32 results about "Pseudo data" patented technology

A method, computer system, and a computer program product for data-free knowledge amalgamation are provided. Multiple pre-trained teacher machine learning models are obtained. Each is trained on a respective different set of training data. Pseudo-data samples that mimic original training data of the teacher models are generated. A block-wise amalgamation with a self-regulative strategy to integrate knowledge from the multiple teacher models is implemented by inputting the pseudo-data samples into the teacher models and into a student machine learning model. The implementing also includes aligning intermediate representations of the student model with a unified representation capturing relevant features from the teacher models.

The objective is to improve the accuracy of the generated abnormal signal data. [Solution] The signal management device 1 includes a generator that generates pseudo-data statistically similar to true data, with each of the sequences of intensity for each frequency component corresponding to the second data being treated as true data, and a discriminator that identifies whether the pseudo-data generated by the generator belongs to the class corresponding to true data or the class corresponding to pseudo-data. The device also includes a third learning unit configured to learn multiple generative models, each having an objective function set by the probability distributions of the first data and the second data determined from the ratios estimated by the first learning unit, and the second parameter estimated by the second learning unit.

When an application execution error occurs, an entry for the execution error is included in an error log. In instances where user data associated with the error should be preserved, a placeholder corresponding to the error is included in the error log. The placeholder contains pseudo data representing the user data, but is formatted in such a way to prevent the vendor from accessing the content of the data without additional authorization. When reviewing the log file, the vendor has visibility to the pseudo data contained in the placeholders, but is not able to discern the content of the user data from the pseudo data. In response to a request from the vendor to access instances of user data, the customer can selectively provide access to particular instances of user data that are deemed to not contain sensitive information.

The invention belongs to the technical field of equipment monitoring, and particularly relates to a bearing state recognition method and system based on pseudo data, a terminal and a storage medium, and the method comprises the steps: obtaining historical bearing vibration data, carrying out the data processing, and constructing an initial data set; inputting the initial data set into the constructed TimeVQ-VAE model, generating pseudo data of the bearing state, and constructing an enhanced training set based on the pseudo data; training a BiIndRNN (Bidirectional Independent Recurrent Neural Network) model by utilizing the enhanced training set; and adopting a particle swarm optimization (PSO) algorithm to obtain an optimal hyper-parameter combination of the BiIndRNN model. According to the method, the TimeVQ-VAE model is introduced, so that high-quality pseudo data (the similarity index u is greater than or equal to 0.9) highly similar to real data can be generated, model training difficulty caused by scarcity of fault samples and imbalance of the number of various types of samples in an industrial field is effectively relieved, and dependence on a large amount of real labeled data is reduced.

A forward-direction model for predicting a processing result for a substrate from processing conditions pertaining to the substrate is generated on the basis of a plurality of actual datasets. Using the forward-direction model, a pseudo processing result for the substrate is predicted from individual pseudo processing conditions pertaining to a plurality of substrates, whereby a plurality of pseudo datasets are generated. A reverse-direction model for predicting a processing condition pertaining to the substrate from a processing result for the substrate is generated on the basis of at least some of the plurality of pseudo datasets.

The objective is to improve the accuracy of the generated anomalous traffic data. [Solution] The communication management device 1 includes a generator 131 that generates pseudo-data statistically similar to true data, using each of the series of communication volume observed for each time period corresponding to the second data as true data, and a discriminator 132 that identifies whether the pseudo-data generated by the generator 131 belongs to the class corresponding to true data or the class corresponding to pseudo-data. The device also includes a third learning unit 13 configured to learn multiple generative models, each having an objective function set with the probability distribution of the second data and the probability distribution of the first data determined from the ratio estimated by the first learning unit 11, and the second parameter estimated by the second learning unit 12.

The present application provides a kind of network security system based on virtual environment, and the pseudo database constructed with general honeypot has significant difference.In the network security system provided by the present application, the original data is classified according to the relevance (i.e., sensitivity) of the log record before the attacker, and different intensities of noise are introduced to different dimensions of data with different sensitivities in different ways, so that the different dimensions of data in a data string in the finally formed pseudo database have different camouflage effects, which not only ensures that the sensitive dimension part is completely hidden to ensure data security, but also ensures that the insensitive dimension part is more confusing to network attackers due to the weak noise.

The present invention is a pseudo-data creation system (S1) for creating pseudo-data (D1) pertaining to a pseudo buried pipe (T1), the pseudo-data creation system comprising: a reflection profile point calculation unit (P1) for calculating a reflection profile point (T4), which is the foot of a vertical line drawn from a pseudo search point (T3) to a profile line (T2); a reference reflection profile point calculation unit (P2) for calculating a reference reflection profile point (R4), which is the foot of a vertical line drawn from a reference search point (R3) to a reference profile line (R2); a reference pseudo search point calculation unit (P3) for calculating a reference pseudo search point (T3'), which is a point where transformation for matching a minute line segment of the reference profile line (R2) at the reference reflection profile point (R4) to a minute line segment of the profile line (T2) at the reflection profile point (T4) is used in the pseudo search point (T3); and a buried pipe signal calculation unit (P4) for calculating a pseudo buried pipe signal at the reference pseudo search point (T3') and associating the pseudo buried pipe signal with the pseudo search point (T3) to calculate the pseudo-data (D1).

A system for implementing data security using dynamic data fragmentation and storage is disclosed. The system accesses digital content and splits the digital content into a set of data shards according to a splitting rule. The splitting rule indicates that each data shard combined with a respective pseudo data shard should appear as the digital content. The system generates a pseudo data shard such that the first data shard together with the pseudo data shard appears as the digital content. The pseudo data shard comprises pseudo information that is a counterpart to the original information indicated in other data shards. The system communicates the first data shard and the pseudo data shard to a database.

The application provides a data enhancement method for Chinese text proofreading, and relates to the technical field of artificial intelligence. The method judges the position and type of errors prone to occur in the correct source sentence through a sequence labeling model, makes up for the defects of current methods of randomly selecting error positions and error types, and makes the data closer to existing training data; in generating multi-word errors, syntax error data generated by using the model BERT is added, so that the semantic correlation of the generated error sentences is stronger; in the process of generating spelling errors, syntax error data generated by using the model BERT is added to simulate the situation of vocabulary selection errors in writing; at the same time, the spelling errors caused by pressing the wrong key when entering the text by using the keyboard in the real entry process are considered; the generated pseudo data contains common syntax error types, which can improve the robustness of the syntax correction model and the spelling correction model to a certain extent, and make the model learn more diverse and similar error sentence features to the real data.

The invention discloses a Mongolian-Chinese neural machine translation method fusing a pre-training model and pseudo data enhancement, the model adopts a shared encoder and bidirectional decoder architecture, the shared encoder is composed of a fusion encoding layer and a structure sensing layer, and the bidirectional decoders respectively correspond to Mongolian-Chinese and Chinese-Mongolian translation directions; the fusion coding layer is used for extracting semantic features by adopting MongolBERT and XLM-R-Mongolian respectively, and performing linear weighted fusion through a learnable weight so as to generate cross-language semantic representation; a lightweight syntactic information integration mechanism is introduced into the structure perception layer, syntactic distance is embedded into position coding, and the structure perception capability is enhanced in combination with a relative position attention mechanism. In the training stage, a staged multi-task learning strategy is adopted, and Mongolian-Chinese bilingual data generated by a practical pseudo-parallel corpus enhancement method is used for model optimization. Complementation is formed in the aspects of model structure, data expansion, multi-task optimization and the like, and an extensible, efficient and robust solution is provided for neural machine translation under the low-resource condition.

The information processing device 1X is an AI based device to support a decision making and includes an acquisition means 20X, a calculation means 22X, a selection means 23X, and a generation means 24X. The acquisition means 20X is configured to acquire a plurality of sets each including a sample and a label. The calculation means 22X is configured to calculate, for each of the plurality of sets, accuracy of the label based on a difference between the label and a predicted result of the label predicted from the sample paired with the label. The selection means 23X is configured to select a set to be used for generating pseudo data from the plurality of sets, based on the accuracy. The generation means 24X is configured to generate the pseudo data, based on the selected set.

The invention discloses a federated learning model protection method based on a super network, and relates to the technical field of federated learning and privacy protection. According to the invention, a super network is adopted to generate a global model and a local model for a server and each client; in order to better capture unique data distribution characteristics between a server and different clients, an embedded network is adopted to generate descriptors, and the descriptors are used as input of a super network; in addition, the server is lack of data, and a generator is trained by adopting a data-knowledge-free distillation method, so that a generated pseudo data set can reflect global data features. According to the method, the privacy of the model can be effectively protected while high performance is guaranteed; in addition, the method still retains the capability of generating a high-quality local model for the client.

This invention discloses a method for generating tabular data based on local context retrieval and fine-tuning, comprising: obtaining raw tabular data samples for machine learning training from a public benchmark database, performing preprocessing and data partitioning to obtain feature columns and label columns, and constructing a training set; fine-tuning the local calibration parameters of a TabPFN model based on the training set to obtain a parameter-fine-tuned TabPFN model; obtaining pseudo-data samples based on the training set by manually setting the target number of samples and target category labels; and iteratively updating the pseudo-data samples and the parameter-fine-tuned TabPFN model using an iterative sampling method based on dynamic local context to finally obtain a tabular dataset. This invention, through local calibration parameter fine-tuning and dynamic local context iterative sampling, can generate high-quality, diverse tabular datasets, effectively improving the realism and usability of the generated data.

The invention discloses a federal learning backdoor attack defense method and system based on SAGAN and fine tuning. The method comprises the following steps: S1, generating pseudo data through SAGAN; s2, cleaning pseudo data, and constructing a fine tuning data set; s3, finely adjusting the global model; s4, dynamically adjusting the weight of the regular term; s5, gradient calculation and parameter updating; and S6, iterative training is carried out. According to the method, the self-attention generative adversarial network is introduced to the server side to generate pseudo data, so that the problem caused by the fact that the server cannot directly access real data of the client side in federated learning backdoor defense is effectively solved, and a clean classification model can be constructed to perform data cleaning only by using a small amount of clean data; the dependency on real data is greatly reduced, and the privacy protection capability is enhanced.

The invention discloses a transmission system cross-sensor collaborative diagnosis method based on dynamic competitiveness balance. The method comprises the following steps: acquiring monitoring data of sensors in a transmission system and preprocessing the monitoring data; the preprocessed monitoring data is input as a sample set, a cross-sensor collaborative distillation network is constructed, and the cross-sensor collaborative distillation network comprises a teacher model and a student model; the teacher model and the student model execute standard judgment respectively, and a heterogeneous sensor dynamic competitiveness balancing mechanism is constructed after the standard is reached; iteratively training a student model through optimization of distillation loss and dynamic competitiveness balance mechanism regularization loss; based on the optimal model obtained by iterative training, inputting the monitoring data to be diagnosed into the model for diagnosis, and outputting a corresponding diagnosis result; based on a cross-sensor collaborative distillation network, association between multi-sensor fusion fault features and single-sensor local features is established, pseudo data does not need to be additionally generated in the whole process, and missing data can effectively participate in model training.

The invention relates to the field of data encryption, and provides a supervision submission-oriented data encryption storage method and system, and the method comprises the steps: firstly obtaining to-be-encrypted data, and carrying out the preprocessing of the to-be-encrypted data, and obtaining a sensitive field; secondly, performing pseudo data filling and reconstruction on the sensitive field to obtain a pseudo data field and a reconstruction packet, and recording a mapping relation among the sensitive field, the pseudo data field and the reconstruction packet; generating a verification code through a digital signature algorithm and the integrity check bit, and embedding the verification code into the reconstruction packet; and finally, symmetrically encrypting the reconstructed packet embedded with the verification code, and storing the encrypted reconstructed packet into a system database. The system comprises a data preprocessing module, a pseudo data filling module, a reconstruction packet construction module, a verification code generation module and an encryption module, and can realize security encryption storage, source authentication, integrity verification and tamper-proof verification of data on the premise of ensuring that sensitive data is not directly exposed, thereby meeting the requirements of supervision departments on the security and compliance of submitted data.

The present application relates to the technical field of artificial intelligence, and in particular to an image classification enhancement processing method and device, equipment and medium. The above method is applied to the medical field, obtains a pre-training model corresponding to a previous task, a pre-training generator, real data corresponding to a current task, and real label values corresponding to the real data, uses pseudo data generated based on the pre-training generator and the real data as training data, trains a model in the current task using the training data, obtains an initial pre-training model in the current task, performs enhancement training on the initial pre-training model, and determines a trained model as a target pre-training model. In the present application, training with historical pseudo data ensures the stability of the feature distribution of the model, knowledge distillation is used to minimize the difference between the pseudo data in the adjacent task models, the feature deviation caused by the pseudo data is reduced, and the output accuracy of the target pre-training model in the current task obtained by training is improved.

The invention relates to the technical field of computers, in particular to an elimination condition storage method and device, a compiling optimization method and device and a storage medium. The method comprises the following steps: acquiring a candidate storage instruction corresponding to a reuse memory address in a loop; taking the candidate storage instruction in the branch structure as a condition storage instruction; deleting the condition storage instruction in a condition storage basic block where the condition storage instruction is located, and creating a new storage instruction in an exit basic block to complete the original write-in behavior of the condition storage instruction. Through the method provided by the embodiment of the invention, the problem of pseudo data reuse caused by condition storage after loop fusion can be avoided, so that the memory access performance of the loop is improved through data reuse in a wider application scene, and the execution efficiency is improved.

The processor includes core circuits and a cache unit having L2 to LN caches (N is 3 or more). L1 cache has a move-in buffer including entries in which memory access instruction resulted in cache miss in L1 cache is stored. The move-in buffer, when issuing a normal memory request to L2 cache, issues a pseudo memory request to L3 to LN caches in parallel and receives a pseudo data response that has coherency-unsecured data from any one cache. The re-order buffer executes a normal instruction execution completion process in response to the normal data response, executes a pseudo instruction execution completion process in response to the pseudo data response, and, when the pseudo data response is a failure, rewinds an arithmetic operation circuit that speculatively executed instructions after the memory access instruction in response to the pseudo instruction execution completion process, back to a state before the speculative execution.

The application discloses a drive system cross-sensor collaborative diagnosis method based on dynamic competition force balance, which comprises the following steps: obtaining and preprocessing monitoring data of sensors in a drive system; inputting the preprocessed monitoring data as a sample set, constructing a cross-sensor collaborative distillation network, and the cross-sensor collaborative distillation network comprising a teacher model and a student model; the teacher model and the student model respectively performing up-to-standard discrimination, and constructing a heterogeneous sensor dynamic competition force balance mechanism after reaching the standard; iteratively training the student model through optimization of distillation loss and dynamic competition force balance mechanism regularization loss; inputting monitoring data to be diagnosed into an optimal model obtained based on iterative training to perform diagnosis and output corresponding diagnosis results; relying on the cross-sensor collaborative distillation network, establishing the association between multi-sensor fusion fault features and single-sensor local features, and enabling missing data to effectively participate in model training without generating additional pseudo data in the whole process.

This enables data analysis using external language models while reducing the risk of data leakage. [Solution] The sensitive data analysis support device uses its own first language model to extract data from validation data that shows cases related to the clinical question, generates pseudo-data from the extracted data that is statistically similar to the extracted data and has reduced personal identifiability, generates a pseudo-analysis specification that is formatted for the pseudo-data based on the clinical question and validation data, inputs the pseudo-analysis specification and pseudo-data into an external second language model to obtain a draft analysis code for the pseudo-data based on the pseudo-analysis specification, and formats the draft source code for analysis of the extracted data based on the analysis specification.

A data generation device (100) comprises a pseudo-data generation unit (113) that selects two pieces of data with different labels from a dataset representing a collection of data in which each piece of data is a feature vector given a label indicating a class to which said feature vector belongs, and generates a point on a line segment connecting the two pieces of data in a vector space as pseudo-data. The data generation device (100) may further comprise a pseudo-data correction unit (114) that modifies a feature of the pseudo-data such that a deviation, which is the degree to which said feature deviates from a constraint condition on a feature of data, is not more than a prescribed value.

The present disclosure provides a three-dimensional interactive two-hand rendering method and device based on knowledge distillation. The method takes a single image as input of a teacher network and performs training; when training a student network, input a training image into the teacher network, obtain coordinates of each light ray down-sampling point from the teacher network processing as input of the student network, take a rendering image output by the teacher network as ideal output, and form a student network training sample; and train the student network by using the student network training sample. In actual rendering, input an image to be rendered into the student network, and the student network predicts a pixel value corresponding to a direction of a target view direction by using a direction vector of the direction, and outputs a rendering image. The present disclosure applies knowledge distillation technology to interactive two-hand rendering, uses high-quality pseudo data generated by a pre-trained teacher network to train a student network based on residual convolution, and realizes efficient and real-time two-hand rendering effect.

This invention relates to a method for generating CSI pseudo-data, comprising the following steps: inputting noisy data into a CSI data generation model to obtain CSI pseudo-data. The CSI data generation model includes: an encoder for extracting feature maps from the input image; a diffusion model for generating pseudo-feature maps based on the feature maps; and a decoder for restoring the pseudo-feature maps back to CSI pseudo-data. This invention can generate reliable multi-class CSI pseudo-data, and by embedding the encoder and decoder into the diffusion model, it achieves a lightweight CSI data generation model, saving significant computational power.

A system for implementing data security using dynamic data fragmentation and storage is disclosed. The system accesses digital content and splits the digital content into a set of data shards according to a splitting rule. The splitting rule indicates that each data shard combined with a respective pseudo data shard should appear as the digital content. The system generates a pseudo data shard such that the first data shard together with the pseudo data shard appears as the digital content. The pseudo data shard comprises pseudo information that is a counterpart to the original information indicated in other data shards. The system communicates the first data shard and the pseudo data shard to a database.

The invention provides an evaluation method and system for a multi-modal deep pseudo data discrimination algorithm, and the method comprises the steps: calculating the cosine similarity of the last two rounds of weight vectors after a judgment matrix is updated each time, carrying out the bit-by-bit median fusion and consistency verification of the two rounds of weight vectors if the cosine similarity is lower than a preset threshold value, and generating a target weight after the result reaches the standard; and training the identification algorithm evaluation model by using the evaluation data set, during training, applying a dynamic mask to each training sample of the evaluation data set according to a target weight, marking the position of which the modal consistency score is lower than a preset weighted mean value as a perturbable region, adding disturbance information, and recording an identification result of the model. The method comprises the following steps: acquiring a model, accumulating differences of identification results of the model before and after disturbance to obtain a robustness increment, adding the robustness increment to a corresponding index weighted score, carrying out loop iteration, and after the model is converged, evaluating a multi-modal deep pseudo data identification algorithm by using the model to obtain an evaluation result so as to accurately measure the actual performance and generalization ability of the identification algorithm.

The application relates to a Vietnamese dependency syntax analysis method and system based on multiple annotators, and belongs to the field of natural language processing. The application fine-tunes the parameters of an XLM RoBERTa model using unlabeled data. Then, an initial Vietnamese dependency syntax analysis model is trained using a UD tree library, a sentence is input into the model for analysis, and pseudo data containing noise is generated. The pseudo data is input into a pre-designed prompt template, a large language model DeepSeek is used for secondary annotation, noise data is gradually corrected through prompt learning, and high-quality annotation results are output. Finally, the pseudo data after secondary annotation is used as additional training corpus, combined with original annotation data, and a new Vietnamese syntax analysis model is trained. The application introduces a pseudo data enhancement and multiple annotator collaborative optimization mechanism, and significantly improves the performance of the model in the Vietnamese dependency syntax analysis task.