The present disclosure provides a method and apparatus for detecting a traffic anomaly, relates to the field of artificial intelligence and specifically to computer vision and deep learning technologies, and can be applied to video analysis scenarios. A specific implementation comprises: acquiring at least two frames of consecutive traffic images; identifying respectively a position of a target vehicle from the at least two frames of consecutive traffic images to obtain a position information set; determining a direction of travel and speed of the target vehicle according to the position information set; and comparing the direction of travel and speed of the target vehicle with a pre-generated vehicle vector field to determine whether the target vehicle is abnormal.

This disclosure relates to method and system for training of deep learning based time-series models based on self-supervised learning. The problem of missing data is taken care of by introducing missing-ness masks. The deep learning model for univariate and multivariate time series data is trained with the distorted input data using the self-supervised learning to reconstruct the masked input data. Herein, the one or more distortion techniques include quantization, insertion, deletion, and combination of the one or more such distortion techniques with random subsequence shuffling. Different distortion techniques in the form of reconstruction of masked input data are provided to solve. The deep learning model performs these different distortion techniques, which force the deep learning model to learn better features. It is to be noted that the system uses a lot of unlabeled data available cheaply as compared to the label or annotated data which is very hard to get.

A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.

A system for automatically analyzing a video recording of a colonoscopy includes a processor and memory storing instructions, which when executed by the processor, cause the processor to receive the video recording of the colonoscopy performed on the colon and detect informative frames in the video recording. A frame is informative if the clarity of the frame is above a threshold or if the frame includes clinically relevant information about the colon. The instructions cause the processor to generate scores indicating severity levels of a disease for a plurality of the informative frames, estimate locations of the plurality of the informative frames in the colon, and generate an output indicating a distribution of the scores over one or more segments of the colon by combining the scores generated for the plurality of the informative frames and the estimated locations of the plurality of the informative frames in the colon.

According to one embodiment, a condition monitoring device includes a processor. The processor is configured to acquire a time-series signal about a condition of a monitor target from a first sensor, acquire operation timing information indicating start of operation of the monitor target, detect a first operation segment signal from the time-series signal based on the operation timing information, detect a second operation segment signal from the first operation segment signal based on a waveform feature of the first operation segment signal, and determine the condition of the monitor target based on the second operation segment signal.

A system for managing an infrastructure includes extraction engine is in communication with a managed infrastructure that includes physical hardware. A signalizer engine includes one or more of an NMF engine (Non-negative matrix factorization), a k-means clustering engine (a method of vector quantization), and a topology proximity engine. The signalizer engine determines one or more common characteristics of events and produces clusters of events relating to the failure or errors in the infrastructure. The signalizer engine uses graph coordinates and optionally a subset of attributes assigned to each event to generate one or more clusters to bring together events whose characteristics are similar. One or more interactive displays provide a collaborative interface coupled to the extraction and the signalizer engine with a collaborative interface (UI) for decomposing events from the infrastructure. The events are converted into words and subsets to group the events into clusters that relate to security of the managed infrastructure. In response to grouping the events physical changes are made to at least a portion of the physical hardware. In response to production of the clusters security of the managed infrastructure is maintained.

A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

A system can identify a group of computer services that correspond to respective computer hardware. The system can extract features that respectively identify characteristics of the computer services, a feature of the features comprising ratio information representative of a ratio of a first price of a computer service of the group of computer services to a second price of a corresponding computer hardware of the respective computer hardware that corresponds to the computer service. The system can convert the features into a numerical representation. The system can cluster the computer services into multiple clusters based on the numerical representation of the features. The system can identify, from a cluster of the multiple clusters and based on a machine learning model, an anomalous price for at least one computer service of the group of computer services that belongs to the cluster. The system can store an indication of the anomalous price.

In at least one embodiment, the present description is directed to a computer system, having a processor to at least: electronically receive a model for one or more operating conditions, and facility operating data; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model, including: determining model predicted values, comparing the model predicted values to the facility operating data, removing bias facility operating data from the facility operating data of the plurality of facilities, and constructing, based at least in part on the non-biased facility operating a data, an updated model with one or more updated coefficients; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the no-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

Provided is an abnormal data generation device capable of generating highly accurate abnormal data. The abnormal data generation device includes an abnormal data generation unit for generating pseudo generated data of abnormal data that has, in the same latent space, a normal distribution as a normal data generation model and an abnormal distribution expressed as a complementary set of the normal distribution and that is optimized such that pseudo generated data cannot be discriminated from observed actual abnormal data by a latent variable sampled from the abnormal distribution.