Abstract: A user terminal, a debugging device, and a data backup method are provided. The user terminal includes a storage component, an I/O controller, a main controller, a first CC controller, a first MUX, a second MUX, a third MUX, and a first interface. The first CC controller is connected to each of the first interface and a first signal selection input end of the first MUX; a first signal input end of the first MUX is connected to the I/O controller, a second signal input end of the first MUX is connected to the main controller, and a first signal output end of the first MUX is connected to the first interface; the main controller is connected to each of a second signal selection input end of the second MUX and a third signal selection input end of the third MUX.

Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised time-series feature learning. The method generates a set of reference time-series of random lengths, in which each length is uniformly sampled from a predetermined minimum length to a predetermined maximum length, and in which values of each reference time-series in the set are drawn from a distribution. The method generates a feature matrix for raw time-series data based on a set of computed distances between the generated set of reference time-series and the raw time-series data. The method provides the feature matrix as an input to one or more machine learning models.

Abstract: Techniques that facilitate time series analysis using machine learning are provided. In one example, a system includes a matrix generation component, a matrix factorization component and a machine learning component. The matrix generation component converts at least a first stream of time series data and a second stream of time series data (e.g., raw time series data) into a data matrix (e.g., a partially-observed similarity matrix) that comprises void data and numerical data associated with the first stream of time series data and the second stream of time series data. The matrix factorization component factorizes the data matrix into a first factorization data matrix and a second factorization data matrix. The machine learning component processes a machine learning model based on first matrix data associated with the first factorization data matrix and second matrix data associated with the second factorization data matrix.

Abstract: Techniques that facilitate time series analysis using machine learning are provided. In one example, a system includes a matrix generation component, a matrix factorization component and a machine learning component. The matrix generation component converts at least a first stream of time series data and a second stream of time series data (e.g., raw time series data) into a data matrix (e.g., a partially-observed similarity matrix) that comprises void data and numerical data associated with the first stream of time series data and the second stream of time series data. The matrix factorization component factorizes the data matrix into a first factorization data matrix and a second factorization data matrix. The machine learning component processes a machine learning model based on first matrix data associated with the first factorization data matrix and second matrix data associated with the second factorization data matrix.

Abstract: A method and system are provided. The method includes receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data. The method further includes generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer. The recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for image identification and classification. The method, computer program product and computer system may include a computing device which may receive one or more images of a first object from at least two angles linguistic data associated with the first object. The computing device may input the one or more images of the first object into one or more first neural networks and the linguistic data of the first object into one or more second neural networks. The computing device may combine the output of the one or more first neural networks and the one or more second neural networks and generate an identification model based on the combined output of the one or more first neural networks and the one or more second neural networks.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: A method and system are provided. The method includes converting, by a computer having a processor and a memory, categorical sequence data for a customer journey into a numerical similarity matrix. The method further includes learning, by the computer, features of the customer journey by applying a distance metric learning based matrix factorization approach to the numerical similarity matrix.

Abstract: Methods and systems for generating prediction data are described. In an example, a processor may retrieve preferential data from a memory. The preferential data may include a set of preferences that corresponds to a first subset of objects among a set of objects, and may exclude preferences associated with a second subset of objects among the set of objects. Each preference may indicate a preferred object between a respective pair of objects among the first subset of objects. The processor may determine first predicted ratings corresponding to the first subset of objects based on the preferential data. The processor may determine second predicted ratings corresponding to the second subset of objects based on the preferential data. The processor may generate prediction data by populating entries of the prediction data with the first and second predicted ratings, where the prediction data may include predicted ratings of the set of objects.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for image identification and classification. The method, computer program product and computer system may include a computing device which may receive one or more images of a first object from at least two angles linguistic data associated with the first object. The computing device may input the one or more images of the first object into one or more first neural networks and the linguistic data of the first object into one or more second neural networks. The computing device may combine the output of the one or more first neural networks and the one or more second neural networks and generate an identification model based on the combined output of the one or more first neural networks and the one or more second neural networks.

Abstract: A method for generating a classification model using original data that is sensitive or private to a data owner. The method includes: receiving, from one or more entities, a masked data set having masked data corresponding to the original sensitive data, and further including a masked feature label set for use in classifying the masked data contents; forming a shared data collection of the masked data and the masked feature label sets received; and training, by a second entity, a classification model from the shared masked data and feature label sets, wherein the classification model learned from the shared masked data and feature label sets is the same as a classification model learned from the original sensitive data. The sensitive features and labels cannot be reliably recovered even when both the masked data and the learning algorithm are known.

Abstract: A system, method and computer program product for generating a classification model using original data that is sensitive or private to a data owner. The method includes: receiving, from one or more entities, a masked data set having masked data corresponding to the original sensitive data, and further including a masked feature label set for use in classifying the masked data contents; forming a shared data collection of the masked data and the masked feature label sets received; and training, by a second entity, a classification model from the shared masked data and feature label sets, wherein the classification model learned from the shared masked data and feature label sets is the same as a classification model learned from the original sensitive data. The sensitive features and labels cannot be reliably recovered even when both the masked data and the learning algorithm are known.

Abstract: Methods and systems for generating prediction data are described. In an example, a processor may retrieve preferential data from a memory. The preferential data may include a set of preferences that corresponds to a first subset of objects among a set of objects, and may exclude preferences associated with a second subset of objects among the set of objects. Each preference may indicate a preferred object between a respective pair of objects among the first subset of objects. The processor may determine first predicted ratings corresponding to the first subset of objects based on the preferential data. The processor may determine second predicted ratings corresponding to the second subset of objects based on the preferential data. The processor may generate prediction data by populating entries of the prediction data with the first and second predicted ratings, where the prediction data may include predicted ratings of the set of objects.

Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised time-series feature learning. The method generates a set of reference time-series of random lengths, in which each length is uniformly sampled from a predetermined minimum length to a predetermined maximum length, and in which values of each reference time-series in the set are drawn from a distribution. The method generates a feature matrix for raw time-series data based on a set of computed distances between the generated set of reference time-series and the raw time-series data. The method provides the feature matrix as an input to one or more machine learning models.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: Techniques that facilitate time series analysis using machine learning are provided. In one example, a system includes a matrix generation component, a matrix factorization component and a machine learning component. The matrix generation component converts at least a first stream of time series data and a second stream of time series data (e.g., raw time series data) into a data matrix (e.g., a partially-observed similarity matrix) that comprises void data and numerical data associated with the first stream of time series data and the second stream of time series data. The matrix factorization component factorizes the data matrix into a first factorization data matrix and a second factorization data matrix. The machine learning component processes a machine learning model based on first matrix data associated with the first factorization data matrix and second matrix data associated with the second factorization data matrix.

Abstract: Techniques that facilitate time series analysis using machine learning are provided. In one example, a system includes a matrix generation component, a matrix factorization component and a machine learning component. The matrix generation component converts at least a first stream of time series data and a second stream of time series data (e.g., raw time series data) into a data matrix (e.g., a partially-observed similarity matrix) that comprises void data and numerical data associated with the first stream of time series data and the second stream of time series data. The matrix factorization component factorizes the data matrix into a first factorization data matrix and a second factorization data matrix. The machine learning component processes a machine learning model based on first matrix data associated with the first factorization data matrix and second matrix data associated with the second factorization data matrix.