Abstract: A method and a system for device edge learning is disclosed. The method includes training an artificial intelligence (AI) model for extracting the visual embeddings with pre-trained visual deployment networks; checking the performance of AI model by feeding real-time data and by performing an inference; initiating an edge learning; extracting visual embeddings with pre-trained visual deployment networks; performing the inference and adding a text image embedding; taking the text embeddings using text embedders embeddings; converting the text to image embeddings to generate augmented image embeddings and adding text embeddings; training learning networks on a plurality of agents; and performing forward prop with the mapping networks and calculating the loss and backprop.

Abstract: A system and a method for integer only quantization aware training on an edge device is disclosed. The method includes 1) computing a pseudo cross entropy and a loss function based on a gradient stabilization and a gradient delta stabilization, and a residual weight error; 2) computing a gradient and performing a back propagation by converting of integer values to floating point values and updating the gradient; 3) updating weights parameters corresponding to gradients with a low precision; and 4) adjusting the residual weight error and repeating the steps 1 to 4 for a predetermined number of epochs.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: An intelligent question and answer (Q&A) system and method for interactively guiding users through a procedure is disclosed. The intelligent Q&A system can dynamically generate process trees (or procedural trees) from the content or procedures presented in a raw document, such as a reference manual. The intelligent Q&A system can include a virtual agent that uses the dynamically generated process trees for interactive conversation with a user. Using the system, the virtual agent can interactively guide users through completing tasks such as updating software or connecting an IoT device to an existing system.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: An intelligent question and answer (Q&A) system and method for interactively guiding users through a procedure is disclosed. The intelligent Q&A system can dynamically generate process trees (or procedural trees) from the content or procedures presented in a raw document, such as a reference manual. The intelligent Q&A system can include a virtual agent that uses the dynamically generated process trees for interactive conversation with a user. Using the system, the virtual agent can interactively guide users through completing tasks such as updating software or connecting an IoT device to an existing system.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: A method for performing a task of a cleaning robot is provided. The method according to an embodiment includes generating a navigation map for driving the cleaning robot using a result of at least one sensor detecting a task area in which an object is arranged, obtaining recognition information of the object by applying an image of the object captured by at least one camera to a trained artificial intelligence model, generating a semantic map indicating environment of the task area by mapping an area of the object included in the navigation map with the recognition information of the object, and performing a task of the cleaning robot based on a control command of a user using the semantic map. An example of the trained artificial intelligence model may be a deep-learning neural network model in which a plurality of network nodes having weighted values are disposed in different layers and exchange data according to a convolution relationship, but the disclosure is not limited thereto.

Abstract: Buying stage determination techniques in a digital medium environment are described to model and control user interaction with a service provider to purchase a good or service through identification of stages in a buying cycle associated with users. Usage data is obtained that at least describes interactions by one or more users with the service provider that occurred during previous sessions. The marketing interactions in the obtained usage data are classified and quantified for respective said users using one or more features. Generation of a model of the stages of the buying cycle is controlled using the quantified usage data. The model is configured to identify a stage in the buying cycle that is associated with a respective said user, which is usable to control further marketing interactions supported for that user.