305 results about "Application programming interface" patented technology

A method for establishing a cross-issuer chargeback fraud detection system includes receiving a fraud analysis request for one or more chargebacks from a payment processor using an Application Programming Interface (API) over a computer network, extracting identifying information of transactions associated with the one or more chargebacks from the fraud analysis request, searching for a fraud analysis profile linked to the extracted identifying information in a profile database, determining whether the fraud analysis profile linked to the extracted identifying information exists in the profile database, upon determining that the fraud analysis profile linked to the extracted identifying information does not exist in the profile database, retrieving historical transaction data associated with the extracted identifying information from a historical transaction database, retrieving reported fraudulent activities from one or more financial institutions associated with the extracted identifying information, and aggregating the retrieved historical transaction data and reported fraudulent activities.

This application provides a code digest generation method, model, apparatus, medium, and device, relating to the field of artificial intelligence technology. The method includes: determining an application programming interface (API) information graph corresponding to the code to be processed, wherein the node types in the API information graph include: a first type, and at least one of a second type and a third type, wherein the associated information of the first type of nodes is the application programming interface, the associated information of the second type of nodes is variables, and the associated information of the third type of nodes is API description information; encoding the API information graph to obtain API encoding features of the code to be processed; and determining a code digest of the code to be processed based on the API encoding features and target features of the code to be processed, wherein the target features include: source code encoding features corresponding to the code to be processed, or source code encoding features and AST encoding features corresponding to the code to be processed. This application embodiment can improve the semantic integrity of the code digest.

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a decision tree to generate a recommended action in response to detecting a change in a data map representing a computing environment. The disclosed system determines relationships among data objects representing digital data, digital assets, and data processing activities and generates a data map according to the relationships. The disclosed system monitors the data map for changes and, responsive to determining a change in the data map, traverses a decision tree by executing one or more calls to one or more application programming interfaces according to the change in the data map and one or more data policies relevant to the changes. The disclosed systems utilize the decision tree and application programming interfaces to generate a recommended action for modifying digital assets, digital data, and / or data processing activities.

The present disclosure provides an application programming interface (API) governance method, device, equipment and program product, and relates to the technical field of software development. The API governance method comprises: generating a target API based on a data configuration request, the target API being used to provide a data query service corresponding to the data configuration request; generating a permission identifier corresponding to an application request of the target API in response to the application request; the application request at least comprising a first request party identifier and a target API identifier corresponding to the target API, the permission identifier being used to mark the calling permission of the target API of a first request party with the first request party identifier; verifying based on the permission identifier in response to a calling request of the target API, the calling request at least comprising the target API identifier and the permission identifier, and providing the data query service based on the target API after the verification is passed. The whole process management of the API from generation, permission application to calling can be realized.

The present application relates to the field of artificial intelligence, and provides a multi-chip operator automatic generation method, device and equipment, the method comprising: obtaining operator demand description; in response to the operator demand description, generating a standard operator code conforming to a preset intermediate representation specification and independent of specific hardware; based on a hardware rule library of a target chip, converting the standard operator code into target operator code adapted to the target chip; wherein the hardware rule library at least includes application programming interface mapping rules, hardware characteristic constraint rules and performance and engineering experience rules; compiling and verifying the target operator code, and outputting the target operator code that passes the verification. The present application solves the problem of poor reusability and high migration cost of operator automatic generation logic in the prior art in a multi-chip scenario, realizes decoupling of algorithm logic generation and chip adaptation, and efficient and controllable multi-platform operator automatic generation.

This invention discloses a method, apparatus, device, and medium for malware detection based on subspace ensemble learning. The method includes: acquiring the installation package file of the software to be detected; parsing the installation package file to obtain the application programming interface (API) call relationships of the software to be detected; determining the API call relationship graph based on the API call relationships; filtering each API in the call relationship graph to obtain a target API set; and determining the software detection result of the software to be detected based on the target API set and a feature subspace ensemble learning model. This invention obtains the target API set by filtering the API call relationships and employs a feature subspace ensemble learning model composed of multiple machine learning algorithms, which can capture malicious behavior in software from different perspectives, thereby improving the accuracy of malware detection.

An exemplary computing system retrieves information from one or more applications using an application programming interface and generates personal context information for a user based on at least one section of that information. Upon receiving a natural language request to generate at least one graphical component, the computing system applies a machine learning model to the natural language request to determine at least one user intent. Using one or more pieces of information from the one or more applications and the user's personal context information, the computing system generates a set of instructions that include instructions for generating the at least one graphical component, such as a widget, based on the at least one user intent.

The invention relates to an application programming interface for indicating strided memory. Apparatuses, systems, and methods for performing thread memory addressing are disclosed. In at least one embodiment, a processor includes one or more circuitry to execute an application programming interface (API) to cause execution of one or more instructions based at least in part on one or more API parameters, the one or more API parameters indicate a size of one or more operands to be used by the one or more instructions.

A queue application programming interface (QAPI) in a first data center receives incident messages, each with an incident identifier. The first data center is in communication with additional data centers. The messages are either ordered or unordered. Ordered messages must be processed in the order received, while unordered messages do not. The QAPI transmits the messages to a message stream data structure. A first worker sub-system retrieves unordered messages from the message stream for processing. In parallel, the QAPI transmits the messages to a group stream data structure which arranges the messages into groups. A second worker sub-system retrieves groups of ordered messages for processing. At least one of the first or second worker sub-systems is associated with the additional data centers.

An apparatus and method for efficiently performing efficient data storage and data transfer of machine learning data. In various implementations, a host processing circuit of a computing system executes a machine learning (ML) application. The application includes a computational graph that indicates the computational order of the ML nodes, layers, and stages of the ML model. The host processing circuit translates function calls in the application to commands particular to an accelerator circuit. The accelerator circuit preloads weights to be used by ML nodes of the ML model by retrieving compressed weights from a storage device different from system memory. The accelerator circuit uses a streaming application programming interface (API) and bypasses the host processing circuit to retrieve the compressed weights from the storage device. The accelerator circuit decompresses the retrieved weights and executes the ML node using the decompressed weights.

Systems and methods for configuring a smart contract are disclosed. A system may receive unstructured data and identifying, based on the unstructured data, an intent to perform a transfer of data. The system may then identify, based on the unstructured data, parameters associated with the transfer of data. The parameters may include an amount of data to be transferred, a transferee, and at least one condition associated with the transfer of data. The system may then retrieve identifier data from a storage module, the identifier data including a first unique identifier. The system may then send, to a Large Language Model (LLM) via a first prompt engine module and an LLM Application Programming Interface (API), a prompt based on the unstructured data and the identifier data. The system may then receive LLM output from the LLM, and based on the LLM output, configure a smart contract.

A computer-implemented method involves receiving input with information about treatments for targets. A large language model (LLM) agent identifies tools for configuring treatments, selecting targets, and allocating treatments. The LLM agent generates and provides application programming interface (API) inputs to the tools. The LLM agent receives results from the tools, determines if the results satisfy a condition, and outputs an indication based on the results. The method includes generating computer code for treatments, applying treatments, and performing statistical analysis. The system iteratively adjusts treatments, targets, or allocations until conditions are met. The system identifies ambiguities and obtains clarifications, training the LLM agent accordingly. The processing system includes memory and processors to execute instructions, receive input, identify tools, generate API inputs, provide inputs to tools, receive results, determine conditions, and output indications.

Systems, methods, and apparatuses are described for automatically generating mock Application Programming Interface (API) endpoints corresponding to a plurality of APIs provided by a third-party endpoint, selectively servicing API requests using internal data or routing those requests to a third-party endpoint, and collecting data to support such an internal model of a third-party API endpoint. A computing device may use a third-party API specification to generate an internal model of a third-party API and may service the API using, for example, internal data. In turn, requests received by an internal endpoint may be handled internally or routed to the third-party API endpoint providing the third-party API based on a variety of considerations. The computing device may collect and prepare data to support the internal model using internal data and / or services.

The invention relates to the technical field of artificial intelligence, and discloses an operation management method and system of industrial equipment, a medium and a program product. The method comprises the steps that a current equipment side agent robot interacts with equipment operation software of corresponding current non-standard customized equipment based on an extended application programming interface, and first running state data corresponding to the current non-standard customized equipment is acquired; if it is detected that the current non-standard customized equipment is abnormal according to the first operation state data, an abnormal assistance request is generated, and the abnormal assistance request and the first operation state data are sent to a manufacturer-side agent robot; and calling a first instant messaging tool through the manufacturer-side agent robot, and sending the abnormity assistance request and the first operation state data to a manufacturer after-sales manager, so that the manufacturer after-sales manager carries out abnormity handling. Efficient and accurate abnormity diagnosis of the non-standard customized equipment can be realized, and the operation management efficiency of the industrial equipment can be improved.

Methods, systems, and devices for data management are described. A wallet service may include one or more application programming interface (API) endpoints. An API endpoint may receive a set of requests to broadcast messages via one or more blockchain networks, the set of requests including a first request to broadcast a first message via a first blockchain network and a second request to broadcast a second message via a second blockchain network. A first blockchain message may be generated using a mapping of a plurality of sets of network-specific parameters to respective blockchain networks, and the first blockchain message may include a first set of network-specific parameters. A second blockchain message may also be generated using the mapping, and the second blockchain message may include a second set of network-specific parameters. The first and second blockchain messages may be broadcast via respective nodes of the blockchain networks.

A device (120) comprising the following: at least one memory (122) in which computer-readable instructions are stored which, when executed by at least one processor (124) coupled to the at least one memory (122), cause the at least one processor (124) to: to receive data from a web service client (150), wherein the data includes at least one operational request associated with a vendor-neutral application programming interface (API) procedure, wherein the at least one operational request includes at least one request to configure a tenant VPC in a Smart NIC (132), and wherein the vendor-neutral API procedure includes a RESTful data model (Representational State Transfer Web API); which modify at least one operational requirement to match a Fabric data model; Decomposing the modified at least one operation requirement into a multitude of operations that act on data associated with a vendor-specific API method, where the vendor-specific API method differs from the vendor-neutral API method; and Performing the multitude of operations that act on the data using the vendor-specific API method for passing to the vendor firmware (160).

Embodiments of the present application disclose a method, device, equipment, medium and program product for generating an adversarial sample. The method comprises: obtaining a preset code file; determining a non-conditional jump instruction in the preset code file; modifying the non-conditional jump instruction into a reconstructed jump instruction according to a preset instruction modification rule; performing control flow flattening processing on a basic block of the preset code file; based on a result of the control flow flattening processing, inserting a preset application programming interface (API) sequence into the preset code file according to a preset rule to obtain a target code file containing a malicious code feature; inserting noise into the target code file to obtain an initial sample; and performing iteration on the initial sample according to a preset evolution algorithm to obtain an adversarial sample. The embodiments of the present application can generate a large number of malicious code adversarial samples that can evade antivirus software and sandboxes.

The invention provides a data service application programming interface API opening method and related equipment, and relates to the technical field of computers. The method comprises the steps of determining a target data table; analyzing a field in the target data table, and determining metadata information of the field; according to the metadata information of the field, determining output field information and input parameter information in the field, and obtaining a processing structured query language (SQL) statement; service registration information of a data service API is generated to obtain data service API configuration information, and the data service API configuration information comprises the service registration information, a processing SQL statement, the output field information, the input parameter information and an access address of the data service API; and automatically registering and generating the data service API according to the data service API configuration information.

The present invention relates to a system (120) and a method (500) for managing Application Programming Interface (API) traffic in a communication network (105) is disclosed The system (120) includes a transmitter (220) configured to transmit a request from an API consumer (405) to a server (115). The system (120) includes a traffic management unit (225) configured to dynamically configure API traffic management rules. The system (120) includes a status request unit (235) configured to ping an API current status request to the server (115) to determine current configurations of the one or more APIs. The system (120) includes a response receiving unit (240) configured to receive a response from the one or more APIs in response to the API current status request. The system (120) includes a report generation unit (245) configured to prepare and transmit a final response configuration report of the APIs to the API consumer (405).

Disclosed is a blockchain-based security method that is performed by an integrated web security solution (web application and API protection, WAAP) and using WAAP and application programming interface (API) level management. The blockchain-based security method may comprise: receiving an API safety grade check request from the blockchain under a session establishment with the blockchain, wherein, in the checking of the API safety grade, a security level for vulnerability of the API is checked by itself in management API data of the WAAP with a highest level of safety; and transmitting information on the API safety grade to the blockchain.

Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate two or more blocks of threads to be scheduled in parallel.

Apparatuses, systems, and techniques to perform one or more operations using a tensor. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause parameters indicating operands and dimensions of operands of two or more tensor contractions to be stored. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause two or more tensor contractions to be performed based, at least in part, on one or more input parameters of the API.

A system and method for facilitating a collaborative conversation with an AI companion are provided. The system utilizes artificial intelligence and machine learning to monitor end user well-being, provide reminders for routine activities, provide real-time notifications to facilitators, and facilitate simulated social interactions with an AI companion. The system comprises a computing device, a server platform, and at an Application Programming Interface (API), configured to facilitate communication between the computing device and the server platform. The server platform houses a knowledge base and a learning language model. Information and conversational prompts are received from the end user via the computing device and relayed via the API to the server platform and processed by the learning language model with reference to the knowledge base, such that a response is formulated by the learning language module and transmitted from the server platform to the end user via the API and computing device.

Aspects of the disclosure are directed to performing hierarchical queries based on summary reporting from an application programming interface (API) for digital content estimation. Performing the hierarchical queries can include denoising API outputs while ensuring consistency across different levels of a hierarchy. Performing the hierarchical queries can further include optimizing a privacy budget across different levels of the hierarchy.

Devices, systems, and techniques for mapping tokens generated by neural networks to application programming interfaces (APIs). In at least one embodiment, a processor includes one or more circuits that cause one or more tables to map one or more tokens generated by one or more neural networks to one or more tokens that are to be used by one or more APIs.

This invention discloses a data processing method, a data processing system, an electronic device, and a readable storage medium. In this embodiment, after determining the resource query method corresponding to a resource query request, the terminal determines and sends a configuration acquisition request to the server based on the parameters carried in the resource query method. The server queries resource configuration information in the resource management system based on these parameters and returns the resource configuration information to the terminal, enabling the terminal to execute the received resource configuration information and obtain a first execution result, including the target resource. In this embodiment, the resource configuration information in the resource management system is obtained by decomposing interface documents. Therefore, the first execution result of the resource can be obtained based on the parameters carried in the resource query method, including the resource identifier and resource type. This improves the reusability of resource configuration information, reduces the growth rate of application programming interfaces, and thus limits the complexity of the data processing system.

The application discloses an intelligent mine management and control platform and method based on a beneficiation whole-process optimization large model, relates to the technical field of industrial control, and comprises the following steps: constructing a unified data warehouse, collecting multi-source heterogeneous data, performing quality verification and space-time alignment, and generating beneficiation data; using the beneficiation data to train a twin prediction sub-model and an optimization decision sub-model respectively, forming a linkage model set; running the linkage model set in the intelligent mine management and control hub node to generate future state trends and candidate set values, and distributing the candidate set values to each business item; each business item forms an optimization instruction according to the candidate set value and a preset optimization target, and issues the optimization instruction to the bottom control layer for execution; and the new beneficiation data generated by the execution is returned to the unified data warehouse. The application constructs a callback closed loop in an application programming interface, prepositions the candidate set value as a twin prediction input, and returns the candidate set value in the same data closed loop, so that the cross-business item execution stability is improved.