Abstract: Examples of analyzing documents are defined. In an example, a request to analyze a document may be received. A knowledge model corresponding to a guideline associated with the document may be obtained. The knowledge model may include at least one of a hypothetical question and a logical flow to determine an inference to the hypothetical question. The hypothetical question relates to an element of the guideline. Based on the knowledge model, data from the document may be extracted for analysis using an artificial intelligence (AI) component. The Ai component may be configured to extract and analyze data, based on the knowledge model. Based on the analysis, a report indicating whether the document falls within a purview of the guideline may be generated.

Abstract: A database migration system, according to an example of the present disclosure, builds a dynamic object-relationship model of a database and determines a sequence of data migration for moving data from one database to another. The system may determine a dynamic object-relationship model based on a traversal of the database schema from a root object. The system may determine object attributes for child objects in the object database. In an example, an object attribute may be a relationship between the object and another object, or a property of the object. The system may build a dynamic object-relationship model based on the determined object attributes. The system may also determine a sequence for migrating the objects in the database that does not result in missing objects or missing object attributes based on the dynamic object-relationship model.

Abstract: A system for providing data security and protection using distributed ledgers to store validated data in a global knowledge graph for fraud detection is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with an individual from a data source. The processor may convert the data into knowledge graph data by: extracting entities from the data; extracting relations from the data; and translating the data into knowledge graph triples to generate the knowledge graph data. The processor may validate the knowledge graph data using a cryptographic validation, such as blockchain or distributed ledgers, on the knowledge graph data to provide secured contents to update a global knowledge graph. The processor may then determine a fraudulent activity level associated with the individual based on the updated global knowledge graph.

Abstract: A device may receive a code set that includes a set of objects. The set of objects may include one or more successor objects and one or more predecessor objects corresponding to the one or more successor objects. The device may populate a data structure based on the code set. The data structure may identify each object of the set of objects. The device may identify relationships between the one or more successor objects and the one or more predecessor objects. The device may add, to the data structure and in association with the one or more predecessor objects, dependency information that identifies the relationships between the one or more successor objects and the one or more predecessor objects. The dependency information may identify the one or more successor objects. The device may perform an action based on the dependency information.

Abstract: A platform may obtain, for a first set of entities involved in a money laundering investigation, target entity information for a target entity and related entity information for a set of related entities. The platform may analyze the target entity information and the related entity information to identify money laundering candidates. The platform may determine one or more relationships indicating a degree of similarity between the target entity and the one or more related entities. The platform may generate a graph data structure that associates the target entity and the one or more related entities using the one or more relationships. The platform may determine a score for the target entity and one or more scores for the one or more related entities. The platform may provide a recommendation indicating whether the target entity and/or the one or more related entities are likely to be engaging in money laundering.

Abstract: A device receives application information associated with a monolithic application, and generates a recommendation based on utilizing an artificial intelligence technique. The recommendation relates to a service to be generated, a service category for the service, and a deployment model for the service. The artificial intelligence technique generates the recommendation based on the application information. The device automatically generates code for the service based on the service category and the application information, receives a request to deploy the generated code for the service via the deployment model, and deploys the generated code, based on the request, to provide the service via the deployment model.

Abstract: A method, computer-readable medium, and system for monitoring usage of computing resources provisioned across multiple cloud providers and/or data centers are disclosed. Events associated with usage of a plurality of computing resources may be accessed, where the plurality of computing resources may implement a virtual machine, a plurality of virtual machines of a cloud computing environment, etc. The events may be associated with a start, a stop, a status change, etc., of the plurality of computing resources. The events may be used to generate usage data for the plurality of computing resources. The usage data may include historical data associated with previous usage of the plurality of computing resources. Additionally, the usage data may be displayed using a graphical user interface, thereby enabling monitoring and/or tracking of usage of computing resources provisioned across at least one cloud provider and/or at least one data center.

Abstract: A device may receive, from a user device, a request to verify a machine learning (ML) application using a metamorphic testing procedure. The device may determine a type of ML process used by the ML application, and may select one or more metamorphic relations (MRs), to be used for performing the metamorphic testing procedure, based on the type of ML process. The device may receive test data to be used to test the ML application, wherein the test data is based on the one or more MRs, and may perform, by using the one or more MRs and the test data, the metamorphic testing procedure to verify one or more aspects of the ML application. The device may generate a report that indicates whether the one or more aspects of the ML application have been verified and may provide the report for display on an interface of the user device.

Abstract: A device may receive configuration information for an experiment associated with optimizing an application. The configuration information may identify a group of experimental treatments and information identifying a target event. The device may identify an experimental treatment to be implemented in the application, and may implement the experimental treatment in the application. The device may collect response information based on implementing the experimental treatment. The device may detect the target event associated with the experiment, indicating that the response information is to be provided to an experiment server. The device may determine that the experiment server is unreachable and may store the response information. The response information may be stored to allow the response information to be provided to the experiment server at a later time. The response information may be provided to the experiment server to permit the experiment server to determine a result of the experiment.

Abstract: A device may receive information associated with a collection including a set of uniform resource locators (URLs). The information associated with the collection may include a set of titles and a set of images corresponding to the set of URLs, and attribute information associated with the collection. The device may provide, for display to a user and based on the information associated with the collection, a collection user interface that includes the set of titles and the set of images, the attribute information, and one or more input elements associated with editing the information associated with the collection. The device may determine, based on user input via the one or more input elements, updated information associated with the collection, and may provide the updated information associated with the collection to allow the updated information associated with the collection to be provided for display to another user.

Abstract: A device may receive information associated with an entity-specific designation of a role, and determine a standardized designation of the role that corresponds to the entity-specific designation of the role after comparing the information associated with the entity-specific designation of the role and information associated with a set of standardized designations of roles. The device may receive information associated with a set of tasks that is associated with the role, and compare the information associated with the set of tasks and information associated with a set of automation-capable tasks. The device may determine a set of automation scores associated with the set of tasks, and provide information associated with the set of automation scores to permit an action to be performed based on the set of automation scores.

Abstract: A device may receive, from a consumer device, an application programming interface (API) request that includes information associated with a consumer of data and information associated with a producer of the data. The data may be collected by one or more data sources associated with the producer. The device may identify a consumer context, associated with the consumer, based on the information associated with the consumer. The device may identify a producer context, associated with the producer, based on the information associated with the producer. The device may generate an API response based on the consumer context and the producer context. The API response may include data in accordance with the consumer context and the producer context. The device may provide, to the consumer device, the API response to permit the consumer to view or access the data.

Abstract: A certification management system helps an organization develop and maintain a repository of current certification status of employees. The system may integrate multiple learning management systems and other enterprise level systems across the organization. The system facilitates identifying and enrolling targeted employees for any number and type of certification programs. The system may also implement and support reconfiguring certification programs, for example, during training, and enforcing recertification requirements according to maturing business needs. The system provides automated workflows that facilitate a formal, structured approach to the development and recognition of specific specialized skills at scale by infusing more consistency, rigor, and objectivity.

Abstract: A device receives source data, target data, external data, and a target task, and generates features of and differentiators between the source data and the target data. The device identifies a set of mappings between the source data and the target data based on the features and the differentiators, and determines different clusters of the source data based on the external data, the features, and the differentiators. The device generates, based on the external data, a set of artificial intelligence (AI) models as candidates to perform the target task, and generates a performance measure for the set of AI models based on the features, the differentiators, and the external data. The device refines the set of mappings, and identifies an AI model, from the set of AI models, to perform the target task based on the different clusters of the source data and based on the performance measure.

Abstract: A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The system facilitates building, deploying, and/or training analytical models, by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The system can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The system also provides a dynamically reconfigurable user interface for controlling the system.

Abstract: Systems and methods for active state synchronization between distributed ledger technology (DLT) platforms are provided. A system may store an origin blockchain compliant with an origin DLT. The system may further store a target blockchain compliant with a target DLT. The target DLT may be different from the origin DLT. The system may include a DLT object synchronizer with access to the origin blockchain and the target blockchain. The DLT object synchronizer may receive, from an exchange node, a request to synchronize an origin instance of a DLT object between the origin blockchain and the target blockchain. The DLT object synchronizer may select a target instance of the DLT object on the target blockchain. The DLT object synchronizer may format origin data from the origin instance for compliance with the target DLT. The DLT object synchronizer may synchronize the origin instance and the target instance.

Abstract: Methods, systems, and apparatus for providing an enhanced hotel guest experience. Disclosed methods include the actions of receiving data indicating the presence of a hotel guest from one or more sensors; generating a likelihood score that the hotel guest is going to their hotel room; determining that the generated likelihood score exceeds a likelihood threshold score; and based on determining that the generated likelihood score exceeds the likelihood threshold score, sending one or more instructions to one or more devices located within the hotel room.

Abstract: Systems and methods for entitlement tracking and control with blockchain technology are provided. A server node may receive usage information indicating usage of a licensed component by a remote device. The server node may generate a datablock that includes the usage information and append the datablock to a blockchain. The server node may acquire, from the blockchain, a license smart contract. The license smart contract may include control logic to control access to the license component. The server node may control access to the license component by the remote device based on the usage information and the control logic.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

Abstract: The Next Best Action (NBA) management system and method is a real time decision solution usable in multiple industries, including Telco industry. The NBA system identify offers for call agents to pitch to customers, calculate and allow call center organizations and agents to view performance analytics, including an Inbound Campaign Agent Performance Index (ICAPI) for each agent. The NBA system uses usage oriented key performance indicators (KPIs) and sales oriented KPIs to add a weighted portion of the usage oriented KPIs and the sales oriented KPIs to calculate the ICAPI for each agent.