The following relates generally to diagnosing problems with websites. In some embodiments, a webpage interaction processor receives a list of potential user experience problems. The webpage interaction processor then extracts click data from the website, and processes the extracted click data into grams. Subsequently, an analytics engine is trained based on the processed click data. The trained analytics engine may then diagnose the problem of the website with a potential user experience problem from the received list of potential user experience problems. In some embodiments, the process is entirely automated.

In a method, a computer and a medical computer for automatic failure analysis in order to provide a cause of failure of the medical imaging apparatus during operation, input data are read into the computer that include raw data or image data, acquired by the imaging apparatus. A set of performance indicators in the input data is calculated by the computer. A trained neural network system is accessed with the calculated performance indicators, in order to provide result data that, in the case of a failure, identify a failure source.

Mobile phones and methods for mobile phone failure prediction include receiving respective log files from one or more mobile phone components, including at least one user application. The log files have heterogeneous formats. A likelihood of failure of one or more mobile phone components is determined based on the received log files by clustering the plurality of log files according to structural log patterns and determining feature representations of the log files based on the log clusters. A user is alerted to a potential failure if the likelihood of component failure exceeds a first threshold. An automatic system control action is performed if the likelihood of component failure exceeds a second threshold.

Methods for system failure prediction include clustering log files according to structural log patterns. Feature representations of the log files are determined based on the log clusters. A likelihood of a system failure is determined based on the feature representations using a neural network. An automatic system control action is performed if the likelihood of system failure exceeds a threshold.

A method and system of imputing corrupted sequential data is provided. A plurality of input data vectors of a sequential data is received. For each input data vector of the sequential data, the input data vector is corrupted. The corrupted input data vector is mapped to a staging hidden layer to create a staging vector. The input data vector is reconstructed based on the staging vector, to provide an output data vector. adjusted parameter of the staging hidden layer is iteratively trained until it is within a predetermined tolerance of a loss function. A next input data vector of the sequential data is predicted based on the staging vector. The predicted next input data vector is stored.

A method is provided to predict a disaster for a computer system based on logs. The method includes representing existing logs as first vectors by tokenizing the existing logs and partitioning the first vectors into clusters. The clusters represent disaster types. The method further includes selecting representative vectors for the clusters, representing a new log of the computer system as a second vector by tokenizing the new log, matching the second vector to a cluster by comparing the second vector and the representative vectors, and categorizing the new log as a disaster type represented by the cluster.

Memory devices, and methods of operating similar memory devices, include an array of memory cells comprising a plurality of access lines each configured for biasing control gates of a respective plurality of memory cells of the array of memory cells, wherein the respective plurality of memory cells for one access line of the plurality of access lines is mutually exclusive from the respective plurality of memory cells for each remaining access line of the plurality of access lines, and a controller having a plurality of selectively-enabled operating modes and configured to selectively operate the memory device using two or more concurrently enabled operating modes of the plurality of selectively-enabled operating modes for access of the array of memory cells, with each of the enabled operating modes of the two of more concurrently enabled operating modes utilizing an assigned respective portion of the array of memory cells.

A method or apparatus for monitoring a system by detecting misbehaving components in the system is presented. A computing device receives historical data points based on a set of monitored signals of a system. The system has components that are monitored through the set of monitored signals. For each monitored component, the computing device performs unsupervised machine learning based on the historical data points to identify expected states and state transitions for the component. The computing device identifies one or more steady components based on the identified states of the monitored components. The computing device also receives real-time data points based on monitoring the set of signals from the system. For each identified steady component, the computing device examines the received real-time data points for deviation from the expected state and state transitions of the steady component. The computing device reports anomaly in the system based on the detected deviations.

Provided is a method for detecting an abnormal session including a request message received by a server from a client and a response message generated by the server, the method including transforming at least a part of messages included in the session into data in the form of a matrix, transforming the data in the form of the matrix into a representation vector a dimension of which is lower than a dimension of the matrix of the data using a convolutional neural network, and determining whether the session is abnormal by arranging the representation vectors obtained from the messages in an order in which the messages are generated to compose a first representation vector sequence, and analyzing the first to representation vector sequence using an long short term memory (LSTM) neural network.

The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the deep convolutional neural network model.