Abstract: An amount of data change associated with a version of a content file with respect to one or more previous versions of the content file is determined. The amount of change associated with the version of the content file is determined using a tree data structure associated with the content file that is stored on a storage cluster. One or more statistics associated with backup snapshot are provided to a server. The server is configured to determine that the amount of data change associated with the version of the content file is anomalous based in part on the one or more statistics associated with the backup snapshot. A notification that data associated with the backup snapshot is potentially infected by malicious software is received from the server. The version of the content file is indicated as being potentially infected by malicious software.

Abstract: A method, computer system, and a computer program product for enhancing a customer experience is provided. The present invention may include collecting data for a store visit. The present invention may include determining whether to offer the user one or more rewards based on the data collected. The present invention may include determining the one or more rewards to offer the user. The present invention may include presenting the one or more rewards to the user.

Abstract: A computer system may obtain reference ticket data for reference tickets of an online system, with the reference ticket data indicating a reference issue for a reference component and a reference solution, and also obtain learning content items from a learning management system. The computer system may then create a mapping between the reference issues indicated by the reference ticket data for the reference tickets and the learning content items using an unsupervised machine learning algorithm. The computer system may detect target ticket data that has been provided by a user to the online system via a computing device, with the target ticket data indicating a target issue for a target component, identify a target solution for the target issue based on the target ticket data using the mapping, and cause a recommendation of the target solution to be displayed on the computing device.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate switching a model training process from a ground truth training phase to an adversarial training phase based on performance of a model trained in the ground truth training phase are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that identifies a performance condition of a model trained in a model training process. The computer executable components can further comprise a trainer component that switches the model training process from a ground truth training process to an adversarial training process based on the identified performance condition.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate switching a model training process from a ground truth training phase to an adversarial training phase based on performance of a model trained in the ground truth training phase are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that identifies a performance condition of a model trained in a model training process. The computer executable components can further comprise a trainer component that switches the model training process from a ground truth training process to an adversarial training process based on the identified performance condition.

Abstract: A method and system for quality control of a facility based on Internet of things. The system obtains data of the facility from plurality of architectural sources. The system creates a digital replica of the facility and defines plurality of control point levels for each region of the plurality of regions. The system allocates plurality of smart devices based on plurality of micro-descriptors. The system collects data associated with the facility and deciphers the data into one or more matrix. The system analyzes data with the pre-defined standard quality matrix in real time. The system assigns a degree of severity of the identified one or more issues. The system apprises one or more workforces associated with the facility to resolve the one or more issues including high degree of severity and the plurality of smart devices to take action to resolve the one or more issues.

Abstract: A method and system for quality control of a digital facility based on machine learning. The system connects a plurality of elements associated with a plurality of regions of the digital facility. The system allocates a unique identity to the plurality of elements. The system receives a set of data associated with the plurality of regions. The system collects a set of data associated with a plurality of micro descriptors. The system processes the second set of data to discover a plurality of patterns. The system predicts issues associated with the plurality of elements. The system assigns high severity issue to the one or more severe issues. The system stores information associated with the digital facility. The system updates the patterns associated with the plurality of elements. The system recommends characteristic parameters to the plurality of elements. The system notifies manpower associated with the digital facility.

Abstract: A method and system to conduct an audit for control and maintain the quality of a digital facility. The system executes instructions to causes one or more processors to perform a method. The method includes a first step of collecting a first set of data and a second step of creating one or more tasks. The method includes a third step of receiving a second set of data. The method includes a fourth step of analyzing the first set of data and the second set of data and a fifth step of assigning the one or more tasks. The method includes a sixth step of obtaining the third set of data. The method includes a seventh step of determining the deviation in the first set of data and the third set of data and an eighth step of rating a plurality of auditors.

Abstract: A system for quality control of a facility based on feedback from multiple sources. The system receives data of the facility from plurality of data sources. The system creates one or more digital replica of the facility and classifies each region of the plurality of regions into plurality of checkpoint levels. The system assigns a plurality of micro-descriptors to the last level of the plurality of checkpoint levels and collects a feedback for the plurality of micro-descriptors from plurality of sources. The system collects data associated with the facility including facility-specific matrix and manpower specific matrix. The system maps the facility specific matrix with pre-defined quality specific matrix and evaluates deviation of the mapped data to identify one or more issues associated with the facility. The system assigns degree of severity to the identified issue and alerts if degree of severity is high.