Abstract: Disclosed herein are system, method, and computer program product embodiments for providing application resiliency using a machine learning model trained to detect potential failures based on computational transaction metrics. A resiliency system may monitor metrics related to an application executing on an enterprise data system. The resiliency system may apply these metrics to a machine learning model trained to identify a potential application failure based on application usage trends. In response to detecting a potential failure of the application, the resiliency system may instruct the application to execute one or more resiliency actions. These may include one or more circuit breaker, rate limiter, time limiter, and/or bulkhead actions. The resiliency actions may aid the application in avoiding failure states. The resiliency actions may also be modified based on feedback metrics to aid the application in quickly restoring service once the failure state has been avoided.

Abstract: A system and method for predicting a set of probable classes for test data is described. The method comprises retrieving, from a memory a training dataset, a plurality of classes, and a plurality of corresponding training feature vectors for each of the plurality of classes. The method comprises receiving an input indicative of a target probability required for the test data. The method comprises determining a set of membership probabilities for the test data that include a corresponding membership probability associated with each of the plurality of classes, the corresponding membership probability being indicative of a probability of the test data belonging to a corresponding class of the plurality of classes. The method comprises determining, based on the input and the set of membership probabilities, the set of probable classes, from the plurality of classes, for the test data.

Abstract: A system to optimize scheduling of a data backup and/or restore of a backup data in a data backup/restore environment is presented. The system includes a training module configured to train an artificial intelligence (AI) model based on historical data corresponding to data backup and/or restore of one or more training datasets. The system further includes a time estimator configured to estimate an estimated time taken for the data backup and/or restore of the backup data to a data backup server or a restore location based on the trained AI model and operating data corresponding to operating states of one or more resources in the data backup/restore environment. A related method is also presented.

Abstract: A method for generating a hyper-stabilized video by an electronic device includes detecting, by the electronic device, a preview of at least one image frame on a screen of the electronic device, wherein the at least one image frame is captured by one or more cameras of the electronic device; determining, by the electronic device, an optimal Central Square Field of View (CSFoV) in the at least one image frame; determining, by the electronic device, a maximum rotation angle of the electronic device for each camera of the one or more cameras based on the optimal CSFoV; determining, by the electronic device, a current rotation angle of the electronic device by using at least one sensor of the electronic device; and generating, by the electronic device, the hyper-stabilized video based on the maximum rotation angle of the electronic device for the each camera and the current rotation angle of the electronic device.

Abstract: A method for generating a hyper-stabilized video by an electronic device includes detecting, by the electronic device, a preview of at least one image frame on a screen of the electronic device, wherein the at least one image frame is captured by one or more cameras of the electronic device; determining, by the electronic device, an optimal Central Square Field of View (CSFoV) in the at least one image frame; determining, by the electronic device, a maximum rotation angle of the electronic device for each camera of the one or more cameras based on the optimal CSFoV; determining, by the electronic device, a current rotation angle of the electronic device by using at least one sensor of the electronic device; and generating, by the electronic device, the hyper-stabilized video based on the maximum rotation angle of the electronic device for the each camera and the current rotation angle of the electronic device.

Abstract: A system for dynamic resource allocation during a data backup and/or restore of a backup data is presented. The system includes a resource allocation map generator configured to generate a resource allocation map for the current data backup and/or restore based on a mathematical model, real-time operating data corresponding to operating states of one or more resources, and historical data corresponding to data back-up and restore of one or more historical datasets. The system further includes a resource allocation recommender configured to generate a recommendation for resource allocation for the current data backup and/or restore based on the resource allocation map and a threshold value corresponding to a particular resource. The system moreover includes a resource allocator configured to dynamically initiate a change in resource allocation based on the generated recommendation. A related method is also presented.

Abstract: A system to optimize scheduling of a data backup and/or restore of a backup data in a data backup/restore environment is presented. The system includes a training module configured to train an artificial intelligence (AI) model based on historical data corresponding to data backup and/or restore of one or more training datasets. The system further includes a time estimator configured to estimate an estimated time taken for the data backup and/or restore of the backup data to a data backup server or a restore location based on the trained AI model and operating data corresponding to operating states of one or more resources in the data backup/restore environment. A related method is also presented.

Abstract: A robotic device management service obtains, from a client device operating in a first network, a request to obtain data from a robotic device operating in a second network. In response to the request, the robotic device management service issues a token to the client device that can be provided in future queries to obtain the data. The robotic device management service provides parameters of the request to the robotic device to cause the robotic device to generate and provide the data to the robotic device management service. In response to another request to obtain the data, where the other request includes the token, the robotic device management service queries a database to determine whether the data is available from a storage location of the service. If the data is available, the service provides the data to the client device to fulfill the other request.

Abstract: Methods and systems are provided for recording mixed audio signal and reproducing directional audio. A method includes receiving a mixed audio signal via plurality of microphones; determining an audio parameter associated with the mixed audio signal received at each of the plurality of microphones; determining active audio sources and a number of the active audio sources from the mixed audio signal; determining direction and positional information of each of the active audio source; dynamically selecting a set of microphones from the plurality of microphones based on at least one of the number of the active audio sources, the direction of each of the active audio sources, the positional information of each of the active audio sources, the audio parameter, or a predefined condition; and recording, based on the selected set of microphones, the mixed audio signal for reproducing directional audio.

Abstract: A robotic device management service obtains, from a customer, a first set of parameters of a robotic device and a second set of parameters for a simulation environment for testing a robotic device application installable on the robotic device. The set of parameters are used to indicate a storage location of the application and a selection of a simulation environment for testing the application. In response to the request, the robotic device management service selects a set of resources on which to execute the simulation in the simulation environment. The robotic device management service obtains the robotic device application from the storage location and loads the application on to the set of resources to execute the simulation.

Abstract: Provided are an electronic device and method for managing the perceived loudness of an input audio signal. The electronic device includes a communication unit configured to communicate with internal units and external devices and receive an audio source signal; a loudness adjustment unit configured to receive the audio source signal from the communication unit and obtain an input audio source signal from the audio source signal; a noise separator configured to separate noise from the input audio source signal; a voice booster configured to boost an output audio signal received from the loudness adjustment unit; a storage unit storing instructions; and at least one processor configured to communicate with the communication unit, the loudness adjustment unit, the noise separator, the voice booster, and the storage unit and control operations thereof by executing the stored instructions.

Abstract: Methods and systems are provided for recording mixed audio signal and reproducing directional audio. A method includes receiving a mixed audio signal via plurality of microphones; determining an audio parameter associated with the mixed audio signal received at each of the plurality of microphones; determining active audio sources and a number of the active audio sources from the mixed audio signal; determining direction and positional information of each of the active audio source; dynamically selecting a set of microphones from the plurality of microphones based on at least one of the number of the active audio sources, the direction of each of the active audio sources, the positional information of each of the active audio sources, the audio parameter, or a predefined condition; and recording, based on the selected set of microphones, the mixed audio signal for reproducing directional audio.

Abstract: A robotic device management service obtains a set of parameters of a simulation environment and a set of components for execution of a simulation of a robotic device application. Based on these parameters, the robotic device management service selects a set of resources for executing the application in a simulation environment. The robotic device management service may launch the set of components among the set of resources and monitor execution of the application in the simulation environment to ensure completion of the simulation.

Abstract: A robotic device management service obtains, from a customer, a first set of parameters of a robotic device and a second set of parameters for a simulation environment for testing a robotic device application installable on the robotic device. The set of parameters are used to indicate a storage location of the application and a selection of a simulation environment for testing the application. In response to the request, the robotic device management service selects a set of resources on which to execute the simulation in the simulation environment. The robotic device management service obtains the robotic device application from the storage location and loads the application on to the set of resources to execute the simulation.

Abstract: Provided are an electronic device and method for managing the perceived loudness of an input audio signal. The electronic device includes a communication unit configured to communicate with internal units and external devices and receive an audio source signal; a loudness adjustment unit configured to receive the audio source signal from the communication unit and obtain an input audio source signal from the audio source signal; a noise separator configured to separate noise from the input audio source signal; a voice booster configured to boost an output audio signal received from the loudness adjustment unit; a storage unit storing instructions; and at least one processor configured to communicate with the communication unit, the loudness adjustment unit, the noise separator, the voice booster, and the storage unit and control operations thereof by executing the stored instructions.