7 results about "Probabilistic generative model" patented technology

The invention discloses a network security uncertainty quantitative detection method based on generative AI, and belongs to the technical field of network security detection, and the method comprises the steps: collecting security event data from a plurality of heterogeneous data sources, and forming a standardized to-be-detected event sequence; constructing a dynamic baseline model based on the historical event sequence of the normal behavior by using a probability generation model, and generating at least one anomaly indication feature for the to-be-detected event sequence; inputting the anomaly indication features into a Bayesian deep learning model, and obtaining a mean value representing an anomaly probability and a variance representing prediction confidence in the event sequence to be detected through multiple forward propagation sampling; and carrying out risk grading on the security events, and carrying out retraining on the probability generation model and / or the Bayesian deep learning model based on feedback annotation of a grading result to realize closed-loop optimization. According to the invention, the decision-making efficiency is improved in a leap-over manner, and the gray attack behavior is effectively detected and the precision is improved.

Disclosed are an apparatus and method for providing a speech video. The apparatus for generating a speech video according to an embodiment comprises: an encoder for encoding a person image to generate an identity latent vector; a voice emotion recognition model for recognizing emotion from speech audio; a motion latent vector generation model which is a probabilistic generative model for generating a motion latent vector on the basis of the speech audio and the emotion; and a decoder for generating a speech video of speech spoken by the corresponding person on the basis of the identity latent vector and the motion latent vector.

The invention relates to a service quantity prediction method fusing multi-scale decomposition and adaptive probability modeling. The method comprises the following steps: acquiring a historical service quantity time sequence, and decomposing the historical service quantity time sequence into historical sub-sequence components with fluctuation characteristics and dependency modes of different time scales by utilizing multi-scale decomposition; respectively extracting historical time sequence characteristics of each component, performing independent modeling by using a probability generation model, and training the model to generate a plurality of groups of probability distribution parameters to fit probability distribution corresponding to each component; obtaining time sequence characteristics of a to-be-predicted moment, and inputting the time sequence characteristics into the trained model to obtain probability distribution of each prediction component; and adopting Monte Carlo sampling to generate a prediction sample, performing adaptive dynamic fusion according to the time-varying weight, and outputting a fused service quantity prediction result. By adopting the method, the precision and reliability of service quantity prediction can be remarkably improved through multi-scale collaborative modeling and adaptive probability fusion.

Disclosed are an apparatus and a method for generating a speech video. The apparatus for generating a speech video, according to one embodiment, comprises: an encoder for encoding a person image so as to generate an identity latent vector; a motion latent vector generation model which is a flow matching-based probabilistic generative model for generating a motion latent vector on the basis of a speech audio; and a decoder for generating a speech video of the corresponding person uttering, on the basis of the identity latent vector and the motion latent vector.

A computerized method is disclosed analyzing maliciousness of an electronic message including operations of analyzing header information of the electronic message including a heuristic analysis and a name entity recognition analysis, determining a probability that the electronic message is directed to one of a predefined set of topics by deploying a probabilistic generative model, generating a prompt for a language model based on a first topic, providing the prompt to the language model and generating a semantic result based on a response thereto. Additional operations include classifying the electronic message as malicious or benign based on a semantic result based on the response to the prompt from the language model, and semantics of the header information, and generating a graphical user interface display that indicates whether the electronic message has been classified as malicious or benign.

A computerized method is disclosed that includes operations of obtaining and parsing a copy of an email into components including body and subject line information, performing a plurality of analyses on the copy of the email including deploying a probabilistic generative model resulting in a determination of a likelihood that the email is directed to one of a predefined set of topics, classifying semantics extracted from the body or subject based on results generated by the probabilistic generative model, and performing a determination operation by a relationship compiler or a neural network resulting in a maliciousness determination as to whether the email is malicious. Responsive to the maliciousness determination being malicious, generating a graphical user interface display indicating that the email has been classified as malicious and performing a mail retrieval attempt that causes the email to be removed from an inbox of the mail client of the intended recipient.

Embodiments of the present disclosure disclose a model training method, a prediction probability information generation method, an apparatus and an electronic device. A specific implementation of the method comprises: obtaining a training data set; inputting a variable data set into an initial feature data vector representation model to output an initial variable vector set; performing model training on each initial discriminant model in an initial discriminant model set included in an initial target value payment method probability generation model to obtain a discriminant model set; and performing model training on an initial generative model set and the initial feature data vector representation model included in the trained target value payment method probability generation model according to the initial variable vector set, an actual value reduction information set and an actual value payment method information set to obtain the target value payment method probability generation model. The implementation is related to artificial intelligence, and the trained target value payment method probability generation model can accurately generate target value payment method prediction probability information.