An example operation may include one or more of acquiring, by an organization node, a user profile from a blockchain, determining, by the organization node, a role of the user based on the user profile, detecting, by the organization node, user actions directed to graph expansions, and executing, by the organization node, a smart contract to analyze user behavior based on the detected user actions and the role to produce role-based user behavior parameters.

The implementations of the subject matter described herein relate to an octree-based convolutional neural network. In some implementations, there is provided a computer-implemented method for processing a three-dimensional shape. The method comprises obtaining an octree for representing the three-dimensional shape. Nodes of the octree include empty nodes and non-empty nodes. The empty nodes exclude the three-dimensional shape and are leaf nodes of the octree, and the non-empty nodes include at least a part of the three-dimensional shape. The method further comprises for nodes in the octree with a depth associated with a convolutional layer of a convolutional neural network, performing a convolutional operation of the convolutional layer to obtain an output of the convolutional layer.

Implementations generally relate to extended schemas. In some implementations, a method includes displaying a first extensible markup language schema definition (XSD) schema, where the first XSD schema includes a plurality of XSD elements arranged in a tree structure. The method further includes receiving a selection of at least one XSD element of the plurality of XSD elements. The method further includes displaying one or more XSD extension selections associated with the at least one XSD element. The method further includes receiving at least one XSD extension selection of the one or more XSD extension selections. The method further includes appending in the tree structure at least one XSD extension element based on the at least one XSD extension selection.

An asset monitoring and reporting system (AMRS) implements an interface to establish an asset hierarchy to be monitored and reported against. The interface employs a search query of extant asset data from which definitional aspects of the asset hierarchy can be identified, and therefrom the interface automatically determines control information reflective of the asset hierarchy to direct the ongoing operation of the AMRS. The interface further allows for configuration of a metric definition for a metric of an asset node of the asset hierarchy, the metric representing a point in time or a period of time and derived from a metric-time search of machine data produced by or about the asset node and receives an identification of a metric determination specification for the metric definition, the metric determination specification comprising at least identification of a metric component and identification of a calculation operation to apply to the metric component.

A plurality of different views of data associated with a storage domain stored on a deduplicated storage are traversed to determine data chunks belonging to each view of the plurality of different views of data associated with the storage domain. A request for a metric associated with disk space utilization of a group of one or more selected views of data included in the plurality of different views of data associated with the storage domain that are stored on the deduplicated storage is received. Data chunks belonging to the one or more selected views of data associated with the storage domain of the group but not other views of the plurality of different views of data associated with the storage domain that are stored on the deduplicated storage are identified. An incremental disk space utilization of the group is determined, including by determining a total size of the identified data chunks. The metric associated with disk space utilization is provided based on the determined incremental disk space utilization of the group.

The disclosed technology relates to a system configured to detect a modification to a node in a tree data structure. The node is associated with a content item managed by a content management service as well as a filename. The system may append the filename and a separator to a filename array, determine a location of the filename in the filename array, and store the location of the filename in the node.

A key value (KV) store, a method thereof, and a storage system are provided herein. The KV store may include a key logger; and a processor configured to receive a first command for storing a first KV in the KV store, write a first value of the first KV to a first NAND page, generate an extent map for identifying the first memory page including the first value, write the extent map to a second memory page, append an entry for storing the first KV to the key logger, and update a device hashmap of the KV store to include a first key of the first KV, upon a threshold being met within the key logger.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

A method of generating a searchable representation of an electronic document includes obtaining an electronic document specifying a graphical layout of content items. The content items include at least text in a table. The method also includes selecting masking rules, generating a vertical mask based on the masking rules, and generating a horizontal mask based on the masking rules. The vertical mask indicates estimated locations of vertical boundaries of table columns of the table, and the horizontal mask indicates estimated locations of horizontal boundaries of table rows of the table. The method also includes identifying cells of the table based on the vertical mask and the horizontal mask and generating a searchable data structure based on text corresponding to the identified cells of the table.

Systems and methods include receiving a trained machine learning model that has been processed with training information removed therefrom, wherein the training information is utilized in training of the trained machine learning model; monitoring traffic, inline at the node, including processing the traffic with the trained machine learning model; obtaining a verdict on the traffic based on the trained machine learning model; and performing an action on the traffic based on the verdict.