28 results about "Positive data" patented technology

A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third-party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.

The invention relates to the technical field of data processing, in particular to a threat recognition system and method.The method comprises the steps that a self-adaptive collection module obtains network flow data and operation log output feature vectors; the multi-dimensional rule module generates rule confidence; the statistical modeling module outputs an abnormal probability score; the machine learning module outputs a classification probability in response to the abnormal probability score dynamic selection model combination; the simulation decision module is fused with the multi-source data to execute threat diffusion simulation and output hazard indexes; the grading execution module triggers grading response according to the hazard index; and the feedback optimization module adjusts model parameters in a cross-layer manner. According to the method, noise interference is eliminated through feature optimization, a multi-dimensional model is fused to calibrate an output result, a threat grading mechanism preferentially responds to key threats, and closed-loop feedback is continuously injected into a false alarm data training model, so that the problem of high false alarm rate caused by insufficient training data coverage of a machine learning model in a complex dynamic network environment is solved; security resource allocation is optimized and key threat response delay is shortened.

A data driver according to an embodiment and a display device including the same are disclosed. The data driver includes a voltage output circuit including a first amplifier configured to amplify input positive pixel data and output the amplified positive pixel data as a positive data voltage and a second amplifier configured to amplify negative pixel data and output the amplified pixel data as a negative data voltage; a selection circuit configured to alternately connect the first amplifier and the second amplifier to a first data line and a second data line, and connect the first and second data lines before polarities of data voltages that are applied to the first and second data lines are inverted; and a control circuit configured to vary a charge sharing time for connecting the first and second data lines and performing charge sharing according to change in grayscale of the input pixel data.

An autonomous vehicle (AV) can utilize quantitative data, such as data received from sensors with known intrinsic parameters (e.g., positive data) to determine a second subsequent action from a set of possible actions, using inputs such as an original subsequent action. To improve the decision-making process of the second subsequent action, the AV can utilize qualitative data, such as data received from a data storage system (e.g., negative data). Qualitative data can be text or image based, such as from a crash report or sensor data missing sufficient intrinsic parameters to make a quantitative evaluation. By weighting the quantitative data against the qualitative data, the decision-making process of the AV towards its high-level goal (such as moving toward a parking lot) can be more efficient, such as ensuring lower levels goals are met. The decision process can utilize multi-modal large language models and latent space models to weight data appropriately.

The invention discloses a data transmission method and device, an electronic device, equipment and a storage medium. The method comprises the following steps: a sending end determines to-be-transmitted target bit data in current to-be-transmitted first target byte data, outputs electric levels with opposite symbols to a positive data line and a negative data line according to the target bit data, pulls down a clock line, and a receiving end samples the positive data line and the negative data line when detecting a falling edge of the clock line, so as to obtain a to-be-transmitted first target byte data; and determining the received bit data according to the sampling result and pulling up the clock line. According to the technical scheme, a timer does not need to be used for data logic judgment in data transmission, the receiving end is triggered by the falling edge of the clock line to conduct data judgment and reading, the data response speed can be effectively increased, the data transmission speed is increased, the positive data line and the negative data line are adopted to output levels with opposite symbols, and the data transmission efficiency is improved. The transmission distance can be increased, the anti-interference capability in a complex electromagnetic environment can be improved, and the accuracy of data transmission is ensured.

A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third- party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.

A data driver according to an embodiment and a display device including the data driver are disclosed. The data driver includes: a voltage output circuit including a first amplifier and a second amplifier, the first amplifier being configured to amplify input positive pixel data and output the amplified positive pixel data as a positive data voltage, and the second amplifier being configured to amplify negative pixel data and output the amplified negative pixel data as a negative data voltage; a selection circuit configured to selectively connect the first amplifier and the second amplifier to a first data line and a second data line, and to connect the first data line and the second data line before the polarity of the data voltage applied to the first data line and the second data line is reversed; and a control circuit configured to change the charge sharing time for connecting the first data line and the second data line and performing charge sharing according to the grayscale change of the input pixel data.

A display device includes a display panel, a drive controller, and a data driver. The drive controller processes input image data according to a variable input frequency and generates a data signal with a variable frame length. The data driver converts the data signal into a data voltage and outputs the data voltage to the display panel. The drive controller determines a variable frequency mode and generates an asymmetric data signal in the variable frequency mode, including a positive data signal and a negative data signal for the same grayscale value that are asymmetric with respect to a common voltage.

A classification device (10) creates a loss function that decreases the better the classification performance for imbalanced data of a classification model for a test data distribution using unlabeled data that is data based on the test data distribution and does not have binary labels that represent a positive class and a negative class, unlabeled data that is data based on a training data distribution and does not have the binary labels, and labeled data (for example, positive data) that is data based on the training data distribution and has the binary label for the positive class. The classification device (10) updates the parameters of the classification model so as to reduce the loss function.

The present disclosure relates to a data classification method and device, electronic equipment and storage medium, and relates to the field of insurance and finance technology. The present disclosure solves the problem of low accuracy of data classification results caused by large differences in data volume of positive data and negative data. The method comprises: determining a confidence interval according to the confidence of a plurality of target positive historical data; the confidence represents the correlation degree of the position distribution of each target positive historical data and other target positive historical data in the plurality of target positive historical data; the confidence interval represents the distribution interval of the target positive historical data with a confidence higher than a preset confidence; and classifying the data to be classified according to the confidence interval to determine abnormal data and / or non-abnormal data in the data to be classified.

The application discloses an inter-chip communication method and a storage medium, and relates to the technical field of communication. The inter-chip communication method comprises the following steps: packing slave machine data through a first communication protocol layer of the slave machine to obtain a first protocol package; packing the first protocol package through a first data link layer of the slave machine to obtain a first data package; pulling a positive data pin of the slave machine to a low level, so that the host machine detects the low level and reads the slave machine data according to the low level; and sending the first data package to the host machine. The inter-chip communication method can guarantee the timeliness of data transmission, and makes data interaction more rapid.

Systems and methods for extracting rule lists from tree ensembles are provided. A system extracts first stage candidate rules from individual trees. The system identifies the first stage candidate rules that satisfy a precision threshold and places those rules in a solution set. Subsequently, a determination is made whether a further stage is needed based on whether a predetermined number of positive data samples of the data set are covered by the solution set. In the further stage, the system generates next stage candidate rules from previous stage candidate rules that have not been pruned and identifies the next stage candidate rules that satisfy the precision threshold, placing those rules in the solution set. A simplified rule list is generated by identifying a minimum subset of rules in the solution set that covers the positive data samples within the precision threshold.

The invention relates to the field of fire image recognition and the field of fire image monitoring, and discloses an electric vehicle fire recognition system based on image enhancement, which comprises a picture sample acquisition module, an intelligent sampling module, a dynamic layout combination module, an edge data processing module, a high-quality sample recognition module and an enhanced data set output module. The dynamic layout combination module is used for asymmetrically combining the randomly selected i pictures, and the edge data processing module filters low-quality samples through color recognition. According to the invention, a fire image sample and a false alarm data sample are input, and a sample set and a label set which are identified as fire images are output through data processing of an edge data processing module and a high-quality sample identification module. According to the system, the model learns the fine difference between the fire and the non-fire in the same field of view, easily-confused feature information contained in misinformation data is fully mined, the situation that the model has insufficient discrimination capability on similar features is avoided, and the fire false alarm rate is greatly reduced.

Methods, systems, and computer-readable media for identifying true positive data within a set of blast exposure data. An equation fit is applied to generate one or more equations corresponding to portions of pressure data within the set of blast exposure data. The one or more equations are compared to the pressure data to determine if respective portions of the blast exposure data relates to true positive data.

An assembly of electronic components for reception of data using an optical fiber wherein data is received in bursts, the assembly comprising: a photodiode; a transimpedance amplifier coupled to said photodiode, the gain of the transimpedance amplifier being adjusted based on a level of a gain control signal: a received input signal sensor configured to sense a received input signal level and provide the gain control signal, the gain control signal being varied according to the received input signal level; wherein the received input signal level is sensed via a sampling circuit arrangement; a comparator; and an adjustor for adjusting a low-frequency behavior and a high-frequency behavior of the comparator so that the detection of both positive data symbol level transitions and negative data symbol level transitions in the received signal have identical behavior within accepted engineering tolerances.

The application discloses a charging method, comprising: a charging head outputs a preset voltage; detecting the level value of a positive data line terminal of the charging head; determining the charging voltage according to the level value and outputting the charging voltage. The application also discloses a charging head, a portable device and a computer readable storage medium. The application determines the charging voltage according to the level value of the positive data line terminal of the charging head, which simplifies the charging steps.

Methods, systems, and computer-readable media for identifying true positive data within a set of blast exposure data. An equation fit is applied to generate one or more equations corresponding to portions of pressure data within the set of blast exposure data. The one or more equations are compared to the pressure data to determine if respective portions of the blast exposure data relates to true positive data.

The USB chip includes a positive data pin, a negative data pin, a first transceiver circuit, a second transceiver circuit, a switching circuit, and a control circuit. During the high-speed handshake phase, the control circuit controls the switching circuit to a second state to disconnect the connection between the positive and negative data pins and the first termination impedance circuit, and actuates the second transceiver circuit to alternately transmit a second voltage signal at the positive and negative data pins. During the high-speed transmission phase, the control circuit controls the switching circuit to a first state to connect the positive and negative data pins to the first termination impedance circuit, and actuates the first transceiver circuit to alternately transmit a first voltage signal with a voltage level greater than the second voltage signal at the positive and negative data pins.

The invention relates to the technical field of vehicle network interaction, and particularly provides an artificial intelligence model enhancement method and device for a vehicle network interaction scene, and the method comprises the steps: S101, obtaining a negative data set and a positive data set corresponding to an AI attack, and constructing training data through the negative data set and the positive data set; step S102, training a to-be-enhanced artificial intelligence model by using the training data to obtain a disease-carrying artificial intelligence model; and S103, judging whether the artificial intelligence model with the disease meets protection effect constraints and performance constraints or not, if yes, outputting the artificial intelligence model with the disease, and if not, performing enhancement processing on the artificial intelligence model with the disease and then repeatedly executing the step S103. According to the technical scheme provided by the invention, support can be provided for developing algorithm safety protection in a vehicle network interaction scene.

The application provides a real-time data classification and hierarchical encryption system and method based on deep learning, which comprises the following steps: dividing real-time data into a plurality of independent data frames, identifying the data attributes corresponding to each independent data frame, constructing a positive data representation of the corresponding real-time data according to the data attributes, dividing the real-time data into corresponding data sets according to the positive data representation, constructing a random ciphertext sequence based on the data attributes to encrypt the corresponding independent data frame, constructing primary encrypted real-time data according to the encryption effect of each independent data frame, selecting a plurality of random encrypted modes contained in the data set to locally encrypt the primary encrypted real-time data for several times, determining the secret level of the real-time data and saving it, which can classify different data first and then encrypt them accordingly, thereby guaranteeing the confidentiality of the data to be encrypted and reducing the processing of non-encrypted data, and fully guaranteeing the security of the data to be encrypted.

The invention provides a model training method and device, equipment, a program product and a storage medium, and the method comprises the steps: obtaining a first training data set and a second training data set, and enabling data attributes in the first training data set to be forward data attributes; the data attributes in the second training data set are negative data attributes; training the initial positive dialogue model and the initial negative dialogue model by using the first training data set and the second training data set to obtain a positive dialogue model and a negative dialogue model; a third training data set and a fourth training data set are obtained, and the third training data set and the fourth training data set comprise question and answer information; training the initial reward model by using the third training data set to obtain a reward model; and training the initial question and answer network model by using the fourth training data set, the positive dialogue model, the negative dialogue model and the reward model to obtain the question and answer network model.

The application provides a multi-source age-related macular degeneration analysis system based on a fusion deep learning model, comprising: a preprocessing module, used for acquiring a data source, performing preprocessing on the data source, and obtaining preprocessing data, wherein the data source is used for representing multi-source age-related macular degeneration picture data; a classification prediction module, used for performing feature extraction on the preprocessing data to obtain feature data, performing fusion on the feature data to obtain fusion features, and performing classification on the fusion features to obtain an age-related macular degeneration analysis result. The application has the beneficial effect that, compared with a complete picture level black box classification, the technical solution of the application improves clinical interpretability, is compatible with AMD analysis of multiple data sources, and solves the problems of a small amount of positive data and labeled data and different data formats.

The invention belongs to the technical field of optical computing, and particularly relates to a deep optical neural network training method and system in a noise environment. The training method comprises the steps of setting an initial weight of the optical neural network; processing positive and negative input data in the computer; encoding the positive and negative input data to optical signals with different wavelengths and inputting the optical signals into an optical neural network; forward propagation is carried out in the optical neural network chip to obtain a calculation result of positive and negative data; a calculation result is input into a balance detection module; calculating a layer gradient and updating the weight of the neural network of the layer; setting the weight updated in the current iteration into an optical neural network chip; transmitting the calculated positive and negative data results of the layer to the next layer, and repeating the steps; transmitting the last layer of positive data result to a final classification layer; and performing loop iteration to obtain trained optical neural network parameters. The training scheme has robustness to noise, and can be used in practical optical neural network chip training.

Novel techniques are described for positive entity-aware augmentation using a two-stage augmentation to improve the stability of the model to entity value changes for intent prediction. In one particular aspect, a method is provided that includes accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels; applying one or more positive data augmentation techniques to the first set of training data, depending on the tuning requirements for hyper-parameters, to result in a second set of training data, where the positive data augmentation techniques comprise Entity-Aware (“EA”) technique and a two-stage augmentation technique; combining the first set of training data and the second set of training data to generate expanded training data; and training the intent prediction model using the expanded training data.

A display device includes a display panel, a drive controller, and a data driver. The driving controller processes input image data according to a variable input frequency and generates a data signal having a varying frame length. The data driver converts the data signal into a data voltage and outputs the data voltage to the display panel. The drive controller determines a variable frequency mode and generates an asymmetric data signal in the variable frequency mode including a positive data signal and a negative data signal that are asymmetric with respect to the common voltage for the same gradation value.

Generally, the present disclosure is directed to systems and methods that perform spreadout regularization to enable learning of a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a limited number of classes (e.g., a single class). Examples of such settings include decentralized training of face recognition models or speaker identification models, where in addition to the user specific facial images and voice samples, the class embeddings for the users also constitute sensitive information that cannot be shared with other users.

Systems and methods for validating a classification code assigned to a data object by a first artificial intelligence (AI) model using a second AI model are provided. A data object associated with an entity including a classification code that is assigned to the data object via the first AI model can be received. The classification code for the data object that is assigned to the data object via the first AI model can be validated using the second AI model. The second AI model can be trained using positive data objects that include assigned classification codes that are correct and negative data objects that include assigned classification codes that are incorrect. A validation result can be transmitted to a user device based on validating the classification code for the data object using the second AI model.