Abstract: A method includes receiving data collected from a plurality of managed devices in a plurality of data collections. The data collections are performed using a plurality of collection protocols. A trigger that generated each of given ones of the data collections is determined. The method further includes identifying a collection protocol of the plurality of collection protocols used for each of the given ones of the data collections, and determining one or more attributes of a plurality of attributes of the plurality of managed devices that have been collected using given ones of the collection protocols. A mapping is generated between the triggers, the collection protocols and the attributes using one or more machine learning algorithms. The generated mapping is used to predict one or more collection protocols of the plurality of collection protocols to use to collect data from one or more of the managed devices.

Abstract: Methods, apparatus, and processor-readable storage media for automated configuration determinations for data center devices using artificial intelligence are provided herein. An example computer-implemented method includes obtaining input information pertaining to one or more device-related changes to a data center; obtaining telemetry data attributed to one or more devices in the data center; determining one or more device configurations for implementation in at least one device in the data center in connection with the one or more device-related changes by processing the input information and the obtained telemetry data using one or more artificial intelligence techniques; and performing at least one automated action based at least in part on the one or more determined device configurations.

Abstract: A processing platform comprises at least one processor coupled to at least one memory, and is configured to obtain a set of profiles for configuring a set of devices in an information processing system wherein, for a given device, a given profile comprises one or more configuration attribute values corresponding to one or more components of the given device. The processing platform divides each of at least a portion of the profiles of the set of profiles into two or more sub-profiles to form a set of sub-profiles wherein, for a given divided profile of a given device, two or more sub-profiles respectively correspond to two or more components of the given device. The processing platform stores the set of sub-profiles for use in configuring another device in the information processing system.

Abstract: A charge/discharge pattern battery training system includes a battery subsystem connected to a battery charging subsystem, and a battery charging determination engine coupled to the battery charging subsystem. The battery charging determination engine monitors a plurality of charge/discharge cycles performed by the battery subsystem and, based on the plurality of charge/discharge cycles performed by the battery subsystem, identifies a first charge/discharge pattern exhibited by the battery subsystem. The battery charging determination engine then determines a charging schedule that will cause the battery subsystem to exhibit a second charge/discharge pattern that is different than the first charge/discharge pattern, and applies the charging schedule via the battery charging subsystem to the battery subsystem that causes the battery subsystem to exhibit the second charge/discharge pattern.

Abstract: In some examples, a server receives configuration data from a device. The server receives a software or firmware update from a vendor and determines, based on the configuration data, that the update is installable on the device. The server creates and configures a container, based on the configuration data, to create a replica of the device. The server installs the update in the replica and performs multiple tests that generate logs. If the logs indicate that the update caused no issues, the server sends the update to the device. If the logs indicate that the update caused an issue, the server sends the update to the vendor. In response, the server receives, from the vendor, a modified update that addresses the issue, installs the modified update in the replica, performs the tests, determines that the modified update causes no issues, and sends the modified update to the device.

Abstract: In some examples, a computing device may gather data associated with activities performed by individuals from multiple locations (e.g., code repositories). The computing device may determine data gathered by a data monitor application at individual locations of the multiple locations over a predetermined amount of time. The computing device may filter, based on criteria, the gathered data and perform an analysis of the filtered data using a machine learning algorithm (e.g., an artificial neural network and a logistic sigmoid). The criteria may be selected based at least in part on a job function associated with the particular individual. The machine learning algorithm may create a human resource evaluation of a particular individual of the plurality of individuals recommending an increase in salary, a bonus, or a promotion. The human resources evaluation may include a probability that the particular individual will leave a current job in the organization.

Abstract: Methods, apparatus, and processor-readable storage media for device temperature impact management using machine learning techniques are provided herein. An example computer-implemented method includes obtaining one or more notifications pertaining to temperature information associated with one or more devices; generating one or more predictions pertaining to at least one potential problem with at least one of the one or more devices by applying one or more machine learning models to the one or more obtained notifications; determining one or more automated actions related to the one or more predictions by utilizing at least one neural network to process data associated with temperature control for the at least one device; and automatically initiating the one or more automated actions.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

Abstract: Techniques for localization of a content presentation in an information processing system are provided. In one example, a method adapts translated content to accommodate an original content design structure (e.g., wireframe) when such translated content is inconsistent with the original content design structure. In another example, a method adapts the original content design structure when translated content is inconsistent with the original content design structure. In yet another example, an image analysis method is used to adapt translated content determined to be inconsistent with the original content design structure.

Abstract: Techniques for localization of a content presentation in an information processing system are provided. In one example, a method adapts translated content to accommodate an original content design structure (e.g., wireframe) when such translated content is inconsistent with the original content design structure. In another example, a method adapts the original content design structure when translated content is inconsistent with the original content design structure. In yet another example, an image analysis method is used to adapt translated content determined to be inconsistent with the original content design structure.

Abstract: Techniques for localization of a content presentation in an information processing system are provided. In one example, a method adapts translated content to accommodate an original content design structure (e.g., wireframe) when such translated content is inconsistent with the original content design structure. In another example, a method adapts the original content design structure when translated content is inconsistent with the original content design structure. In yet another example, an image analysis method is used to adapt translated content determined to be inconsistent with the original content design structure.

Abstract: Various systems and methods are provided for analyzing the effect(s) that a configuration change to one device has on other connected devices. In one embodiment, the disclosed functionality includes determining connectivity information associated with a data center, where the data center comprises at least a first device and a second device; discovering one or more changes to a configuration of the first device; determining, based at least in part on the connectivity information, that the second device is impacted by the one or more changes to the configuration of the first device; and determining one or more impacts to the second device as a result of the one or more changes, where each of the one or more impacts indicates a positive impact to the second device, a negative impact to the second device, or no impact to the second device.

Abstract: Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

Abstract: Various systems and methods are provided for using a response tracking plugin to detect and inform users in real time when one or more recipients of an email are actively responding to that email. In one embodiment, the functionality disclosed herein can be applied to an email that was sent to a plurality of recipients, including at least a first and second recipient, and where the email comprises a plugin that is configured to enable response tracking. One such embodiment comprises receiving the email; monitoring for a first pulse from the second recipient of the email, where the pulse is configured to indicate that the second recipient of the email has begun a reply to the email; and in response to receiving the first pulse, displaying an indication that comprises information about the second recipient of the email.

Abstract: A system receives a first alert pertaining to a main part from a managed information handling system, determines whether an alternative part is available at the managed information handling system as a substitute for the main part, and determines a life expectancy of the alternative part. The system may also determine whether an expected level of service can be expected to be provided by the alternative part based on the life expectancy of the alternative part, select between a plurality of levels of criticality to apply to the first alert, and dispatch the first alert for resolution at a selected level of criticality.

Abstract: An access credential is modified at one device, wherein the device is part of a secure private network of multiple devices. Each other device in the secure private network receives notification of the modification. The credential modification is implemented when at least a subset of the other devices each accept the credential modification.

Abstract: A method includes identifying at least one software update available for a given computing device, determining a state of the given computing device, and utilizing a machine-learning based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices. The method also includes generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device.

Abstract: An apparatus comprises a processing device configured to identify software updates available for software applications installed on a computing resource, at least a given one of the software updates for a given one of the software applications comprising information characterizing features of the given software application affected by the given software update. The processing device is also configured to determine frequencies of usage of the features of the given software application, and to select a number of allowable deferrals for the given software update based at least in part on the information characterizing the features of the given software application affected by the given software update and the determined frequencies of usage. The processing device is further configured to generate a software update notification for the given software update comprising an indication of a remaining number of the allowable deferrals for the given software update.

Abstract: Various systems and methods are provided for using dummy characters to provide enhanced security of a user's login credentials. The functionality disclosed herein provides specific steps for the creation, evaluation, storage, and use of such dummy characters in conjunction with a user's login credentials. Dummy characters can be thought of as characters that are not part of a user's actual password, but which are mixed in and around the actual characters when the password is being entered, in order to protect a user's true password from prying eyes and nefarious actors. Dummy characters can be associated with a specific user and/or a specific device or devices associated with a specific user. The functionality provided herein prevents a nefarious actor from simply repeating an entire password string (including the dummy characters) on a device that is not associated with the user whose credentials the nefarious actor is attempting to misappropriate.

Abstract: A system, method, and computer-readable medium are provided to protect passwords for computer applications by providing a user-triggered password scrambling mechanism. Scrambling is enabled with a user configurable character within the password to be a trigger. Once triggered, a user can enter several incorrect password strings without fear of being locked out of the system. Ultimately, a user can enter a correct password string or a modified version of the correct password string to gain access to the system. Through password scrambling, an observer will have a more difficult time determining the correct version of the password. This improves system security, thereby protecting valuable information, resources, and reputation of an enterprise provider.