Abstract: A regenerated container file is detected, and a file in the regenerated container file is determined that is different from any file in an existing container file related to the regenerated container file. To store the regenerated container file, the different file is sent to the data storage for storing.

Abstract: Identification of augmented features based on a Bayesian analysis of a text document is disclosed. One example is a system including a document processing module, a feature processing module, and a feature generation module. The document processing module receives a text document via a processor. The feature processing module automatically identifies, based on a Bayesian analysis of the text document, a plurality of augmented features in the text document, the plurality of augmented features including at least one of local, sectional, and document-level features of the text document, and extracts, via the processor, the identified plurality of augmented features from the text document. The feature generation module generates, via the processor, a feature representation of the text document based on the extracted plurality of augmented features.

Abstract: A method may include selecting a particular entity from a knowledge graph when a level of connectivity between entities in the knowledge graph that are neighbors to the particular entity is above a certain level and determining whether the particular entity is in a character string.

Abstract: A class may be determined of a term from a database. The term may be blocked from being presented to a user, if the determined class does not include a permission for the user to view the term. The term may suggest a remainder of an incomplete query input by the user.

Abstract: According to examples, an apparatus may include a processor that may divide content of a document to be indexed into sections. The apparatus may divide and arrange each section into a hierarchy based on linguistic, spatial, or other analysis. The apparatus may identify a context of each section that may provide an indication of the subject matter of the section. The apparatus may add the context to downstream sections in the hierarchy. The apparatus may generate an index entry for each section based on the content of the section and any added context from upstream sections. Thus, the index entry for a given section may be based on the context of the given section and context of upstream sections in the hierarchy. In this way, the index entries may account for not only the content of the given section, but also context from upstream sections.

Abstract: A plurality of binary support vector machines (SVM) are trained to vote between two of a plurality of classes. Each of the binary SVMs vote between a different pair of the plurality of classes. A set of validated samples is input to each of the binary SVMs. The set of validated samples includes samples of each of the classes. Likelihood estimates are determined based on votes of the binary SVMs. A prior probability distribution (prior) is assigned for each of the classes. A posterior probability is calculated for each of the classes based on the determined likelihood estimates and the assigned priors.

Abstract: In some examples, a system performs language processing of text of an information page to determine whether the text refers to an online resource, and in response to determining that the text refers to the online resource, identify the text as an implicit reference to the online resource. The system derives a link to the online resource based on the implicit reference, the derived link useable in accessing the online resource.

Abstract: Example embodiments relate to asset browsing and restoration over a network using on demand staging. A method may include accessing, by a storage access system, a first portion of a compound asset in a storage system and restoring it as a staged asset in a staging area of the storage access system. The accessing and restoring of the first portion may be performed on demand in response to an indication from a client device to browse a portion of the compound asset. The method may further include accessing the internal structure of the staged asset to generate an asset directory. The asset directory may indicate discrete items within the compound asset. The method may further include providing the asset directory to the client device over a network, and receiving an indication from the client device of at least one of the discrete items to restore to the client device.

Abstract: Examples disclosed herein relate to a person moving in a physical space. In one aspect, a method is disclosed. The method may include obtaining at least two images of a person from at least two cameras directed at a physical space, where the physical space may include a plurality of designated areas. The method may also include obtaining metadata associated with the images, based on the images and the metadata determining within the plurality of designated areas a set of designated areas visited by the person, for each designated area within the set of designated areas, determining an area information, and updating a database based on the set of designated areas and based on at least a portion of the area information associated with each designated area within the set of designate areas.

Abstract: Techniques associated with three-dimensional object modeling are described in various implementations. In one example implementation, a method may include receiving a plurality of two-dimensional images depicting views of an object to be modeled in three dimensions. The method may also include, processing the plurality of two-dimensional images to generate a three-dimensional representation of the object, and analyzing the three-dimensional representation of the object to determine whether sufficient visual information exists in the plurality of two-dimensional images to generate a three-dimensional model of the object. The method may also include, in response to determining that sufficient visual information does not exist for a portion of the object, identifying the portion of the object to a user.

Abstract: Example embodiments relate to embedded processing of structured and unstructured data. Example embodiments may traverse a directory structure of the computing device, wherein a storage medium of the computing device comprises structured data and non-structured data. For a first file traversed in the directory structure, a first file format of the first file may be detected, the first file format comprising an unstructured file type. The content and metadata of the first file may be obtained and included in a directory table stored in the storage medium. For a second file traversed in the directory structure, a second file format of the second file may be detected, the second file format comprising a structured file type. The content and metadata of the second file may be obtained and included in the directory table stored in the storage medium.

Abstract: A pair of video frames may be used for simultaneous localization and mapping (SLAM) initialization. The pair of frames may be determined according to a translation threshold. Whether the translation threshold is met may be determined by obtaining the pair of video frames and estimating the translation between the frames.

Abstract: Systems and methods associated with subscription-based electronic messaging are described. A method includes obtaining registration information associated with a user who has subscribed to receive electronic messages from a subscription-based messaging service. The registration information includes an electronic address of the user and a private token that is known to the user and to the subscription-based messaging service. The method includes embedding the private token in an electronic message associated with the subscription-based messaging service, and such inclusion of the private token may indicate to the user that the electronic message is from a legitimate sender associated with the subscription-based messaging service. The method also includes sending the electronic message to the electronic address of the user.

Abstract: Techniques associated with device notifications are described in various implementations. In one example implementation, a method may include receiving, at a mobile computing device, sensor data captured by a non-touchscreen sensor associated with the mobile computing device. The method may also include analyzing the sensor data, using the mobile computing device, to detect a user activation input that corresponds to a request to identify pending notifications on the mobile computing device. The method may also include identifying pending notifications on the mobile computing device that are responsive to the request. The method may also include generating a non-visual notification response that indicates to a user of the mobile computing device, in a non-visual manner, a result of the identification.

Abstract: At least a computer-implemented method and an apparatus for processing an image are described. In examples, numeric values for at least one property of the image are determined. These values are then converted into at least one corresponding text character, said conversion being independent of any text content within the image. This enables a text representation of the image to be generated that contains said plurality of text characters. This text representation may be used to index and search for the image.

Abstract: Optical character recognition is described in various implementations. In one example implementation, a method may include receiving a plurality of optical character recognition (OCR) outputs provided by a respective plurality of OCR engines, each of the plurality of OCR outputs being representative of text depicted in a portion of an electronic image. The method may also include identifying a document context associated with the electronic image, and generating an output character set by applying a character resolution model to resolve differences among the plurality of OCR outputs. The character resolution model may define a probability of character recognition accuracy for each of the plurality of OCR engines given the identified document context. The method may also include updating the character resolution model to generate an updated character resolution model such that subsequent generating of output character sets are based on the updated character resolution model.

Abstract: A system may include query circuitry. The query circuitry determine a set of structured data terms relevant to a specific data type by performing a preconfigured query for the specific data type on a structured dataset. The preconfigured query may be generated according to a predefined business rule for the specific data type. The query circuitry may further generate an unstructured search query from the set of structured data terms and execute the unstructured search query on an unstructured dataset to obtain unstructured search results.

Abstract: Example implementations relate to rendering a visualization of a data set. In example implementations, a device causes a first plurality of geometric shapes to be generated. The first plurality of geometric shapes may correspond to a first plurality of topics at a first hierarchical level of the data set. The device may cause the first plurality of shapes to be arranged in a tessellation. Sizes of the first plurality of geometric shapes in the tessellation may be based on a ranking or importance of the corresponding first plurality of topics.

Abstract: A plurality of binary support vector machines (SVM) are trained to vote between two of a plurality of classes. Each of the binary SVMs vote between a different pair of the plurality of classes. A set of validated samples is input to each of the binary SVMs. The set of validated samples includes samples of each of the classes. Likelihood estimates are determined based on votes of the binary SVMs. A prior probability distribution (prior) is assigned for each of the classes. A posterior probability is calculated for each of the classes based on the determined likelihood estimates and the assigned priors.

Abstract: A class may be determined of a term from a database. The term may be blocked from being presented to a user, if the determined class does not include a permission for the user to view the term. The term may suggest a remainder of an incomplete query input by the user.