Systems and techniques for profiling asset acquisition agent are described herein. A target profile may be obtained. A set of profile attributes may be determined for the target profile. An acquisition target pool may be identified using the set of profile attributes. An acquisition matrix data structure may be generated for the acquisition target pool. An asset pool may be generated by acquiring equity of the acquisition target pool based on the acquisition matrix data structure.

This disclosure generally relate to a method and system for network policy simulation in a distributed computing system. The present technology relates techniques that enable simulation of a new network policy with regard to its effects on the network data flow. By enabling a simulation data flow that is parallel and independent from the regular data flow, the present technology can provide optimized network security management with improved efficiency.

This disclosure generally relate to a method and system for network policy simulation in a distributed computing system. The present technology relates techniques that enable simulation of a new network policy with regard to its effects on the network data flow. By enabling a simulation data flow that is parallel and independent from the regular data flow, the present technology can provide optimized network security management with improved efficiency.

At least one device is configured to perform a predetermined process associated with a vehicle using data; and send, to a server system, the data used by the predetermined process as learning data. The server system is configured to obtain the learning data sent from the at least one device, and perform a learning process on at least a factor defining the predetermined process using the obtained learning data to generate update data for updating at least the factor defining the predetermined process. The server system is configured to send the update data to the at least one device. The at least one device is further configured to update at least the factor defining the predetermined process based on the update data sent from the server system.

Aspects of the disclosure generally relate to computing devices and/or systems, and may be generally directed to devices, systems, methods, and/or applications for learning an avatar's or an application's operation in various circumstances, storing this knowledge in a knowledgebase (i.e. neural network, graph, sequences, etc.), and/or enabling autonomous operation of the avatar or the application.

A property display device includes: a search content acquirer that acquires search variables and search data; a property value acquirer that acquires property values of a compound corresponding to the search data; a priority setter that sets a priority of the search variables; a display format determiner that determines a display format of the property of the compound by assigning search variables of high priority to coordinate axes A1 and A2 of color maps Ma and assigning search variables of lower priority than the search variables of high priority to array direction axes A3 and A4 of a first array map Mb1; and an image processor that generates at least one first array map Mb1 as a single image on the basis of the search data, the property values, and the display format.

An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

Decentralized machine learning to build models is performed at nodes where local training datasets are generated. A blockchain platform may be used to coordinate decentralized machine learning over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes. Rules in the form of smart contracts may enforce node participation in an iteration of model building and parameter sharing, as well as provide logic for electing a node that serves as a master node for the iteration. The master node obtains model parameters from the nodes and generates final parameters based on the obtained parameters. The master node may write its state to the distributed ledger indicating that the final parameters are available. Each node, via its copy of the distributed ledger, may discover the master node's state and obtain and apply the final parameters to its local model, thereby learning from other nodes.

A system for determining relevant information based on user interactions may include a processor configured to receive application data from one or more applications, the application data including features related to user activity from the one or more applications, the one or more application including applications local to the device that are stored in the memory and applications external to the device. The processor may be further configured to provide, using a machine learning (ML) model, a relevance score for each of one or more user interface (UI) elements based on each of the features. The processor may be further configured to sort one or more UI elements based on a ranking of the relevance scores. The processor may be further configured to provide, as output, the one or more UI elements based at least in part on the ranking.