1069 results about "Classifier (UML)" patented technology

The invention discloses a multi-modal fusion rumor detection method and system based on a dynamic graph convolutional neural network. According to the method, a dynamic feature graph of a language propagation path is constructed, and potential features in the language propagation process are extracted and analyzed by utilizing time sequence changes and key node relations between nodes in a propagation graph. A neural network is adopted to extract and enhance image data, text semantic features are extracted in combination with a text feature modeling network, text feature vectorization expression is achieved based on a BERT model, and rich semantic information is obtained. And a gating mechanism is introduced to dynamically adjust fusion weights of different modal features, and an information fusion strategy is optimized. A collaborative attention mechanism is further adopted for deep fusion, interactive learning of text, image and propagation path features is enhanced, and the relevance of cross-modal and time series data is improved. And finally, inputting the fused feature vectors into a classifier for accurate classification, thereby realizing accurate detection of the social media rumors. According to the method, the multi-modal features are effectively integrated, and the false information identification efficiency is remarkably improved.

Systems and methods for a prompt generation and analysis service for generating and identifying a preferred prompt for performing a function of a large language model (LLM) are provided. The prompt generation and analysis service may generate a set of training prompts for performing a function of an LLM. The prompt generation and analysis service may then query the LLM with the generated set of prompts and characterize the output of the LLM for each prompt. Using the characterization of the output and corresponding prompt, the prompt generation and analysis service can train a classifier model to classify the prompts. The prompt generation and analysis service may generate a set of target prompts for performing a function of an LLM, characterize the target prompts using the training classifier model, and identify a preferred prompt for performing the function based on the classifier model's classification.

The invention relates to the technical field of block chains, in particular to a smart contract vulnerability detection method and device based on multi-modal features, and the method mainly comprises the steps: training a meta-learning model in a dynamic adaptation module, and adjusting the global parameters of a modal feature extraction module, a dynamic gating fusion module and a classifier through the meta-learning model, the dynamic adaptation module comprises a meta-learning model constructed based on an MAML framework, and is used for optimizing global parameters of each module according to vulnerability features learned in pre-training; and inputting the multi-modal fusion feature vector into a classifier, and generating and outputting a vulnerability detection result of the smart contract. According to the method, known vulnerabilities can be accurately detected by fusing multi-modal features, and novel vulnerabilities can be rapidly adapted and detected.

The invention discloses a large reasoning model factuality enhancement method, system and device based on reinforcement learning and a medium. The method comprises the following steps: constructing a large language model reasoning thinking chain factuality detection and enhancement framework (REELANCE), and performing fine-grained fact accuracy evaluation on a reasoning chain output by a large language model; based on the result of the fact check classifier, the reasoning chain factuality is enhanced by adopting a reinforcement learning group strategy optimization technology (GRPO); a multi-dimensional reward mechanism is designed to improve the fact accuracy of the reasoning step; quantitatively analyzing the influence of enhanced training on the nerve activation change in the model through a mechanism interpretability technology; a comprehensive optimization target giving consideration to the fact accuracy and the reasoning quality is established, the fact robustness is improved, the standard reasoning performance is kept, and the balance between the fact and the reasoning ability is achieved; the key problem of inaccurate facts of an inference type large language model in a high-risk scene can be effectively solved; the invention also provides a system, equipment and a medium for realizing the method.

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

An analysis engine receives data characterizing a multimodal prompt for ingestion by a generative artificial intelligence (GenAI) model. The multimodal prompt is processed and fed into a plurality of layers from which an intermediate result of the GenAI model or a proxy of the GenAI model is obtained. The analysis engine, using a prompt injection classifier and the intermediate result, determines whether the prompt comprises or is indicative of malicious content or elicits malicious actions. Data characterizing the determination is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

The invention discloses an incomplete multi-view multi-label data classification method based on semantic enhancement and pseudo-label uncertainty perception, and the method comprises the steps: employing a dual-channel feature extraction and decoupling module to obtain the shared semantic representation and specific representation of each view in each sample for a constructed incomplete multi-view multi-label data classification network model; performing cross-view fusion on the shared semantic characterization and the specific characterization, obtaining a unified shared characterization and a unified specific characterization corresponding to each sample, performing feature fusion, obtaining a fusion characterization of each sample, inputting the fusion characterization of the sample output by the dual-channel feature extraction and decoupling module into a classifier for multi-label prediction, and performing multi-label prediction on the fusion characterization of the sample. Therefore, a multi-label classification prediction result is obtained, and model training is carried out based on a total contrast learning loss function and a joint supervision classification loss function. According to the method, the classification performance and the model robustness on incomplete multi-view multi-label data are remarkably improved through training learning under the guidance of semantic enhancement and uncertainty.

The invention relates to a food reserved sample quality monitoring method based on machine learning, and the method specifically comprises the following steps: deploying a sensor array in a food reserved sample environment, collecting reserved sample multi-dimensional physicochemical characteristic data in real time, constructing a training set through historical normal and degraded batch data, and marking a state; the method comprises the following steps: constructing a machine learning model for food reserved sample quality monitoring, inputting sample data in a training set into the model, sequentially passing through a dynamic distribution alignment module, a physical constraint confrontation enhancement module and a multi-scale residual space-time network, and performing dynamic attention mechanism enhancement by utilizing physical constraint and environment modulation. And finally, outputting a quality category probability by the multi-modal classifier. Then calculating model loss, and carrying out iterative training on the model to obtain a trained model; and deploying the trained model at a reserved sample monitoring terminal, inputting newly collected monitoring data, and predicting the quality state of the food reserved sample. According to the invention, high-efficiency and real-time quality monitoring on the quality of the reserved food sample can be realized.

The invention discloses a karst stratum tunnel disaster sensing method and system based on multi-source information, and belongs to the technical field of tunnel engineering safety monitoring, and the method comprises the steps: building a monitoring index data set, converging the monitoring index data set to a cloud end, carrying out the time-space registration, and generating a multi-dimensional time sequence data field; calculating data uncertainty of each monitoring area by adopting an information entropy theory, calculating spatio-temporal evolution characteristics, and performing classifier identification by combining a deformation field space gradient to obtain a key monitoring area; adaptively adjusting the acquisition frequency of the sensor, and starting supplementary monitoring equipment for encrypted observation; performing space-time response calculation by adopting a machine learning algorithm to generate a tunnel disaster evolution prediction result; and carrying out grading threshold comparison and numerical simulation verification on the prediction result to realize effective identification and perception of the disaster evolution state. According to the method, the technical means of combining multi-source data fusion, the information entropy theory, machine learning and self-adaptive monitoring is adopted, and dynamic, accurate and predictive perception of the tunnel disaster evolution process can be achieved.

The present invention relates to a computer-implemented multi-module classifier method and system for providing a pathogenicity classification score of a variant of a protein of interest. The classifier comprises a sequence module based on a protein language model (PLM); a structure module based on a graph neural network (GNN); a property module; and a unified head module based on a machine learning model. The invention further relates to methods for preparing, training, and implementing the multi-module classifier system.

Typical classifiers must be trained on a large input sample to accurately classify inputs. In addition, if a new classification category needs to be added to a taxonomy after the classifier has already been trained to classify within the taxonomy, the classifier must be recreated and retrained to classify within the updated taxonomy. To address at least these technical problems with classifiers, a generative language model may be used to perform classification. A generative language model is a machine learning model that generates language, typically in the form of a textual response to a data input. A generative language model may utilize a large neural network to determine probabilities for a next token of a sequence of text conditional on previous or historical tokens in the sequence of text. An LLM is an example of a generative language model.

Various embodiments include systems and methods to implement a security platform providing application-level cyberattack detection using multiple stages of classifiers. The security platform may use requests received by a web service to determine training data to train one or more machine learning models. The training data may be determined by instrumenting an application, such as a web service, with a first stage classifier to determine security events indicative of cyberattacks. The security platform may train machine learning models using aggregations of security events over various periods of time. The machine learning models may serve as second stage classifiers for the security platform.

The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts to classify documents; (iii) evaluate the classification performance of prompts; (iv) generate updates to prompts; and / or (v) train classifiers. In some embodiments, one or more processors: generate a prompt for input to the generative AI model; generate classifications for a set of documents from the corpus of documents by inputting the set of documents and the prompt to the generative AI model; based on the classifications, provide the set of documents to a review platform for manual review by a reviewer; obtain review data associated with a subset of documents from the set of documents; and train, by executing a training algorithm, a classifier using the review data as ground truth data, wherein the training algorithm is configured to analyze extracted relevant document portions of the subset of documents to train the classifier.

The invention provides a ship noise multi-feature classifier data enhancement method and system based on a multi-fine-grained conditional diffusion model. And compressing a waveform to a potential space through VQ-VAE, extracting a ship type / ship name cross semantic vector by using ResNet, and optimizing clustering in combination with a loss function. And a one-dimensional U-Net conditional diffusion model is constructed, unconditional / conditional model output is dynamically weighted and fused, and the weight is adaptively adjusted according to training loss. In the generation stage, a semantic prototype is constructed by using a high-fine-granularity label, parameters are determined by using low / medium-granularity mean value sampling and Bayesian optimization, and fine-granularity controllable waveform generation is realized. After the generated data is converted into multiple features such as MFCC and Lofar, the generated data and original data are combined to train a classifier, and a virtual class strategy relieves class imbalance. Experiments show that the MSE of generated data and real data is reduced, the classification accuracy is improved, the data diversity and the model generalization ability are remarkably enhanced, and the method is suitable for scenes such as underwater target recognition.

The invention discloses an FPGA (Field Programmable Gate Array) netlist-level hardware Trojan horse detection method based on a large language model, and relates to the technical field of integrated circuit safety and hardware Trojan horse detection, and the method comprises the following steps: constructing an original FPGA netlist into a text attribute graph containing textualized attributes, generating a path text sequence through bidirectional random walk, and carrying out two-way random walk on the path text sequence; constructing a corpus to pre-train a large language model; then, delimiting a local neighborhood by taking each node as a center, generating a path text set, extracting semantic vector representation of the path text set by utilizing a pre-training model, and further constructing a node-level training sample set; on the basis, supervised fine tuning is carried out by combining a classifier and CB-Focal Loss, and a final model is obtained; in the reasoning stage, representation construction and discrimination are carried out on nodes to be detected, and node-level hardware Trojan horse detection is achieved. According to the method, circuit topology and semantic information can be reserved at the same time, node-level hardware Trojan positioning is achieved, the detection precision and generalization ability are improved, and the automation degree is improved.

The invention belongs to the technical field of industrial visual inspection, and provides an obstacle detection model training method and an obstacle detection method based on comparative learning and feature fusion. The training method comprises the steps of obtaining a point cloud sample set; extracting statistical and geometric feature vectors of each point cloud sample; pre-training the multi-scale feature fusion network by using the point cloud sample set based on comparative learning; constructing an obstacle detection network, wherein the obstacle detection network comprises a pre-trained multi-scale feature fusion network and a classifier; training an obstacle detection network by using the point cloud sample set to obtain an obstacle detection model; the multi-scale feature fusion network comprises M cascaded feature extraction modules; the cross-layer splicing module is used for splicing the statistical and geometric feature vectors of the point cloud samples and M feature matrixes; and a channel attention module and a point cloud feature projection head. According to the method, the precision of 3D point cloud obstacle category identification and the generalization ability of the classifier can be improved, and the model training convergence speed is high.

The invention discloses a ship intelligent fault diagnosis method and system based on open label space identification. The method comprises the following steps: obtaining a multi-source sensor time sequence signal of a ship system and constructing a training sample set; carrying out feature extraction on the training sample set by utilizing a deep neural network model, and strengthening the clustering characteristics of the features by adopting a center loss function in the training process; training a classifier on the basis of feature extraction and introducing an open set loss function to form a comprehensive objective function; extracting features from a to-be-diagnosed sample, embedding the features, calculating the distance between the to-be-diagnosed sample and a known fault category feature center, and judging an unknown fault through comparison between the minimum distance and a preset threshold value; and outputting a known fault category or triggering an unknown fault alarm according to a judgment result, and dynamically expanding and updating a model knowledge base based on accumulated unknown fault samples. The method can break through the limitation of the traditional closed set hypothesis, effectively identifies the unknown fault type, and achieves the self-adaptive learning and continuous optimization of a ship fault diagnosis system.

A vulnerability detection and repair system utilize a classifier model to detect a software vulnerability in a source code snippet and the tokens in the source code snippet attributable to the vulnerability. A large language model is then given the vulnerable source code snippet, its vulnerability type, the vulnerability tokens, and a few-shot examples to determine whether or not the source code snippet includes the identified vulnerability. The few-shot examples include positive and negative samples of the type of vulnerability to guide the large language model towards the correct output.

The invention discloses a federal learning method and system based on global prototype guidance. The method comprises the following steps: a server sends a global model and a global prototype to a local client; the local client performs rebalance comparison learning by using the global prototype, and updates the local model; the local client generates a balanced confrontation positive prototype instance set and trains a classifier; the local client side calculates a local prototype and uploads the local prototype and the updated local model to the server side; and the service aggregates the updated local model and local prototype, and updates the global model and global prototype. The system comprises a server and a local client. According to the method, the Non-IID problem in federated learning is solved, and the accuracy and generalization of the model are improved. The method can be widely applied to the technical field of distributed machine learning.

The invention discloses a vulnerability detection method and system based on abstract syntax tree decomposition and large language model assistance. The vulnerability detection method and system are used for solving the problem that an existing pre-training model is insufficient in detection accuracy under complex code logic and multiple execution paths. The method comprises the following steps: firstly, analyzing a code snippet into an abstract syntax tree, splitting the abstract syntax tree into a plurality of sub-trees through an improved decomposition algorithm, and combining each sub-tree with a natural language annotation generated by a large language model to form an abstract sub-tree with the annotation; then, a semantic aggregator based on Transform is used for modeling the relation between the sub-trees, features are fused to a target vulnerability vector, and finally, vulnerabilities are predicted through a classifier. Based on the technical scheme, the vulnerability detection accuracy is effectively improved, and the performance of the vulnerability detection model is greatly improved.

Embodiments include an activity monitoring machine learning model method. One embodiment the method includes transforming HTTP network requests into feature vectors, each feature vector representing selected features from a corresponding HTTP network request and an action selected from a plurality of actions to be monitored and inputting the feature vectors into a machine learning model to train the machine learning model to classify new HTTP requests according to the plurality of actions, wherein the plurality of actions include an upload action and a download action.

The invention provides a multi-objective optimization method fusing a classifier and an active learning strategy, and belongs to the technical field of artificial intelligence and optimization design, and the method specifically comprises the steps: obtaining a sample data set containing a plurality of target performance function values; performing non-dominated sorting on the sample data set to extract a Pareto frontier point set, and extracting geometric features from the point set as a geometric feature vector sequence; using the geometric features and the corresponding convex marks to train a classifier model of a Transform architecture, wherein the classifier model supports a multi-head attention mechanism and a position coding structure; judging the convex confidence coefficient of the current Pareto front by using a classifier model, and obtaining a candidate scheme; and inputting the candidate scheme into a double-attention mechanism prediction model based on context reasoning, and predicting the multi-target performance of the candidate scheme. According to the method, the experiment frequency can be reduced, the target performance can be accelerated to be achieved, and the intelligence and efficiency of the multi-target optimization process are improved.

An adaptive classification retrieval-augmented generation model system and a method, relating to the technical field of artificial intelligence. The system comprises: a query splitting module, a classifier module, a processing module, a user interaction module, and an evaluation and optimization module. The user interaction module is used to receive an original query of a user, and display a generated answer. The query splitting module is used to split the original query received by the user interaction module into multiple equivalent queries. By means of integrating a classifier module, the complexities of different tasks are effectively identified and, corresponding processing methods therefor are determined, so that the understanding and application efficiency of the augmented generation technology is optimized. Advanced RAG technologies and specially adjusted classification algorithms are combined, so that not only is the accuracy of question answering improved, but processing speed is also improved, and consumption of running resources is significantly reduced.

An analysis engine receives data characterizing a multimodal prompt for ingestion by a generative artificial intelligence (GenAI) model. The multimodal prompt is processed and fed into a plurality of layers from which an intermediate result of the GenAI model or a proxy of the GenAI model is obtained. The analysis engine, using a prompt injection classifier and the intermediate result, determines whether the prompt comprises or is indicative of malicious content or elicits malicious actions. Data characterizing the determination is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Systems and methods are disclosed herein for identifying and predicting maintenance events with respect to assets, such as mobile machinery. A computing platform can include control circuit(s) (on-board and / or remote) configured to acquire machine fault data and machine utilization data. A feature set can be generated by transforming the machine fault data (e.g., by fusing the machine fault data with machine utilization data), normalizing the data, generating embeddings, and / or executing imputation algorithms to fill in missing values. A classifier model can be trained using the transformed machine fault data in conjunction with labeled maintenance event data to enable the trained classifier model to learn to recognize utilization-specific fault code patterns. The trained classifier model can generate inferences and predictions regarding machinery maintenance events without the use of maintenance data.

The invention discloses a multi-view migration interpretable method based on soft variable embedding and discriminant structure preserving, and particularly relates to the technical field of machine learning, and the method comprises the steps: obtaining a plurality of multi-view features for EEG samples of a tagged source domain and an untagged target domain through the extraction of a plurality of features; after TSK-FS antecedent network mapping, constructing inter-domain connection by adopting migration soft variable embedded consequent learning; a data structure is retained through a local-global structure retention item, so that a discriminant neighborhood relationship of original data is retained to the greatest extent in a migration process; and a target function is constructed in combination with a multi-view learning strategy, iterative optimization is performed through an enhanced Lagrange multiplier algorithm, and finally a result is output by using a given classifier.

An apparatus and method for personalization of educational machine learning models. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive input data comprising one or more of a user profile and a request datum, generate, using a return generator, return output based on the request datum, identify, using a machine learning model, attribute data of the input data, generate, using a classifier, one or more classifications for the input data as a function of the attribute data associated with the input data, modify, using a natural language processor, the return output as a function of the user profile and one or more classifications assigned to the input data to generate user specific output, and display, using a downstream device, the user specific output.

The invention discloses a semi-supervised text data multi-label classification method, system and device and a storage medium. According to the method, a double-branch model of a shared feature processing network is constructed, a pseudo label generator is utilized to automatically generate a pseudo label for an unlabeled sample on the basis of limited labeled data, and the pseudo label generator and a classifier are jointly trained to realize collaborative learning of labeled data and unlabeled data; by introducing an adaptive threshold mechanism and an improved loss function design, the recognition precision of minority class labels is effectively improved. Compared with a traditional full supervision model, the method has the advantages that the dependence on large-scale manual annotation is reduced, the data preparation cost is remarkably reduced, and the application performance of the classification model in multi-label scenes such as medical text analysis, public opinion monitoring and personalized recommendation is improved. The system, the device and the storage medium provided by the invention can realize the method, and have good expandability and engineering application value.

The invention provides a large language model generation code detection method and system, which is applied to the technical field of artificial intelligence, and comprises the following steps: obtaining a to-be-detected code; a to-be-detected code is input to a trained shared encoder, a code feature vector is obtained, the shared encoder is obtained through multi-target joint training, and the multi-target joint training is used for optimizing classification loss, domain confrontation loss, comparison loss and difficult sample loss at the same time; l2 normalization is carried out on the code feature vector, and the code feature vector is mapped to a hyperspherical space to obtain spherical embedding; the sphere is embedded and input into a sphere category classifier based on sphere logistic regression for classification processing, a detection result of the to-be-detected code output by the sphere category classifier is obtained, the detection result comprises AI generation and human writing, and a decision boundary of the sphere category classifier is an intersection line of a hyperplane and a hypersphere for classification. According to the invention, the AI generation code and the human compiled code can be accurately distinguished.