Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for prototyping Internet-enabled devices. One of the methods includes receiving a prototype program defining functionality of a proposed Internet-enabled device having a plurality of sensors and one or more actuators. A sensor hub receives a registration request through a prototyping API for each of the connected sensors and the one or more actuators. The sensor hub initializes each of the connected sensors and the one or more connected actuators, obtains a status of each registered connected sensor, provides the obtained status of each connected sensor to the prototyping system through the prototyping API, receives requested actions corresponding to functionality specified in the prototype program, and executes each of the requested actions on a corresponding connected actuator.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for helping service providers. The methods, systems, and apparatus include actions of obtaining a mobile computing device identifier, identifying a customer corresponding to the mobile computing device identifier, obtaining information regarding the customer that is identified, determining a likely intent of the customer from the information regarding the customer, and providing, using the likely intent of the customer, information that helps a provider.

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 device identifies a subset of network performance indicator (NPI) values that measure network performance during peak usage time periods, selects particular NPI values associated with the peak usage time periods, based on one or more of the subset of NPI values satisfying particular threshold network performance values, and compares the particular NPI values with threshold upper and lower bound network performance values to determine weighted scores for the particular NPI values. The device determines, by processing the weighted scores, a weighted mean score that represents a mean of the weighted scores, and determines, based on an analysis of the particular NPI values and the weighted mean score, an overall site score that represents a viability level in upgrading the cell site. The device generates, based on the overall site score, a plan to upgrade one or more network technologies associated with the cell site.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: In some implementations, messaging services, through which messages from an entity, such as a company, are communicated to various users, are managed. In one aspect, the techniques described may be provided by a messaging management service that keeps track of such messaging services and provides messages to users through these messaging services on behalf of various companies. In this way, the messaging management service may enable companies to quickly establish a presence on new communication platforms and more easily communicate through the mobile communication and social networking services that are most suitable to their existing and potential customers.

Abstract: An extensible key management system includes a developer services portal for accessing an application. The system may identify initiation via the developer services portal of an association of the application with an external API of a third party provider service and automatically obtain a sub-key for the external API from the third party provider service on behalf of a developer of the application. The sub-key may be obtained in accordance with authorization provided by a master key previously provided from the third party provider service. The system may map an association between the sub-key, the application and the external API and store the association. Upon receipt of a service call message resulting from a request from a user device executing the application, the system may identify and replace a developer API key included in the service call message with the sub-key based on the stored association.

Abstract: An optimized test data selection strategy references a sampling file that identifies data attributes that serve as the basis of the test data selection strategy. By analyzing fields and the corresponding field values of the sample imprint, a total number of test data selected for inclusion into a sample dataset is reduced. The test data selection strategy provides an efficient methodology for implementing a data comparison testing process.

Abstract: A system and method for controlling quality of performance of a digital application is disclosed. Performance data representing test results of a requested performance test on the application is obtained. A performance metric is optimized by analyzing the performance data and classifying analysis results of the analyzing into multiple categories, including an immediate remediation category. An automated modification is initiated based on a first analysis result classified into the immediate remediation category. An automated verification performance test determines a measure of improvement in performance of the application based on the modification by comparing a first verification result of a first performance test without the automated modification with a second verification result of a second performance test of the digital application with the automated front end modification.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: A digital voucher system facilitates the convenient application of vouchers against products and services purchased from a merchant by a consumer. A voucher enabled device carried by the consumer stores digital vouchers in memory. The merchant queries and obtains applicable vouchers for the consumer from the voucher enabled device carried by the consumer, applies the vouchers to a transaction, and communicates the resulting transaction data back to the voucher enabled device.

Abstract: A device receives, from a virtual assistant device, a first user input associated with a first account of a user, and causes a natural language processing analysis to be performed on the first user input to identify first information, the first account, and a first operation to be performed in association with first information in the first account. The device identifies a first data management platform, associated with the first account, that is configured to maintain the first information in a first data structure associated with the first data management platform, and determines that the first data management platform is a first type of data management platform based on the first data structure. The device causes the first operation to be performed using a RPA, that uses a user interface of the first data management platform, based on the first data management platform being the first type of data management platform.

Abstract: A device may receive a computing resource request. The computing resource request may be related to allocating computing resources for a job. The device may process the computing resource request to identify a set of parameters related to the computing resource request or to the job. The set of parameters may be used to determine an allocation of the computing resources for the job. The device may utilize multiple machine learning models to process data related to the set of parameters identified in the computing resource request. The device may determine the allocation of the computing resources for the job based on utilizing the multiple machine learning models to process the data. The device may generate a set of scripts related to causing the computing resources to be allocated for the job according to the allocation. The device may perform a set of actions based on the set of scripts.

Abstract: Successfully deploying resources into the cloud is a constantly increasing technical challenge as more cloud service providers emerge, each offering disparate computing platforms, services, assets, supported technical components, and other features, including serverless execution. An infrastructure build and collaboration architecture for serverless execution environments solves the technically complex challenges around allowing developers to work independently in physically separated locations. To this end, the architecture allows shared object repositories to be brought together when required. The architecture performs deep validation of the infrastructure built for the developer.

Abstract: A system may support distributed multiple tier multi-node serverless analytics task execution. At a data ingestion tier, data ingestion serverless tasks may receive detail data for analytic processing. data integration serverless tasks, executing at a data integration and consolidation tier and initiated by the data ingestion serverless tasks, may sort the detail data and identify patterns within the detail data to generate grouped pre-processed data. The data integration serverless tasks may initiate partitioning serverless tasks which may divide the grouped pre-processed data into data chunks. Multi-node analytic serverless tasks at an analytic tier, at least some of which being initiated by the partitioning serverless tasks, may analyze the data chunks and generate prescriptive outputs.

Abstract: A method for provisioning a computer includes providing a graph that defines relationships between one or more hardware components of a plurality of computers and component characteristics of the one or more hardware components, and relationships between one or more applications and requirements of the one or more applications. The method further includes receiving a selection of an application and determining, via the graph, whether at least one computer with hardware components capable of meeting the requirements of the application exists. If a computer exits, the method also includes communicating the application to the computer; triggering the computer to execute the application; and communicating, from the computer, data processed by the application to an external system.

Abstract: A system may facilitate a distributed ledger technology (DLT) record based (for example, blockchain-based) ballot organization. A node configured to support an organizer role may generate a ballot that designates answers, and in some cases, conditions for valid vote-value transfers. The organizer may distribute vote-value to one or more voters. The voters may then commit portions (including null portions) of the received vote value to answers using committed tokens configured to bind the voter to a particular vote-value without divulging the particular vote value while in a cryptographic form.

Abstract: A multi-layer activation timetable stack may generate prescriptive activation timetables for controlling activation states for computing resources. An input layer of the activation timetable stack may generate time-scaled pattern data. A transformation layer may identify trends and variables at era timescales. A data treatment layer may flag activation states based on the trends identified at the era timescales. Once the activation states a flagged, the prescriptive engine layer may generate an activation timetable that may be used to control computing resource activation prescriptively.

Abstract: A system may receive task information identifying tasks to be performed by workers of one or more crowds. The system may obtain worker information describing the workers. The system may determine task completion probabilities based on the task information and the worker information. A task completion probability may identify a likelihood that a particular crowd will complete a particular task. The system may determine crowd recommendation information based on the task completion probabilities, the task information, and/or the worker information. The crowd recommendation information may identify recommended crowds to which a task is recommended to be provided. The system may output the crowd recommendation information and/or the worker information to permit selection of the crowds to perform the task.