330results about "Transformation of program code" patented technology

High-level synthesis (HLS) is a complex design flow that exploits the benefit of modern language features such as loops, inheritance, templates, etc. for rapid prototyping of hardware designs. Hardware engineers explore various design space parameters in a time-consuming trial-and-error approach to meet a target design specification. This invention provides a framework where machine learning methods are integrated with Bayesian optimization (BO) to accelerate hardware design in HLS. This tool is built around the context of HLS pragma exploration and kernel transformation. It can produce best set of pragma installments to determine parameters by optimizing on multi-objective functions, such as reduction of digital signal processor, flip-flop, look-up table, power consumption, and latency. The method uses classical deep neural network and quantum neural network for reinforcement meta learning to predict the synthesis characteristics and synthesizability under a given time budget. A large language model can also assist transpilation to modify the original codes so that the best tradeoff between hardware complexity, latency, and power consumption is realized.

The invention relates to the technical field of software reliability guarantee, in particular to a code vulnerability detection method based on efficient parameter fine tuning of a code pre-training model, which comprises the following steps: preprocessing source codes of a vulnerability data set; s2, converting the preprocessed source code, and extracting a token sequence and a code attribute graph path corresponding to the source code; s3, constructing a code vulnerability detection model, wherein the code vulnerability detection model comprises a semantic feature extractor, a structural feature extractor, a feature fusion layer and a classifier; s4, jointly training a code vulnerability detection model by using the token sequence corresponding to the extracted source code and the code attribute graph path; and S5, through the optimized code vulnerability detection model, outputting a result that whether the source code to be detected has vulnerabilities or not. According to the method, the model is finely adjusted through efficient parameter fine adjustment, training resources are saved, good performance is obtained, and the code vulnerability detection efficiency and accuracy are improved by fusing and learning semantic and structural features of source codes at the same time.

An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.

Disclosed are a mapping method for a quantum program and a quantum chip, a quantum computer, and a computer. The method comprises: obtaining topological structures of physical bits in a quantum chip, a logic gate set of an initial quantum program, and an initial mapping relationship between logic bits and the physical bits; determining an execution timing of the logic gate set of the initial quantum program bits; according to the topological structures of physical bits and the initial mapping relationship, adjusting the mapping relationships between the logic bits and the physical bits corresponding to each logic gate so as to obtain the final mapping relationship; and according to the final mapping relationship, constructing the to-be-mapped quantum program equivalent to the initial quantum program to minimize the number of SWAP quantum logic gates in the to-be-mapped quantum program.

A system includes an application generating website building system (AGWBS) for converting a website to an application (app) which operates on a mobile device. The AGWBS includes at least one database storing at least one layout option for the app and BI (business information) for the website, where the BI includes at least use of settings and features by a designer of the website and of apps of the AGWBS from previous editing sessions and an app-editor to determine an editing mode for a designer of the app according to at least the use of settings and features and to provide at least one layout option from the at least one database for the app according to the editing mode and to receive modification to a designer selected layout.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing transformed code snippets using a large language model. In particular, the disclosed systems can determine a code snippet from a code, for instance, based on user selection of the code snippet. The disclosed system can analyze the code and / or the code snippet to generate a prompt comprising context. The context contains information about the functionality of the code. The disclosed systems further use a large language model to analyze the code snippet and the prompt comprising the context and generate transformed code. The disclosed systems may provide the transformed code snippet to one or more devices.

Systems and methods are disclosed for executing large quantum programs efficiently and securely. It breaks down programs into small, logical components, leveraging runtime analysis and AI-driven labeling. These components are then deployed across distributed quantum hardware nodes based on factors like noise levels, capabilities, and geolocation, optimized by a deep learning engine. Ownership and deployment paths are tracked using non-fungible tokens (NFTs) on a blockchain network, ensuring security and transparency. Outputs from each node are validated and aggregated based on their NFT linkage, resulting in accurate and reliable program results. This distributed quantum DevOps framework promotes scalability, performance optimization, and secure management, accelerating the development and application of quantum computing across diverse fields.

A method and system includes a network interface and a processor. The processor is configured to augment third-party code, via the network interface, with auxiliary code implementing a process for facilitating enforcement of one or more computer-usage rules, such that execution of the third-party code carries out the process. Other embodiments are also described.

Tagging deterministic code in artificial intelligence-generated code, including: receiving code generated by a generative artificial intelligence (AI) model; identifying at least one portion of code by identifying at least one of: one or more portions of deterministic code or one or more portions of non-deterministic code; and tagging the identified at least one portion of code.

Techniques are disclosed relating to transforming, by a computer system in response to a request to develop an evaluation strategy, the request into a first query for an artificial intelligence (AI) tool. The computer system may retrieve historical information that is related to the first query. The computer system may submit the first query to the AI tool. In response to receiving evaluation strategy information, the computer system may transform the evaluation strategy information into a second query for the AI tool. The computer system may submit the second query to the AI tool. In response to receiving executable code, the computer system may modify the executable code for compatibility with a particular computer system. The computer system may test performance of the modified executable code, and indicate that the modified executable code satisfies one or more performance thresholds for a code release.

The invention discloses an efficient code feature extraction method suitable for different programming languages, which comprises the following steps: loading different programming language configuration files from a specified project path, analyzing the language configuration files through a predefined language analysis rule mapper, and generating a language rule mapping table suitable for different programming languages; identifying programming language types, creating a multi-task queue according to the types and distributing task threads; preprocessing the source code according to the language rule mapping table, performing word segmentation and marking, and normalizing a word segmentation result to generate a normalized source code file; performing code block segmentation on the normalized source code file according to a given window and step size, generating hash values as characteristic values, and obtaining a code file taking a hash value sequence as a characteristic fingerprint; and carrying out serialized storage and output on the code file taking the hash value sequence as the feature fingerprint and the marked content according to a standardized format. According to the method, the code analysis efficiency and accuracy can be improved.

A GPI device includes a communication port for receiving a GPI signal from a first device, one or more processors; and a memory comprising program instructions. The program instructions, when executed by the one or more processors, cause the GPI device to assign a command to the GPI signal when the GPI device receives the GPI signal from the first device and transmit the command to a second device. The transmission of the command may be triggered by the received GPI signal. The command may include a script, such as a python script. The command may also include triggering transmission of a live broadcast, switching between graphics during the live broadcast, or a combination thereof.

The invention discloses an intermediate representation generation method and device based on a domestic modeling language X language. The method comprises the steps that X language code information is acquired; preprocessing the X language code information to obtain X language information; the X language information comprises N rows of code information; n is a positive integer; and processing the X language information to obtain X language intermediate representation information. Therefore, according to the method, the domestic modeling language X language can be quickly and efficiently converted into the intermediate representation capable of being explained by the program, and the method is an important step for converting the X language model into the executable program and an important link for engineering of the X language.

Methods and systems for improving modernization of legacy software using an intelligent discovery platform are described herein. A client-based agent may generate metadata regarding the received legacy software. The code metadata may be analyzed by a code classifier module, which computes a plurality of score factors from the metrics from the legacy software metadata using a knowledge base from a modernization platform. The classified code metadata may be used by a project-specific model to derive a plurality of sub-scores based on the plurality of score factors associated with the legacy software. An analytics engine may then identify a code module from the legacy software having a greatest derived vulnerability score factor. A graphical interface including reconstructed code, corresponding modern code, and an explanation of vulnerabilities may then be generated by the analytics and reporting component for the identified code module.

The embodiment of the invention provides a basic block linking method, a translator, electronic equipment and a readable storage medium, and the method comprises the steps: in the process of executing a target search code, judging whether a jump source basic block meets a de-linking condition or not; the target search code is used for searching an initial address of the translated basic block corresponding to the jump target basic block in the code cache; the jump target basic block is a basic block to which the jump source basic block is to jump; the unlinking conditions comprise that the jump target basic block crosses pages, the virtual-real address mapping relation of the jump target basic block is changed, and the jump source basic block is in a linked state; if it is determined that the jump source basic block meets the unlinking condition, basic block unlinking is carried out on the jump source basic block; and carrying out basic block linking on the unlinked jump source basic block again. According to the embodiment of the invention, the number of times of searching the jump target basic block when the direct jump instruction is executed can be reduced, and the efficiency of executing the application program by the translator is improved.

The invention belongs to the field of Web application technology, XML document processing technology and online editor technology, and discloses an XML semantic editing system based on an aviation knowledge graph. The system comprises a front-end application module, an XML processing engine module, a DTD rule verification module, a visual editing module, a plug-in extension module, a collaborative editing module and an aviation knowledge graph integration module. According to the method, professional and efficient editing of XML documents in the aviation field is achieved through the full-process design of front-end interaction, XML processing, DTD verification, visual editing, semantic assistance and collaborative management, and the Web technology, the XML processing technology, the DTD verification technology, the knowledge graph technology and the collaborative editing technology are integrated, so that the XML documents in the aviation field are edited in a professional and efficient mode. The problems that an existing XML editing tool is difficult to deploy, poor in compliance, low in efficiency and weak in cooperation in the aviation maintenance field are solved, and a specialized and efficient XML semantic editing scheme is provided for the aviation maintenance industry.

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

The invention discloses an AI component calling method and device, a medium and a computer program product, and relates to the technical field of container mirroring, the method comprises the following steps: when a first calling request sent by a large model application through a gateway is detected, determining a target AI component container instance corresponding to the first calling request, the AI component container instance is a container instance obtained after containerization packaging processing is carried out on an AI component capable of being deployed independently, and comprises a model file and a protocol adapter; performing format conversion on the first calling request through a protocol adapter in the target AI component container instance according to a preset protocol format to obtain a second calling request; calling a model corresponding to the model file according to the second calling request for task processing to obtain a processing result; and packaging the processing result into a response message through the protocol adapter according to a preset protocol format, and sending the response message to the large model application through the gateway. According to the method, the calling efficiency of calling each AI component by the large model application is improved.

The embodiment of the invention provides a binary translation method, a translator, electronic equipment and a readable storage medium, and the method comprises the steps: when a direct jump instruction in a first basic block is translated, identifying whether a second basic block to which the direct jump instruction is to jump crosses pages or not; if it is determined that the second basic block crosses pages, a target instruction sequence is inserted in front of an unoptimized instruction sequence translated by the direct jump instruction, the target instruction sequence is used for detecting whether the direct jump instruction meets an optimization condition or not, and jumping is conducted through the link instruction sequence under the condition that the direct jump instruction is detected to meet the optimization condition; the optimization condition comprises that the virtual and real address mapping relation of the second basic block is not changed; the link instruction sequence is used for skipping to an initial address of the translated basic block corresponding to the second basic block in the code cache. According to the embodiment of the invention, the execution of the direct jump instruction is optimized under the condition that the optimization condition is satisfied, so that the program running efficiency can be improved.

Tagging deterministic code in artificial intelligence generated code, including: receiving code generated by a generative artificial intelligence (AI) model; identifying at least one portion of the code by identifying at least one of: one or more portions of deterministic code or one or more portions of non-deterministic code; and marking at least one portion of the identified code.

The invention relates to a programming language migration operation method and device, an M language function can be recognized from M language script codes and converted into a Python function, in addition, for other codes with complex M language grammar forms, the other codes can be uniformly mapped and converted into the Python function, and through the functions of context and the like in the Python function, the programming language migration operation can be achieved. Different functions are implemented or different processing logics are executed for different types. The realization difficulty is greatly reduced, complex functions can be quickly realized, and the M language can be effectively operated without depending on a core library of Matlab.

A system for distributed quantum computing execution is provided. The system comprises a processor configured to receive at least one user instruction set, process user data to output a flow of quantum programs, determine allocation of available quantum resources need for the flow of quantum programs, remap the flow of programs to the allocation of available quantum resources produce distributed machine level instructions, serialize said distributed machine level instructions, and send the distributed machine level instructions to a distributed quantum computing system. The system further includes a distributed quantum computing system comprising, at least one quantum processing unit configured to receive at least one set of machine level instructions, communicate with a central controller, and send measurement results back to the processor.

The invention relates to the field of programmable logic controller programming software, in particular to a method for converting a ladder diagram of a PLC into an instruction list, which comprises the following steps of: obtaining a mapped AOV diagram by adopting a mode of adding and deleting virtual nodes; generating a logic tree by adopting a series-parallel scanning method of dynamic priority; and after the logic tree structure is obtained, performing subsequent traversal on the logic tree according to the logic relationship described by the logic tree to generate an instruction table structure. Compared with the prior art, a complex ladder diagram is greatly simplified through a preprocessing mechanism, the scanning steps of continuous series connection or continuous parallel connection original parts can be reduced, unnecessary node scanning can be fully reduced through dynamic priority series-parallel connection scanning, and the scanning efficiency is greatly improved; in addition, the method can be widely applied to a complex ladder diagram program in the PLC, and the series-parallel scanning efficiency can be greatly improved.

The invention provides an automatic operation and maintenance platform implementation method. The method comprises the steps that operation and maintenance work responsible for a plurality of operation and maintenance groups is split into basic function units; for each basic function unit, in response to acquisition of a task configuration instruction for the basic function unit, establishing a corresponding atomic task for the basic function unit; based on all the atomic tasks and the operation and maintenance work execution sequence of the operation and maintenance groups, constructing a directed acyclic graph reflecting the execution dependency sequence of the atomic tasks; determining an executable task sequence based on the directed acyclic graph; and according to the executable task sequence, calling a uniform interface to distribute the atomic tasks in the executable task sequence to an agent program for execution. According to the technical scheme, the automation level of the operation and maintenance platform is remarkably improved.

The invention provides a complex data model non-intrusive field conversion platform, which comprises a determination unit used for determining a source model and a target model; the configuration unit is used for configuring at least one mapping rule scheme, and an effective condition of the current mapping rule scheme and at least one field mapping rule in the current mapping rule scheme are defined in each mapping rule scheme; the distribution unit is used for determining a mapping rule scheme as an effective rule scheme; the mapping selection unit is used for selecting a source field needing to be mapped and a target field needing to be mapped, and selecting a field mapping rule between the source field and the target field from the effective rule scheme; and the execution unit is used for mapping the source field to the target field based on the field mapping rule. According to the scheme, multiple sets of mapping rule schemes are configured on the platform according to the source model and the target model, and the appropriate mapping rule scheme is selected to flexibly perform field conversion on the complex data model.

A GPI device includes a communication port for receiving a GPI signal from a first device, one or more processors; and a memory comprising program instructions. The program instructions, when executed by the one or more processors, cause the GPI device to assign a command to the GPI signal when the GPI device receives the GPI signal from the first device and transmit the command to a second device. The transmission of the command may be triggered by the received GPI signal. The command may include a script, such as a python script. The command may also include triggering transmission of a live broadcast, switching between graphics during the live broadcast, or a combination thereof.

A system and method for automatic software code generation utilizing one or more predictive model trained on code-related data, said code-related data involving obtained features and aspects from arbitrary code-related data. At least one instruction, such as natural language description(s), of at least one of desired code components and features can be processed by at least one of the at least one predictive models to produce corresponding code that matches at least one intent behind the at least one instruction. The generated code is analyzed and tested for correctness. At least one portion of the at least one trained predictive model leverages techniques such as attention mechanisms and transfer learning to improve contextual code generation. The present technology can be at least enabled with version control and codebases to ensure seamless integration with existing workflows. Automated code generation improves programmer productivity and enables rapid software prototyping.

The invention discloses a warehouse level code translation method and device based on a large model, and is used for solving the technical problem that the existing warehouse level code translation method only relates to a function translation pair, so that a code translation task in a warehouse level context is poor in performance. The method comprises the steps that a tree-shaped analyzer is utilized, a large model is preset, and a target language code sample self-evolution knowledge base, a dependent use example self-evolution knowledge base and a successful translation function pair self-evolution knowledge base are constructed according to an obtained open source project, a to-be-translated warehouse and a plurality of historical successful translation function pairs; and outputting the target triple translation knowledge corresponding to each to-be-translated function, and outputting a warehouse level code translation result in combination with the obtained implementation code corresponding to each to-be-translated function and the generated warehouse architecture.

In the context of software security, reachability analysis provides a mechanism to assess the ease of exploitability of a particular vulnerability or whether a vulnerability is exploitable at all. The present techniques provide a mechanism to compute reachability for one or more binary executables within the context of an execution environment. These reachability analyses can be used to determine whether the executables or components therein present any potential vulnerabilities which, in turn, can cause a computing system executing such binary executable to exhibit undesired behavior. The analyses include determining reachability metrics for each of a plurality of program locations. These metrics are used to determine an environment-aware reachability metric reflecting the runtime properties of a computing environment specified by artefacts associated with the software. Remediation actions can then be undertaken to address any security issues in the software or take other actions before it is deployed or allowed to execute.

The invention belongs to the technical field of vehicle software development, and particularly relates to a method for converting a form file into a graphic program, which is used for converting the form file into the graphic program and comprises the following steps of: reading the form file so as to read text contents and coordinate information of all cells in at least one page of the form file into a corresponding two-dimensional object array, the name of each two-dimensional object array is set as a corresponding page name; analyzing each two-dimensional object array into a flow chart area and an attribute area in sequence according to pages; constructing a graphic program according to an analysis result; and / or converting the graphic program into a table file, including creating table empty pages, the number of the table empty pages being equal to the sum of the number of the main program and the number of the nested subprograms of the graphic program; corresponding table pages are sequentially constructed according to program categories in the graphic program, one page corresponds to the main program, and the other pages correspond to the corresponding nested subprograms; and storing the constructed form file.