23 results about "Identity mapping" patented technology

This invention relates to a driving method and system for improving the compatibility of network interface cards (NICs) and optical modules, comprising: acquiring optical module identity data, NIC identity data, and an identity mapping table; the optical module identity data includes an optical module manufacturer's equipment identifier and an optical module manufacturer's name; if the optical module manufacturer's equipment identifier is a first identifier, a first information table is generated based on the NIC identity data and the identity mapping table; new optical module identity data is obtained based on the NIC identity data and the first information table, and the new optical module identity data is sent to the NIC's receive buffer; the NIC chip reads the new optical module identity data from the NIC's receive buffer. This invention generates a first information table, selects new optical module identity data compatible with the NIC from the first information table, and sends the new optical module identity data to the NIC chip. This enables data transmission between the optical module and the NIC when the NIC and optical module cannot connect or data transmission is unstable, thereby improving the compatibility of the NIC and optical module.

The present disclosure is directed to a managed cloud-based FIDO authenticator implemented with a distributed switchboard system. The disclosed switchboard implementation enables proxy automation and management of FIDO-related service, including registration and / or access operations, between FIDO service requesting and relying parties. The described process is based on generation of a single pre-validated and personalized identity token, based on validation, tokenization and identity mapping of an identification record associated with a FIDO service-requesting entity (SRE). The generated FIDO identity token can be used as a validity signature for streamlined resolution of identity for generation of FIDO access credentials. The managed FIDO authenticator further provides a FIDO credential management and recovery features for user and / or client applications reliant upon FIDO-authenticated services. The distributed switchboard implementation further enables implementation of a federated FIDO services for a distributed application whereby various FIDO-reliant application components can perform their own FIDO authentication via the distributed switchboard.

This invention discloses a unified image anomaly detection method based on pseudo-anomalies and contrastive learning. The method constructs a dual-teacher model containing teacher and student networks with identical structures. During training, a twin data flow strategy is employed: on the one hand, the original normal image data flow is preserved; on the other hand, multi-scale simplex noise and external textures are fused to generate pseudo-anomaly images and pixel-level masks, forming a pseudo-anomaly data flow. The network is segmented at the deep feature output end according to the data flow direction, calculating the spatial contrast loss to ensure the fidelity of normal feature reconstruction, the anomaly boundary loss guided by the mask to push away anomalous features, and the global class-aware contrast loss to separate multi-class semantics. This invention overcomes the identity mapping bottleneck of traditional unsupervised networks in multi-class unified scenarios, solves the feature confusion problem, and achieves high-precision multi-class unified anomaly detection without class priors.

This invention belongs to the technical field of cross-e-commerce platform information processing, specifically involving an AI-based method and system for joint prediction and recommendation of cross-platform e-commerce user behavior. The method collects user behavior data from different e-commerce platforms and converts the raw behavior into unified event records. Based on anonymized identity mapping information, cross-platform aggregation is performed, and behavioral fragment units are generated. Event density, category overlap, cross-platform jump time interval, search click consistency, time difference between adding to cart and placing an order, and platform switching frequency are extracted to generate migration trigger tags. The behavioral fragment units and migration trigger tags are then input into a dual-channel sequence prediction network, which outputs click probability, add-to-cart probability, and purchase probability, forming a recommended content list. This list is incrementally updated based on actual response behavior. This scheme can uniformly represent and jointly predict continuous behavior across platforms.

The application relates to the technical field of communication, and provides a packet loss processing method, device, equipment, medium and product. The method comprises the following steps: obtaining a total sequence number of a to-be-sent data packet, determining a container identifier and a container internal sequence number according to the total sequence number, and encapsulating the container identifier, the container internal sequence number and a path switching identifier for the to-be-sent data packet to obtain a first data packet; and sending the first data packet, so that a network switching device forwards the first data packet to a receiving end device based on the container identifier and the path switching identifier. The container identifier and the container internal sequence number are uniquely determined by the total sequence number, thereby establishing a constant identity mapping for each data packet, which can be used for accurate retransmission of the corresponding data packet; the existence of the path switching identifier enables the sending end to continuously use the original packet loss determination rule without the need of re-computing or maintaining a dynamic mapping table, thereby reducing retransmission delay and computation cost and ensuring the consistency of packet loss determination logic before and after path switching and the accuracy of retransmission.

The application relates to the technical field of computers and discloses an implementation method and device of a reentrant lock, which comprises the following steps: performing a preset identity mapping detection operation on a first task triggering a lock request to obtain a detection result; when the detection result indicates that the first task is a new task, based on a preset double-lock architecture, a preset atomic operation is performed to control the first task to acquire a master lock; when the master lock is successfully acquired, the first task is controlled to acquire an operation lock, and based on the current scene of the first task, the first task performs a resource access control operation through the master lock and a state management operation through the operation lock; and when the master lock is not successfully acquired, the first task is added to a preset waiting queue. It can be seen that the application can provide a new access control mode, which is beneficial to ensuring that a task can safely access a shared resource.

This application discloses a quantum circuit and quantum system for a quantum residual neural network used to process target data, belonging to the field of quantum computing technology. The target data includes at least one feature data arranged according to position. The quantum circuit includes: a first qubit and a second qubit for encoding and evolving each feature data; a first quantum logic gate and a second quantum logic gate acting on the first qubit for performing an identity mapping operation on the feature data; and a third quantum logic gate acting on the second qubit and entangled with the first qubit between the first and second quantum logic gates. The third quantum logic gate includes an encoding module for encoding the feature data and parameterized training logic gates located before and after the encoding module for realizing residuals. The encoding module includes the encoding logic gate and entanglement gates located before and after the encoding logic gate, and the entanglement gates realize the entanglement of the first qubit and the second qubit. The second qubit corresponding to one feature data at an adjacent position is the first qubit corresponding to another feature data. By applying this application, the importance of features can be distinguished, thereby focusing on key information.

The application discloses an electromagnetic abnormal signal detection method and system, and relates to the technical field of signal detection and identification. The method comprises the following steps: obtaining a target electromagnetic signal; performing feature extraction on the target electromagnetic signal to obtain a signal feature; determining a signal detection result of the target electromagnetic signal according to the signal feature and a signal detection model; the signal detection model comprises an identity mapping module and a convolution layer module connected in sequence; when the signal detection result is that the target electromagnetic signal is a suspicious abnormal signal, performing signal time-frequency domain statistical distribution feature calculation on the target electromagnetic signal, and performing signal identification on the calculation result according to a signal distribution feature map to determine whether the target electromagnetic signal is abnormal; the signal distribution feature map is determined according to signal time-frequency domain statistical distribution feature calculation on a measured electromagnetic signal. The application can improve the applicability and accuracy of abnormal signal detection in a complex environment.

The application discloses a two-dimensional user identity mapping and large-scale license-free access method based on prime number interleaving series spread spectrum, which comprises the following steps: at the user end, sequentially performing cyclic redundancy check addition, data segmentation and GRS coding, modulation and aggregation on original information packets; embedding a prime number interleaver in the two-dimensional user identity into the aggregated frame vector; concatenating and spreading the interleaved symbol vector to form a transmission signal vector; the two-dimensional user identity further comprises a spread spectrum signature, and the spread spectrum signature is an outer layer identity, and the prime number interleaver is an inner layer identity; at the base station, the superimposed signal is divided into multiple spread spectrum segments, and the spread spectrum signature is detected to determine the collision group corresponding to the candidate spread spectrum signature; the prime number interleaver is used to separate the collision users in the collision group, and the user data of the collision users is decoded and verified. The application can improve the fine separation ability of the collision users without increasing the length of the spread spectrum sequence, without expanding the size of the finite field, and without increasing the regulation of the low power consumption and low operation amount of the Internet of Things terminal.

System for continuous identity verification and access control in cross-company computing infrastructures, comprising an identity acquisition unit, a cross-company identity mapping unit, a device state verification unit, a behavior analysis unit, a context evaluation unit, a dynamic trust value calculation unit, an access decision unit, and a permission adjustment unit, wherein the system is configured to repeatedly acquire technical identity, device, behavior, and context parameters during an ongoing access session, generate a current trust value from them, and automatically adjust access permissions to the current trust value.

This invention discloses a method, system, and apparatus for optimizing MIMO radar waveforms based on signal-to-interference-plus-noise ratio (SINR), relating to the field of radar signal processing technology. The method includes: initializing MIMO radar system parameters; constructing a deep convolutional neural network model for optimizing initial waveform phase data; inputting the initialized waveform phase data into the deep convolutional neural network, processing it through grouped convolution and identity mapping, and outputting the optimized waveform phase; generating a transmitted signal based on the optimized waveform phase and establishing a received signal model; calculating the SINR value corresponding to the current transmitted waveform; comparing the calculated SINR value with a threshold value; if it is greater than the threshold, continuing iterative optimization; otherwise, outputting the current waveform as the optimal waveform. This method utilizes the nonlinear modeling capability and powerful data processing capability driven by neural network data to solve the problem of high computational cost in traditional robust optimization.

The application discloses a gradient shielding and height-aware automatic driving BEV occupancy network construction method, and belongs to the technical field of automatic driving perception. Two complementary mechanisms are used to solve the defects of module integration disturbance and insufficient height perception in the prior art. Firstly, a zero initialization residual gate mechanism is introduced in the GS-RDA to strictly maintain an identity mapping in the initialization stage, so that the newly introduced attention module can be grafted onto the pre-training encoder without disturbance, thereby avoiding the destruction of the existing feature representation. Secondly, the HAL uses a voxel-by-voxel adaptive convex combination fusion strategy to replace a simple channel replication mode, fuses BEV features and learnable height embedding, and further refines through three-dimensional convolution to model the structural continuity in the vertical direction. The accuracy and spatial consistency of three-dimensional occupancy prediction are significantly improved, the training is stable, the calculation cost is small, and the automatic driving real-time perception demand is adapted.

The present application relates to a binary function similarity detection method using hypergraph twin neural network, belonging to the field of computer and information science technology. The present application firstly constructs a directed heterogeneous hypergraph containing data flow and control flow hyperedges based on the abstract syntax tree of binary functions; then uses the improved hypergraph twin network to obtain hypergraph embedding vectors, and calculates the cosine distance as the similarity measure of function pairs, effectively learns the structure and semantic features of long-distance statements through multi-layer convolution, uses the initial residual connection and identity mapping to retain the initial features of the statements and avoid the performance degradation caused by the stacking of network layers; finally, the similarity measure is compared with the threshold to determine whether the function pairs are similar. The present application aims at the problem that the existing methods do not generate sufficient information representation of function semantics and structure, and it is difficult to accurately model the mutual influence between long-distance statements in the function, and proposes a binary function similarity detection method using hypergraph twin neural network, which improves the detection accuracy.

This invention relates to an automated detection and analysis method based on mobile phone forensic data parsing, belonging to the field of electronic data forensics and intelligent analysis technology. The method includes: S1 standardizing and cleaning the multi-source heterogeneous raw data extracted from the mobile phone, constructing a global data view and establishing a dual-dimensional global index of time and entities; S2 extracting multi-dimensional features of entities, calculating similarity, performing unified identification and identity mapping of related entities, and aggregating records from all sources of the same subject to generate an entity association table; S3 constructing a multi-dimensional abnormal behavior detection model, identifying various abnormal behaviors and connecting them to form a complete abnormal behavior chain, and completing risk quantification scoring; S4 automatically extracting related evidence nodes based on the abnormal behavior chain, generating a logically closed-loop case evidence chain, and completing legality and completeness verification. This achieves fully automated processing of mobile phone forensic data from preprocessing, entity association, anomaly detection, and evidence generation.

The present disclosure is directed to a managed cloud-based FIDO authenticator implemented with a distributed switchboard system. Exemplary implementations enable proxy automation and management of FIDO-related service, including registration and / or access operations, between FIDO service requesting and relying parties. Exemplary implementations are based on generation of a single pre-validated and personalized identity token, based on validation, tokenization and identity mapping of an identification record associated with a FIDO service-requesting entity. The generated FIDO identity token can be used as a validity signature for streamlined resolution of identity for generation of FIDO access credentials. The managed FIDO authenticator further provides a FIDO credential management and recovery features for user and / or client applications reliant upon FIDO-authenticated services. Exemplary implementations further enable implementation of a federated FIDO services for a distributed application whereby various FIDO-reliant application components can perform their own FIDO authentication via the distributed switchboard.

This application discloses a training method and related apparatus for a quantum generative adversarial network (GAN), belonging to the field of quantum computing technology. The GAN includes a generator and a discriminator. The method includes: using the generator to obtain generated samples against random noise; using the discriminator to distinguish between real samples and the generated samples, obtaining a discrimination result; updating the parameters of the generator and the discriminator based on the discrimination result, the loss function of the generator, and the loss function of the discriminator, to obtain a trained GAN. At least one of the generator and the discriminator includes a quantum convolutional layer and a quantum residual neural module connected sequentially. The quantum residual neural module includes a first qubit and a second qubit for encoding and evolving each feature data. Two qubits; a first quantum logic gate and a second quantum logic gate acting on the first qubit for performing an identity mapping operation on feature data; a third quantum logic gate acting on the second qubit and entangled with the first qubit between the first and second quantum logic gates, wherein the third quantum logic gate includes an encoding module for encoding feature data and parameterized training logic gates located before and after the encoding module for implementing residuals, and the encoding module includes an encoding logic gate and entanglement gates located before and after the encoding logic gate, the entanglement gates realizing the entanglement of the first and second qubits; the second qubit corresponding to one feature data at an adjacent position is the first qubit corresponding to another feature data, the feature data being obtained based on a quantum convolutional layer. Applying this application can effectively suppress the gradient vanishing problem and reduce the waste of training resources caused by training failures.

This application belongs to the field of computer vision and 3D point cloud processing technology. It provides a point cloud feature extraction and registration method based on manifold-constrained hyperconnections. The embodiments of this disclosure extend the feature dimension of the standard residual flow to multiple flows and introduce pre-mapping, post-mapping, and residual mapping matrices. By applying the Sinkhorn-Knopp algorithm to impose double random manifold constraints on the residual mapping matrix, the identity mapping characteristics of signal forward propagation and backward gradient are strictly maintained while enhancing multi-flow feature interaction. By replacing the standard residual block in the original Predator encoder with a KPConv-mHC block and combining it with a recomputation memory optimization strategy, the network's ability to express local geometric features of the point cloud can be significantly improved while controlling computational overhead. This effectively solves the problems of limited feature extraction and the difficulty in stabilizing large-scale networks in the registration of point clouds in complex 3D scenes and with low overlap rates.

The subject technology includes an identity mapping module that implements a system of networked communications that establish a consent based framework for creating and managing cross site identifiers across multiple publisher websites. The identity mapping module implements a call flow that coordinates multiple publisher sites with an authenticator website and an identity database to create a cross site identifier upon receiving a consent confirmation. The cross site identifier may be stored in local storage of a web client. One or more tags on the publisher website may communicate with the identity mapping module 106 to provide access to the cross site identifiers to enable other publisher websites with access to the authenticator website to quickly access the cross site identifier.

The application discloses a WeChat ecological multi-channel OpenID unified identity mapping and event driving system and method, which is applied to intelligent park and factory security management scenes. The scheme solves the WeChat OAuth callback limitation problem under the front-end and back-end separation architecture by deploying the intermediate page service in the record callback domain name, and stably obtains the multi-channel OpenID and the globally unique wechat_unionid. Taking the wechat_unionid as the core, the multi-channel identity identifier is normalized and associated to the unique business user ID through the non-overlay field update rule. Based on the business user ID, the memory cache and the message queue are combined to realize the low-delay accurate push of the security event. The scheme has completed the full code development and production verification, solves the problems of unstable multi-channel identity collection, identity fracture, field coverage and alarm push delay, and can be widely applied to various intelligent security systems.