A computing system receives a training data set that includes a first subset of labeled events and a second subset of unlabeled events for an event type. The computing system generates an event model configured to detect the event type and classify the event type by actively training the event model. The computing system receives a target game file for a target game. The target game file includes at least tracking data corresponding to players in the target game. The computing system identifies a plurality of instances of the event type in the target game using the event model. The computing system classifies each instance of the plurality of instances of the event type using the event model. The computing system generates an updated event game file based on the target game file and the plurality of instances.

A computing system receives a training data set that includes a first subset of labeled events and a second subset of unlabeled events for an event type. The computing system generates an event model configured to detect the event type and classify the event type by actively training the event model. The computing system receives a target game file for a target game. The target game file includes at least tracking data corresponding to players in the target game. The computing system identifies a plurality of instances of the event type in the target game using the event model. The computing system classifies each instance of the plurality of instances of the event type using the event model. The computing system generates an updated event game file based on the target game file and the plurality of instances.

A technological approach can be employed to protect data. Datasets from distinct computing environments of an organization can be scanned to identify data elements subject to protection, such as sensitive data. The identified elements can be automatically protected such as by masking, encryption, or tokenization. Data lineage including relationships amongst data and linkages between computing environments can be determined along with data access patterns to facilitate understanding of data. Further, personas and exceptions can be determined and employed as bases for access recommendations.

A technological approach can be employed to protect data. Datasets from distinct computing environments of an organization can be scanned to identify data elements subject to protection, such as sensitive data. The identified elements can be automatically protected such as by masking, encryption, or tokenization. Data lineage including relationships amongst data and linkages between computing environments can be determined along with data access patterns to facilitate understanding of data. Further, personas and exceptions can be determined and employed as bases for access recommendations.

In some implementations, a prediction and monitoring system may processing, using a machine learning model, account data associated with an account that is associated with a user of a user device to identify a series of recurring events associated with the user device. The prediction and monitoring system may generate, using the machine learning model, a predicted transaction date and a predicted transaction amount that are both associated with the series of recurring events. The prediction and monitoring system may select, based on additional account data associated with the account and at least one of the predicted transaction date or the predicted transaction amount, a particular communication scheme, of a plurality of communication schemes, for communicating with the user. The prediction and monitoring system may transmit at least one message according to the particular communication scheme to facilitate authentication of the user.

In some implementations, a prediction and monitoring system may processing, using a machine learning model, account data associated with an account that is associated with a user of a user device to identify a series of recurring events associated with the user device. The prediction and monitoring system may generate, using the machine learning model, a predicted transaction date and a predicted transaction amount that are both associated with the series of recurring events. The prediction and monitoring system may select, based on additional account data associated with the account and at least one of the predicted transaction date or the predicted transaction amount, a particular communication scheme, of a plurality of communication schemes, for communicating with the user. The prediction and monitoring system may transmit at least one message according to the particular communication scheme to facilitate authentication of the user.

Techniques are described for enabling the automatic refactoring of software application source code to mitigate identified anti-patterns and other software modernization-related issues. A software modernization system analyzes software applications to generate various types of modernization report information, where the report information can include identifications of various types of design and cloud anti-patterns, proposed decompositions of monolithic applications into subunits, refactoring cost information, recommended modernization tools and migration paths, among other such information. A software modernization system further includes a refactoring engine that can automatically refactor source code based on such application analysis information, e.g., to automatically address identified anti-patterns, restructure code for decomposition, etc. A refactoring engine performs refactoring actions based on refactoring templates, machine learning (ML) refactoring models, or other input.

Techniques are described for enabling the automatic refactoring of software application source code to mitigate identified anti-patterns and other software modernization-related issues. A software modernization system analyzes software applications to generate various types of modernization report information, where the report information can include identifications of various types of design and cloud anti-patterns, proposed decompositions of monolithic applications into subunits, refactoring cost information, recommended modernization tools and migration paths, among other such information. A software modernization system further includes a refactoring engine that can automatically refactor source code based on such application analysis information, e.g., to automatically address identified anti-patterns, restructure code for decomposition, etc. A refactoring engine performs refactoring actions based on refactoring templates, machine learning (ML) refactoring models, or other input.

Data compression includes: inputting data comprising a vector that requires a first amount of memory; compressing the vector into a compressed representation while preserving information content of the vector, including: encoding, using one or more non-quantum processors, at least a portion of the vector to implement a quantum gate matrix; and modulating a reference vector using the quantum gate matrix to generate the compressed representation, wherein the compressed representation requires a second amount of memory that is less than the first amount of memory; and outputting the compressed representation to be displayed, stored, and/or further processed.

The technology disclosed describes determination of which elements of a sequence are nearest to uniformly spaced cells in a grid, where the elements have element coordinates, and the cells have dimension-wise cell indices and cell coordinates. The determination includes generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells. The subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, and the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates. The determination further includes generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence.