Abstract: Some aspects of the disclosed technology can create a virtual object based on user container selections. Further aspects of the disclosed technology can provide one or more product recommendations corresponding to a current context of user activity. Additional aspects of the disclosed technology can generate and export non-fungible tokens using object recognition. Yet further aspects of the disclosed technology can augment a digital environment with NFT content corresponding to an NFT wallet.

Abstract: One embodiment is directed to training a machine-learning model using sample data by partitioning the machine-learning model into sub-portions and training the sub-portions in different nodes. Another embodiment is directed to training machine-learning models using features determined based on different data layers. Another embodiment is directed to determining a validity of a request for accessing data based on the processing results of policy modules. Another embodiment is directed to a policy engine including a policy knowledge module and a policy intelligence module. Another embodiment is directed to a smart data warehouse using natural language processing and nested heterogeneous graphs to visualize results.

Abstract: In one embodiment, a computing system may access first data associated with a first user. The system may determine, based on the first data associated with the first user, a number of content recommendations for the first users. The content recommendations may be associated with one or more interests or one or more operations of the first user. The system may execute one or more operations associated with the content recommendations. The one or more operations may cause one or more contents to be displayed to the first user.

Abstract: Graph data processing methods and system are disclosed. One example method comprises obtaining, by a master node, graph data, wherein the graph data comprises M vertexes and a plurality of directional edges, each edge connects two vertexes, a direction of each edge is from a source to a destination vertex in the two vertexes, and M is an integer greater than two. The node divides the graph data into P non-overlapping shards, where each shard comprises at least one incoming edge directed to at least one vertex in the corresponding shard. The node schedules at least two edge sets comprised in a first shard of the P shards and an associate edge set comprised in a second shard of the P shards for processing by at least two worker nodes.

Abstract: A graph data processing method and a distributed system is disclosed. The distributed system includes a master node and a plurality of worker nodes. The master node obtains master node graph data, divides the graph data to obtain P shards, where the P shards include a first shard and a second shard. The master node determines at least two edge sets from each shard, schedules at least two edge sets included in the first shard to at least two worker nodes for processing, and schedules an associate edge set included in the second shard to the at least two worker nodes for processing, where the associate edge set is an edge set that includes an outgoing edge of a target vertex corresponding to the first shard.

Abstract: This application discloses a graph computing method and apparatus, so that concurrent graph computing performed by using a plurality of algorithms can be supported. A plurality of subgraphs of a graph are loaded into a plurality of computing units, and the plurality of computing units execute a plurality of algorithms in parallel, so that a same graph can be shared by the plurality of algorithms, and the plurality of algorithms are executed in parallel on the same graph. In this way, a delay caused when one algorithm needs to executed after execution of another algorithm ends is saved, so that overall efficiency of performing graph computing by using the plurality of algorithms is improved, and overall time of performing graph computing by using the plurality of algorithms is shortened.

Abstract: In one embodiment, a computing system may receive query information associated with a machine-learning model. The system may access a knowledge graph that defines relationships between a number of machine-learning models and a number of features of the machine-learning models. The system may determine, based on the knowledge graph and the query information, one or more correlation metrics indicating correlations between the machine-learning model and one or more features of the features in the knowledge graph. The system may determine one or more recommended features for the machine-learning model based on the one or more correlation metrics and the one or more features.

Abstract: A graph processing system for concurrent property graph queries of a property graph implemented in a distributed network computes on respective nodes a subgraph shard represented as edge-sets containing vertices within a certain range. Each node stores data for a subgraph shard that contains a range of local vertices that are a subset of all vertices of the property graph. Each subgraph shard also has boundary vertices having edges that connect the subgraph shard to boundary vertices of another subgraph shard. Upon receipt of concurrent queries of the property graph, a query of the subgraph shards is scheduled in accordance with an initial vertex for each concurrent user query. The property graph is traversed by traversing edge-sets within a subgraph shard on each node and during traversal messaging is used to send values of boundary vertices to at least one other node having another subgraph shard sharing the boundary vertices.

Abstract: A query image is obtained. In a database including a plurality of reference image graphs, at least one of the reference image graphs, with feature vectors similar to the query image, is identified. Image querying is carried out by graph traversal on the at least one of the reference image graphs with the feature vectors similar to the query image. An image from the at least one of the reference image graphs having a highest matching score in the graph traversal is returned as a response to the query image. techniques for building the database are also disclosed.

Abstract: An artificial intelligence device for identifying an object in a data set includes processing circuitry configured to receive the data set and a query including object. The processing circuitry selects one or more models using an entity knowledge database that includes a plurality of entities corresponding to objects to be identified. Each of a plurality or recognition models is linked to multiple entities of the entity knowledge database so that the processing circuitry may select multiple recognition models. The processing circuitry then processes the data set using the selected recognition model or models to provide an indication of whether the data set includes the at least one object. The entities may be ontologically coupled in the database so that, even if the object does not have a corresponding entity in the database, the object may be identified using models selected based on the ontology.

Abstract: A Resilient Distributed Dataset (RDD) processing method, system, and computer program product include sending a differential portion of a message from a receiver to a sender along with information of a global identifier of an RDD and differential values after matching incoming message.

Abstract: Method and apparatus for monitoring eating behavior are disclosed. A wearable device including an imaging sensor, an electromyography (EMG) sensor, and a processing unit is configured to collect EMG data via the EMG sensor, transmit the EMG data to a computing device, receive a control signal to capture one or more images via the imaging sensor, capture, one or more images, and transmit the one or more images. A computing device is disclosed including a processor and a memory containing a machine learning model and computer program code that, when executed, performs an operation. The operation includes receiving EMG data, processing the EMG data as input to a trained machine learning model, transmitting the control signal to capture one or more images via the imaging sensor, receiving the one or more images, and determining nutritional content of the food or beverage using the one or more images.

Abstract: A data storage and retrieval system for a computer memory includes a stream graph engine extracting graph data from streaming data, the graph data occupying a sliding window and comprising a plurality of slices representing a set of contiguous graphs, and where each slice of the plurality of slices corresponds to a given graph structure and its properties at a particular time, the stream graph engine causing the computer memory to store an on-disk portion of the graph data including a key table, a timestamp table and a plurality of property files, wherein the key table comprises a plurality of pointers to corresponding entries of the timestamp table, wherein each of the entries of the timestamp table comprise a corresponding timestamp and a pointer to the properties files, wherein the properties files comprise properties of a corresponding graph of the set of contiguous graphs, an in-memory portion of the graph data having a cache data structure storing a subset of the key table, and a versioning control modu

Abstract: A Resilient Distributed Dataset (RDD) processing method, system, and computer program product include sending a different portion including a global identifier and different values of a message from a receiver to a sender and at a receiver side, an already-processed message is extracted from an RDD object repository corresponding to the global identifier of the different portion and the incoming differential portion is applied to the already-processed message.

Abstract: Materialized views of a database are generated for query optimization by creating a query definition of a materialized view including a left outer or a right outer join to generate a joined database table and adding a non-nullable virtual tagging column to the right original database table with a value indicating whether a null entry came from an original database table or has been inserted to indicate that a row or column value does not exist as a result of the join operation. A materialized view is created using the query definition and query results of the joined database table with the virtual tagging column.

Abstract: A graph processing system for concurrent property graph queries of a property graph implemented in a distributed network computes on respective nodes a subgraph shard represented as edge-sets containing vertices within a certain range. Each node stores data for a subgraph shard that contains a range of local vertices that are a subset of all vertices of the property graph. Each subgraph shard also has boundary vertices having edges that connect the subgraph shard to boundary vertices of another subgraph shard. Upon receipt of concurrent queries of the property graph, a query of the subgraph shards is scheduled in accordance with an initial vertex for each concurrent user query. The property graph is traversed by traversing edge-sets within a subgraph shard on each node and during traversal messaging is used to send values of boundary vertices to at least one other node having another subgraph shard sharing the boundary vertices.

Abstract: A computer-implemented method of determining data lineage based on database queries is provided. A received database query is parsed to identify a plurality of data entities associated with a plurality of data flows. A query graph associated with the received database query is generated, where the query graph includes a plurality of nodes connected via edges. The plurality nodes correspond to the plurality of data entities and the edges correspond to the plurality of data flows. A data lineage query is retrieved from memory. The data lineage query includes one or more of the plurality of data entities associated with the plurality of nodes within the generated query graph. A representation of the generated query graph is output based on the data lineage query.

Abstract: A remote control system detects a directional signal at an alerting sensor package; activates an appliance to accept a control signal in response to the directional signal; accepts the control signal at the appliance from a muscle sensor package; and adjusts operation of the appliance in response to the control signal.

Abstract: An apparatus and method are provided for scheduling graph computing on heterogeneous platforms based on energy efficiency. A scheduling engine receives an edge set that represents a portion of a graph comprising vertices with at least one edge connecting two or more of the vertices. The scheduling engine obtains an operating characteristic for each processing resource of a plurality of heterogeneous processing resources. The scheduling engine computes, based on the operating characteristics and an energy parameter, a set of processing speed values for the edge set, each speed value corresponding to a combination of the edge set and a different processing resource of the plurality of heterogeneous processing resources. The scheduling engine identifies an optimal processing speed value from the set of computed speed values for the edge set.

Abstract: A network graph analysis tool identifies clusters of nodes in a network graph based on edges connecting the nodes. It then distributes the clusters of nodes in a two-dimensional plane to generate a two-dimensional representation of a network. For each cluster, the tool distributes the nodes in the cluster in the two-dimensional plane to calculate respective coordinates of the nodes in the cluster. The result is a two-dimensional mapped network graph of the cluster. The tool then generates a density map of the network based on the calculated coordinates of the nodes in the mapped network graph, and in response to a selection of a sub-area of the density map, provides, for display, selected nodes and edges in the mapped network graph having coordinates corresponding to the selected sub-area of the density map. The selected nodes and edges may be magnified in response to a software visualization lens.