Abstract: A method includes causing files that are part of an application to be packaged to form an application package. During a packaging process, a hash calculator is caused to calculate a hash for the application package and a signing module is caused to generate an application manifest including the hash for the application package. The method also includes causing the application manifest to be added to the application package, causing firmware executed by a processor to verify a bootloader, and causing the bootloader to be executed to verify a kernel. The method also includes, causing the kernel to be executed to verify a trust agent, and causing the trust agent to process an application list to identify the one or more files that are part of the application included in the application package and generate a hash for the one or more files included in the application package.

Abstract: An apparatus executes firmware to verify a bootloader. The verified bootloader is caused to be executed to verify a kernel. In response to verifying the kernel, the kernel is caused to be executed to verify a trust agent. In response to verifying the trust agent, the trust agent is caused to process an application list to identify one or more files that are part of an application and to generate a hash for all of the one or more files combined or for each of the one or more files individually. The apparatus also compares the hash for all of the one or more files combined or for each of the one or more files individually with a hash included in an application manifest file and, in response to confirming a hash match, causes the one or more files that are part of the application to be executed.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user’s search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: A method of classifying webpages using a data processing system includes generating a plurality of topic models from a first plurality of training documents. The method further includes performing inference using the plurality of topic models on a second plurality of training documents, to generate a first set of feature vectors and a second set of feature vectors. The method further includes performing supervised classification of a third plurality of training documents using the first set of feature vectors, to generate a plurality of candidate topic models. The method further includes evaluating the plurality of candidate topic models using the second set of feature vectors and storing, in a production model datastore, at least some of the plurality of candidate topic models as production topic models, responsive to the evaluation, wherein the first plurality of training documents comprise text obtained from an inventory of web pages.

Abstract: Systems and methods are provided for automatically classifying videos based on faces discovered in the videos, wherein the discovered faces are not known to be associated with a particular category of videos. The detected face is compared to a set of unknown faces to generate a cluster of unknown faces that each match with the detected face. A set of categorized videos is identified based on the cluster of unknown faces. One or more categories are assigned to the video based on categories from the set of categorized videos so that the video can be automatically classified based on the detected face even though the detected face is not associated with a known person.

Abstract: Systems and methods are provided for automatically classifying videos based on faces discovered in the videos, wherein the discovered faces are not known to be associated with a particular category of videos. The detected face is compared to a set of unknown faces to generate a cluster of unknown faces that each match with the detected face. A set of categorized videos is identified based on the cluster of unknown faces. One or more categories are assigned to the video based on categories from the set of categorized videos so that the video can be automatically classified based on the detected face even though the detected face is not associated with a known person.