2198 results about "Program code" patented technology

Disclosed herein are computer-implemented systems and methods for code value assessment. For example, in one aspect, the system comprises, an input module configured to receive program code submissions from developers, a processing unit equipped with Text-Based Models with Contextual Understanding (TBM-CUs) configured to evaluate the functional meaning, purpose, and value of submitted code segments and distinguish between code contributions from human programmers and machine learning systems, a visualization module configured to present the assessed value of the analyzed code over various time periods and dimensions, offering insights into trends, patterns, and comparative performance, a communication module configured to translate programming code meaning or function into plain language summaries for non-technical stakeholders, and an AI peer review module configured to automatically review, accept or reject code contributions.

In one embodiment, a computer-implemented method executed using one or more processors of an incident management system comprises receiving a notification of an incident associated with a computer system, and in response to receiving the notification: extracting an error message from the notification; reading a set of computer program code changes that have been implemented in the computer system; matching the error message to the set of computer program code changes and outputting a set of one or more candidate code changes that may correspond to the error message; based on the set of candidate code changes, generating an automatic remediation for the incident; and using the one or more processors of the incident management system, executing the automatic remediation on the computer system.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM). In some examples, such a method includes operations of receiving a user-provided prompt, wherein the user-provided prompt is provided in natural language, identifying an objective of the user-provided prompt, and based on the objective, providing the user-provided prompt to a first operational pipeline of a plurality of operational pipelines, wherein each operational pipeline is associated with a unique prompt template. Additionally, the method may include generating, by the first pipeline, an auto-generated prompt based on a first unique prompt template of the first pipeline, providing the auto-generated prompt to a large language model (LLM), and receiving a response to the auto-generated prompt from the LLM. A graphical user interface (GUI) may then be generated that displays the response to the auto-generated prompt.

The invention relates to the technical field of ultra-low power consumption microcontrollers, and discloses an ultra-low power consumption microcontroller architecture design method and system, and the method comprises the steps: constructing a heterogeneous dual-core processor comprising a high-performance core and a low-power consumption core, and dividing the heterogeneous dual-core processor into a plurality of independent power domains; constructing a sub-threshold voltage operation system on a plurality of independent power supply domains, and configuring a multi-layer storage structure in the heterogeneous dual-core processor; recording task characteristic data by using a multi-level storage structure, executing task dynamic allocation between dual cores, and creating a dynamic task allocation mechanism; and performing static analysis and function block division on the application code based on a dynamic task allocation mechanism to obtain a plurality of function code blocks, and performing storage hierarchy and processor core allocation on the plurality of function code blocks to obtain a function code allocation result. According to the method, the dynamic migration of the workload and the optimal resource allocation are realized, and the energy consumption of the microcontroller in various working scenes is reduced.

The invention discloses a network security vulnerability identification and repair method and system, a medium and a program product, and the method comprises the steps: segmenting a system behavior sequence into a plurality of subsequences; constructing a behavior dependency graph; calculating functional similarity, and dividing the functional similarity into node clusters; calculating a behavior consistency score, and determining a potential vulnerability region; determining call chain information; constructing a vulnerability propagation path, and calculating criticality; dividing first-level hazard vulnerabilities, second-level hazard vulnerabilities and third-level hazard vulnerabilities according to the criticality; determining a repair priority, and generating a vulnerability report; according to the vulnerability types, the repair priorities and the propagation path information, corresponding repair strategies are sequentially matched from a preset repair template library according to the sequence of the repair priorities; and performing hierarchical repair on the application code based on the repair strategy obtained by matching to obtain a repair code, and deploying the repair code to the target system. According to the invention, the accuracy and efficiency of identifying and repairing network security vulnerabilities are improved.

Certain aspects of the disclosure are directed to searching programming code repositories. In examples, a method is provided that includes receiving a natural language search query via a graphical user interface; converting the natural language search query into a vector representation; determining a proximity score between the vector representation of the natural language search query and at least one vector representation corresponding to a machine learning model generated natural language description of a programming code segment; and providing a search result corresponding to the programming code segment based on the proximity score.

Systems and methods for efficiently translating program code from a source language to a target language. Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a Single Static Assignment (SSA) form, and a System Dependency Graph (SDG) is built from the SSA form. The SDG is traversed to order translation tasks, and ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.

An end-to-end approach for (i) identifying potential vulnerabilities in software applications, including security vulnerabilities, (ii) verifying / confirming the potential vulnerabilities as actual vulnerabilities and (iii) in response, identifying the necessary remedial actions necessary to eliminate or at least mitigate the vulnerabilities. An intelligent agent is implemented that is configured to detect a change to the application's code or computing environment and, as a result of detection of changes to the code or computing environment, identify potential vulnerabilities, verify / confirm the potential vulnerabilities as actual vulnerabilities through determination / creation of vulnerability test cases and automatically identify the necessary remedial actions necessary to eliminate or mitigate the vulnerabilities.

Apparatuses, systems, and techniques to select optimizations to be performed by compilers. In at least one embodiment, a processor includes one or more circuits to perform a compiler to select one or more optimizations to one or more first versions of a program based, at least in part, on a result of performing said one or more optimizations on one or more second versions of said program.

Provided is a terminal for transmitting information. The terminal includes a memory and a processor, wherein the memory stores at least one program code, and the processor is configured to load and execute the at least one program code, to cause the terminal to: report assistance information in a case that a first condition is satisfied, wherein the assistance information indicates service information of the terminal, the service information comprising at least one of a service characteristic of the terminal, remaining service information of the terminal, or remaining data information of the terminal.

One embodiment of a method for generating program code to control a robot includes receiving user input specifying a task to be performed by the robot, processing the user input via a first machine learning model to generate a plurality of subtasks for performing the task, and for each subtask included in the plurality of subtasks, processing the subtask via a second machine learning model to generate program code for controlling the robot to perform the subtask.

Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains queries from sources via a query interface. The program code analyzes the queries to obtain attributes of the queries. The program code applies a trained classifier to the attributes to classify the queries into finite categories based on a predicted usage of a given resource type exceeding a pre-determined threshold and an anticipated execution duration for each query. The program code determines, based on the finite categories of the one or more queries and resource configurations of resources comprising the database service, which resource of the resources can efficiently execute each query of the one or more queries. The program code schedules each query of the one or more queries for execution by the determined resources.

Methods and systems are presented for providing a large language model-based query optimizer to interface between program developers and database systems. The query optimizer receives programming code corresponding to a set of queries intended for a database system from a program developer. The query optimizer then uses a machine learning model to analyze the programming code and to determine a set of strategies for executing the set of data queries corresponding to the programming code. To determine the set of strategies, the machine learning model analyzes dependencies among the set of data queries and retrieves sample data from the database system. The machine learning model implement the set of strategies by incorporating additional instructions in the programming code for the database system such that the database system would execute the set of data queries according to the set of strategies.

Automatic code generation for robotic process automation (RPA) that uses a cognitive artificial intelligence (AI) layer to automatically generate computer program code, text, or other items based on an input source is disclosed. Expressions and / or code snippets may be generated for RPA workflows that provide code in a programming language or convert code from one language to another. The input source may include natural language sentence(s), source document(s), pseudocode written by a user for a desired task, etc. These inputs may be used to modify an RPA workflow, generate a new RPA workflow, create a document describing a task, generate an application, etc.

An apparatus comprising: at least one processor; and at least one memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: determine whether to report a power headroom report value to a network node, the power headroom report value being applicable to a mixed slot where simultaneous transmissions in downlink and uplink are scheduled in non-overlapping sub-bands of the same carrier; wherein the power headroom report value is based on a difference between an uplink transmit power given an expected cross link interference for a user equipment, and an uplink transmit power at the mixed slot; and determine whether to reduce the uplink transmit power at the mixed slot, based on information received from the network node.

The invention belongs to the technical field of industrial automation and artificial intelligence crossing, and particularly relates to an AI industrial control intelligent master system and method based on deep learning. The system comprises an intelligent programming module, a real-time monitoring module, a fault management module, an optimization decision module and a man-machine interaction module. The system automatically generates a PLC program code through a deep learning model, monitors an equipment operation state through a multi-dimensional anomaly detection technology, analyzes a fault mode and a propagation path by using a knowledge graph and a graph neural network, optimizes process parameters and a maintenance strategy based on digital twinning and reinforcement learning, provides a 3D virtual interface and an AR remote cooperation function, and improves the reliability of the system. And intelligent management of the whole life cycle of the industrial control system is realized. The PLC programming efficiency is greatly improved, accurate anomaly detection and fault prediction are realized, system operation parameters are optimized, the service life of equipment is prolonged, the man-machine interaction experience is improved, and the problem of unified management of multiple brands of PLCs is solved.

The invention provides a software code defect detection method and system based on program code feature fusion, and the method comprises the steps: carrying out the grammar and structure inspection of a code through a static analysis tool, and extracting defect features related to control flow chaos, exception handling and resource leakage; running a preset test case through a dynamic analysis tool, capturing an execution path and an abnormal behavior in a runtime state, and obtaining defect features which are not covered by static analysis; if the output result of the decision tree model is that defects exist, grouping comprehensive defect feature vectors by adopting a density-based clustering algorithm, determining the types and distribution ranges of the defects, and selecting the density clustering algorithm to process defect features in non-spherical distribution; and according to a clustering result, generating a defect repairing suggestion, combining code logic and execution path information, and determining a specific position and an influence range of the defect. According to the method, the stealth defect in the code can be effectively identified and positioned, the software quality and reliability are improved, and timely and accurate defect repairing guidance is provided for developers.

The invention discloses an automatic compiling and adapting method for an RISC-V extension instruction, and belongs to the technical field of compilers. According to the method, a dynamic DSL (Digital Subscriber Line) for registering a custom instruction, adding register use constraints and defining a specific code mode is designed. The LLVM plug-in is used for automatically integrating the registered custom instruction in the compiling process. The invention relates to a self-defined instruction rapid adaptation mechanism which is specially provided for a continuously provided RISC-V self-defined instruction expansion scene, and by automatically generating correct assembly codes of registered self-defined instructions, correct registers are automatically distributed and reserved according to register use constraints; the method comprises the following steps of: searching a defined specific code mode and semantics, and automatically generating and inserting a registered custom instruction in a program, so as to realize the rapid adaptation of a compiler to an RISC-V custom extension instruction and the automatic use of a program code to the custom instruction, and the whole process does not need a developer to manually modify the compiler or a program source code.

The invention discloses natural language computer-aided engineering and a computer-aided design method. CAE simulation analysis and design optimization on complex product design are high in threshold and difficulty. According to the method, an artificial intelligence large language model is finely adjusted by using professional knowledge of CAE and CAD, an agent is developed to call an API service of the large language model, an engineer dialogues with the agent by using a natural language, and the large language model can understand purposes and requirements of the engineer and generate corresponding operation instructions or program codes of CAE software and CAD software; the agent drives the CAE software and the CAD software to execute instructions or run programs to complete simulation analysis and design optimization tasks, and an engineer can cooperate with a plurality of professional CAE software and CAD software to perform simulation analysis and design optimization of multi-physics coupling only by dialogue with the agent. The intelligent agent can autonomously complete loop iteration simulation analysis and design optimization tasks according to formulated scheme steps and rules.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through a post processing. In some examples, such a method includes operations of receiving, by a post processing engine, a response to an auto-generated prompt from the LLM that includes programming code generated by the LLM, performing a post processing of a response from the LLM that includes the programming code generated by the LLM including performing an error correction process when a term of the programming code generated by the LLM is inconsistent with terms of a schema of the user, and generating a graphical user interface (GUI) that displays the response to the auto-generated prompt when the terms of the programming code generated by the LLM including any replacement terms are consistent with the terms of the schema.

An apparatus for measuring the stopping distance of a working machine is provided. The working machine includes a user interface, at least one processor, and at least one memory including a computer program code. The at least one memory and the computer code are configured to, with the at least one processor, cause the apparatus at least to: receive at least one speed parameter based on at least one user input on the user interface, receive at least one speed information value from a controller or the engine control unit of the working machine, send at least one command to the controller or the engine control unit to at least stop the working machine when the received speed information value is equal to the at least one speed parameter, and calculate the stopping distance of the working machine. A method and a system are also disclosed.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through novel processes for performing retrieval augmented generation (RAG). In some examples, such a method includes operations of receiving a natural language user-provided prompt, performing a first RAG process including retrieving first RAG data including natural language, programming syntax pairings from a vector database and performing a second RAG process including retrieving second RAG data including additional programming syntax examples from a vector database that have a second level of similarity with programming syntax of the natural language, programming syntax pairings retrieved during the first RAG process. The method may also include generating an auto-generated prompt requesting generation of programming code by a large language model (LLM) and generating a graphical user interface that displays the response to the auto-generated prompt from the LLM.

Some implementations of the disclosure provide a computer-implemented method including operations of receiving a user question by an orchestration agent, where generating a response to a user question includes generation of programming code, executing, by a sub-large language model (LLM), an instruction to generate the programming code and performing, by the sub-LLM, a validation process including determining whether the programming code generated by the sub-LLM includes a syntax error. When the validation process indicates the programming code does not include the syntax error, the method includes operations of invoking a logic module configured to execute the programming code, wherein the logic module is provided the programming code generated by the sub-LLM and executes the programming code and generating, by the orchestration agent, a graphical user interface that displays the response to the user question that includes or is based on results of execution of the programming code.

The invention relates to the technical field of intelligent machining, in particular to a machining simulation verification method and device, computer equipment and a storage medium. The machining simulation verification method comprises the steps of generating an initial machining program code of a to-be-machined object based on machining equipment and a machining path; calling a second simulation program, determining a target machine tool model matched with the machining equipment in the second simulation program, and performing machining simulation on the to-be-machined object based on the initial machining program code and the target machine tool model to obtain a machining simulation result; adjusting the machining equipment and the machining path based on the machining simulation result; and performing machining simulation on the to-be-machined object based on the new machining program code and the target machine tool model until an obtained new machining simulation result meets a preset condition, and determining the current machining program code as a target machining program code of the to-be-machined object. A method for manufacturing industry machined product yield is provided.

In some embodiments, a computer cluster system comprises a plurality of nodes and a software package comprising a user interface and a kernel for interpreting program code instructions. In certain embodiments, a cluster node module is configured to communicate with the kernel and other cluster node modules. The cluster node module can accept instructions from the user interface and can interpret at least some of the instructions such that several cluster node modules in communication with one another and with a kernel can act as a computer cluster.

Disclosed herein are computer-implemented systems and methods for code value assessment. For example, in one aspect, the system comprises, an input module configured to receive program code submissions from developers, a processing unit equipped with Text-Based Models with Contextual Understanding (TBM-CUs) configured to evaluate the functional meaning, purpose, and value of submitted code segments and distinguish between code contributions from human programmers and machine learning systems, a visualization module configured to present the assessed value of the analyzed code over various time periods and dimensions, offering insights into trends, patterns, and comparative performance, a communication module configured to translate programming code meaning or function into plain language summaries for non-technical stakeholders, and an AI peer review module configured to automatically review, accept or reject code contributions.

In one aspect, computer program code is synthesised based on program design artefacts. At least one code synthesis prompt is created based on a program design artefact, and synthesised program code is obtained by submitting the at least one code synthesis prompt to a machine learning (ML)-based generator. The program design artefact for synthesizing the first program code is obtained by submitting at least one program design prompt to the ML- based generator. When the synthesised program code is modified, at least one design update prompt is submitted to the ML-based generator based on the modified program code, causing the ML-based generator to generate an updated program design artefact. Alternatively, when the program design artefact is modified, at least one code update prompt is submitted to the ML-based generator based on the modified artefact, causing the ML-based generator to generate updated program code.

An information handling system node includes a hardware processor, a memory device, a power management unit with a peripheral device node operatively coupled to the information handling system node. The hardware processor may execute computer-readable program code of a custom event and device manageability service sub-agent to discover the peripheral device node operatively coupled to the information handling system node including a unique peripheral device identification (ID), request and receive, via a unified device application programming interface module, peripheral device capabilities and event IDs, where the event IDs describe custom events that are transmittable via any bus protocol from the peripheral device node, and register for custom event notifications with the peripheral device node by sending callback messages to the peripheral device node describing which custom events to receive custom event notifications about and setting custom event triggers that trigger the custom event notifications.

In example embodiments, an apparatus, a method, and a computer program product are provided. An example apparatus include processing circuitry; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processing circuitry, cause the apparatus at least to: overfit a neural network on each media item, from a batch of media items, for a number of iterations to obtain an overfitted neural network model for the each media item; evaluate the overfitted neural network model on the each media item to obtain evaluation errors; and update parameters of the neural network to be based on the evaluation errors.

An electronic device, a method for wireless communication, and a computer readable storage medium are provided. An electronic device on a terminal side may include at least one processor and at least one memory, wherein the at least one memory includes computer program code. The at least one memory and the computer program code are configured, by the at least one processor, to cause the electronic device to perform: obtaining a beam measurement result for a measurement beam; and under the condition that the obtained beam measurement result does not meet the requirement of the beam prediction model on the beam measurement result to be input, a non-conformance report is sent to the network side equipment.