Abstract: A method for predicting a time to a failure condition of an asset includes a first model, trained to reconstruct input image pairs to resemble a healthy condition asset. Similarity coefficients are generated for respective historical image pairs that include a visible-light and infrared light image by use of the reconstructed image pair by the first model as a similarity base and the respective historical image pairs include a timestamp of image capture. A second model is trained to predict a time to a failure condition of the asset based on similarity coefficients and timestamps of real-time image pair capture, timestamps of an asset failure condition, and similarity coefficients of the respective historical images. Responsive to receipt of a first real-time image pair, the method computes a predicted time to the failure condition of the asset.

Abstract: Systems and methods for automated mainframe database maintenance are provided.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining target application container configuration data to identify one or more target container base image referenced in the target application container configuration: subjecting script data associated to the one or more target container base image to text based processing for evaluation of security risk associated to the one or more container base image, the script data obtained from at least one candidate hosting computing environment; and selecting a hosting computing environment from the at least one computing environment for hosting the target application container, the selecting in dependence on the text based processing.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining target application container configuration data to identify one or more target container base image referenced in the target application container configuration: subjecting script data associated to the one or more target container base image to text based processing for evaluation of security risk associated to the one or more container base image, the script data obtained from at least one candidate hosting computing environment; and selecting a hosting computing environment from the at least one computing environment for hosting the target application container, the selecting in dependence on the text based processing.

Abstract: An approach is provided in which the approach calculates at least one weighting factor based on a word frequency analysis of an unlabeled document against a set of word frequencies corresponding to a set of labeled documents. The approach computes an a posteriori classification probability of the unlabeled document based on the at least one weighting factor, and creates an inferred classifier based on the a posteriori classification probability. The approach classifies the unlabeled classifier using the inferred classifier.

Abstract: A method for predicting a time to a failure condition of an asset includes a first model, trained to reconstruct input image pairs to resemble a healthy condition asset. Similarity coefficients are generated for respective historical image pairs that include a visible-light and infrared light image by use of the reconstructed image pair by the first model as a similarity base and the respective historical image pairs include a timestamp of image capture. A second model is trained to predict a time to a failure condition of the asset based on similarity coefficients and timestamps of real-time image pair capture, timestamps of an asset failure condition, and similarity coefficients of the respective historical images. Responsive to receipt of a first real-time image pair, the method computes a predicted time to the failure condition of the asset.

Abstract: Systems and methods for automated mainframe database maintenance are provided.

Abstract: Provided are a computer-implemented method, a computer program product, and a computer system for drone deployment for distributed asset maintenance and repair. Embodiments identify a fix for a problem at an asset and identify a drone to perform the fix. Embodiments generate an initial flight plan that describes a drone flight path for the drone, and embodiments generate an updated flight plan for the drone by updating the drone flight path using real-time air traffic data, real-time road traffic data, and real-time drone flight path conditions obtained from one or more edge devices. Embodiments generate an overall flight plan for the drone and one or more other drones using a predicted cost and a predicted period of time for the updated drone flight path. Embodiments send a drone flight path from the overall flight plan to the drone with instructions to fix the problem.

Abstract: A method for predicting a time to a failure condition of an asset receives image pairs of a healthy condition asset and historical image pairs of the asset from inspections and at a failure condition of the asset. A first model is trained to rebuild input images to resemble a healthy condition asset. Similarity coefficients are generated for the historical image pairs of a regular and infrared image by use of the rebuilding of images by the first model as a similarity base and historical image pairs include a timestamp of image capture. A second model is trained to predict a time to an asset failure condition based on timestamps of image capture, timestamps of an asset failure condition, and similarity coefficients of the historical images. Responsive to receipt of a real-time image pair, the method computes a predicted time to a failure condition of the asset.

Abstract: The present specification describes a computer-implemented method. According to the method, a programming task to be executed is received. A completed task that is similar to the programming task to be executed is identified from a database of completed tasks. Supporting documentation associated with the completed task is extracted and supporting material associated with the completed task is generated and transmitted to a computing device which is to execute the programming task.

Abstract: Computer-implemented methods of training machine learning models and using the machine learning models for tailoring a multi-channel help desk environment. One or more computers train a machine learning model of selecting best attendance channels for respective customer clusters and for respective issue clusters. One or more computers train machine learning models of tailoring respective attendance channel types. One or more computers employ the machine learning models to determine a best attendance channel for resolving an information technology problem of a user and to predict channel tailoring characteristics for the best attendance channel. One or more computers employ genetic algorithm operators to determine a random attendance channel with random tailoring characteristics. One or more computer use random routing to route the user to one of the best attendance channel and the random attendance channel, avoiding undesired bias favorable toward the best attendance channel.

Abstract: A method and a system for automating security updates in a multi-tiered architecture of a system environment. The method includes receiving a software update note relating to a multi-tiered architecture system environment and determining a patch in the software update note is initially compatible with the system environment. The method also includes predicting, by a patch acceptance machine learning model, the patch is patchable within the system environment and predicting, by an installation machine learning model, an installation patch time for the patch on the system environment. The method also includes installing the patch at the installation patch time on the system environment, and validating the patch installation using a validation machine learning model.

Abstract: Weather data, asset variable values, and failure data may be received. A correlation factor may be generated by comparing the weather data and the asset variable values. One or more weather variables may be selected based on the correlation factor. A predicted asset variable value may be determined based on the weather data and the selected weather variables. The weather data and the predicted asset variable values may be compared to predict a failure probability for a first asset segment.

Abstract: A method for asset maintenance includes determining a location of an asset, receiving meteorological data corresponding to the location, determining forecasted meteorological conditions for the asset from the meteorological data, determining a forecasted probability of degradation or failure of the asset based, at least in part, on the forecasted meteorological conditions, and dispatching asset maintenance services for the asset responsive to determining that the forecasted probability of degradation or failure of the asset is above a selected threshold. The method may also include collecting sensor data corresponding to the asset, and determining the forecasted probability of degradation or failure of the asset based, at least in part, on the forecasted meteorological conditions and the sensor data. A corresponding apparatus, computer program product, and computer system are also disclosed herein.

Abstract: A method for predicting a time to a failure condition of an asset receives image pairs of a healthy condition asset and historical image pairs of the asset from inspections and at a failure condition of the asset. A first model is trained to rebuild input images to resemble a healthy condition asset. Similarity coefficients are generated for the historical image pairs of a regular and infrared image by use of the rebuilding of images by the first model as a similarity base and historical image pairs include a timestamp of image capture. A second model is trained to predict a time to an asset failure condition based on timestamps of image capture, timestamps of an asset failure condition, and similarity coefficients of the historical images. Responsive to receipt of a real-time image pair, the method computes a predicted time to a failure condition of the asset.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining target application container configuration data to identify one or more target container base image referenced in the target application container configuration: subjecting script data associated to the one or more target container base image to text based processing for evaluation of security risk associated to the one or more container base image, the script data obtained from at least one candidate hosting computing environment; and selecting a hosting computing environment from the at least one computing environment for hosting the target application container, the selecting in dependence on the text based processing.

Abstract: Weather data, asset variable values, and failure data may be received. A correlation factor may be generated by comparing the weather data and the asset variable values. One or more weather variables may be selected based on the correlation factor. A predicted asset variable value may be determined based on the weather data and the selected weather variables. The weather data and the predicted asset variable values may be compared to predict a failure probability for a first asset segment.

Abstract: Automated classification of non-structure data streams from a plurality of Internet of Things (IoT) devices includes receiving, by a computer, from the plurality of IoT devices a data stream including a set of labeled readings S with a predetermined sample size n and a predetermined partition size m. The received data stream is partitioned into a partition set S? including m readings. The computer determines a set of features associated with the data stream based on the partition set S? by applying feature engineering techniques. A vector representation of the obtained set of features is built by the computer to place each feature on a same range scale. A predetermined minimum number of layers and neurons is then selected based on the set of features for training a neural network. Finally, non-structured data streams from new or unknown data sources can be classified using the trained neural network.

Abstract: Described are techniques for identifying anomalies in log files. The techniques including a method encompassing mapping a plurality of log entries into a bag-of-words matrix and determining respective cosine similarities between consecutive log entries in the bag-of-words matrix. The method further includes converting respective cosine similarities to respective states and determining respective state transitions between consecutive log entries. The method further includes generating a stochastic matrix based on the respective state transitions and identifying an anomalous log entry based on an anomalous state transition. The anomalous state transition has a probability below a probability threshold, and the probability is based on the stochastic matrix.

Abstract: A method and a system for automating security updates in a multi-tiered architecture of a system environment. The method includes receiving a software update note relating to a multi-tiered architecture system environment and determining a patch in the software update note is initially compatible with the system environment. The method also includes predicting, by a patch acceptance machine learning model, the patch is patchable within in the system environment and predicting, by an installation machine learning model, an installation patch time for the patch on the system environment. The method also includes installing the patch at the installation patch time on the system environment, and validating the patch installation using a validation machine learning model.