Abstract: Methods, systems and computer program products are provided for spoiler prevention. Media consumption applications may be placed in “spoiler-free” mode, for example, to prevent media content from spoiling first-hand user experience. A user may provide and/or authorize access to and use of spoiler prevention information. A user may request media content (e.g., while surfing the Internet). Digital media content to be presented to a user may be searched in real-time and/or pre-searched for spoiler content and/or associated spoiler indications relative to spoiler prevention information. Identified spoiler content may be concealed from users. A procedure may be provided for users to determine one or more reasons why content is concealed, to selectively reveal concealed content, and to provide feedback whether concealed content was or was not spoiler content for a user. Feedback may be used to improve spoiler prevention, for example, by retraining a machine learning model, which may be user-specific.

Abstract: Systems and methods are provided to process a digital photo and other media. An apparatus to process digital photos can include a tangibly embodied computer processor (CP) and a tangibly embodied database. The CP can perform processing including: (a) inputting a photo from a user device, and the photo including geographic data that represents a photo location at which the photo was generated; (b) comparing at least one area with the photo location and associating an area identifier to the photo as part of photo data; and (c) performing processing based on the area identifier and the photo data.

Abstract: Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

Abstract: Systems and methods are described to address shortcomings in conventional conversation systems by executing a customized supplemental function for a natural language query based on the user's profile. In some aspects, the systems and methods receive a user query for a media asset, determine a query template in the user's profile matching the user's query, and identify and execute a supplemental function corresponding to the query template in addition to responding to the user's query. The user's profile is customized on the user's usage patterns of an interactive media guidance application. By monitoring the user's usage patterns and learning from the user's actions in conjunction with the user's natural language queries on which supplemental functions to execute, the interactive media guidance application may provide a smarter and more efficient user experience.

Abstract: Methods, systems and computer program products are provided personalizing recommendations of items with associated explanations. The example embodiments described herein use contextual bandits to personalize explainable recommendations (“recsplanations”) as treatments (“Bart”). Bart learns and predicts satisfaction (e.g., click-through rate, consumption probability) for any combination of item, explanation, and context and, through logging and contextual bandit retraining, can learn from its mistakes in an online setting.

Abstract: In some examples, a system receives function descriptors for different types of functions to be used when processing database queries, each function descriptor of the function descriptors comprising information relating to a respective function of the different types of functions. The system computes, based on a first function descriptor for a first function of the different types of functions, an estimate of a runtime metric associated with execution of the first function for processing a database query.

Abstract: In implementations of search input generation for an image search, a computing device can capture image data of an environment scene that includes multiple objects. The computing device implements a search input module that can detect the multiple objects in the image data, and initiate a display of a selectable indication for each of the multiple objects. The search input module can then determine a subject object from the detected multiple objects, and generate the subject object as the search input for the image search.

Abstract: A mapping support apparatus includes a search unit that performs a web search by using a part or a whole of dictionary mapping target data as a search key, and performs a search on a predetermined classification dictionary using a part or a whole of the dictionary mapping target data as a search key, a UI providing unit that displays a screen including a search result of the web search and a search result of the predetermined classification dictionary, and a data creation unit that applies, to the dictionary mapping target data, classification information selected by a user from the search result of the predetermined classification dictionary.

Abstract: A computer implemented method for creating and managing a database system comprising data structures for storing, in a memory, data and relations between the data, the method comprising the steps of creating a mind map structure wherein each node of the mind map represents a set in the first data structure and each branch represents a relation in the fifth data structure of the database in which there are defined five data structures that hold all information relating to tables, records and relations, namely: a first data structure comprising a definition of at least one data set, a second data structure comprising definitions of properties of objects, a third data structure comprising definitions of objects, a fourth data structure comprising definitions of properties of each object, a fifth data structure comprising definitions of relations and a sixth data structure for storing definitions of relations between objects.

Abstract: A system for retrieving information from an instructional document, includes a processor configured to: receive a query from a user; compare the query with one or more text sections in the instructional document; obtain, from the one or more text sections, top x text sections relevant to the query using a pre-trained encoder; compare the query with one or more images in the instructional document; obtain, from the images, top y images relevant to the query; generate top y image-text sections based on the top y images; obtain top k sections from the top x text sections and the top y image-text sections; obtain one or more most relevant sections from the top k sections using a domain-specific pre-trained encoder and a sequential classification model; and generate an answer based on the one or more most relevant sections and device context information using the domain-specific pre-trained encoder.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for obtaining a digital twin asset. The exemplary embodiments may include collecting input data, extracting one or more features from the input data, searching for a digital twin asset based on the extracted one or more features and one or more models, and facilitating an exchange of the digital twin asset.

Abstract: A disparate data source aggregation system and methods are provided which may pull or retrieve talent data or features from disparate data sources, automatically correlate the data across the different data sources, build a self-adjusting system database that captures the talent data from the disparate data sources, and lets users search, query and build model insights on the aggregated data of the system database without human intervention. A method for disparate data source aggregation may include: extracting a first feature set having a first extracted feature and a second feature set having a second extracted feature; determining, if the first extracted feature of the first feature set matches the second extracted feature of the second feature set; and aggregating the first feature set with the second feature set if the first extracted feature of the first feature set matches the second extracted feature of the second feature set.

Abstract: An apparatus comprises at least one processing device configured to receive a query to perform sentiment analysis for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying sentiment of the words of the document, and to determine, for a given aspect term, attention weights for a given subset of the words of the document surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, a given sentiment classification of the given aspect term based on the attention weights and a given portion of the second set of encodings for the given subset of the words, and to provide a response to the query comprising the given sentiment classification.

Abstract: A method generates data visualizations. A computing device retrieves a set of tuples from a database according to user selection. Each tuple has the same set of fields. The device identifies a relation between tuples. The relation is a non-empty set of ordered pairs of tuples from the set of tuples. A user selects a base tuple from the set of tuples and the device forms a filtered subset of tuples consisting of the selected base tuple and those tuples that are connected to the selected base tuple by a sequence of tuples that are related by the relation. The user selects an aggregation level, which consisting of fields from the set of fields. The device generates and displays a data visualization by aggregating the filtered subset of tuples at the selected aggregation level to form a set of aggregated tuples, and displaying each aggregated tuple as a visible mark.

Abstract: In implementations of multi-resolution color-based image search, an image search system determines a color vector for a query image based on a color histogram of the query image by concatenating two color histograms having different resolutions. The image search system can compute distance measures between the color vector of the query image and color vectors of candidate images. The image search system can select one or more of the candidate images to return based on the distance measures utilizing the distance measures as indication of color similarity of the candidate images to the query image.

Abstract: The present application relates to a field of smart searching technology, and provides a method and an apparatus for searching a multimedia content, a device, and a storage medium. The method includes: acquiring a query vector of query information; determining, from a search library, a first category matching the query vector, wherein the search library comprises a plurality of categories, each of the categories comprises a plurality of vectors, and each of the vectors is associated with a corresponding multimedia content; and inquiring, in the first category, a target vector matching the query vector, and acquiring the multimedia content corresponding to the target vector.

Abstract: A method is provided that includes obtaining one or more pieces of crowd-sourced information. A respective crowd-sourced information is at least indicative of a location at which the respective crowd-sourced information was gathered. The method determines a set of tiles at least partially based on the one or more geographical areas. For at least one tile, the method obtains one or more pieces of fingerprint information comprised by the one or more pieces of crowd-sourced information that were gathered within the respective area of the respective tile and determines an area type and/or context information indicative of a type of venue located within the respective tile and/or context the respective area of the respective tile is used for. The area type and/or context information is determined at least partially based on the one or more pieces of fingerprint information. A corresponding apparatus and computer program product are also provided.

Abstract: Various embodiments are provided for providing machine learning with noisy label data in a computing environment using one or more processors in a computing system. A label corruption probability of noisy labels may be estimated for selected data from a dataset using temporal inconsistency in a machine model prediction during a training operation in a neural network.

Abstract: Method, apparatus and computer program product for generating filter cache are described herein. For example, the apparatus includes at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least generate an index document array, generate a group identifier lookup array, generate a channel identifier lookup array, and generate a filter cache for the index document array based at least on the group identifier lookup array and the channel identifier lookup array.

Abstract: Various methods, apparatuses/systems, and media for implementing a payment application module for searching billets are disclosed. A plurality of data sources include customer profile data, customer payment history data, biller information data, and biller directory data. A processor operatively coupled to the plurality of data sources via a communication network builds a customer profile service area popularity model based on the customer profile data, customer payment history data, biller information data, and biller directory data; receives search query data for searching a biller; builds a biller search machine learning model to optimize weights applied to the search query data; and ranks search results by utilizing the machine learning model in response to the search query data.