65 results about "Local learning" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an English teaching training system and method fusing semantic matching and cognitive assessment, and the method comprises the steps: synchronously capturing a text response, a voice intonation, an eye movement track, a facial micro-expression and a touch rhythm generated in a learning process; semantic deviation deconstruction and cognitive intention quantization processing are carried out on the learning interaction original sequence, and a bidirectional deep semantic matching network is adopted to carry out context alignment on student answers and target corpora; based on the word meaning divergence point set and the cognitive load multi-scale vector, extracting a nonlinear diffusion trajectory of a learning state by using a time gating multi-layer recursive trajectory evolution algorithm; the knowledge point nodes, the deviation type nodes and the emotion triggering nodes associated with the emotion instability candidate segments are fused to construct a local learning map; and forming an emotion cognition feedback result driven by learning interest based on the local learning map and the self-adaptive error correction intervention sequence. The method has the advantage of improving the learning interest of students.

The invention provides a personalized federal learning method and system for a shared model, and the method comprises the steps: enabling a server to store and initialize the shared model, a global model and a global class prototype of a client, and enabling the client to initialize a local learning weight vector; the server sends sharing models of other clients to one client, and the client sets a local sharing model as a global model and trains the global model, and uploads the sharing model and a local class prototype to the server; and the server calculates and obtains a global class prototype and a global model according to all the received shared models and local class prototypes. According to the method, the knowledge sharing problem in federal learning is solved, and the performance of a personalized model is improved; the problem of offset in local model training of the client is solved, and the obtained personalized prototype contains more abundant global information than the personalized prototype; while state dependence and communication overhead are reduced, effective fusion of global knowledge and local characteristics is realized so as to improve robustness and adaptability of the model in a complex scene.

The invention discloses a terminal active emergency method and device, terminal equipment and a medium, and relates to the technical field of communication, and the method comprises the steps: obtaining safety condition information, carrying out the processing of the safety condition information based on a preset emergency triggering rule, and judging whether the terminal equipment needs to be switched to an emergency mode or not; the safety condition information is used for evaluating whether an emergency event exists or not; after the terminal equipment enters the emergency mode, automatically executing one or more emergency operations according to a preset emergency response rule; wherein the emergency trigger rule and the emergency response rule are dynamically updated according to local learning or remote pushing. By adopting the method, the terminal equipment can have an automatic response capability under an emergency situation, so that the response efficiency and the user safety guarantee capability under emergency scenes such as disasters and the like are improved.

The invention relates to the technical field of e-commerce, in particular to a multi-merchant user behavior deep learning analysis method, which comprises the steps of collecting interaction data of each merchant end, calculating a gradient based on a local learning model, packaging slices and uploading the slices to a cloud end; aggregating gradients, constructing a behavior relation graph and generating a causal tensor, and inputting the causal tensor and the graph domain multi-scale features into a time sequence embedding model to obtain time embedding; generating commercial tenant high-dimensional pulse vectors through random projection and binary mapping, and aggregating the commercial tenant high-dimensional pulse vectors into cross-commercial tenant vectors; executing a generative reverse process on the cross-merchant vector according to a noise strategy to obtain a prediction vector; the prediction vector and time are embedded and mapped into an energy matrix, an action vector is sampled through quantum optimization, and the action vector and a cross-merchant vector are input into a reinforcement learning network to output a recommendation decision; and generating an update gradient according to user clicking and payment feedback, and returning the update gradient to the merchant end to form a self-calibration closed loop. According to the method, cross-merchant collaborative recommendation is realized on the premise of not exposing original data, and cold start recall and real-time conversion rate are improved.

Method, comprising: receiving an indication of one or more supported bit-widths for local learning by a first node among plural nodes; generating a respective quantized version of a model for at least one of the supported bit-widths; providing the generated respective quantized versions of the model for the at least one of the supported bit-widths or a link to location from where the first node may download the at least one quantized version of the model for the at least one of the supported bit-widths to the first node.

Described is a Neuromorphic Adaptive Core (NeurACore) cognitive signal processor (CSP). The NeurACore CSP includes a NeurACore local learning layer block that is operable for receiving as an input a mixture of in-phase and quadrature (I / Q) signals and mapping the I / Q signals onto a neural network to determine complex-valued output weights of neural states of the neural network. A global learning layer is included that is operable for adapting the complex-valued output weights to predict a most likely next value of the input I / Q signal. Further, a neural combiner is included that operable for combining a set of delayed neural state vectors with weights of the global learning layer to compute an output signal, the output signal being separate in-phase and quadrature signals.

The invention discloses a remainder value evaluation method and device for second-hand electronic equipment and a medium, belongs to the technical field of electronic equipment value evaluation, and aims to solve the problems that in existing remainder value evaluation of the second-hand electronic equipment, manual detection evaluation is generally adopted, the subjectivity is high, the remainder value detection dimension is shallow, the labor time consumption cost is high, and the evaluation efficiency is high. And dynamic data support is also lacked. The method comprises the following steps: carrying out structural data acquisition processing related to a physical appearance mode on second-hand electronic equipment to be evaluated; performing data test processing based on a hardware performance mode under non-invasive flaw detection on the second-hand electronic equipment; using mode quantitative analysis under federal local learning is carried out on the software system using mode of the second-hand electronic equipment; carrying out equipment use mode scoring processing under the microscopic state analysis network; and carrying out residual value evaluation processing in a related market residual value evaluation interval between the comprehensive state score and the multi-dimensional value influence factors of the second-hand electronic equipment.

The invention relates to an electroencephalogram signal processing method and device based on a memristor two-dimensional collaborative learning architecture, and the method comprises the steps: importing a multi-channel electroencephalogram signal from an electroencephalogram emotion data set, and carrying out the preprocessing of the electroencephalogram signal, so as to construct a frequency-time two-dimensional feature map; inputting the frequency-time two-dimensional feature map into a local feature learning module to extract multi-level spatial features, and judging an emotion category corresponding to the electroencephalogram signal according to the spatial features; carrying out physical disturbance modeling based on memristor hardware to obtain a population variation mechanism; inputting variation sources of population individuals into a global evolution optimization module to increase memristive hardware disturbance, and constructing an optimization strategy to evaluate emotion categories corresponding to the electroencephalogram signals and select suitable persons, so as to screen out an optimal emotion category recognition result of the electroencephalogram signals and feed back the optimal emotion category recognition result to a local feature learning module; and forming a closed-loop process of local learning and global evolution cooperative iteration. The accuracy of the emotion category recognition result of the electroencephalogram signal is effectively improved.

The application relates to a dynamic particle swarm optimization-based X structure Steiner minimum tree construction method. The method mainly comprises the following three effective strategies: (1) a dynamic subgroup and information exchange strategy enables subgroups to exchange information with other subgroups while maintaining independence, and increases subgroup diversity; (2) an improved particle learning strategy can combine the advantages of local topological structure in particle diversity and optimization precision with the advantages of global topological structure in algorithm convergence speed; and (3) a transition from multi-group local learning to single-group global learning strategy enables particles to obtain a better line length optimization rate. The application takes optimization of line length as the target, and finally optimizes the important target of line length.

A model generation apparatus according to one or more embodiments may include: a generating unit that generates data using a generation model; a transmitting unit that transmits the generated data to a plurality of trained identification models that each have acquired, by machine learning using local learning data, a capability of identifying whether given data is the local learning data, and causes the identification models to perform an identification on the data; a receiving unit that receives results of identification with respect to the transmitted data executed by the identification models; and a learning processing unit that trains the generation model to generate data that causes identification performance of at least one of the plurality of identification models to be degraded, by performing machine learning using the received results of identification.

The federated learning method is performed by sampling at least one edge device to perform federated learning based on local data of each edge device, performing local learning on the sampled edge device to aggregate the results of the local learning, generating global weights based on the aggregated results of the local learning, and broadcasting them to each edge device.

The invention provides a portrait generation method and system based on privacy calculation, and relates to the technical field of data processing, and the method comprises the steps: carrying out the data collection of a target user through calling a plurality of data channels, and obtaining a user multi-party data set; traversing the data set to carry out local learning training to obtain a local learning log, carrying out security encryption on the data set to determine an encrypted data set, transmitting the encrypted data set to a central service holder to carry out privacy calculation to generate a data aggregation result, carrying out result decryption according to the aggregation result, and updating the data set to generate an update result; and synchronizing the updating result to a plurality of data channels to carry out portrait modeling to generate comprehensive portrait information of the target user and formulate recommendation suggestions, so that the problems of low data processing efficiency and insufficient privacy protection of a portrait generation method are solved; and the effects of efficiently generating the comprehensive portrait of the target user and formulating personalized recommendation suggestions according to the comprehensive portrait on the premise of ensuring the privacy security of the user data are achieved.

The invention provides a channel gain map reconstruction method based on a k-nearest neighbor refined neural network, relates to the technical field of wireless communication, and has the technical key points that the invention provides a two-stage CGM reconstruction framework under a sparse regular grid sampling condition, and in the stage 2, local learning is carried out during query to refine prediction, so that explicit decoupling and robust extrapolation are realized. K nearest neighbors are selected from a total sample by adopting a global k nearest neighbor thought which is not constrained by a clustering boundary, spatial continuity is preferentially utilized, and k nearest neighbors in a cluster which can be used as a contrast are selected only in the same cluster. The whole process does not depend on LoS / NLoS labels or high-cost environment prior, the CGM can be reconstructed only by regular grid sampling measurement, and high-precision benefits are kept under different sampling interval values d and k. On the premise of not increasing expensive measurement and label cost, the MSE of CGM reconstruction is obviously reduced.

Provided is a data processing apparatus and the like that easily and suitably contribute to marketing activities. In a data processing apparatus, an input unit receives input data regarding a predetermined consumption behavior. A federated learning model is generated such that at least a part of local learning models generated for a plurality of different business operators are federated. The federated learning model described above is generated by learning a relationship between a plurality of customer groups respectively generated from business customer data owned by the business operators and consumption behaviors corresponding to the business operators. The federated learning model is set to be able to output prospective customer data with respect to the input data. An output unit outputs the prospective customer data.

The present invention discloses a network embedding method, device, electronic device, and computer program product based on a federated learning network. The method comprises: obtaining local network matrices of multiple data holders in a federated learning network, wherein the data holders are participants in the federated learning network, the federated learning network is pre-divided into multiple local learning networks, and the local network matrix is ​​a matrix representation of the local learning network; encrypting the local network matrix using a preset mask matrix to obtain a local encryption matrix; uploading the local encryption matrices corresponding to the multiple data holders to an embedding server, and receiving a global encryption matrix obtained by integrating the multiple local encryption matrices from the embedding server; and decrypting the global encryption matrix based on the preset mask matrix to obtain a global embedding matrix. The present invention solves the technical problem of poor data security in network embedding in a distributed environment in the prior art.

The application discloses a vehicle image recognition system based on local feature learning, and relates to the field of vehicle image recognition.The system comprises an image preprocessing module, which is used for denoising an input image; an image segmentation module, which is used for segmenting the image into a plurality of superpixel regions; a dense feature extraction module, which is used for extracting local gradient features of the image and constructing a statistical histogram; a feature recombination and representation module, which is used for clustering analysis on the local gradient features and reconstructing feature distribution into a category distribution histogram; a local learning model construction module, which is used for selecting representative samples as a local learning center and constructing a local training subset; and a collaborative classification module, which comprises a local classifier, is used for training respectively and selecting a local classifier for category discrimination, and finally determines a vehicle region.The application scheme can realize high-accuracy and high-stability vehicle target recognition in a static picture without relying on time sequence information.

The present disclosure relates to a machine learning (ML) orchestrator entity for a ML system. The ML system comprises one or more local learning agents (LLAs) and is configured to compute an analytics output for an analytics service. The ML orchestrator entity comprises first processing circuitry configured to: receive an analytics service request for the analytics service from a consumer entity; define a ML profile for the analytics service based on the analytics service request; and determine ML job information based on the ML profile, wherein the ML job information indicates, for each LLA of the one or more LLAs, a computation operation to be performed by that LLA to compute the analytics output.

The application discloses a high-efficiency local learning system supporting an arbitrary network topology, which is applied to various local learning tasks and is realized by an electronic device with computing capability and comprises the following modules: an rlayer module, a datasets module, a grads module, a metrics module and an optimizer module; operator classes, gradient solving classes, optimizer classes, dataset classes and measurement index functions in an operator layer are used for providing interfaces of the modules in the module layer; the operator classes, the dataset download and preprocessing classes, the optimizer classes, the gradient derivation classes and the index calculation functions in the module layer all have input and output interfaces, a user can create and instantiate a network on demand through the rlayer module, download and preprocess a dataset through the datasets module, select a corresponding optimizer through the optimizer module, select a local learning gradient derivation class through the grads module, select a loss and precision calculation function through the metrics module, complete initialization of the network and corresponding hyperparameters, and realize network training and testing.

This invention discloses a method, device, and medium for assessing the residual value of used electronic devices, belonging to the field of electronic device valuation technology. It addresses the technical problems of existing used electronic device residual value assessment methods, which generally rely on manual inspection and evaluation, are highly subjective, have shallow residual value detection dimensions, are time-consuming and costly, and lack dynamic data support. The method includes: structured data acquisition and processing of the physical appearance modalities of the used electronic device to be assessed; data testing and processing of the hardware performance modalities of the used electronic device based on non-invasive flaw detection; quantitative analysis of the usage patterns of the software system usage modalities of the used electronic device under federated local learning; and equipment usage modal scoring processing under a micro-state analysis network; and residual value assessment processing within a relevant market residual value assessment range based on the comprehensive state score and the multi-dimensional value influencing factors of the used electronic device.

The invention discloses a block chain-based federated learning cross-border trade service platform model verification method, and belongs to the technical field of block chain federated learning of cross-border trade, and the method comprises the steps: in the federated learning of the current round, each client node carries out the local learning of a global model through employing a first data set, and obtains a local model, submitting the data to all verifier nodes; each verifier node trains the global model by using the second data set to obtain a verification model, verifies the quality of each target local model based on the verification model to obtain each verification result, and submits each verification result and the corresponding target local model to an aggregation node; and the aggregation node aggregates each target local model based on all verification results to obtain a new global model, and adds the new global model to the block chain network. According to the method, the quality of each local model can be accurately verified, so that the quality of the aggregated global model is guaranteed.

The invention discloses a federal learning model aggregation optimization method for adaptive adjustment of weight and learning rate, and the core of the method lies in collaborative optimization of aggregation weight and local learning rate: on one hand, through introducing an adaptive weight mechanism, the contribution degree of a low-performance client is improved, and the low-performance client is prevented from being marginalized in long-term training; on the other hand, an appropriate learning rate is configured for the client according to the computing power, the learning process of the vulnerable party is accelerated, and global convergence is balanced. According to the method, the problems of slow convergence and generalization reduction caused by calculation performance difference of clients in federated learning are solved, the method has remarkable advantages in convergence speed, communication round and final precision, and the training efficiency and accuracy are improved.

Provided is a federated learning model, a generation apparatus thereof, and the like that easily and suitably contribute to marketing activities. A federated learning model generation apparatus includes a local learning model acquisition unit and a federated learning model generation unit. The local learning model acquisition unit acquires a plurality of different local learning models that have learned a relationship between a plurality of customer groups respectively generated from business customer data owned by a plurality of business operators and consumption behaviors corresponding to the business operators. The federated learning model generation unit receives a predetermined consumption behavior of a customer as input data by federating at least a part of the acquired local learning models, and generates a federated learning model that outputs prospective customer data for the input data.

The present invention relates to a learning system, a learning method, and a learning computer program product. Each local learning device (2) of the learning system (1) learns a subset of correction weight coefficients that corrects a part of a set of weight coefficients of a base model that is a basis of a generative model using a local training dataset, generates distribution data that represents a distribution of features of data included in the local training dataset, and transmits the learned subset and the distribution data to a server (3). The server (3) generates a simulation training dataset that reproduces a distribution of features represented by the distribution data based on the distribution data received from each local learning device (2), and learns a gate network used for selecting a subset of correction weight coefficients to be used using the simulation training dataset.

A system and a method for adaptively synchronizing learning of multiple learning models are disclosed. Several local learning models are executed on multiple nodes. Learning model parameters are shared by such nodes to a master node, in multiple iterations, after a predefined synchronization interval. Such learning model parameters are aggregated and central learning models are generated based on aggregated set of learning model parameters. Accuracies of the central learning models and an average accuracy of the central learning models are determined. Accuracy of an immediate central learning model i.e. the one received after determining the average accuracy, is compared with the average accuracy. Based on the difference between the accuracy of the immediate central learning model and the average accuracy, the synchronization interval is modified, and the multiple nodes are updated about this modified synchronization interval.

This application proposes a training method, device and electronic device for a federated learning model, wherein the training method includes: obtaining corporate portraits and feature information of multiple companies on a data provider; based on the corporate portraits and the feature information, coordinating with other data providers to conduct federated training of the model to obtain the model intermediate results of each local learning model; sending the model intermediate results to a third-party platform for aggregation, and receiving the global model intermediate results sent by the third-party platform; based on the global model intermediate results, adjusting the model parameters of the local learning model to continue the federated training of the model to generate a target federated learning model, wherein the target federated learning model is used to predict the credit status of the company. Therefore, this method can apply federated learning to the acquisition of the credit status of the company, and the accuracy of the target federated learning model obtained by federal training of the model through corporate portraits and feature information of multiple data providers is better.

The application provides a credit risk control prediction model training method, a credit risk control prediction method and a system, and relates to the technical field of artificial intelligence. The training method comprises the following steps: inputting the data in the respective training sets of the two credit institutions into respective local learning models, obtaining the hidden features of the data in the respective training sets, and using the hidden features to complete the data in the training sets; inputting the data into the local prediction model; comparing the model output result with the corresponding risk label to obtain a loss value and an alignment loss value; sending the loss value of one party to the other party to obtain a reconstruction loss value and a comprehensive gradient, which is equivalent to using the user data of the self and the user data of the other party to train the local prediction model, thereby avoiding the data island problem; updating the parameters of the local prediction model according to the comprehensive gradient until the loss value and the reconstruction loss value of each party converge to the corresponding preset accuracy, and stopping the training. The local prediction model is used as a credit risk control prediction model to ensure the safety of loan funds.

To detect an anomaly in association learning while balancing information security and reduced calculation cost.SOLUTION: An integration processing unit generates global learning model parameters by performing integration processing of association learning using multiple local learning model parameters. A server difference calculation unit performs server difference calculation processing that shows the differences between each of the multiple local learning model parameters and the global learning model parameters. A learning unit performs learning processing of local learning models using global learning model parameters and learning data. A local difference calculation unit performs a local difference calculation process that indicates the differences between global learning model parameters and local learning model parameters. A detection unit performs detection processing to detect abnormalities in the server device, client device, or communication channel by comparing the server difference and the local difference.SELECTED DRAWING: Figure 2

The application relates to an activation learning method, system and picture classification method of an artificial neural network for picture classification, which comprises the following steps: constructing an artificial neural network while taking data and category labels or multi-modalities as inputs; training the artificial neural network through an unsupervised local learning method, so that the output activation intensity of the last layer of neurons in the artificial neural network reflects the typicality of input samples; inputting data to be identified into the trained artificial neural network and optimizing missing category labels so that the output activation intensity is maximum, thereby obtaining a data classification result; or inputting category labels into the trained artificial neural network with random noise and optimizing missing data parts so that the output activation intensity is maximum, thereby realizing data generation. The application can better learn the statistical probability distribution of input sample data, unifies the framework of supervised learning, unsupervised learning and a generation model, and has strong practicability.

The application provides a personalized federated learning method and system of a shared model, comprising: a server end saving and initializing a shared model, a global model and a global class prototype of a client, and the client initializing a local learning weight vector; the server sending a shared model of other clients to one client, the client setting the local shared model as the global model and training, and uploading the shared model and the local class prototype to the server end; and the server calculating the global class prototype and the global model according to all received shared models and local class prototypes. The application solves the knowledge sharing problem in federated learning, improves the performance of the personalized model, solves the deviation problem in the local model training of the client, and the obtained personalized prototype contains more global information than the personalized prototype, so that the effective fusion of the global knowledge and the local characteristics is realized while reducing the state dependence and the communication overhead, so as to improve the robustness and adaptability of the model in a complex scene.

One aspect of the present invention relates to an inference apparatus that obtains inference results for each inference model by providing object data to multiple inference models derived from local learning data obtained in different environments to perform prescribed inferences, and by having each inference model perform prescribed inferences. Then, the inference apparatus generates an inference result for the object environment by determining the values ​​of each integration parameter based on the environmental data, weighting the inference results of each inference model using the determined values ​​of each integration parameter, and integrating the weighted inference results.