The present disclosure provides radio-frequency (RF) systems that can detect the presence of RF signals received by the system, as well as determine characteristics such as the operating frequency of RF signals, the type of RF source that transmitted each RF signal, and/or the location of each RF source with high precision and sensitivity while using low cost, scalable electronics that are versatile enough for deployment in a variety of environments. Such systems can employ a network of RF sensors that can coordinate in response to communication with a computer to perform any such detection and/or determination using trained models executed onboard the RF sensors and/or the computer. RF signals may have unique characteristics when received at one or more RF sensors that may be detected using trained models described herein, even in high noise or non-line of sight (LOS) environments and with low cost, low resolution RF receiver hardware.

System and method for determining an outcome probability of an event based on videos are disclosed. The method includes receiving the videos of an event, creating a building block model, extracting one of an audio content, a video content from the videos, analysing extracted content, generating an analysis result, analysing an engagement between speaker and participant of event, generating a data lake comprising a keyword library, computing the outcome probability of the event, enabling the building block model to learn from the data lake and the outcome probability computed and representing the at least one outcome probability in a pre-defined format.

A method for receiving a full training data set including a plurality of individual training data set, dividing the plurality of individual training sets into N classes, where N is an integer greater than three, dividing the N classes into M full data classes and N-M partial data classes, performing training to obtain a trained fixed size machine learning (ML) classification model and a trained in-class confidence model, outputting a first set of prediction value(s) based on the performance of training, distributing each class of the N classes of individual training data sets to a different node of a distributed machine learning system; and outputting, from the nodes of the distributed machine learning system, a second set of prediction value(s) for each class of the N classes.

The technology disclosed implements Human-in-the-loop (HITL) active learning with a feedback look via a user interface that is expressly designed for the suggested images to admit multiple fast feedbacks, including selection, dismissal, and annotation. Then, the downstream selection policy for subsequent sampling iterations is based on the available data interpreted in the context of the previous selections, dismissals, and annotations.

Provided are a device and method for recommending educational content. The method includes acquiring a user's learning data, wherein the learning data includes at least one of the user's first learning ability information at a first time point, the user's second learning ability information at a second time point, and the user's question answering information, acquiring the user's target learning ability information on the basis of the learning data, determining a neural network model on the basis of the target learning ability information, distributing resources corresponding to the determined neural network model, and acquiring educational content to be recommended to the user through the determined neural network model.

A system can receive a first set of data. The first set of data can include information indicating a first set of user sessions and for each of the first set of user sessions having an associated summary and a corresponding agent indicated intent. The system can also, based on the first set of data, determine a set of utterances and for each of the set of utterances a corresponding set of intents. Additionally, the system can receive a second set of data. The second set of data including information indicating a second set of user sessions and for each of the second set of user sessions having an associated determined utterance and corresponding interaction of a user. Moreover, the system can validate a corresponding intent of one or more utterances of the set of utterances, based on the second set of data.

Computer systems and associated methods are disclosed to implement a model development environment (MDE) that allows a team of users to perform iterative model experiments to develop machine learning (ML) media models. In embodiments, the MDE implements a media data management interface that allows users to annotate and manage training data for models. In embodiments, the MDE implements a model experimentation interface that allows users to configure and run model experiments, which include a training run and a test run of a model. In embodiments, the MDE implements a model diagnosis interface that displays the model's performance metrics and allows users to visually inspect media samples that were used during the model experiment to determine corrective actions to improve model performance for later iterations of experiments. In embodiments, the MDE allows different types of users to collaborate on a series of model experiments to build an optimal media model.

Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.

A system and methods relate to, inter alia, determining a prediction confidence level associated with machine identification of production data based on a machine learning model. The system and methods further relate to routing the production data to at least one of a human analyzer device associated with the human analyzer or a prediction engine of the server based on the prediction confidence level for identification of the data. The machine learning model of the system and methods may be configured to be modifiable in response to feedback from at least one of the human analyzer device or the prediction engine.

A deep learning device and system including the same is provided. The deep learning device comprising processing circuitry configured to determine whether a received image is abnormal using an anomaly detection model; merge at least some vectors extracted from the anomaly detection model; input, to a probability approximation model, principal components generated by a principal component analysis (PCA) to detect whether out of distribution (OOD) occurs in data of the received image; store a result of the determinations; and extract at least some the data in which the OOD occurs, as target labeling, using a target labeling extraction model when a rate of the data in which the OOD occurs is greater than or equal to a threshold value, wherein the anomaly detection model determines whether the received image is abnormal using the target labeling.