A method of using an adaptive fault diagnostic system for motor vehicles is provided. A diagnostic tool collects unlabeled data associated with a motor vehicle, and the unlabeled data is transmitted to a central computer. An initial diagnostic model and labeled training data associated with previously identified failure modes and known health conditions are transmitted to the central computer. The central computer executes a novelty detection technique to determine whether the unlabeled data is novelty data corresponding with a new failure mode or normal data corresponding with one of the previously identified failure modes or known health conditions. The central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data. The central computer updates the labeled training data to include the labeled novelty data.

A method may include receiving an event from an event source. The event may correspond to event data. The event source may be a container executing an image. The image may correspond to image metadata including attributes describing the image. The method may further include combining the event data with the image metadata to obtain enriched data, detecting, using the enriched data, a deviation from a policy, and in response to detecting the deviation from the policy, performing an action to enforce the policy.

The present disclosure provides a method and apparatus for detecting an abnormal event from a monitoring image accurately and speedily in a video surveillance system. A method of detecting an abnormal event in a series of temporally successive images includes: generating a predicted current frame based on a previous frame temporally ahead of an actual current frame and a subsequent frame temporally behind the actual current frame; calculating an anomaly score indicating a difference between the predicted current frame and the actual current frame; and determining that an abnormality is included in the actual current frame when the anomaly score satisfies a predetermined condition.

According to one embodiment, a learning apparatus includes a processor. The processor acquires data with a label indicating whether the data is normal data or anomalous data. The processor calculates an anomaly degree indicating a degree to which the data is the anomalous data using an output of a model for the data. The processor calculates a loss value related to the anomaly degree using a loss function based on an adjustment parameter based on a previously calculated loss value and the label. The processor updates a parameter of the model so as to minimize the loss value.

A Deep-Learning (DL) explainable AI system for Radio Frequency (RF) machine learning applications with expert driven neural explainability of input signals combines three algorithms (A1, A2, and A3). A1 is a neural network that learns to classify spectrograms. During training, A1 learns to map a spectrogram to its paired label. It outputs a label estimate from a spectrogram. Labels account for device number and spectrum utilization. The neural network is built on two-dimensional dilated causal convolutions to account for frequency and time dimensions of spectrogram data. A2 is a user-defined function that converts an input spectrogram into a vector that quantifies human-identifiable elements of the spectrogram. A3 is a random forest feature extraction algorithm. It takes as input the outputs of A2 and A1. From these, A3 learns which elements in the vector output by A2 were most important for choosing the labels output from A1.

Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.

A store management system includes: a first setting unit that sets a first priority indicating a degree of priority about a display, for a plurality of display items relating to a store; a second setting unit that sets a second priority that is different from the first priority, on the basis of a captured image of the store; a sorting unit that rearranges the plurality of display items on the basis of the first priority and the second priority; and a display unit that displays the plurality of display items, in order of rearrangement performed by the sorting unit. According to such a store management system, it is possible to appropriately display a plurality of information about the store.

Object identification may be provided herein. A feature extractor may extract a first set of visual features, extract a second set of visual features, concatenate the first set of visual features, the second set of visual features, and a set of bounding box information, determine a number of object features and a global feature for a scene, and receive ego-vehicle feature information associated with an ego-vehicle. An object classifier may receive the number of object features, the global feature, and the ego-vehicle feature information, generate relational features with respect to relationships between each of the number of objects from the scene, and classify each of the number of objects from the scene based on the number of object features, the relational features, the global feature, the ego-vehicle feature information, and an intention of the ego-vehicle.

According to one embodiment, a method, computer system, and computer program product for using mobile devices for anomaly detection in a vehicle. The present invention may include a computer receives sensor data from at least one mobile device associated with the vehicle, where the mobile device having one or more sensors. The computer analyzes data from the one or more sensors to identify an anomaly associated with the vehicle. The computer identifies a message associated with the anomaly. The computer determines an urgency value of the message based on the anomaly. The computer transfers the message with the urgency value to the vehicle and causes the vehicle to notify the message using a vehicle notification device.

Systems and methods for detecting anomaly in video data are provided. The system includes a generator that receives past video frames and extracts spatio-temporal features of the past video frames and generates frames. The generator includes fully convolutional transformer based generative adversarial networks (FCT-GANs). The system includes an image discriminator that discriminates generated frames and real frames. The system also includes a video discriminator that discriminates generated video and real video. The generator trains a fully convolutional transformer network (FCTN) model and determines an anomaly score of at least one test video based on a prediction residual map from the FCTN model.