Abstract: Methods and apparatuses are described for dynamic option reselection in virtual assistant (VA) communication sessions. A chat session is established between a VA application and a remote computing device. The VA application captures a first conversation intent generated by a user of the remote device, where the first conversation intent is part of a first workflow. The VA application generates an option selection feature based upon the first conversation intent, comprising a plurality of options and associated with a reference identifier. The VA application receives a selection of a first option in the option selection feature from the remote device. The VA application initiates a second workflow in response to the selected. The VA application detects a selection of a second option in the option selection feature from the remote device and restores the chat session to a state in the first workflow using the reference identifier.

Abstract: A method may include defining a rule identifying an event that triggers a change in a value of a metric. The defining of the rule includes generating a subscription to receive the event from an event stream. Occurrences of the event identified by the rule may be detected based on receiving the event from the event stream. In response to each occurrence of the event, the value of the metric and/or the change in the value of the metric may be evaluated. A notification may be sent to a software application consuming the metric based on the value of the metric and/or the change in the value of the metric satisfying a threshold. Related systems and computer program products are also provided.

Abstract: A computer-implemented method of comparing one or more genetic traits of two users. Each user has a wearable device storing data indicative of the one or more genetic traits, the data having been obtained by an analysis of a biological sample provided by the user. The method comprises: transmitting the data indicative of the one or more genetic traits from a first of the wearable devices to a first computer device using a short-range wireless data connection; transmitting the data from the first computer device to a second computer device over a data network; transmitting the data from the second computer device to a second of the wearable devices over a short-range wireless data connection; and comparing the data from the first wearable device with the data stored on the second wearable device to determine whether there is a match between the users' one or more genetic traits.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for application programming interface (API) request throttling. A request throttling system monitors incoming API requests and applies delays to repeated API requests to avoid an overload and/or disruption in service. Upon receiving an API request from a requesting device, the request throttling system determines a counter value that is used to determine whether to apply a delay to API request. The counter value indicates a number or previous requests received from the requesting device that were directed to the same resource and which resulted in an error. The request throttling system compares the counter value to a threshold value to determine whether to apply a delay to the API request. Applying a delay reduces the speed at which subsequent repeat API requests are received from the requesting device, while still allowing for API requests from the requesting device to be processed.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a dynamic api, includes receiving a first message in a first format from a client, the first format being specified for the client, the first message including information sufficient to cause a target service to execute an operation. The method includes identifying, using the API message, the target service. The method includes determining based of the target, a second format. The method includes generating a second message in the second format prescribed by the target service using the first message, the second message including information sufficient to cause the target service to execute the operation. The method also includes sending the second message to the target service over the network connector.

Abstract: Methods and systems are configured for monitoring operations of a computing device by associating threads executing in a user space with kernel events in a kernel space. The systems and methods are configured for detecting a kernel event in the kernel space of the computing device; in response to detecting the kernel event, accessing, from a mapping table that maps a computing thread in the user space to a span that is active on the computing thread, a base address of a memory in the user space of the computing device, the memory storing a span identifier for each span in the user space, the span comprising one or more operations of a computing thread that is active in the user space; accessing, based on the base address, a span identifier in the memory; and associating the span identifier with the kernel event.

Abstract: The subject matter of this specification can be implemented in, among other things, a method that includes accessing a plurality of target tasks for a computing system, the computing system comprising a plurality of resources, wherein the plurality of resources comprises a first server and a second server, accessing a plurality of configurations of the computing system, wherein each of the plurality of configurations identifies one or more resources of the plurality of resources to perform the respective target task of the plurality of target tasks, and performing, for each of the plurality of configurations, a simulation to determine a plurality of performance metrics, wherein each of the plurality of performance metrics predicts performance of at least one of the plurality of resources executing the plurality of target tasks on the computing system.

Abstract: Aspects of a cloud service framework are described. In some examples, a noncompliance event is received. The noncompliance event is identified using parameters detected from a deployment of hardware resources and software resources provided by a cloud service according to a service level agreement (SLA). An application programming interface (API) associated with the noncompliance event is identified. The API is disabled by updating an API availability status. A request to invoke the API is received, and the API is prevented from being forwarded to a backend service that provides the API.

Abstract: Cloud computing techniques utilizing distributed application execution are disclosed herein. One example technique includes receiving a command to launch an application, and in response, determining an execution location corresponding to a type of data consumed by individual components of the application. Upon determining that one of the components is to be executed in a local computing facility, the example technique includes transmitting, from a public computing facility to the local computing facility, a request to execute the one of the components in the local computing facility instead of the public computing facility. Upon being authorized by the local computing facility, data is requested and received from the one of the components executed at the local computing facility without having direct access from the public computing facility to a data source at the local computing facility.

Abstract: Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning model lifecycle management. An example computing method includes receiving a machine learning model selection, a machine learning model experiment creation input, a machine learning model experiment run type, and a machine learning model input data path. The example method further includes determining a machine learning model execution engine based on the machine learning model experiment creation input and the machine learning model experiment run type. The example method further includes retrieving input data based on the machine learning model input data path. The example method further includes executing a machine learning model experiment based on the machine learning model execution engine, machine learning model experiment creation input, and the input data.

Abstract: Systems and methods for processing events are disclosed. Event data comprising passive event data, active event data, or both is received. It is determined whether the received event data is available for a pattern of passive event data and active event data. In response to determining that the received event data is available for the pattern of passive event data and active event data, one or more constraints between the passive event data and the active event data are converted into one or more query terms. The query terms are used to construct at least one query. Remaining passive event data that is related to some, but not all, of the active event data is obtained using the constructed at least one query.

Abstract: Provided are an application interface implementation method and apparatus in a host platform layer, a device, and a storage medium, which relate to the field of computer technologies. The implementation scheme includes: the host platform layer acquiring description data of a terminal capability interface; parsing the description data to acquire a communication mode of the terminal capability interface; and configuring a corresponding processor according to the communication mode, configuring a corresponding concept mapping relationship according to the communication mode, or configure a corresponding processor and a corresponding concept mapping relationship according to the communication mode to encapsulate a platform layer interface of the terminal capability interface, where the platform layer interface is configured to process data of communication interactions in a process in which a mini program calls the terminal capability interface through the host platform layer.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: A real-time dynamic API traffic shaping and infrastructure resource protection in a multiclient network environment is provided. A traffic rules engine (TRE) applies traffic shaping only to customers that are utilizing “more than their fair share” of the currently available bandwidth without allowing them to negatively impact the user experience of other users. The present invention takes current API traffic into consideration, allowing one or a few high volume users to utilize most of all available bandwidth as long as other users do not need that bandwidth. This includes dynamically measuring and adjusting which users had traffic shaping applied to them based on the overall traffic during any given second. The solution of the present invention avoids any slowdown of customer API requests unless the maximum allowable TPS limit is near to being reached.

Abstract: A fault simulation system, a server (100), a user device (200) comprising a user interface and a wire controller (300), intended for teaching and validating practical knowledge in the long-distance training of vehicle (500) maintenance personnel, allowing the emulation or simulation of faults by manipulating the signals of the electronic system of the vehicle (500). Some embodiments of the invention are developed with the purpose of simulating the most diverse defects in vehicles, acting, for example, in the injection calculator module, air conditioning system, CAN bus system, dynamic stability control, ABS system, accelerator, speed governor, speed limiter, airbag system, fuel pump, gear selector lever, wiper nozzle, among others, enabling a highly realistic emulation of a fault scenario by manipulating the signals of the electronic communication system of the vehicle (500).

Abstract: An electronic device for detecting a business system and a detection method thereof are provided. The electronic device includes a communication module, a storage module, and a processing module. The communication module communicates with the business system. The storage module stores a detection engine and a detection rules corresponding to the business system. The processing module is electrically connected to the storage module and the communication module. The processing module executes the detection engine. The detection engine accesses a detection application program interface of the business system through the communication module according to the detection rule, so that the detection application program interface returns business data corresponding to the business system to the detection engine. Therefore, the electronic device of the disclosure can effectively detect the business system to obtain required relevant business data.

Abstract: Device group configurations can be managed across workspaces based on context. A service can be employed on each client computing device that a user may use in his or her workspaces. The service can be configured to determine when a profile applies to a workspace and can automatically apply a device group configuration to the workspace. The service can also monitor for context changes while a device group configuration is applied and can adjust the device group configuration based on the context changes.

Abstract: Methods, systems and computer program products for extensible content object workflows. A content management system stores a plurality of content objects. The content management system integrates applications, which integrated applications make modifications to the content objects based on computer-aided invocation of workflows. Upon receipt of a message from a first integrated application indicating an intent to invoke a workflow at a second integrated application, the content management system relays information pertaining to the desired workflow to the second integrated application. The workflow of the second integrated application modifies one or more of the content objects or its metadata or other data associated with the workflow. Selection of the second integrated application can be based on attributes of the content objects, and/or can be based on user interactions within a user interface.

Abstract: Disclosed embodiments are directed at systems, methods, and architecture for configuring event hooks in a microservice architecture-based system. The control plane links a plurality of APIs for a microservice architecture application. Each API includes a data plane proxy that serves traffic for the API from the control plane. The control plane receives an event hook including an event, a source, and a handler. The control plane configures a component of the microservice-architecture-based system to run the handler when the event occurs at the source. When the event occurred at the source, the component runs the handler.

Abstract: One or more virtual machines are launched at an application platform. At each of the one or more virtual machines, a machine learning model execution environment is instantiated for an instance of a machine learning model. A respective instance of the machine learning model is loaded to each machine learning model execution environment. Each loaded instance of the machine learning model is associated with an application programming interface (API) endpoint which can receive input data for the loaded instance of the machine learning model from a client device and return output data produced by the loaded instance of the machine learning model based on the input data.