word mode that includes keyboard inputs for specific transcription inputs; a line mode that facilitates looping through an audible portion of at least one of the whole verbal messages to focus on a single line of transcribed text at a time; and a whole message mode, in which the operator working at the workbench station listens to the whole verbal message to produce the corresponding text.

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11. The method of claim 10 wherein the first hyperlink is configured to include one or more of the extracted terms, and wherein the first hyperlink, when activated, causes the processor to access the search system using the one or more extracted terms.

10. The method of claim 1 wherein a first hyperlink of the one or more hyperlinks identifies a search system, wherein the first hyperlink, when activated, causes a processor to access the search system.

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20. The computer-implemented system of claim 19 , wherein to quantify an effect on the person's brain health of at least one of social interaction, mobility, physiology, cognitive stimulation, behavioral therapy, diet, medication taken and traumatic exposure, the computer-implemented system: a. recording at the one or more electronic devices inputs associated with a measurement of at least one of social interaction, mobility, physiology, cognitive stimulation, behavioral therapy, diet, medication taken and traumatic exposure; b. learning a measurement function mapping from said inputs associated with the measurement to the brain health metric; and c. outputting an attribution to the brain health metric that is explained by the inputs associated with the measurement.

19. A computer-implemented system for assessing brain health of a person, wherein the computer-implemented system comprises a processor executing instructions for learning a function mapping, the computer-implemented system: receiving inputs from a data collection module installed at one or more electronic devices, the inputs associated with one or more interactions of the person with the one or more electronic devices, the inputs recorded by the data collection module without requiring additional actions by the person while the inputs are recorded; computing, by the computing system, a brain health metric by learning the function mapping from the inputs to the brain health metric, wherein the brain health metric is a percentile rank relative to a population of users, the brain health metric indicating the person's likelihood of being in at least one of a cognitive state and a neuropsychological state; and outputting, by the computing system, the brain health metric.

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22. A computer program product that includes a computer usable storage medium, the computer usable storage medium comprising a sequence of instructions which, when executed by a processor, causes said processor to execute a process for performing software performance analysis for a target machine, the process comprising: describing a system design as a network of logical entities; selecting at least one of the logical entities for a software implementation; implementing a source software program for the logical entities selected for the software implementation; generating an optimized assembler code for the software program, wherein the optimized assembler code is an assembly-language representation of the software implementation; performing a performance analysis using the optimized assembler code, wherein the act of performing the performance analysis is performed by a processor; generating a software simulation model in a high level language format based at least in part upon the optimized assembler code by annotating the software simulation model with information related to hardware on which the software implementation runs based at least in part upon a result of the act of performing the performance analysis to capture a dynamic interaction between tasks during runtime, wherein the act of annotating the software simulation model is performed during a time of the act of generating the software simulation model; storing the software simulation model on a computer usable storage medium; generating a hardware and software co-simulation model using the software simulation model; and storing at least the hardware and software co-simulation model on the computer usable storage medium or a second computer usable storage medium or displaying the at least the hardware and software co-simulation model on a display apparatus.

24. The computer program product of claim 22 , the process further comprising selecting at least one of the logical entities for a hardware implementation, and synthesizing a software model of the hardware implementation from the selected logical entities, wherein the hardware and software co-simulation model is generated using the software model of the hardware implementation.

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9. The method of claim 7 , wherein a series of document collations are processed sequentially, each document collation of the series of document collations having a first page mark and a last page mark, and wherein adjacent document collations in the series of document collations each have a different predetermined first page zone where the first page mark is located, and have a different predetermined last page zone where the last page mark in located, and wherein processing the information obtained from the marking comprises determining the presence of marks on a document collation in different zones than the predetermined zones for the document collation to detect if different pages from adjacent document collations become intermixed and inserted into the enclosure.

7. The method of claim 6 , further comprising processing the information obtained from the marking to validate the completeness of the document collation within the enclosure.

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8. A computer program product for dynamically evaluating an electronic communication, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by a processor to: identify an idiom in an electronic communication; conduct a primary evaluation of the identified idiom; assign a confidence level to the identified idiom in response to the primary evaluation; identify an explanation of the idiom; and selectively update a corpus based on the assigned confidence level, including extract and transform the explanation and the idiom into an entry in the corpus including create a primary association between the explanation and the idiom.

13. The computer program product of claim 8 , wherein the identification of the explanation includes program code to: parse the electronic communication and isolate two or more component phrases; compare a structure of the isolated component phrases to a structure of stored explanation tags in the corpus; detect a match between at least one of the isolated component phrases and a stored explanation tag in the corpus; and identify the explanation utilizing the detected match.

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12. The computer system of claim 10 , wherein the identifying the cluster of the similar digital text volumes comprises: clustering the digital text volume and the second digital text volume together in response to the number of minimum hash values in common exceeds a threshold.

10. The computer system of claim 9 , wherein the comparing the reduced feature set for the digital text volume to the reduced feature sets for the other digital text volumes comprises: determining a number of minimum hash values in the reduced feature set for the digital text volume that are in common with minimum hash values in a reduced feature set of a second digital text volume in the corpus.

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8. The distributed computer system implemented method of claim 1 , wherein the classifying further comprises classifying the extracted identifiers within a hierarchical structure in which the parent identifier is a top level domain and the child identifiers are base domains, sub-domains, or paths.

1. A distributed computer system implemented method comprising: extracting, by a computer system, identifiers from URLs, each of the identifiers identifying an entity associated with a URL from among the URLs; classifying the extracted identifiers of the URLs as parent identifiers and child identifiers; designating, by the computer system, a sequence of identifiers as attributable for a URL from among the URLs by: (1) determining whether any of one or more child identifiers of a parent identifier of the URLs account for more than a threshold percentage of traffic flowing from the computer system to an entity associated with the parent identifier, (2) responsive to a negative determination, designating a sequence of identifiers including the parent identifier as attributable, and (3) responsive to a positive determination, designating a sequence of identifiers including the one or more child identifiers as attributable; and attributing responsibility for each of the URLs to the entity associated with one of the designated attributable sequences of the URL.

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8. The machine-readable medium of claim 7 further comprising: predicting future data points based on at least one of coefficients c or a hyperplane function w associated with the selected regularization parameter.

7. A machine-readable medium encoded with instructions, that when executed by one or more processors, cause the processor to carry out a process for sound discrimination, the process comprising: receiving a training set of sound waveforms; generating a cortical representation of the training set; computing a matrix decomposition using the cortical representation of the training set including computing a singular value decomposition (SVD) using the cortical representation of the training set, wherein the SVD is the matrix decomposition; receiving a plurality of regularization parameters; computing coefficients for each regularization parameter using the matrix decomposition; computing a leave-one-out (LOO) error for each of the regularization parameters; and selecting the regularization parameter with the lowest LOO error.

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1. A method for providing audio-based guidance in a media guide, the method comprising: receiving a plurality of listings, wherein each of the plurality of listings is associated with a respective media item of a plurality of media items, the plurality of media items including a first media item having audio-friendly characteristics that make media content desirable to users who have difficulties viewing or understanding a visual portion of the content, the plurality of media items including a second media item not having the audio-friendly characteristics; generating for display the plurality of listings; receiving a first user command to navigate to a listing associated with the first media item; automatically determining that the first media item has the audio-friendly characteristics; and playing an audio notification when the first media item is determined to have the audio-friendly characteristics.

8. The method of claim 1 further comprising deactivating the audio notification in response to a second user command.

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2. The method according to claim 1 wherein performing the at least one input evaluation function comprises performing a subject evaluation function for extracting subject information from at least one of keywords; phrases; sentences; concepts; compound, complex or orthogonal inputs; and a simple web query.

5. The method according to claim 2 wherein performing the subject evaluation function for concepts comprises performing an evaluation for at least one of subject, object and context information.

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9. A method, comprising: presenting a document template with an enhanced repeating section content control having formatted content in a presentation field of the enhanced repeating section content control, wherein the enhanced repeating section content control is placed around at least one of a: paragraph, a row, and a table in a document part, and is operative to insert a cloned copy of the enhanced repeating section content control into the document part in response to receiving a control directive at the enhanced repeating section content control; receiving control directives to modify the formatted content; and synchronizing the modified formatted content between the content control and a data store associated with the enhanced repeating section content control.

10. The method of claim 9 , comprising receiving control directives to modify the formatted content presented by the enhanced repeating section content control or stored in the data store.

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3. The method of claim 1 , wherein said first keystroke is a press on a first key.

4. The method of claim 3 , wherein said second keystrokes are tilts on said first key or presses on keys adjacent to said first key.

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11. The method of claim 9 wherein the fourth row of the 4.times.2 dot-position array previews information contained in other frames.

9. The method of claim 8 wherein the top frame comprises a 4.times.2 dot-position array.

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8. A computer-implemented method for providing multi-language support for data mining models, the method comprising: receiving at a computer system an extension document having first and second entries associated with a unique identifier in a textual description field of a data mining model, the first entry including textual information in a first language, and the second entry including textual information in a second language; processing at the computer system a request from a front-end application to execute an analytical task associated with the data mining model, the request from the front-end application including input data that is employed by a back-end analytical engine to execute the data mining model to generate a back-end model output, the back-end model output including the unique identifier; and in response to receiving the back-end model output from the back-end analytical engine, outputting to the front-end application an updated model output that includes the first entry such that the textual information is output in the first language.

10. The method of claim 8 , further comprising storing contents of the extension document in a database, the contents including the first and second entries.

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10. The method of claim 9 , wherein the sharing tool generates a URL link to a web-based location of the created collaboration discussion.

9. The method of claim 8 , wherein each collaboration application includes a sharing tool, which, when selected by a user, allows the user to share the created collaborative discussion with one or more new users who did not previously have access to the created collaborative discussion.

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8. The method of claim 7 , further comprising dynamically removing, via the processor, a role from a security context of a user after completion of the action.

7. The method of claim 6 , further comprising performing, via the processor, an action on behalf of the user using the security context of the user.

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12. The method of claim 11 , wherein the weights associated with the first and second types of items ordered in common are determined based on a prioritization provided by the first user with respect to the first and second types of items.

11. The method of claim 1 , wherein generating the score comprises: identifying items of a first type ordered in common between the first and second plurality of items; identifying items of a second, different type ordered in common between the first and second plurality of items: and weighting each of the identified common items as a function of the respective type of the identified common item.

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9. The method of claim 8 , wherein the ranker array configuration data is stored in the ranker array configuration data structure in association with an identifier of a domain of the training question.

8. The method of claim 2 , further comprising: in response to the ranker array reward value satisfying the predetermined relationship with regard to the reward value threshold value, storing ranker array configuration data specifying a configuration of the ranker array, in a ranker array configuration data structure.

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1. A method in a computing system for improving the relevance of search results retrieved from one or more keyword-based search engines, comprising: receiving an indication of a designated entity having a name; determining whether the name of the designated entity is likely to lead to relevancy errors when used in a keyword-based search by performing a test to determine if the name of the designated entity matches a word that is not an entity, the name of the designated entity is a substring of a different entity's name, the name of the designated entity matches a name of a different entity having a facet that is not shared by the designated entity, or the name of the designated entity matches a name of a different entity with a facet that is shared with the designated entity; when determined that the name of the designated entity is likely to lead to relevancy errors, determining whether the name of the designated entity should be enhanced with an entity-specific enhancement, a facet-specific enhancement, or both types of enhancements, to formulate an enhanced query strategy, wherein the enhanced query strategy comprises a plurality of automatically generated queries that include disambiguation information, wherein an entity-specific enhancement includes a name of an entity, action, or property value related to the designated entity or related through a facet of the designated entity, and wherein a facet-specific enhancement includes one or more terms related to a facet; and when determined that the name of the designated entity is to be enhanced to formulate the enhanced query strategy, using one or more query enhancer components of the computing system selected based upon the determination of whether the name of the designated entity is to be enhanced with the entity-specific enhancement and/or the facet-specific enhancement, automatically adding one or more entity-specific queries and/or one or more facet-specific queries to the name of the designated entity to generate the enhanced query strategy, wherein at least one of the enhancements is based upon one or more facets associated with the designated entity; and forwarding the automatically generated query strategy to the one or more keyword-based search engines to generate on-topic information related to the designated entity.

15. The method of claim 1 wherein the automatically generated query strategy is used with a keyword-based search engine API for searching social network related messages.

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18. The computer system of claim 17 , the instructions further comprising: instructions for computing a first topic affinity score for a first user not in the first topic group by applying the topic profile to information of the social networking system about the first user; and instructions for predicting, based on the first topic affinity score, that the first user has an affinity for the first topic.

17. A computer system comprising: a processor; and a non-transitory computer-readable medium storing instructions executable by the processor, the instructions comprising: instructions for identifying a first topic group of users of a social networking system that have expressed an affinity for a page of the social networking system that corresponds to a first topic; instructions for determining, for a category, that the category includes the first topic and a plurality of other topics; instructions for identifying a category group of users of the social networking system that have expressed an affinity for a page corresponding to at least one of the topics included in the category; instructions for identifying a plurality of candidate pages of the social networking system, each candidate page having a corresponding candidate topic about which users of the first topic group and users of the category group can have differing opinions; instructions for, for each candidate page of the plurality of candidate pages of a social networking system: computing a user interest measure quantifying an amount of interest expressed by users of the first topic group for the candidate topic corresponding to the candidate page, computing a category interest measure quantifying an amount of interest expressed by users of the category group for the candidate topic corresponding to the candidate page, and computing a divergence measure between the first topic group and the category group based on the user interest measure and the category interest measure; instructions for generating, for the first topic, a first topic profile based on the divergence measures; and instructions for storing the first topic profile.

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10. The information processing apparatus according to claim 1 , wherein said description information is represented by a tree structure including a plurality of leaves bearing the same keyword; each of said plurality of leaves corresponds to an end of said tree structure, and said information processing apparatus further comprises: a description information conversion means for assigning said keyword and ID information representing said keyword to at least one of said plurality of leaves, and assigning reference information corresponding to said ID information to the other leaves, and said storage means stores the converted description information provided from said description information conversion means.

11. The information processing apparatus according to claim 10 , wherein said description information conversion means assigns said keyword and said ID information to the one leaf appearing first in said tree structure among said plurality of leaves.

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8. The method of claim 5 , wherein determining the set of probing characters comprises: selecting a first character; determining a second character that has a primary strength level difference from the first character; and determining a third character that has a secondary strength level difference from the first character.

5. The method of claim 1 , wherein determining the strengths of differences between characters comprises: selecting a repertoire of characters from the sets of characters in the information; determining a set of probing characters; testing the repertoire of characters based on the set of probing characters; and determining the strengths of differences between characters in the repertoire of characters based on the testing with the set of probing characters.

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1. A method performed by data processing apparatus, the method comprising: identifying one or more queries that were received with a reference to a given factual entity, wherein the one or more queries identified for the given factual entity are different from one or more queries identified for one or more other factual entities; identifying one or more resources related to the given query; obtaining search results that are responsive to a received query; determining that the given factual entity is referenced by the received query; identifying a type of entity for the given factual entity; identifying, from a set of different knowledge panel templates, a knowledge panel template specified for the type of entity, the identified knowledge panel template including placeholders for content relevant to the type of entity; selecting, from the one or more resources related to the given factual entity, content for display in a knowledge panel for the given factual entity, the selected content including a first content item obtained from a first resource and a second content item obtained from a second resource different than the first resource, each given content item of the selected content being selected based on a number of the one or more received queries that reference both (i) the given factual entity and (ii) information presented by the given content item; generating the knowledge panel for the given factual entity by populating the placeholders of the identified knowledge panel template with the selected content; and providing data that causes the identified search results and the knowledge panel to be presented on a search results page, the knowledge panel presenting the selected content in a knowledge panel area alongside at least a portion of the search results.

12. The method of claim 1 , wherein selecting content for display in the knowledge panel for the given factual entity comprises: identifying content items for each placeholder of the identified knowledge panel template based on a type of content specified for the placeholder and a type of content for each content item; ranking, for each placeholder, each content item identified for the placeholder relative to each other content item identified for the given content item, each content item being ranked based on a number of the one or more received queries that reference both (i) the given factual entity and (ii) information presented by the content item; and selecting, for inclusion in the knowledge panel, one or more of the content items for each placeholder based on the ranking for each placeholder.

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22. One or more non-transitory computer-readable storage media embodying software for execution by one or more computer systems and being operable when executed to: construct a plurality of first binary decision diagrams (BDDs), each of the first BDDs representing a different one of a plurality of words, each of the words having a unique word identifier (ID), each first BDD being constructed based on the word ID of the word represented by the first BDD; construct a plurality of second BDDs, each of the second BDDs representing a different one of a plurality of search queries, each of the search queries comprising one or more of the words, each second BDD being constructed by performing an AND operation on the first BDDs representing the words in the search query represented by the second BDD, wherein the plurality of search queries comprise a plurality of cached searched queries that have been previously submitted to a search engine; construct a plurality of third BDDs, each of the third BDDs representing a different one of a plurality of web pages, each of the web pages having a unique page ID, each of the third BDDs being constructed based on the page ID of the web page represented by the third BDD; construct a plurality of fourth BDDs, each of the fourth BDDs representing a different one of a plurality of search results generated in response to the search queries, each of the search results comprising one or more of the web pages, each fourth BDD being constructed by performing an OR operation on the third BDDs representing the web pages in the search result represented by the fourth BDD; construct a plurality of fifth BDDs, each of the fifth BDDs representing a different one of a plurality of search tuples, each of the search tuples comprising a different one of the search queries and a different one of the search results corresponding to the search query, each fifth BDD being constructed by performing an AND operation on the second BDD representing the search query and the fourth BDD representing the search result that the search tuple represented by the fifth BDD; and constructing a sixth BDD by performing an OR operation on the fifth BDDs, the sixth BDD representing the search queries and the search results.

28. The media of claim 22 , wherein the plurality of computer-readable instructions, when executed, are further operable to cause one or more computing devices to add a new fifth BDD representing a new search tuple to the sixth BDD by performing an OR operation on the sixth BDD and the new fifth BDD.

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2. A method according to claim 1 , wherein the text document is a Hypertext Markup Language (HTML) document.

6. A method according to claim 2 , wherein the new text body includes HTML document image tag text specified by an ALT attribute.

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23. A non-transitory computer-readable storage medium according to claim 19 , wherein at least one of the historical queries is associated with a recorded action of purchasing a thereby identified item.

19. A non-transitory computer-readable storage medium having stored thereon a computer-executable program that when executed directs a computing system to, at least: receive a query from a searching entity; submit the query to a plurality of search indexes, each search index corresponding to a respective category of items, at least one of the plurality of search indexes utilizing at least one of a different ranking property, scale, function, or definition for ranking items relative to other search indexes; receive one of a plurality of search index result sets from each of the plurality of search indexes in response to the query; determine a plurality of appropriateness scores each corresponding to one of the plurality of search indexes, each of the plurality of appropriateness scores indicating an appropriateness of the category of items corresponding to the respective search index with respect to the query and being based at least in part on historical queries similar to the query that were submitted to the respective search index; determine a universal item score for each of a plurality of items in the plurality of search index result sets at least in part by normalizing the at least one of the different ranking property, scale, function, and definition to a ranking scale common to all the search index result sets; for each of the plurality of items, determine a probability that the item satisfies the query based at least in part on the appropriateness score for the search index associated with the item and the universal item score for the item; include in the universal query result set ones of the plurality of items selected in an order based at least in part on the probabilities of the plurality of items satisfying the query; and provide the universal query result set to the searching entity, wherein determining the plurality of appropriateness scores comprises modifying the plurality of appropriateness scores differently based at least in part on different types of recorded actions associated with the historical queries that were submitted to a corresponding search index.

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10. The method of claim 6 wherein the information about the scanning device is stored at least partially in hardware.

6. The method of claim 2 wherein the session key includes information about a scanning device.

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1. A method comprising: determining, by a processor, one or more metric values for a news source based at least in part on at least one of a number of articles produced by the news source during a first time period, an average length of an article produced by the news source, an amount of coverage that the news source produces in a second time period, a breaking news score, an amount of network traffic to the news source, a human opinion of the news source, circulation statistics of the news source, a size of a staff associated with the news source, a number of bureaus associated with the news source, a number of original named entities in a group of articles associated with the news source, a breadth of coverage by the news source, a number of different countries from which network traffic to the news source originates, or a writing style used by the news source determining, by the processor, an importance metric value representing the amount of coverage that the news source produces in a second time period, where the determining an importance metric includes: determining, by the processor, for each article produced by the news source during the second time period, a number of other non-duplicate articles on a same subject produced by other news sources to produce an importance value for the article, and adding, by the processor, the importance values to obtain the importance metric value; generating, by the processor, a quality value for the news source based at least in part on the determined one or more metric values; and using, by the processor, the quality value to rank an object associated with the news source.

2. The method of claim 1 where the determining includes: determining, by the processor, a plurality of metric values for the news source.

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6. The method of claim 1 , further comprising: at a data collector layer, parsing a page component; and at the data collector layer, invoking a data source call corresponding to the page component.

1. A computer-implemented method of dynamically extracting context associated with a web request, comprising: at a context analyzer layer, receiving a web request context, wherein the web request context comprises a coded expression; at the context analyzer layer, determining if the coded expression contains a property name of multiple property names; if the context analyzer layer determines the coded expression contains the property name of the multiple property names, updating the web request context at the context analyzer layer by replacing the coded expression with a value of the property name of the multiple property names; if the context analyzer layer determines the coded expression does not contain any property name of the multiple property names, updating the web request context by replacing at least a part of the coded expression by at least one of: at the context analyzer layer, determining that the coded expression starts with a request parameter attribute and replacing the at least the part of the coded expression with a value of a parameter name at the context analyzer layer; at the context analyzer layer, determining that the coded expression starts with a request cookie attribute and replacing the at least the part of the coded expression with a value of a cookie name at the context analyzer layer; at the context analyzer layer, determining that the coded expression starts with a request attribute and replacing the at least the part of the coded expression with a value of an attribute name at the context analyzer layer; at the context analyzer layer, determining that the coded expression starts with a request header attribute and replacing the at least the part of the coded expression with a value of a header name at the context analyzer layer; or at the context analyzer layer, determining that the coded expression starts with a context attribute and attempting to identify a context analyzer name class and replacing the at least the part of the coded expression with a value of a context key of the context analyzer name class at the context analyzer layer; at the context analyzer layer, determining if a value of the at least the part of the coded expression is null after replacing the at least the part of the coded expression; at the context analyzer layer, determining if the value of the at least the part of the coded expression is marked as required; if the value of the at least the part of the coded expression is (1) determined to be null and (2) not marked as required, then ignoring the value of the at least the part of the coded expression; if the value of the at least the part of the coded expression is (1) determined to be null and (2) marked as required, then, at the context analyzer layer, generating a warning in a log based at least in part on the determining that the value of the at least the part of the coded expression is null after replacing the at least the part of the coded expression; at the context analyzer layer, invoking the context key of the context analyzer name class; and at the context analyzer layer, determining that the context analyzer name class is not identified or that the value of the context key is not specified and then throwing an invalid configuration exception.

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15. The computer readable medium of claim 14 , wherein the computer readable medium further comprises the steps of: utilizing a Bayesian network learning algorithm to analyze causes and effects of observed evidence using Bayesian Networks; and, creating real-time mathematical models to predict actions.

14. The computer readable medium of claim 13 , wherein the computer readable medium further comprises the steps of: linking algorithms with a user interface; and, parsing each sentence based on parts of speech and relative positions, using a semantic role labeling algorithm, to extract entities and discover relationships between entities.

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1. A method for replacing an at least one symbol in a first image, the method comprising: obtaining the first image comprising a plurality of pixels representing the at least one symbol and a plurality of pixels representing a background area; defining a first and a second boundary in the first image, wherein the first and the second boundaries are each a path comprising a plurality of pixels that minimizes a cost associated with change in color for the path and each of the paths are positioned on opposite sides of the at least one symbol; generating a plurality of pixels representing an at least one translated symbol of the at least one symbol; generating a plurality of pixels representing an augmented version of the background area, by interpolating a plurality of background pixel values between the first and the second boundaries; and constructing a second image comprising the plurality of pixels representing the at least one translated symbol and the plurality of pixels representing the augmented version of the background area.

2. The method of claim 1 , wherein each of the first and second boundaries is defined as a string of pixels along one side of the at least one symbol.

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24. The system of claim 21 , wherein the instructions cause the one or more computers to identify one or more sets of matching labels by causing the one or more computers to perform operations comprising identifying at least one set of matching labels in which each label references a same topic.

21. The system of claim 17 , wherein the instructions further cause the one or more computers to perform operations comprising: identifying one or more sets of matching web pages from the plurality of web pages, each set of matching web pages being a two or more web pages that each have a characteristic that matches a characteristic of another web page in the set of matching web pages; and identifying one or more sets of matching labels from the set of initial labels, each set of matching labels including two or more initial labels having at least a threshold measure of similarity.

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19. The method of claim 1 , wherein the content item tag comprises an indication that the tagged user is identified as being in an image in the content item.

1. A computer-implemented method for communication in a social networking system, the method comprising: receiving a content item from an author, the author being a user of a social networking system; receiving a definition of a first audience from the author, the first audience comprising one or more users of the social networking system; receiving a content item tag from the author, the content item tag indicating an association between the content item and a tagged user, wherein the tagged user is a user of the social networking system different than the author; receiving a definition of a second audience from the tagged user, the second audience comprising one or more users of the social networking system, wherein at least one user in the second audience is not in the first audience; determining if a viewing user is a member of a union of the first audience and the second audience; and sending the content item and the content item tag for display to the viewing user if the viewing user is a member of a union of the first audience and the second audience.

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8. A method for engaging a storage device with a telescoping finger mechanism, the method comprising: moving a translation member along a picker frame, the translation member mechanically coupled with a finger mechanism, wherein the translation member includes a leadnut operable to translate along a leadscrew, and a gear assembly is rotatably mounted to the translation member, and a first portion of the gear assembly engages a portion of the picker frame and a second portion of the gear assembly engages a portion of the finger mechanism such that translation of the translation member results in relative movement of the translation member and the finger mechanism such that the finger mechanism translates a distance different than the translation member.

11. The method of claim 8 , where the finger mechanism further includes a rack that engages the second portion of the gear causing relative translation of the finger mechanism to the leadnut.

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3. The method of claim 1 , wherein the adapting of the DNN model further comprises adding a new layer into the DNN model, wherein the new layer comprises the decomposed matrix a non-linear layer.

1. A method of adapting and personalizing a deep neural network (DNN) model for automatic speech recognition (ASR), comprising: receiving, by a computing device, at least one utterance comprising a plurality of speech features for one or more speakers from one or more ASR tasks; applying, by the computing device, a decomposition process to two or more matrices in the DNN model; in response to applying the decomposition process, adapting the DNN model to include a decomposed matrix that is generated from decomposition processing of the two or more matrices; and exposing the adapted DNN model as a service.

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8. The method of claim 7 , wherein identifying the user further comprises applying pattern recognition techniques to the content.

7. The method of claim 6 , further comprising identifying the user of the second device using content of the digital file, the nametag, and the identifiers.

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6. The method of claim 1 , wherein the incompatible input comprises unrecognized speech.

1. A method comprising: detecting, by a system including a processor, input incompatible with an original script for an interactive communication over a communication network, wherein the detecting is performed during the interactive communication; modifying, by the system, the original script during the interactive communication into a dynamically updated script different from the original script in accordance with a type of the incompatible input; and providing, by the system over the communication network, information to a device participating in the interactive communication in accordance with the dynamically updated script, wherein at least a portion of a remainder of the interactive communication is conducted in accordance with the dynamically updated script.

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5. The method of claim 1 , further comprising: determining, by the one or more processors, a likelihood of receiving additional electronic information from the first user computing device; generating, by the one or more processors, based on at least the determining the likelihood of receiving the additional electronic information from the first user computing device, second advertising information comprising electronic user interest information associated with at least one of the plurality of persons; and transmitting, via the one or more communication devices, by the one or more processors, the second advertising information to the first user computing device.

1. A method comprising: accessing at least one database on one or more processor readable media, by one or more processors, the at least one database comprising: electronic user information including at least one from a group consisting of a trait, a brand-specific preference, and a person-specific identifier of each of a plurality of persons; and electronic advertiser information comprising information associated with at least one branded product and/or service associated with at least one respective advertiser of a plurality of advertisers; receiving, via one or more communication devices that are operatively connected to the one or more processors, first electronic information from a first user computing device associated with a first user, wherein the first electronic information includes at least some user information associated with the first user and information representing at least a trait, a preference, and/or a person-specific identifier; defining, by the one or more processors and in accordance with the first electronic information, a first group of at least two respective persons of the plurality of persons, wherein the first group is custom to the first user; determining, by the one or more processors and in accordance with at least a relationship of at least some user information associated with the first user and the electronic advertiser information, a relevance of the first user to at least two advertisers of the plurality of advertisers; processing, by the one or more processors, to identify one respective person of the first group in accordance with information representing a previously received selection of the one respective person by the first user; selecting, by the one or more processors, one of the at least two advertisers based at least on a relevance of the one respective person of the first group to at least some of the user information representing at least one person's preference of a brand associated with the one of the at least two advertisers; generating, by the one or more processors, advertising information that includes the brand associated with the one of the at least two advertisers, and a person-specific identifier associated with the one respective person of the first group; and transmitting, via the one or more communication devices, by the one or more processors, at least some of the advertising information to the first user computing device.

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16. The system of claim 12 , wherein the classifier is employed to predict the type of terrain proximal to the current terrain.

17. The system of claim 16 , further comprising the step of the mobile device changing course based on the prediction.

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1. A method of coding information comprising; creating sets of independent structures of any size, including elements carrying information allowing the identification and descriptive and functional control of structure of the elements; defining links with other structures; creating the structures to further include characters representing conjointly the information on a physical medium by dots and in computing format by bits; creating the structures of the physical characters which comprise on the one hand detection or marking dots that are peculiar to the physical characters and on the other hand dots which each correspond to 1 bit in computing format; and creating as mixed dot which is half white and half black.

6. The method according to claim 1 , wherein the first column of the structure of physical characters has the mixed dot, which is the last dot, formed from a white half dot and a black half dot.

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16. A data processing system for performing dynamic textual complexity analysis using machine learning artificial intelligence comprising: one or more client devices, wherein each client device is connected to a network system; a data center unit comprising: a network interface unit for interfacing with the one or more client devices and the network system; a user interface; one or more storage devices, wherein the one or more storage devices comprise one or more databases; a storage device controller and database manager for controlling the operations of the one or more storage devices and one or more databases; a web server for providing web services to the one or more clients; and a database server for providing database services to the one or more clients; a machine learning artificial intelligence (“MLAI”) application server for predicting textual complexity of data, the machine learning artificial intelligence application server includes one or more databases for storing data used to refine textual complexity analysis for improved accuracy of textual complexity predictions.

19. The data processing system of claim 16 , wherein the MLAI application server includes a second database for storing feature resolution data.

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1. A method of controlling an operational line in a DSL system, the method comprising: constructing a Hidden Markov Model (HMM) to model one or more internal states of the DSL system, the states characterizing at least one of data activity, impulse noise, crosstalk, noise margin, maximum attainable data rate or bit distributions; collecting update operational data from the operational line; analyzing the collected update operational data, the analyzing farther comprising: estimating an observation probability, a state-transition probability matrix, and an initial state distribution of the HMM based on the collected update operational data; estimating a likelihood of at least one state of the HMM based on the collected update operational data; and controlling operation of the DSL system based on the constructed HMM and analyzed update operational data, the controlling further comprising: determining control parameters based on the estimated likelihood of at least one state of the HMM; and modifying operation of the operational line through application of the determined control parameters to reflect the information available from the HMM.

21. The method of claim 1 wherein the HMM model is dependent on time and the states are modeled as a function of a time index; wherein the collected operational data pertains to data activity and CV counts over time; and wherein a forward error correction (FEC) parameter is determined as a function of the time index.

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39. The software product of claim 23 , wherein at least one of the subsets of information comprises annotations.

23. A software product tangibly stored on a machine-readable medium, the software product comprising instructions that cause a programmable processor to perform operations for reversing a format of a reversible electronic document comprising two formatting states, the operations comprising: in a first document conversion for reversing an electronic document in a binary format into a markup language format, transforming the electronic document from the binary format into the markup language format by extracting a subset of information from the electronic document in the binary format according to predefined extraction parameters, inserting the subset of the information into portions of the electronic document in the markup language format, and inserting a reduced-information version of the electronic document in the binary format into a storage location of the electronic document in the markup language format such that the version of the electronic document in the binary format is retrievable upon reversal of the electronic document in the markup language format in a second document conversion, wherein the reduced-information version comprises data that is unconvertible to the markup language format; and in the second document conversion for reversing an electronic document in the markup language format into the binary format, transforming the electronic document from the markup language format into the binary format by extracting a first subset of information, from the electronic document in the markup language format, that is recognized in the binary format, extracting a second subset of information, from the electronic document in the markup language format, that is not recognized in the binary format, inserting the first subset of information, from the electronic document in the markup language format, into portions of the electronic document in the binary format, and placing the second subset of information from the electronic document in the markup language format into a storage location of the electronic document in the binary format such that the unrecognized information is retrievable upon reversal of the electronic document in the binary format in the first document conversion, wherein the second subset of information comprises data that is unconvertible to the binary format.

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19. The system of claim 12 , wherein determining that the particular search result is highly ranked for the second search query includes determining that the particular search result appears within a threshold number of highest-ranked second search results of the second search results responsive to the second search query.

12. A system comprising: one or more data processing apparatus; and a computer-readable storage device having stored thereon instructions that, when executed by the one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising: obtaining a plurality of first search results responsive to a first search query, the plurality of first search results being ranked in an order; obtaining one or more second search results responsive to a second search query, the second search query being related to the first search query; identifying a particular search result of the second search results; determining that the particular search result is related to the first search query and that the particular search result is highly ranked for the second search query; determining that the particular search result is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query; in response to determining that the particular search result is highly ranked for the second search query and the particular search result is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query, modifying the first search results by providing the particular first search result within the threshold number of highest-ranked first search results of the first search results responsive to the first search query; and providing the modified plurality of first search results in response to the first search query.

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1. A computer system for generating a representation of time-based media, the system comprising: a feature extraction module for: extracting, using a feature extraction technique, features from the time-based media, the feature extraction technique specified by a document format specification file; and generating a media representation of the time-based media that represents the extracted features, the media representation including a waveform representing the time based media including the extracted features, a corresponding timeline and a plurality of user-selectable identifiers indicating locations on the timeline corresponding to the extracted features; a formatting module communicatively coupled to the feature extraction module, the formatting module for: formatting the media representation according to layout parameters specified by the document format specification file; and a printer communicatively coupled to the formatting module, the printer for: printing the formatted media representation, wherein each of the plurality of user-selectable identifiers in the printed, formatted media representation can be selected to access a corresponding part of the time-based media.

4. The system of claim 1 , further comprising processing logic for controlling a printer console on the printer.

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2. The method of claim 1 , where: the electronic document has an internal structure that is different from a structure defined in the database schema.

3. The method of claim 2 , where generating the metadata of the unstructured data includes: extracting the metadata from the document; and incorporating user created document attributes into the extracted metadata.

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1. A system, comprising: a memory configured to store a simulated model comprising a plurality of simulated components, each of the plurality of simulated components arranged in a component hierarchical graph such that a combination of the simulated components forms the simulated model; a processor that executes an inference engine configured to generate one or more redesign recommendations for at least one simulated component of the plurality of simulated components in the simulated model based on redesign recommendation rules and the at least one simulated component in the component hierarchical graph; and a display generator configured to display the redesign recommendations to a first user.

3. The system of claim 1 , wherein the redesign recommendation rules comprise previously generated simulated component by a second user.

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5. The computer-implemented method for extracting information from an individual handle of claim 1 , further comprising the step of identifying any sentinel characters and removing at least a part of the identified sentinel characters in the handle prior to the marking step.

6. The computer-implemented method for extracting information from an individual handle of claim 5 , further comprising the step of identifying and removing only non-disambiguation sentinel characters.

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18. The system of claim 11 , wherein the speech recognizer generates the first carrier phrase based on a first language model, wherein the speech recognizer selects, based on the first carrier phrase, a second language model from a plurality of language models that are assigned to carrier phrases, and wherein the speech recognizer generates, from the voice query, a remaining portion of the textual query that does not include the carrier phrase, based on use of the second language model.

11. A computer-implemented system comprising: a computer-implemented speech recognizer that is configured to (i) receive data that represents a content of a voice query that was orally provided to a computing device, and (ii) generate a textual query that is a textual representation of the data; a carrier phrase database that is programmed to (i) identify a plurality of third-party application programs that are provided by organizations that are different from an organization that provides the system, and (ii) identify a plurality of carrier phrases that have been assigned to respective of the third-party application programs in response to requests by the third-party application programs or providers of the third-party application programs to reserve the respective carrier phrases, wherein the application programs are configured to be installed on the computing device; and a computer-implemented query distributor programmed to (i) identify, in the textual query, a first carrier phrase from the plurality of carrier phrases, and (ii) identify a first third-party application program from the plurality of third-party application programs to which the first carrier phrase has been assigned in the carrier phrase database, so as to cause the query distributor to provide all or some of the textual query for receipt by the first third-party application program.

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7. The method of claim 1 , wherein the categories are represented by language independent categories.

1. A method of performing text classification based on language-independent text features, the method comprising: performing, by a processor, a first syntactic and semantic analysis of a training natural language text to produce a first plurality of language-independent semantic structures representing a plurality of sentences of the training natural language text; producing, based on the first plurality of language-independent semantic structures, a text classifier model; performing a second syntactic and semantic analysis of an input natural language text to produce a second plurality of language-independent semantic structures representing a plurality of sentences of the input natural language text; extracting, using the second plurality of language-independent semantic structures, a set of features, wherein at least one feature references a semantic class of a language-independent semantic hierarchy comprising a plurality of semantic classes, in which the semantic class exhibits one or more properties inherited from its parent semantic class; applying the text classifier model to the set of features to produce a classification spectrum comprising a plurality of weight values, wherein each weight value reflects a degree of association of the input natural language text with a particular category of natural language texts; and associating the input natural language text with one or more categories using the classification spectrum.

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10. The method of claim 9 further comprising prompting the user for additional information if a discrete path associated with an answer to the user's question is not found.

9. One or more non-transitory computer-readable media storing computer-executable instructions which, when executed by a processor on a computer system, perform a method for natural language searching comprising: searching an index using key words from a user's natural language question and the context of the user's question, said index contains information which references a domain model; saving a plurality of documents obtained in response to the search of the index; mapping each of the documents as a node into an object graph, wherein each node is associated with a parent node, except when the node is a root node; identifying the root node of each document; identifying the path of each node from the node to the node's root node, wherein said path comprises a plurality of nodes that connect the node to the root node; identifying a plurality of matching paths, wherein each matching path provides an answer to the user's natural language question, each of said plurality of paths that comprises a discrete path of nodes; filtering out paths depending on a set of the user's pre-specified criterion, said criterion including a weighting factor specifying a relative level of importance associated with receiving information at a high speed; ranking the remaining paths; and displaying a selected answer to the user, in response to the ranking and filtering.

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10. The method of claim 9 , wherein said query document and said plurality of additional documents are each associated with a maximum number of most relevant concept vectors M such that the proximity of each document belonging to said plurality of additional documents to said query document is determined by the degree of overlapping of said maximum number of most relevant concept vectors M such that said output module enables displaying said at least a subset of said plurality of said additional documents based, at least in part, on said degree of overlapping.

12. The method of claim 10 , wherein said maximum number of most relevant concept vectors M is one hundred.

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15. An apparatus comprising: means for obtaining a request for translation information from a mobile station, said translation information being associated with one or more written and/or spoken languages; means for associating a location with said request for translation information; and means for generating requested translation information based, at least in part, on said request for translation information, said location, and predicted information, wherein the predicted information is associated with at least one other request for translation information associated with at least one other location and previously obtained from at least one other mobile station.

16. The apparatus as recited in claim 15 , further comprising: means for transmitting a response comprising said requested translation information to said mobile station.

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26. A computer-readable medium encoded with a computer program according to claim 24 , wherein said semantic information comprises a graphics information.

24. A computer-readable medium encoded with a computer program for causing a computing entity to operate to: display on the computing entity a view of a displayable item, a first activatable transport-control element with associated first semantic information, and a second activatable transport-control element with associated second semantic information that is different from said first semantic information; and respond to activation of a said transport-control element both by moving the displayed item view within or between displayable items and by storing or outputting data indicative of the semantic information associated with the activated element, the item-view move that is effected as a result of activation of a said transport-control element being the same whichever of said elements is activated.

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6. One or more computer readable storage devices comprising computer executable instructions that, when executed by a computer processor, direct the computer processor to perform operations including: receiving a query including an image; automatically collecting at least one visually similar image to the included image and text from a file containing the visually similar image, the text being in a proximity of the visually similar image within the file; determining a name of an entity in the included image based on the collecting; and outputting the name of the entity.

7. The one or more computer readable storage devices of claim 6 , wherein the image is a face image and the entity is a person.

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1. A method of visually presenting information to a computer user, the method comprising: providing a notification at an interface of a computing device that a status of at least one of an initial data object and a semantic relationship between the initial data object and another data object has changed; receiving a first user input at the interface, the first user input corresponding to at least one of the initial data object and an initial set of data objects; determining, in response to the first user input, semantic relationships among a plurality of data objects, each data object being of a type, wherein the type comprises one or more of a customer, a supplier, a purchase order, and a material, and information about each data object and the semantic relationships among the plurality of data objects being stored in a data object repository; filtering the plurality of data objects based on one or more of a particular type of a data object or an instance of the data object to provide filtered data objects; grouping the filtered data objects into sets of data objects, the data objects in a set of data objects being of a same type and having a same type of semantic relationship to the at least one of the initial data object and the initial set of data objects; presenting, on a display of the interface, a first graphical user interface including multiple path graphical elements presenting respective paths of the semantic relationships between one of the initial data object and the initial set of data objects and a respective one of a destination object and a destination set of data objects, the first graphical user interface allowing the computer user to perform analyses and actions not predefined in existing workflows and to select a first number of data object relationship levels to be presented in the first graphical user interface; receiving a second user input at the interface, the second user input indicating a first graphical element, the first graphical element corresponding to a second data object or a second set of data objects provided in a path graphical element; and in response to receiving the second user input, presenting, on the display, a second graphical user interface including the first graphical element and at least one semantic relationship between the first graphical element and another graphical element, the second graphical user interface allowing the computer user to perform analyses and actions not predefined in the existing workflows and to select a second number of data object relationship levels to be presented in the second graphical user interface.

2. The method of claim 1 , further including presenting at least one semantic relationship between a business object and other data objects, wherein the business object comprises a data object.

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1. A method comprising: receiving private data, the private data (1) comprising private time-sensitive data associated with activities of a user, and (2) being stored in a computer system; generating an alphanumeric word using the private data; prompting the user to speak the alpha-numeric word; extracting a voice feature from a received phrase in response to the prompting; comparing, via a processor, the voice feature with a voice profile, to yield a comparison; and determining whether to accept a speaker identity based on the comparison.

2. The method of claim 1 , further comprising receiving the received phrase.

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1. A computer implemented method for navigating a multi-page electronic document displayed on a touchscreen, comprising: selectively displaying different pages of the multi-page electronic document in a display area of the touchscreen; in response to sensing a touch by a first finger on the display area, automatically bookmarking a first selected page currently displayed on the touchscreen; navigating to a second selected page for display on the touchscreen in response to receiving a user navigation command while the first finger maintains uninterrupted contact with the display area; while displaying the second selected page, sensing a touch to the display area by a second finger at a location spaced apart from the location touched by the first finger; bookmarking the second selected page in response to the touch by the second finger; switching from the second selected page back to the first selected page in response to a sliding gesture of the first and second touch in a direction away from the first touch toward the second touch; alternately switching from the first selected page back to the second selected page in response to a sliding gesture of the first and second touch in a direction away from the second touch toward the first touch; and automatically returning to the bookmarked first selected page for display on the touchscreen in response to sensing a first predefined gesture relative to the display area using the first finger.

2. The computer implemented method of claim 1 , further comprising: bookmarking the currently displayed page only in response to the combination of sensing a touch by a first finger on the display area and navigating to another page while the first finger maintains uninterrupted contact with the display area.

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1. A method for identifying synonymous expressions, comprising: determining synonymous expression candidates for a target non-facial-based expression; identifying a plurality of target images related to the target non-facial-based expression and a plurality of candidate images related to each of the synonymous expression candidates; and comparing features extracted from the plurality of target images with features extracted from the plurality of candidate images using a processor to identify a synonymous expression of the target non-facial-based expression, wherein the synonymous expression includes at least one of a word, a phrase, and a sound.

7. The method as recited in claim 1 , wherein the features extracted from the plurality of target images and the features extracted from the plurality of candidate images includes color information.

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1. A method for identifying n-grams about an object, comprising: identifying, as a result of computing hardware and programmable memory, an object-specific corpus, that is a subset of a first corpus, where approximately all statements of the object-specific corpus are about a same first object; identifying, as a result of computing hardware and programmable memory, statements of the object-specific corpus, for inclusion in a corpus of interest, upon a basis of a statement relating to a time period of interest; identifying, as a result of computing hardware and programmable memory, statements of the object-specific corpus, for inclusion in a reference corpus, upon a basis of a statement relating to a reference time period that is different from the time period of interest; identifying, as a result of computing hardware and programmable memory, n-grams of the corpus of interest, for inclusion in a corpus-of-interest list of n-grams; identifying, as a result of computing hardware and programmable memory, n-grams of the reference corpus, for inclusion in a reference-corpus list of n-grams; identifying, as a result of computing hardware and programmable memory, for each n-gram of the corpus-of-interest list of n-grams, for subsequent access in conjunction with an n-gram, a number of occurrences of the n-gram in the corpus of interest; identifying, as a result of computing hardware and programmable memory, for each n-gram of the reference-corpus list of n-grams, for subsequent access in conjunction with an n-gram, a number of occurrences of the n-gram in the reference corpus; determining, as a result of computing hardware and programmable memory, a selected list of n-grams from the corpus-of-interest list of n-grams or the reference-corpus list of n-grams; determining, as a result of computing hardware and programmable memory, for each n-gram of the selected list of n-grams, for subsequent access in conjunction with an n-gram, an average number of occurrences of the n-gram, in the reference-corpus; determining, as a result of computing hardware and programmable memory, for each n-gram of the selected list of n-grams, for subsequent access in conjunction with an n-gram, a difference value, between a number of occurrences of the n-gram in the corpus of interest and an average number of occurrences of the n-gram in the reference-corpus; normalizing, as a result of computing hardware and programmable memory, for each n-gram of the selected list of n-grams, for subsequent access in conjunction with an n-gram, the difference value to produce a normalized difference value; and determining, as a result of computing hardware and programmable memory, from the selected list of n-grams, a second selected list of n-grams, on a basis of the normalized difference value.

7. The method of claim 1 , wherein the step of normalizing, to produce a normalized difference value, further comprises: dividing, for each n-gram of the selected list of n-grams, a difference value by a number of occurrences of the n-gram in the corpus of interest.

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15. One or more non-transitory computer-readable media having computer-executable instructions stored thereon that, when executed, cause at least one computing device to: parse a string of terms into a plurality of individual terms; determine whether the plurality of individual terms includes at least one term matching a keyword of a keyword listing; responsive to determining that the plurality of individual terms includes at least one term matching the keyword, determine a part of speech of the at least one term matching the keyword; responsive to determining that the part of speech of the at least one term is one of: a noun and a verb, identify the other of the noun and the verb in the plurality of individual terms; identify a category of risk associated with the identified noun and a category of risk associated with the identified verb; determine whether the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are a same category of risk; responsive to determining that the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are the same category, determine a first risk rating of the string of terms including the identified noun and the identified verb, the first risk rating being based on the identified noun, the identified verb and the same category; and responsive to determining that the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are different categories, determine a second risk rating of the string of terms including the identified noun and the identified verb, the second risk rating being based on the identified noun, the identified verb, the identified category of risk associated with the noun and the identified category of risk associated with the verb, the second risk rating being different from the first risk rating.

20. The one or more non-transitory computer-readable media of claim 15 , wherein the string of terms is input into a computing device by a user, and wherein the user is an employee of an entity and the computing device is provided to the user by the entity for use as an employee of the entity.

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13. An apparatus for processing information on nucleotide sequence, comprising: a transmitter/receiver that receives positional information representing a position in a nucleotide sequence in accordance with a request for an object and/or service; and a CPU that: obtains from a memory device, from among a plurality of pieces of polymorphism pattern, a polymorphism pattern associated with the positional information received by the transmitter/receiver, wherein the obtained polymorphism pattern is information on nucleotide sequence which differs among individual organisms and shows a pattern of nucleotide or nucleotide sequence in a polymorphism; causes the transmitter/receiver to transmit the polymorphism pattern obtained from the memory device; causes the transmitter/receiver to receive semantic information corresponding to the transmitted polymorphism pattern and/or information associated with the semantic information in association with positional information, wherein the semantic information refers to information on phenotypes caused by one or more differences in polymorphism patterns; makes a determination as to whether the received positional information matches positional information related to the transmitted polymorphism pattern; and causes the apparatus to alert a party that received the transmitted polymorphism pattern, and wherein the causing of the apparatus to alert is performed in response to the determination as to whether the received positional information matches positional information related to the transmitted polymorphism pattern.

19. The apparatus of claim 13 , wherein the apparatus is arranged to transmit a warning that is made based on an evaluation that the probability of unauthorized use and acquisition is high, a notification that all the transmitted polymorphism patterns were not used, or an announcement regarding notification to a third party that all the transmitted polymorphism patterns were not used.

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2. The system of claim 1 , wherein the document comprises one of: textual content; and a musical score.

3. The system of claim 2 , wherein the document comprises textual content, the method further comprising: converting text to speech to create the at least one audio file.

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13. A computer implemented method as in claim 1 , further comprising the steps of: accepting from said actor, by said at least one computer, a question which is a set of formal texts containing at least one well-formed syntactic element, wherein the value of said well-formed syntactic element is unknown; parsing and verifying, by said at least one computer, the formal text of said question to infer the type of syntactic element it contains, and to produce formulas contained in the question; and initializing the rewriting area with the formulas contained in the question, by said at least one computer.

1. A computer implemented method of interpreting written text, said method comprising the steps of: providing an alphabet, at least one symbol for constructing lexical elements, and at least one syntactic element built on said alphabet, said lexical elements, and said at least one symbol on at least one computer; determining, by said at least one computer, a sort of value, an arity, a notation, and a type for each said syntactic element, to provide well-formed syntactic elements and meaningless syntactic elements; building, by said at least one computer, formal definitions from said well-formed syntactic elements, and determining a type for each said formal definition to provide a list of terminologies, assertions, theorems, questions and answers; building, by said at least one computer, at least one formal glossary as ordered sets of said formal definitions; providing, on said at least one computer, to an output of at least one said formal glossary, and a related minimal lexicon; parsing, verifying and normalizing each said formal definition, by said at least one computer, from said formal glossary, to provide a list of defined lexical elements and undefined lexical elements; accepting at least one input, wherein said at least one input includes at least one question containing at least one well formed syntactic element; retrieving for each said defined lexical element, at least one formal definition from said at least one formal glossary; applying said at least one formal definition to said defined lexical elements according to at least one interpretation process to provide at least one meaningful value; coding each said at least one input and said at least one meaningful value as a new questions and answers definition; and saving said new questions and answers definitions in said formal glossary in said computer, to provide an intelligent glossary.

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5. The face recognition method according to claim 1 , further comprising: storing user preference information along with the training dictionaries corresponding to one or more users; and after obtaining the face class of the input face image, recommending personalized contents to a user corresponding to the face class according to the user preference information.

1. A face recognition method on a computing device, comprising: obtaining a plurality of training face images which belongs to a plurality of face classes, wherein a face class includes one or more training face images and represents an identification of the one or more training face images; obtaining a plurality of training dictionaries corresponding to the plurality of training face images, wherein the plurality of training dictionaries include a plurality of deep feature matrices; obtaining an input face image; partitioning the input face image into a plurality of blocks; extracting corresponding deep feature vectors of the plurality of blocks of the input face image using a deep learning network; applying a collaborative representation model to represent the deep feature vectors of the blocks of the input face image with the training dictionaries and representation vectors; computing residual errors for the face classes, a residual error for a face class being a summation of errors for all blocks corresponding to the training face images in the face class, wherein an error for a block exists between a feature vector of the block in the input face image and the collaborative representation model of the block corresponding to the face class; classifying the input face image by selecting a face class that yields a minimum residual error as a recognition face class; and presenting the recognition face class of the input face image.

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1. A computer-based information analysis system for creating a representation of at least a portion of at least one of first and second predetermined bodies of text, the representation comprising records which correspond to text segments each of which have a length determined by the system user, the system comprising: a) input means for entering data, the input means comprising topic input means for inputting a set of user-defined topics; b) organization means for structuring the representation, the organization means comprising: i) record division means for creating at least one of first and second sets of records, wherein each set of records corresponds to a particular body of text and wherein each record comprises data which characterizes a particular text segment chosen by a system user, the record division means comprising demarcation indicia means for entering demarcation indicia representing the user selected length of each record; ii) one-to-one association organizing means comprising demarcation indicia association means for establishing a one-to-one association between a predetermined record and the corresponding demarcation indicia for indicating the length of the record; and iii) one-to-many association organizing means comprising topic organizing means for establishing a user-designated one-to-many association between at least one user-defined topic and at least one of the records; and c) output means for generating a report.

12. The system of claim 1 wherein: a) the input means further comprises item-identifier input means for inputting at least one of first and second sets of item-identifiers corresponding to items comprehended by the text; b) the one-to-many association organizing means further comprises item-identifier association means for establishing a one-to-many association between each item-identifier and the corresponding records which comprehend a reference to the respective item; and (c) the output means further comprises record retrieval means for retrieving one or more records according to one or more of the item identifiers corresponding to items comprehended by the text, and associated with at least one of the records.

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13. A method according to claim 8 , further comprising attempting access to the email account of the user based upon five or less candidate configuration parameters.

8. A method of provisioning an electronic mail (email) account for allowing access to an electronic mailbox to retrieve email, the method comprising: receiving email address parameters of a user and transmitting a domain name system (DNS) query to the Internet for returning mail exchange (MX) records, including email domain names, corresponding to the email address parameters of the user; processing returned MX records to determine candidate configuration parameters for accessing the email account of the user to retrieve user email; and determining candidate configuration parameters based upon expanding a plurality of prioritized email domain names of the returned MX records and including a first preference mail domain and a second preference mail domain based upon relative geographic proximity.

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2. The method of claim 1 , wherein said notification and callback code for events comprises callback subroutines for inputs to said event-sequenced imperative procedures, wherein each of said callback subroutines comprises a switch statement having one or more case blocks, and wherein control flow is provided by setting case variables of said switch statements.

4. The method of claim 2 , further comprising combining adjacent case blocks having no external entry points other than the first statement of a first of said adjacent case blocks into a single case block.

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1. A non-transitory computer-readable medium storing an encryption program causing a computer to operate as: a conversion-table storage unit which stores a conversion table having a plurality of regions each of which stores 2 i character codes associated with index values, where i is a natural number predetermined for each of the plurality of regions, the index values have a minimum necessary bit length for uniquely identifying the 2 i character codes within each of the plurality of regions, the plurality of regions are defined by decomposing a total number of character codes included in a character encoding scheme into a sum of components of 2 i , and each character code included in the character encoding scheme is allocated to any one of the plurality of regions; a plaintext conversion unit which acquires a first plaintext inputted into the computer, and converts first character codes constituting the first plaintext into first index values associated with the first character codes, respectively, by reference to first ones, storing the first character codes, of the plurality of regions of said conversion table stored in said conversion-table storage unit, where the first character codes are defined in said predetermined character encoding scheme; an encryption unit which successively acquires said first index values, and encrypts the first index values into encrypted values having respectively identical bit lengths to the first index values; and an encrypted-text output unit which successively acquires said encrypted values, converts the encrypted values into second character codes respectively associated with index values identical to the encrypted values by reference to said first ones of the plurality of regions of the conversion table, produces a sequence of the second character codes as a first encrypted text corresponding to said first plaintext, and outputs the first encrypted text.

5. A non-transitory computer-readable medium according to claim 1 , causing the computer to further operate as: an encrypted-text conversion unit which acquires a second encrypted text inputted into the computer, and converts third character codes constituting the second encrypted text into second index values associated with the third character codes, respectively, by reference to second ones, storing the third character codes, of the plurality of regions of the conversion table stored in said conversion-table storage unit, where the third character codes are defined in said predetermined character encoding scheme; a decryption unit which successively acquires said second index values, and decrypts the second index values into decrypted values having respectively identical bit lengths to the second index values; and a plaintext output unit which successively acquires said decrypted values, converts the decrypted values into fourth character codes associated with index values identical to the decrypted values by reference to said second ones of the plurality of regions of the conversion table, produces a sequence of the fourth character codes as a second plaintext corresponding to said second encrypted text, and outputs the second plaintext.