4results about How to "Effective against" patented technology

The invention belongs to the technical field of communication services, and provides a fraud prevention and control processing method and system applied to privacy numbers. The method comprises: according to a screening condition, performing risk behavior screening for multiple times on bill data, including: performing feature selection on number behavior features to obtain a corresponding first feature vector as an input of a first-layer model; taking the output of the first-layer model as the input of a second-layer model to obtain a judgment result; content capturing is carried out on ticket data obtained through screening according to the judgment result so as to check whether violation occurs or not through quality inspection; determining violation ticket data according to quality inspection audit, updating the user score, and updating the customer rating at the same time; and managing and controlling the privacy number to be evaluated according to the updated user score and customer level. According to the method, the clients are comprehensively evaluated from the aspects of risk evaluation, listed clients, income contribution, code number coverage, reporting scenes, call completing rates and the like, and differentiated management and control are carried out on the clients of each level.

The invention discloses an adversarial sample defense method based on a contrast learning optimization denoising auto-encoder, electronic equipment and a storage medium, and belongs to the technical field of intrusion detection. In order to solve the problem of effective confrontation sample defense, the method comprises the following steps: collecting data from training to construct a training data set; a TripletLoss loss function is used to train a comparative learning model, and a comparative learning model encoder is output; and optimizing a denoising auto-encoder by using the obtained contrast learning model encoder to obtain an optimized denoising auto-encoder and an optimized decoder, and firstly inputting a sample input into the deep learning model into the optimized denoising auto-encoder and the optimized decoder for recoding and decoding operation to realize sample defense. The method is applied to an intrusion detection system and is deployed between a common flow feature extraction module and a deep learning model classification module in the intrusion detection system, and adversarial sample defense is realized through training of a preposed task contrast learning model and training of a downstream task denoising auto-encoder model.

The present invention provides compounds of formula (I) which are useful for the long-term treatment and control of pests such as fleas and ticks in companion animals and livestock, as well as pharmaceutical compositions and methods of use thereof.

This invention relates to the field of audio processing technology and provides a method and system for individualized noise reduction in Bluetooth headsets. The method includes: acquiring raw time-domain noise signals from outside the headset via an external microphone and error signals from inside the headset via an internal microphone; performing frame-by-frame preprocessing on the raw time-domain noise signals to obtain continuous signal frames; decomposing each signal frame into noise components and their time-domain signals with different time-frequency characteristics; generating corresponding anti-noise signals based on multiple noise components and the error signals; calculating the mixing weights corresponding to each noise component based on the error signals and the time-domain signals; and weighting and synthesizing the anti-noise signals corresponding to multiple noise components into an inverted acoustic wave signal. Through refined processing of external and internal noise signals, the effectiveness and adaptability of noise reduction technology can be significantly improved.