Abstract: A classifier may be trained with less than all datasets manually annotated with labels. A small subset of verbatims may be manually labeled with topic labels as seeds. Data augmentations can be used to acquire seed verbatim sets for known topics and to assign temporary pseudo labels to the rest of the verbatims based on their vector space proximity to the labeled seed verbatims. The training may involve classification epochs during which embeddings are updated with the assumption that the pseudo labels are ground-truth labels. The training may also involve labeling epochs during which the updated embeddings are used to update the vectors corresponding to the verbatims, and pseudo labels are updated based on updated vector coordinates in the vector space. As the training process progresses through the epochs, the embeddings will converge.

Abstract: Described herein are techniques for rendering additional keys on a display of an electronic device based on one or more characteristics of a user's selection of another key. After rendering the additional keys, the user may thereafter select one of the additional keys now available and rendered on the display.

Abstract: A client device executes a display layout application that receives a size of each display item included in a set of display items. The set of display items is associated with a first frame included in a bounding box associated with a display screen. The display layout application determines a reference size based on the sizes of the set of display items. The display layout application determines a size of the first frame based on the reference size. The display layout application determines a position for a first display item included in the set of display items based on a position of the first frame within the bounding box. The display layout application generates a layout for display on the display screen, where the layout includes the first display item.

Abstract: An electronic publication (e.g., an electronic document, e-book, e-pub, etc.) is created and edited using content provided by at least one source. In some aspects, a server-based (e.g., web-based) environment enables multiple authors to seamlessly corroborate to create an electronic publication. In some aspects, a server-based environment enables content to be automatically or manually in a non-linear manner (e.g., designing pages while writing content, and visa versa, or one before the other) become incorporated into an electronic digitally interactive book and print-ready publication (e.g. books, novels, yearbooks, textbooks, comic books, etc.). In some aspects, a server-based environment provides new and innovative ways to incorporate subscriptions and advertisements into an electronic publication, and to display these advertisements in the electronic publication.

Abstract: System receives request from user for decision making options, and provide gameplan document that suggests accessing first and second data source to collect options. System extracts first data records set from content copied by user from first data source, and inserts first data records set in first table in gameplan document. System extracts second data records set from content copied by user from second data source, and inserts second data records set in second table in gameplan document. System inserts combined data records set, based on first data records set joined with second data records set, in combined data table in gameplan document. System enables user to identify candidates, for options, in combined data records set in response to user commands. System determines overall scores corresponding to candidates, based on applying criteria, scored by user, to each candidate. System outputs candidates, ranked based on corresponding scores, as decision making options.

Abstract: The present disclosure pertains to a system and method for determining differences between at least two documents. A preferred embodiment disclosed herein pertains to a system and method for determining differences in the text or terms of legal documents, for example transactional legal agreements, such as but not limited to Trade Confirmations. The document review system can compare text documents to other text documents but is further capable of comparing text documents to structured spreadsheet data. The combination of static component verification in combination with dynamic component verification leads to a powerful tool which greatly reduces the time and efficient of document review while reducing the risk of human errors. Utilizing word embeddings in the static verification in combination with the use of named entity recognition, rule parsers in the dynamic verification, enables the system to produce concise and meaningful summaries of differences between multiple documents.

Abstract: A method for sharing an incrementally trained machine learning (ML) model from an edge device to other edge devices that are associated with a peer to peer network is provided. The method includes (i) receiving event data at an edge device from among one or more edge devices of the peer to peer network upon the occurrence of an event at the edge device, (ii) incrementally training a base version of a ML model at the edge device based on the received event data, (iii) generating, a unique persistent file format of the incrementally trained ML model at the edge device, (iv) optimizing a payload associated with the unique persistent file format to include one or more parameters with associated weights changing beyond a predetermined configurable threshold and (v) encapsulating the unique persistent file format with a unique metadata.

Abstract: A structural analysis system analyzes the pages of a web site, including the content of the pages and their relationships to each other. Based on the analysis, the structural analysis system additionally modifies (or suggests modification of) the web pages of the web site, such as by altering the inter-page linking structure of the web site. This improves the structure of the web site, leading search engines to be able to more quickly find and index its pages, as well as increasing the expected ranking that a search engine would provide to pages of the web site within the set of search results provided by the search engine in response to a user query.

Abstract: A document processing method and apparatus, and an electronic device are provided. The method comprises: determining a target document; and displaying document information of an associated document on the basis of an association relationship between the target document and the associated document, wherein the document information of the associated document comprises a first document having a first reference relationship with the target document, and/or a second document having a second reference relationship with the target document, the first reference relationship indicates that the associated document refers to the target document, and the second reference relationship indicates that the target document refers to the associated document.

Abstract: The present invention provides a multiform separation classifier, including an input module, a data collection module, a multiform separation engine, and an output module. The input module is configured to receive sample data. The data collection module is connected to the input module and configured to store a collection of data from the input module. The collection of data includes a training set and/or a test set. The multiform separation engine is connected to the data collection module and configured to use at least two piecewise continuous functions to perform classification. The piecewise continuous functions are respectively trained with the training set through a learning process. The output module is connected to the multiform separation engine and configured to derive an output result after the sample data is processed through the multiform separation engine.

Abstract: Methods, system, and non-transitory processor-readable storage medium for a website page density and readability system are provided herein. An example method includes capturing an image of a website page rendered in a web browser, where the image comprises text content and graphical content within the website page. The website page density and readability system determines a website page density associated with the website page by removing the text content from the image. The website page density and readability system determines a readability score associated with the website page using the website page density.

Abstract: A computer-implemented method, system and computer program product for creating a descriptor for a dashboard template. The column-to-visualization mappings are extracted from a dashboard of a created or modified dashboard (or a created or modified dashboard template). Furthermore, the concept combinations from each visualization of the dashboard are extracted. Additionally, topics from the dashboard are extracted. The concept combinations, topics and column-to-visualization mappings are aggregated into a dashboard template descriptor. The dashboard template descriptor is then stored. In this manner, the dashboard template descriptor captures how concept combinations are used in the visualizations of the dashboard as well as how high-level concepts (topics) are incorporated in the dashboard. Furthermore, the dashboard template descriptor captures how the concepts of the columns of a dataset are mapped to the visualizations of the dashboard.

Abstract: An auto-tagging engine receives a training set of data comprising documents including a set of tagged fields with each tagged field corresponding to a portion of the document. The auto-tagging engine trains a machine learned model using the training set of data. The trained machine learned model, when applied to a target document in a document management environment, identifies portions of the target document each corresponding to fields of the target document. For each field of the target document, the auto-tagging engine identifies text of the target document associated with the identified portions of the target document corresponding to fields. Natural language processing is performed on the identified text in order to identify field types for the fields. The target document is automatically modified to include a tag identifying the portion of the target document corresponding to each field and identifying a field type of the field.

Abstract: A processor may receive a request for a query item may include a plurality of identifying markers, relating to data associated with the query item. A machine learning model, trained to identify similar items according to the plurality of identifying markers, may then process the plurality of identifying markers and provide a list of one or more similar items and respective similarity distances. The processor may access a respective entity profile including one or more scenario scores for each of the similar items. The processor may then calculate an entity score for each respective entity profile using the respective similarity distances and the scenario scores. The processor may then generate an entity list by ranking the respective entities associated with each respective entity profile using the entity score. The processor may then output the entity list to the client device.

Abstract: An apparatus for pattern recognition includes a generator which transforms noisy feature vectors into denoised feature vectors, a discriminator which takes the denoised feature vectors and the original clean feature vectors corresponding to the denoised feature vectors as input and predicts probability for both of the input features of being an original clean feature, classifies the input feature vectors into its corresponding classes, an objective function calculator which calculates generator and discriminator losses using the denoised feature vectors, the clean feature vectors from which the noisy feature vectors have been made, the estimated classes and their true classes, and a Parameter updater which updates parameters of the generator and the discriminator according to loss minimization.

Abstract: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by a user-based mining system, to determine a plurality of entity names. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Abstract: A controller device is configured to (1) transmit, to a first remote computing device associated with a first music service, a request for a session token that is to be used for identifying an audio playback system with the first music service, (2) receive a first token for authenticating communications from the audio playback system to the first music service, (3) receive first user input indicating a query, (4) transmit a request comprising the first token to the first remote computing device to perform a search in accordance with the query, (5) present indications of music content provided by the first music service, (6) receive second user input indicating selection of given music content provided by the first music service, and (6) based on the second user input and a respective identifier for the given music content, cause at least one playback device to play back the given music content.

Abstract: A non-transitory computer-readable medium includes computer-executable instructions that cause a processing unit of a user device to receive a multi-link uniform resource locator (URL), wherein the multi-link URL is associated with a plurality of routing link URLs configured to route the user device to a plurality of corresponding application pages. The executable instructions cause the processing unit to receive user selection of the multi-link URL and rank the routing link URLs based on application installation data stored on the user device, wherein the application installation data indicates whether applications for the routing link URLs are installed on the user device. The executable instructions cause the processing unit to select a final routing link URL based on the ranking of the routing link URLs and access an application page associated with the final routing link URL.

Abstract: Techniques for obtaining electronic signatures via word processing applications are described. One approach utilizes a code module, also referred to as a “markup module,” that executes within a word processing application and that facilitates the preparation of a document for electronic signature. A user can operate the word processing application together with the markup module in order to add signature tag markers to the document and to provide recipient information about intended signers, such as names and email addresses. Once the document has been prepared, the code module transmits the document (including the added signature tag markers) and the recipient information to a digital transaction service. The digital transaction service then manages the signing of the document, such as by notifying the recipient, receiving the recipient's signature and securely storing the signature in association with the document.

Abstract: Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.