The disclosure is directed to a continuously and automatically updated authentication mechanism for authentication, in real-time, the processing of transactions at point of sales devices based on corresponding target-specific identifiers. In one aspect, a processing server includes one or more memories having computer-readable instructions stored therein, and one or more processors. The one or more processors are configured to execute the computer-readable instructions to receive a request for processing a transaction; identify a merchant-specific identifier for a merchant associated with the transaction; determine, in real-time and using a machine trained model, whether the merchant-specific identifier is a valid merchant-specific identifier or not; and process the transaction based on whether the machine trained model indicates that the merchant-specific identifier is valid or not.

A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.

The present invention relates to a neural network training method. The neural network training method using a de-identified image according to the present invention comprises the steps of: encoding a first image represented by a n-th dimensional vector into a predetermined p-th dimensional second image; decoding the second image into a q-th dimensional third image; inputting the third image to a neural network and extracting object information included in the third image; and training at least one parameter information used for computation in the neural network by using the extracted object information. According to the present invention, de-identified images are used for neural network training such that neural network training is made possible without using personal information included in images.

Embodiments provide a scheme for parametric PV-level modeling and an optimal read threshold voltage estimation in a memory system. A controller performs read operations on cells using read threshold voltages; generates CMF samples based on the read operations; and receives first and second CDF values, which correspond to CMF samples, each CDF value representing a skew normal distribution. The controller estimates first and second probability distribution parameter sets corresponding to the first and second CDF values, respectively; determines first and second PDF values using the first and second probability distribution parameter sets, respectively; and estimates, as an optimal read threshold voltage, a read threshold voltage corresponding to a cross-point of the first and second PDF values.

Interactions between a user and an e-commerce platform are automatically guided to increase the chances of a conversion. Previous sequences of interactions (e.g., conversion journeys and non-conversion journeys) with the e-commerce platform are collected, an artificial neural network (ANN) learns how to estimate a safety value a current user state by learning from previous user interactions (e.g., conversion and non-conversion journeys), a software agent of the e-commerce platform applies a current user state of the user to the ANN to determine a current safety value, and the software agent provides content to the user based on the current safety value and the current user state.

A method and device for detecting acoustic feedback events with an artificial neural network in an in-ear earbud audio system that allows, by user interaction, playback of a recorded and processed signal from an environment-recording microphone by an in-ear speaker that faces or is at least acoustically coupled with the ear canal such that sound played by the speaker enters the ear canal. The audio system employs an acoustic seal that acoustically separates the speaker from the microphone, but due to external factors, the acoustical separation may not be adequate, thereby forming acoustic feedback paths. The neural network facilitates a binary classification of the time-wise segmented microphone signal, which is used to stop playback by the in-ear speaker if a feedback event is detected to protect the hearing of the user. Detection of a feedback event triggers an audible or wireless notification to be delivered to the user.

Systems, methods, and protocols for developing invasive brain computer interface (iBCI) decoders non-invasively by using emulated brain data are provided. A human operator can interact in real-time with control algorithms designed for iBCI. An operator can provide input to one or more computer models (e.g., via body gestures), and this process can generate emulated brain signals that would otherwise require invasive brain electrodes to obtain.

A computer-implemented method includes obtaining a multi-domain (MD) dataset and training a neural network model using the MD dataset with short-form data withheld (MD-SF). The neural network model includes a plurality of layer each having a plurality of parameters. The method also includes resetting each respective layer in the trained neural network one at a time. For each respective layer in the trained neural network model, and after resetting the respective layer, the method also includes determining a corresponding word error rate of the trained neural network model and identifying the respective layer as corresponding to an ambient layer when the corresponding word error rate satisfies a word error rate threshold. The method also includes transmitting an on-device neural network model to execute on one or more client devices for generating gradients based on the withheld domain (SF) of the MD dataset.

A method for training a machine-learning network includes receiving an input data from a sensor. The input data includes a perturbation. The method also includes obtaining a worst-case bound on a classification error and loss for perturbed versions of the input data. The method also includes training a classifier, where the classifier includes a plurality of classes, including a plurality of additional abstain classes. Each additional abstain class of the plurality of additional abstain classes is determined in response to at least bounding the input data. The method also includes outputting a classification in response to the input data indicating one of the plurality of classes and outputting a trained classifier in response to exceeding a convergence threshold. The trained classifier is configured to detect at least one additional abstain class of the plurality of additional abstain classes in response to obtaining the worst-case bound.

A semiconductor device capable of holding analog data is provided. Two holding circuits, two bootstrap circuits, and one source follower circuit are formed with use of four transistors and two capacitors. A memory node is provided in each of the two holding circuits; a data potential is written to one of the memory nodes and a reference potential is written to the other of the memory nodes. At the time of data reading, the potential of the one memory node is increased in one of the bootstrap circuits, and the potential of the other memory node is increased in the other of the bootstrap circuits. A potential difference between the two memory nodes is output by the source follower circuit. With use of the source follower circuit, the output impedance can be reduced.