11 results about "Learning by example" patented technology

Techniques and architecture are described for converting tree structured data such as, for example, JavaScript Object Notation (JSON) data, into multiple feature vectors to train multiple instance learning (MIL) models for providing cybersecurity in networks. In particular, a data set is provided, wherein the data set comprises a sample configured as a hierarchal tree. The sample is converted into a set of path and value pairs, e.g., flattened into a set of path and value pairs, where the path is a sequence of field names and array indices encoding a position of a value. Each path and value pair of the set of path and value pairs is converted into a respective feature vector to form a set of feature vectors. The set of feature vectors is used to train a multiple instance learning (MIL) model, wherein each feature vector has a same, fixed length.

This invention discloses a method and system for identifying encrypted traffic based on spatiotemporal features and semantic alignment. The method first extracts the spatial and temporal feature sequences of the network flow, and uses a byte-pair encoding algorithm to convert the spatial packet length into discrete symbols. Then, the discrete symbols and temporal features are mapped to a high-dimensional space and fused together. A global spatiotemporal feature vector is extracted through a network using a concatenated one-dimensional convolution and multi-head self-attention mechanism. Next, the text semantic bullseye matrix of fine-grained behaviors of various known applications is obtained offline from a large language model. Finally, the similarity between the spatiotemporal features and the text bullseye is calculated, and a multi-instance learning max-pooling mechanism is introduced for dynamic routing. Based on this, a contrastive learning loss function optimization model is constructed or cross-modal inference is performed. This invention completely overcomes the conceptual drift problem caused by changes in encrypted features, achieving extremely high generalization accuracy and feature interpretability across generations.

This invention provides an online monitoring method and system for airborne flight runaway precursors of civil aircraft based on flight test data. The method includes: constructing a set of input flight parameters for identifying airborne flight runaway precursors; acquiring daily operational data and flight test data of the target aircraft model; and extracting physical feature information of airborne flight runaway by referencing the aerodynamic mechanism model and extreme flight envelope boundary of the target aircraft model. Based on the domain adaptation concept, an offline precursor recognition model is constructed that integrates physical feature information embedding, meta-learning, and multi-instance learning. Based on knowledge distillation technology, the recognition capability of the offline precursor recognition model is transferred to a lightweight network constructed from gated recurrent units to generate an online precursor monitoring model, enabling real-time precursor probability calculation and early warning. Finally, an intelligent agent model based on a dual-delay deep deterministic policy gradient algorithm is constructed to verify the effectiveness of the online precursor warning. This invention overcomes the cross-domain data gap and meets the requirements of online lightweight computation.

The application discloses a long video retrieval method and device based on multi-scale multi-example similarity learning. The method acquires video and text preliminary features; uses coarse-to-fine coding mode to extract information of different time granularities from video segment scale and frame scale; based on video representation of two scales, uses segment scale similarity learning branch to filter out video segments most relevant to the text and obtain segment scale similarity; uses frame scale similarity learning branch to aggregate video features guided by the filtered most relevant video segments to obtain more detailed video information, and after similarity calculation with the text, frame scale similarity is obtained; a common space learning algorithm is used to learn multi-scale similarity between long videos and texts, and a model is trained in an end-to-end manner to realize text-to-long video retrieval. The application uses the idea of multi-scale multi-example learning, and can effectively solve the text-to-long video retrieval task.

The application discloses a low-resolution face recognition system based on deep learning and monitoring equipment, and relates to the field of deep learning and monitoring equipment fusion. The system mainly comprises: collecting face images under monitoring videos to construct a data set, including face images under clear videos and face images under low resolution. The method is a multi-example learning method, adopts a self-supervised contrast learning mode to compare the feature differences between positive and negative samples, adopts a multi-scale patch embedding to facilitate improvement of model performance, fuses multi-scale information, adds a Dropout layer in a multi-layer perceptron of a Swin Transformer, increases the generalization ability of data, prevents a certain neuron of the Swin Transformer from dominating the final result, so as to neglect the results of other neurons, and facilitates classification and prediction of the whole image.

This invention discloses a multi-gene mutation prediction method based on a combination of multi-task and multi-instance learning. Belonging to the fields of digital image analysis, pathology, and machine learning, the specific steps are as follows: Preprocessing existing pathological image data by staining normalization; constructing a feature matrix for each pathological image, and further using a two-layer multi-instance learning method to construct a package for each pathological image; constructing a multi-task deep learning network based on transformer and MobileNet; applying the model to a test set and outputting pathological image analysis results. This invention employs a multi-task deep neural network and applies it to the task of predicting multiple gene mutations based on pathological images. Compared to traditional single-task networks, this invention can simultaneously predict the results of multiple tasks, saving computational resources while improving accuracy; furthermore, combining the transformer module with traditional convolution considers both local and global features.

Systems, apparatuses, and methods are provided for generating patch-level predictions in whole slide images (WSIs) using classification models trained with attention and self-attention mechanisms. A WSI is divided into patches, and each patch is processed by a feature extraction model to obtain features. Attention-based aggregation assigns weights to patch features during training, enabling the classification model to generate patch-level predictions for disease-associated or non-disease-associated target classes during inference, including artifacts. A patch is classified as positive for a target class if its predicted probability exceeds a threshold, and negative otherwise. Training uses multiple instance learning and attention-derived data to optimize model performance. This approach supports granular and interpretable outputs for diagnostic and quality assurance applications.

This application relates to an automated detection method and system for latent cracks in poultry eggs based on view AI perception and multi-example learning, comprising: (S1) acquiring multi-view, multi-site images of each poultry egg using an image acquisition device, and assigning an egg-level binary classification label to each egg; (S2) extracting image-level representations using a frozen backbone network, and then adaptively weighting and aggregating all image-level representations through a single-layer gated attention module, and performing end-to-end training using a standard cross-entropy loss function; (S3) obtaining the detection result of whether the current poultry egg to be detected has latent cracks. The accuracy of this method reaches 98.66±0.78%. This method achieves clear imaging of the entire surface of the eggshell while achieving high classification accuracy with low manual annotation input, and can provide an efficient weakly supervised detection scheme for high-throughput online eggshell latent crack detection.

This application discloses a training method, usage method, apparatus, device, and medium for an image classification model, belonging to the field of artificial intelligence. The image classification model includes a feature extraction network and a multiple instance learning model. The method includes: acquiring a sample image set, wherein each sample image in the sample image set includes at least two instances; training the feature extraction network using the sample images in the sample image set through self-supervised learning based on contrastive learning, obtaining a trained feature extraction network; and training the multiple instance learning model using the sample images in the sample image set through multiple instance learning based on a mutual attention mechanism, obtaining a trained multiple instance learning model. The above scheme can reduce the computational complexity of the image classification model. The embodiments of this application can be applied to various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving.

A method includes obtaining, using at least one processor of an electronic device, one or more instance level supervised artificial intelligence (AI) models. The method also includes obtaining, using the at least one processor, aggregated level label information related to the one or more instance level supervised AI models. The method further includes obtaining, using the at least one processor, instance level feature information related to the one or more instance level supervised AI models. In addition, the method includes training, using the at least one processor, the one or more instance level supervised AI models using the instance level feature information and the aggregated level label information to obtain one or more trained instance level supervised AI models.