Dell Products Inc.

Emc IP Holding Company LLC

Eric Joseph Bruno

Amy N Seibel

Zhen Jia

Danqing Sha

Bradley K Goodman

Victor Fong

Joseph Caisse

Shary Beshara

Stephen J Todd

Zijia Wang

Kenneth Durazzo

Maxim Balin

Stav Sapir

Michael Robillard

Mahesh Babu Ramaiah

Farida Shafik

Brendan Burns Healy

Kranti Uppala

Eric L Caron

Ravinder Tamishetty

Dragan Savic

Megan Hyland

Xingyu Wang

Ana Smith

Methods and systems for managing operation of a data processing system are disclosed. To manage operation of the data processing system, new secrets may be established for use in operation of the data processing system when a trusted platform module of the data processing system is replaced. To establish the new secrets, the data processing system may use a recovery secret stored in a partition in storage to establish a recovery connection between the data processing system and a control plane. The recovery connection may be used by the data processing system to establish new secrets and securely store the secrets in a replacement trusted platform module for future use.

Systems and methods for restoring secure connections between data processing systems and control planes

Methods and systems for managing secrets are disclosed. To manage secrets, backups of the secrets may be obtained to facilitate future recoveries of the secrets. While backed up, the secrets may be secured with a security model. The security model may prescribe how entities that manage the backups are to operate, how encryption and decryption keys are maintained, and how various copies of the backed up secrets are to be distributed to manage potential loss of various backups of the secrets. When access to a secret is lost, a recovery may be performed using a corresponding backup of the secret.

Control system management for secret backup in distributed systems

One example method includes generating sound information regarding sound sources in an environment. The sound information is generated by separating and localizing the sound sources. The sound information is then presented as guidance to a user. The guidance may be presented graphically in a user interface, haptically, or in another manner.

Visual guidance of audio direction

One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Intelligent classical/quantum extended memory fabric

One example method includes determining representation bias in a data set. A bias detection engine is trained using a data set that is sufficiently diversified and/or unbiased. Once trained, test data sets can be evaluated by the bias detection engine to determine an amount of representation bias in the test data sets. The representation bias can be visually conveyed to a user and suggestions on how to reduce the representation bias may be provided and/or implemented to reduce the representation bias in the test data set. Suggestions can be implemented by adding or removing data from the test data that will reduce the representation bias.

Detecting representative bias within image data sets

A nanoservice architecture allows a service to include multiple functions or features along with a common skeleton code. Each of the nanoservices in a service may be configured using a common configuration file. The configuration file may include a portion that configures requirements or resources common to all nanoservices and other portions that are specific to associated nanoservices.

Nanoservice computing and architecture

Methods and systems for managing secrets are disclosed. To manage secrets, backups of the secrets may be obtained to facilitate future recoveries of the secrets. While backed up, the secrets may be protected with a security model. The security model may prescribe how decryption keys are maintained, and how various copies of the backed up secrets are to be separated from the decryption keys for encrypted copies of the secrets. When access to a secret is lost, a recovery may be performed using a corresponding encrypted backup of the secret.

Secret management in distributed systems using passphrases

Methods and systems for managing secrets are disclosed. To manage secrets, backups of the secrets may be obtained to facilitate future recoveries of the secrets. The backups may be established by a manufacturer of a device. When ownership in the device is transferred to a new owner, the backups of the secrets may be added to an ownership used to complete onboarding of the device to systems managed by the new owner. The new owner may use the backups, in conjunction with decryption keys, to access and use the secrets in the event of the secrets becoming inaccessible.

Secret management in distributed systems through onboarding

Techniques are provided that employ haptic feedback to influence an engagement level of a remote user. One method comprises obtaining sensor data associated with a remote user interacting with educational content items; applying the sensor data to an analytics engine to obtain an engagement status indicating an engagement level of the remote user with the educational content items; and automatically initiating a provision of a haptic feedback signal to the remote user based on the engagement status. The engagement status may comprise an engagement score, and the haptic feedback signal may be sent to the remote user based on an evaluation of the engagement score relative to one or more threshold-based criteria. A type and/or an intensity of the haptic feedback signal can be based on one or more user preferences of the remote user.

Haptic feedback for influencing user engagement level with remote educational content

Methods, apparatus, and processor-readable storage media for artificial intelligence-driven avatar-based personalized learning techniques are provided herein. An example computer-implemented method includes obtaining multiple forms of input data from user devices associated with a user in a virtual learning environment; determining status information for user variables by processing at least a portion of the multiple forms of input data using a first set of artificial intelligence techniques; determining instruction-related modifications for the user by processing, using a second set of artificial intelligence techniques, at least a portion of the multiple forms of input data and at least a portion of the determined status information; implementing, based on the determined instruction-related modifications, modifications to an instructor avatar with respect to the user in the virtual learning environment; and performing one or more automated actions based on user response to the implemented modifications to the instructor avatar.

Artificial intelligence-driven avatar-based personalized learning techniques

One example method includes performing sound quality operations. Microphone arrays are used to cancel background noise and to enhance speech. With arrays at each environment of each user participating in a call, a first microphone array can cancel or suppress background noise and a second array can generate enhanced speech for transmission to other users. Thus, for user, the audio signal output by the user's device includes an anti-noise signal to cancel background noise present in the user's environment and enhanced speech from other users.

Adaptive noise suppression for virtual meeting/remote education

One example method includes creating an image definition file, using the image definition file to create an image that is deployable to an edge device, copying an agent into the image, stripping any user passwords out of the image, and removing any unnecessary packages from the image, and after the stripping and the removing, the image becomes an immutable image. The immutable image may then be deployed to a group of edge devices, and one or more layers of the immutable image may be updated by the agent.

Immutable image for deployment to edge devices

Methods and systems for managing distribution of data are disclosed. The data may be distributed by edge systems to processing systems. The data may be distributed using a data pipeline. The data pipeline may include functionality to process data using different modalities depending on the type of data and operable connectivity between the edge systems and processing systems. By modifying the manner in which the data is processed, limitations in operable connectivity may be managed and a broader array of different types of data may be more efficiently distributed.

System and method for distribution of data in edge systems

One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

Edge function bursting

Techniques are provided for machine learning-based educational content adaptation based on user personal characteristics. One method comprises obtaining personal characteristics of at least one user; applying the personal characteristics of the at least one user to at least one machine learning model to automatically adapt at least one educational content item for the at least one user using one or more of the applied personal characteristics of the at least one user; and initiating a provision of the at least one automatically adapted educational content item to the at least one user. Technology-related characteristics of the at least one user may also be applied to the at least one machine learning model to further adapt the at least one educational content item for the at least one user using the applied technology-related characteristics.

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Shirley, NY, United States of America

Eric Joseph Bruno