Abstract: Certain embodiments involve ranking search results from an information retrieval system using user query data to provide relevant search results to users of the information retrieval system. For example, a system determines a weight factor associated with a first user that provides a query to the information retrieval system based on a type or role of the first user. The system further determines a boost factor associated with the first user based on the weight factor and a number of consecutive search queries provided by the user. The system uses the boost factor to automatically tune a ranking algorithm to adjust a rank of a search result item resulting from a search query provided by a second user.

Abstract: This disclosure relates to training a classifier algorithm that can be used for automatically selecting tags to be applied to a received image. For example, a computing device can group training images together based on the training images having similar tags. The computing device trains a classifier algorithm to identify the training images as semantically similar to one another based on the training images being grouped together. The trained classifier algorithm is used to determine that an input image is semantically similar to an example tagged image. A tag is generated for the input image using tag content from the example tagged image based on determining that the input image is semantically similar to the tagged image.

Abstract: This disclosure relates to training a classifier algorithm that can be used for automatically selecting tags to be applied to a received image. For example, a computing device can group training images together based on the training images having similar tags. The computing device trains a classifier algorithm to identify the training images as semantically similar to one another based on the training images being grouped together. The trained classifier algorithm is used to determine that an input image is semantically similar to an example tagged image. A tag is generated for the input image using tag content from the example tagged image based on determining that the input image is semantically similar to the tagged image.

Abstract: Certain embodiments involve ranking search results from an information retrieval system using user query data to provide relevant search results to users of the information retrieval system. For example, a system determines a weight factor associated with a first user that provides a query to the information retrieval system based on a type or role of the first user. The system further determines a boost factor associated with the first user based on the weight factor and a number of consecutive search queries provided by the user. The system uses the boost factor to automatically tune a ranking algorithm to adjust a rank of a search result item resulting from a search query provided by a second user.

Abstract: This disclosure relates to training a classifier algorithm that can be used for automatically selecting tags to be applied to a received image. For example, a computing device can group training images together based on the training images having similar tags. The computing device trains a classifier algorithm to identify the training images as semantically similar to one another based on the training images being grouped together. The trained classifier algorithm is used to determine that an input image is semantically similar to an example tagged image. A tag is generated for the input image using tag content from the example tagged image based on determining that the input image is semantically similar to the tagged image.

Abstract: Systems and methods herein provide for a clustered content management comprising at least two computing nodes. A first node comprises an instance of the content repository. The first computing node may perform content management operations on its instance of the content repository. Changes to the instance of the content repository of the first computing node are synchronized with the content repository by way of a second computing node. The second computing node is communicatively coupled to the first computing node through a network and is operable to synchronize the change with the content repository. The second computing node also determines that synchronization of the change is blocked due to an error. The second computing node identifies the error, determines that the error is correctable, and corrects the error to synchronize the change with the content repository.

Abstract: This disclosure relates to training a classifier algorithm that can be used for automatically selecting tags to be applied to a received image. For example, a computing device can group training images together based on the training images having similar tags. The computing device trains a classifier algorithm to identify the training images as semantically similar to one another based on the training images being grouped together. The trained classifier algorithm is used to determine that an input image is semantically similar to an example tagged image. A tag is generated for the input image using tag content from the example tagged image based on determining that the input image is semantically similar to the tagged image.

Abstract: Systems and methods herein provide for a clustered content management comprising at least two computing nodes. A first node comprises an instance of the content repository. The first computing node may perform content management operations on its instance of the content repository. Changes to the instance of the content repository of the first computing node are synchronized with the content repository by way of a second computing node. The second computing node is communicatively coupled to the first computing node through a network and is operable to synchronize the change with the content repository. The second computing node also determines that synchronization of the change is blocked due to an error. The second computing node identifies the error, determines that the error is correctable, and corrects the error to synchronize the change with the content repository.

Abstract: An apparatus for energy efficient location sensing of a mobile device, said apparatus comprising: An orientation sensing module for sensing the orientation of said mobile device; one or more location sensing modules for sensing the location of said mobile device, a control module for controlling said orientation sensing module such that it repeatedly senses the orientation of said mobile device, wherein said control module controls said one or more location sensing modules such that at least one of said one or more location sensing modules is switched on if said sensed orientation indicates an orientation change of said mobile device, wherein the frequency with which said orientation sensing module senses the orientation of said mobile device is changed depending on the time in accordance with the behaviour pattern of the user of said mobile device such that at a time where the behaviour pattern corresponds to a lower activity the sampling frequency is lower and at a time where the behaviour pattern correspon