1676 results about "Coding block" patented technology

The invention provides a neuromorphic visual target tracking method and system based on image processing, and relates to the technical field of neuromorphic calculation, and the method comprises the steps: fusing the input of an event camera and a conventional image sensor, constructing a combined input tensor, and introducing a multi-scale convolution and synaptic event driving mechanism. Quick response and stable feature extraction of a high-speed moving target are realized, and coding block mistaken deletion and tracking loss caused by quick movement of the target are effectively avoided, so that system delay is reduced. Meanwhile, in combination with significance entropy difference evaluation and a dynamic brightness enhancement mechanism, the target judgment accuracy under low illumination and complex backgrounds is improved; redundant noise blocks are screened out through a significance weight function and confidence calculation, the redundancy calculation burden is relieved, and the lightweight characteristic of the system is guaranteed. A memory trajectory tensor and dynamic template adjustment mechanism based on a recurrent neural network is further introduced, time sequence consistency verification and self-adaptive updating are achieved, and the stability and robustness of the tracking process are enhanced.

Systems, methods, and computer-readable storage media for providing a composable cyber resilience object. A method can include identifying, by one or more processing circuits, a posture state of at least one entity, generating or selecting a first plurality of code blocks corresponding with a parameters of at least one third-party, and determining the entity qualifies for protection based on the posture state and the rules or conditions. The method can include generating or selecting a second plurality of code blocks including functions to provide the protection based on the rules or conditions and generating the composable cyber resilience object by integrating a portion of the first plurality of code blocks and the second plurality of code blocks into at least one data structure. The method can include linking the composable cyber resilience object and a computing or networking infrastructure of the entity using a communication interface or structure.

The invention discloses a medical image segmentation method and system based on a hierarchical pre-training model, and the method comprises the steps: carrying out the size adjustment of an input image, and carrying out the data enhancement operation; extracting multi-scale features of the medical image by using an encoder based on a hierarchical pre-training visual model, and inserting an adapter in front of each encoding block; a decoder based on a dense connection structure is adopted to gradually recover the spatial resolution, a feature enhancement module is applied behind each decoder block for feature enhancement, and dense connection is achieved through layer-by-layer up-sampling and cross-layer connection; multi-scale supervision and prediction are realized through three output heads, a main output head generates a main segmentation result, and two auxiliary output heads respectively generate segmentation results from an intermediate decoding layer to provide a multi-scale supervision signal. Therefore, the problems of precision, accuracy and efficiency of an existing medical image segmentation technology in tiny focus recognition are solved, and the recognition capability of tiny boundaries and complex geometrical shapes is remarkably improved.

Systems, methods, and instrumentalities are disclosed for template based cross component linear model / multimode linear model (CCLM / MMLM) adjustment. In an example, a device, such as a video decoding device, or a video encoding device, may obtain a prediction model for predicting a coding block. The device may select an adjustment model, from multiple adjustment models, for adjusting the prediction model. The device may adjust the prediction model based on the selected adjustment model. The device may process (e.g., encode and / or decode) the coding block based on the adjusted prediction model.

The system securely executes code blocks for artificial intelligence (AI) agents by creating nano sandboxes in which customer scripts execute. An input is received that causes an agent executor to execute an AI agent. The AI agent can be defined to include a code block for executing a customer script. To securely execute the customer script while preventing unauthorized access on an AI platform, the agent executor can create a nano sandbox in which the customer script of the code block will execute using read-only system resources and files. The agent executor can wrap the customer script in logic that seeds an input variable from a file in the nano sandbox. Execution results in writing to an output file, from which an output is retrieved and returned to the user device. The nano sandbox can then be destroyed.

The invention provides a code retrieval method and device and related equipment, and relates to the technical field of retrieval. The method comprises the following steps: segmenting each source code file to obtain a plurality of code blocks of each source code file; determining description information of each code block, and determining an association relationship between any two code blocks; generating a multi-layer code knowledge graph based on the description information of each code block and the incidence relation between any two code blocks; obtaining query information of a user, and determining a first code block based on the query information; determining a second code block related to the first code block in the multi-layer code knowledge graph; and generating a retrieval result by using the first code block and the second code block. Through the technical means, the problem of low code retrieval accuracy in related technologies is solved.

The invention provides a script preservation method which can be applied to the technical field of artificial intelligence. The script preservation method comprises the following steps: carrying out semantic understanding on demand related files by utilizing a large model, extracting core elements, and generating a structured demand change list; analyzing to obtain an abstract syntax tree of an existing script code, and constructing a mapping relationship between the software automation test element and a script code block; dynamically comparing the structured demand change list with an existing script code structure tree based on a mapping relation between software automation test elements and script code blocks, identifying a script range influenced by demand change, and outputting a change influence matrix; and based on the change influence matrix, generating an automatic script code snippet needing to be updated, and realizing preservation of the automatic script. The invention further provides a script preservation device and equipment, a storage medium and a program product.

The invention provides an intelligent instruction execution method, system and device and a medium. The intelligent instruction execution method comprises the steps that current environment state information of a robot and a natural language task instruction input by a user are obtained; generating cue words at least based on the environment state information, the task instruction, a predefined standardized function interface document and a thinking chain example, and inputting the generated cue words into a large language model; the method comprises the following steps: decomposing a task instruction into sub-task sequences by utilizing a large language model, and organizing the decomposed sub-task sequences into complete executable code blocks; performing static logic verification on the code blocks before code execution; if verification is not passed, error description is generated and fed back to the large language model for code correction; and in a code execution stage, if abnormal information is returned after the code is executed, the abnormal information is returned to the large language model, so that the large language model regenerates a correction code according to an abnormal reason. The autonomous decision-making level and the task execution success rate of the robot are improved.

The invention relates to the technical field of automatic testing, and discloses an automatic testing scheme generation method and device and a medium, and the method comprises the steps: matching a demand action in a test demand with a code entry name corresponding to a code segment, and determining a code risk level of a current demand code block; analyzing the code change frequency and the code dependency degree of the current demand code block according to historical test records in the test domain knowledge base, and determining the test dynamic priority of the current demand code block; constructing a control flow diagram and a data flow diagram of the current demand code block, and detecting risk nodes in the control flow diagram and the data flow diagram; generating a test scene of the current demand code block according to the risk node, and generating a boundary value test rule according to a key constraint condition in the data flow diagram; and querying an environment simulation rule of the current demand code block, and packaging the test scene, the boundary value test rule and the environment simulation rule into an automatic test scheme. According to the invention, the code coverage when the test scheme is generated can be improved.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention discloses a version updating method and device, equipment and a storage medium, and relates to the technical field of version updating, and the version updating method comprises the following steps: obtaining to-be-updated version information of to-be-updated software, intercepting an original structured query language (SQL), and generating a syntax tree corresponding to the original SQL, the to-be-updated version information comprises to-be-updated SQL and to-be-updated code blocks; according to the to-be-updated SQL, performing field updating on the syntax tree and then converting to generate a target SQL; according to the code task identifier of the to-be-updated code block, determining a code position of an original code block, and generating a target code; based on the target SQL and the target code, version updating and testing are conducted on the software to be updated, and a version updating task is completed. According to the method, a low-code platform can be helped to accurately identify data and code change ranges, codes and scripts are automatically combined, and the purpose of reducing version updating time is achieved.

The invention discloses a method and device for repairing codes in a memory, a storage medium, a chip and a computer program product, and the method comprises the steps: during starting, each target code block in a first memory is executed in sequence, and the target code blocks are obtained by dividing to-be-executed codes in the first memory in advance; when the target code block in the first memory is started to be executed every time, judging whether the target code block contains a code to be repaired or not; if the judgment result is yes, determining a target storage address of a mapping code block corresponding to the target code block in a second memory according to a preset address mapping relation, and skipping to the target storage address in the second memory to execute the mapping code block, and then jumping back to the first memory and continuing to execute the next target code block until it is confirmed that all the to-be-executed codes in the first memory are executed. According to the scheme, more efficient code repair can be realized in a lower storage space.

The invention relates to the technical field of automatic testing, and discloses an automatic test case generation method and device and a medium, and the method comprises the steps: constructing test case knowledge bases of different fields, and carrying out dynamic and static joint feature analysis on code libraries corresponding to field test data; performing intention recognition on the test demand data, and performing atomization operation on the test demand data according to the test demand intention; identifying a test demand code block associated with the test demand data; generating a basic test case of the test demand code block according to the atomization parameter, and injecting an abnormal stream into the basic test case according to the atomization parameter to obtain a target test case; and selecting a similar test case corresponding to the target test case from the test case knowledge base, performing regression test on the test demand code block by using the regression test case, and optimizing the regression test case according to a test result to obtain an automatic test case. According to the invention, the coverage rate during test case generation can be improved.

The invention discloses a method for automatically capturing semantic information of a binary code block and a related device, and relates to the technical field of computers.The method comprises the following steps that code disassembling and instruction level analysis are conducted on the binary code block, and a binary stream original instruction sequence is generated; performing semantic intermediate representation conversion and vocabulary overflow standardization on the binary stream original instruction sequence to obtain a binary code standardized instruction sequence; obtaining an instruction data dependency relationship and an instruction control dependency relationship corresponding to the binary code block, and constructing a corresponding binary code program dependency graph; semantic code node embedding is carried out on the binary code program dependency graph to generate a binary code representation vector corresponding to context semantics; and performing automatic semantic capture reasoning on the binary code representation vector corresponding to the context semantics through a pre-trained semantic understanding model to obtain semantic information of the binary code block. According to the method, the semantic information of the binary code block can be efficiently and accurately captured.

An embodiment detects, in a structured data corresponding to a graphical user interface (UI) design, a UI component. The embodiment determines, using a compliance specification retrieved from a compliance system repository, that the UI component is non-compliant. The embodiment extracts a code block from the compliance system repository, where the code block is executable to render a compliant component in place of the UI component on a UI rendering from the UI design. The embodiment combines, to automatically output a front-end UI code, the code block with generated code, the generated code corresponding to another element of the UI design, and the front-end UI code rendering a complete UI page corresponding to the UI design.

Intelligent code block selection and codebase updating using generative AI is disclosed herein. A user may request a code block for performing a task based on a given quality parameter (e.g., most energy efficient, fastest, or the like). The system may select an AI model for evaluating code blocks to meet the quality parameter. The system may identify code blocks for evaluation and execute each code block in an isolated testing environment. The selected AI model evaluates each code block execution and selects a code block based on completing the task in a way that most adheres to the quality parameter. The selected code block is returned via a user interface. The selected code block may be stored in a configuration code building block library associated with the quality parameter and the task and used when developing and revising software for the industrial automation environment.

The invention provides a multi-agent-based code conversion system and method. The method comprises the following steps: preprocessing an input source code; performing block processing on the source code to obtain source code blocks, obtaining variable information and array size information of the blocks corresponding to the source code, and integrating the variable information and the array size information into complete variable information; fusing the code description file, the code blocks of the source codes and the variable information of the blocks corresponding to the source codes to generate target code blocks; debugging and checking the target code blocks in combination with the source code blocks; the code description file is used for guiding the intelligent agent to establish a jumping type relation in the code conversion process, and it is ensured that the generated target code better meets the actual function requirement of hardware; a block identifier is inserted into a long code to divide the long code into a plurality of blocks, so that the limitation on long code conversion is relieved; codes of different styles can be output by simply adjusting the cue word of the intelligent agent; through multiple times of debugging verification, the conversion quality of the target code is ensured.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

One or more data objects from at least one of HR data system may each contain data fields storing HR information. A graphical user interface (GUI) may present primitive coding blocks representing standard graphic programming functions and custom business blocks representing high-level operations customly directed to the one or more data objects. A visual workflow may be constructed using at least one block selected, by a user of the GUI, wherein the at least one block represents operations linking the data fields of the one or more data objects to corresponding input fields of the at least one block. The at least one block of the visual workflow may be translated into a custom data connector comprising executable instructions for obtaining values for the corresponding input fields from the linked data fields and generating, based on the values, at least one corresponding data object storing the HR information.

The invention discloses a multi-modal sentiment analysis method and device based on feature decoupling and guide correction, and relates to the technical field of sentiment analysis, and the method comprises the steps: building an initial multi-modal sentiment analysis network, the initial multi-modal sentiment analysis network comprises a multi-modal feature extraction mapping block, a cross-modal interaction enhancement coding block, a feature decoupling block, a guide type gating semantic correction block and a low-rank fusion sentiment analysis block, and the total loss value of a training sentiment analysis result for training the multi-modal sentiment data is determined; and performing optimization iteration on the initial multi-mode sentiment analysis network according to the total loss value until a target multi-mode sentiment analysis network is determined, and outputting a target sentiment analysis result of the to-be-analyzed multi-mode sentiment data by adopting the target multi-mode sentiment analysis network. Based on the above scheme, the multi-modal fusion emotion recognition precision is improved.

A method for decoding a picture, a method for encoding a picture, an encoder, and a decoder are provided. The method for encoding a picture includes (i) determining a width and a height of a coding block in the picture; (ii) if the width and the height are equal to N, where N is a positive integer power of 2, determining a matrix-based intra prediction (MIP) size identifier indicating that an MIP prediction size equal to N; (iii) deriving a group of reference samples of the coding block; and (iv) deriving an MIP prediction of the coding block based on the group of reference samples and an MIP matrix corresponding to the MIP size identifier.

The invention relates to a self-adaptive selection restoration method for defocus blurred image restoration, and belongs to the technical field of image restoration. The method comprises the following steps of: establishing a multi-scale branch which consists of three coding blocks and decoding blocks and is used for processing images with different resolutions; in each branch, shallow layer features of an input image with the corresponding resolution are extracted through a convolutional layer, and then image reconstruction is carried out from the shallow layer features by self-adaptive selection modules in corresponding coding blocks and decoding blocks; wherein each self-adaptive selection module comprises a self-adaptive double-branch fractional order module and a double-path fusion strategy based on gating reweighting, so that image reconstruction and feature fusion among different branches are carried out respectively, and finally a reconstructed image is output. Compared with the prior art, the method provided by the invention can realize a more efficient restoration effect on the compressed blurred image with lower model parameter quantity and calculation complexity.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention provides a cross-modal semantic alignment method, device and equipment based on fine-grained semantic decoupling, and the method comprises the steps: inputting a to-be-aligned image into a pre-training image encoder of a cross-modal semantic alignment model, and obtaining a global semantic feature, meanwhile, a semantic feature extraction module is used in a high-level encoding block of the image encoder and based on a preset query vector, high-level visual feature extraction is conducted, visual fine-grained semantic features are obtained and input into a semantic feature decoupling module to be decoupled, and fine-grained semantic features of attributes, objects and combinations are obtained; inputting prompt words corresponding to attributes, objects and combinations in the to-be-aligned image into a text encoder for text feature extraction to obtain soft prompt text features of the attributes, the objects and the combinations; and inputting the fine-grained semantic features and the soft prompt text features corresponding to the global semantic features, the attributes, the objects and the combinations into a loss calculation module for image-text alignment, and performing linear processing on an alignment result to obtain an alignment prediction result.

A wireless transmit receive unit (WTRU) and methods are disclosed for mitigating radar interference on PUSCH transmission. The method may include: transmitting a message informing a network of PUSCH MIMO layer aggregation capability; receiving a MIMO rank; receiving an indication to use PUSCH MIMO layer aggregation; determining a PUSCH layer aggregation level based on the MIMO rank; and transmitting multiple redundancy versions of a PUSCH codeword comprising one or more code blocks based on the PUSCH layer aggregation level. The WTRU may be configured to transmit a message informing a network of PUSCH MIMO layer aggregation capability; receive a MIMO rank; receive an indication to use PUSCH MIMO layer aggregation; determine a PUSCH layer aggregation level based on the MIMO rank; and transmit multiple redundancy versions of a PUSCH codeword comprising one or more code blocks based on the PUSCH layer aggregation level.

An image decoding / encoding apparatus and method are discussed. The image decoding method can include obtaining a coding block by partitioning an image, determining a split type for the coding block based on a size of the coding block, deriving a current coding block split from the coding block based on the split type for the coding block, and reconstructing the current coding block.

A method of decoding video data includes receiving, from bitstream, video data corresponding to a non-skip mode coded block; and determining a parameter set associated with the non-skip mode coded block. In accordance with a determination that the parameter set includes information that satisfies different conditions, the method further includes receiving a first syntax element, a second or a third syntax element from the bitstream, the first syntax element is received and the first syntax element has a first value, the third syntax element is received. The method further includes decoding the non-skip mode coded block based on palette mode when the first syntax element has a first value and the third syntax element has the first value; and decoding the non-skip mode coded block based on intra prediction mode when the first syntax element has the first value and the third syntax element has a second value.

An image prediction method, apparatus, and system, a device, and a storage medium are provided. The method includes: (401) obtaining a split mode of a current node, where the current node is an image block in a coding tree unit in a current image; (402) determining, based on the split mode of the current node and a size of the current node, whether the current node satisfies a first condition; and (403) when it is determined that the current node satisfies the first condition, performing intra prediction on all coding blocks belonging to the current node, to obtain predictors of all the coding blocks belonging to the current node.

The various implementations described herein include methods and systems for coding video. In one aspect, a method includes receiving a video bitstream including a current coding block in a current image frame, obtaining a syntax element value, and determining whether the current coding block is coded with a warped motion mode or a translational motion mode based on the syntax element value. The method further includes in accordance with a determination that the current coding block is coded with the warped motion mode, selecting a first motion vector difference (MVD) setting for the current coding block; in accordance with a determination that the current coding block is coded with the translational motion mode, selecting a second MVD setting distinct from the second MVD setting for the current coding block; and reconstructing the current coding block based at least in part on the selected MVD setting for the current coding block.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.