Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updated to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.

Abstract: Systems and methods for natural language processing (NLP) are described. The systems may be trained by identifying training data including clean data and noisy data; predicting annotation information using an artificial neural network (ANN); computing a loss value for the annotation information using a weighted loss function that applies a first weight to the clean data and at least one second weight to the noisy data; and updating the ANN based on the loss value. The noisy data may be obtained by identifying a set of unannotated sentences in a target domain, delexicalizing the set of unannotated sentences, finding similar sentences in a source domain, filling at least one arbitrary value in the similar delexicalized sentences, generating annotation information for the similar delexicalized sentences using an annotation model for the source domain, and applying a heuristic mapping to produce annotation information for the sentences in the target domain.

Abstract: A computerized method is disclosed that includes accessing domain name server (DNS) record data including a plurality of DNS records spanning a first time period, performing a time-to-live (TTL) analysis to determine a TTL run length distribution for the DNS record data, wherein the TTL analysis includes: generating a vector of the TTL values of each DNS record ordered sequentially in time, parsing the vector of the TTL values into segments, where a segment consists of one or more TTL values where a current TTL value is less than an immediately preceding TTL value, and determining the TTL run length distribution, determining whether DNS beaconing is present based on a result of the TTL analysis and in response to determining that DNS beaconing is present, generating an alert for a system administrator.

Abstract: In an aspect, a method includes determining, by a processor of a vehicle, that an original equipment manufacturer (OEM) subscription of the vehicle is set to a low capability subscription. The method includes determining, by the processor, that the low capability subscription is unable to access a network at a current location of the vehicle based on the low capability subscription. The method also includes determining, by the processor and based on sensor data received from one or more sensors of the vehicle, a probability of an accident occurring to the vehicle. The method further includes determining, by the processor, that the probability satisfies a threshold. The method includes switching, by the processor, the OEM subscription from the low capability subscription to a high capability subscription.

Abstract: This application relates to apparatus and methods for training machine learning models. In some examples, a pool of worker pods are generated that can execute tasks to train a machine learning model. The pool of work pods are assigned tasks by a master that communicates with the worker pods using a work queue. Each worker pod can provide output using a results queue. The embodiments may operate with less reliable memory, such as object stores, which may be less costly than other types of storage mechanisms. To operate in less reliable environments, each worker pod can include a checkpoint mechanism that can recover from interruptions, such as interruptions due to node failure or preemption. For example, the checkpoint mechanism may allow a worker pod to continue processing a task, when the task is interrupted, from a last checkpoint. Processing results are provided to a results queue when a task completes.

Abstract: Systems and methods for natural language processing (NLP) are described. The systems may be trained by identifying training data including clean data and noisy data; predicting annotation information using an artificial neural network (ANN); computing a loss value for the annotation information using a weighted loss function that applies a first weight to the clean data and at least one second weight to the noisy data; and updating the ANN based on the loss value. The noisy data may be obtained by identifying a set of unannotated sentences in a target domain, delexicalizing the set of unannotated sentences, finding similar sentences in a source domain, filling at least one arbitrary value in the similar delexicalized sentences, generating annotation information for the similar delexicalized sentences using an annotation model for the source domain, and applying a heuristic mapping to produce annotation information for the sentences in the target domain.

Abstract: Systems and methods are described for processing ingested pipeline metrics and ingested logs in an asynchronous manner as the data is being ingested to explain anomalies detected in the pipeline metrics using the ingested logs. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and determine whether the logs corresponding to the comparable data structure is anomalous. Separately, the streaming data processor(s) can perform an outlier detection on the pipeline metrics to detect outliers. The streaming data processor(s) can then window the anomalous logs and the pipeline metric outliers to surface explanations for the pipeline metric outliers using the anomalous logs.

Abstract: An embodiment of the present invention is directed to low-maintenance conversion of an advance function presentation (AFP) format to a portable document format. According to an embodiment of the present invention, hidden text may be inserted as locators in the AFP files. For example, the hidden text may be inserted in a small font size (e.g., approximately 1 point) in a color that matches the background, e.g., text in white. A benefit of this hidden text is that it does not need to change with the maintenance of the statement template. Accordingly, the transformation software template may be built, or trained, to look for these hidden text locators to identify the beginning and end of sections. This leads to a robust transformation software template that does not need to be maintained or changed with text changes in the statements.

Abstract: An embodiment of the present invention is directed to low-maintenance conversion of an advance function presentation (AFP) format to a portable document format. According to an embodiment of the present invention, hidden text may be inserted as locators in the AFP files. For example, the hidden text may be inserted in a small font size (e.g., approximately 1 point) in a color that matches the background, e.g., text in white. A benefit of this hidden text is that it does not need to change with the maintenance of the statement template. Accordingly, the transformation software template may be built, or trained, to look for these hidden text locators to identify the beginning and end of sections. This leads to a robust transformation software template that does not need to be maintained or changed with text changes in the statements.

Abstract: Anonymous containers are discussed herein. An operating system running on a computing device, also referred to herein as a host operating system running on a host device, prevents an application from accessing personal information (e.g., user information or corporate information) by activating an anonymous container that is isolated from the host operating system. In order to create and activate the anonymous container, a container manager anonymizes the configuration and settings data of the host operating system, and injects the anonymous configuration and settings data into the anonymous container. Such anonymous configuration and settings data may include, by way of example and not limitation, application data, machine configuration data, and user settings data. The host operating system then allows the application to run in the anonymous container.

Abstract: Anonymous containers are discussed herein. An operating system running on a computing device, also referred to herein as a host operating system running on a host device, prevents an application from accessing personal information (e.g., user information or corporate information) by activating an anonymous container that is isolated from the host operating system. In order to create and activate the anonymous container, a container manager anonymizes the configuration and settings data of the host operating system, and injects the anonymous configuration and settings data into the anonymous container. Such anonymous configuration and settings data may include, by way of example and not limitation, application data, machine configuration data, and user settings data. The host operating system then allows the application to run in the anonymous container.

Abstract: A system and method for repairing corrupt software components of a computer system. Corrupt software is detected and repaired utilizing an automated component repair service. Repair files are downloaded from an external storage location and used to repair the corruption. The downloaded files are preferably the smallest amount of data necessary to repair the identified corruption. The process of repairing corrupt files is used in conjunction with a software updating service to resolve problems that occur when corrupt software is updated by allowing a corrupt component to be repaired and then uninstalled such that an updated component can be properly installed.

Abstract: A system and method for repairing corrupt software components of a computer system. Corrupt software is detected and repaired utilizing an automated component repair service. Repair files are downloaded from an external storage location and used to repair the corruption. The downloaded files are preferably the smallest amount of data necessary to repair the identified corruption. The process of repairing corrupt files is used in conjunction with a software updating service to resolve problems that occur when corrupt software is updated by allowing a corrupt component to be repaired and then uninstalled such that an updated component can be properly installed.

Abstract: A system and method for repairing corrupt software components of a computer system. Corrupt software is detected and repaired utilizing an automated component repair service. Repair files are downloaded from an external storage location and used to repair the corruption. The downloaded files are preferably the smallest amount of data necessary to repair the identified corruption. The process of repairing corrupt files is used in conjunction with a software updating service to resolve problems that occur when corrupt software is updated by allowing a corrupt component to be repaired and then uninstalled such that an updated component can be properly installed.

Abstract: A software system, such as an operating system, that has multiple parts can be deployed to a computing device incrementally, rather than all at once. The software system self-adapts to the user's needs by installing and removing system extensions automatically without user intervention. A core part of the software system is deployed in the computing device, and system extensions are delivered to the computing device when needed. The software system is aware of the system extensions, so they appear to be part of the system, even though they have not yet been downloaded. The system extensions can be delivered to the computing system on demand or over time during idle times of the computing device. Unused system extensions can be removed from the computing device, and can be downloaded again when next needed.

Abstract: A software system, such as an operating system, that has multiple parts can be deployed to a computing device incrementally, rather than all at once. The software system self-adapts to the user's needs by installing and removing system extensions automatically without user intervention. A core part of the software system is deployed in the computing device, and system extensions are delivered to the computing device when needed. The software system is aware of the system extensions, so they appear to be part of the system, even though they have not yet been downloaded. The system extensions can be delivered to the computing system on demand or over time during idle times of the computing device. Unused system extensions can be removed from the computing device, and can be downloaded again when next needed.

Abstract: A system and method for repairing corrupt software components of a computer system. Corrupt software is detected and repaired utilizing an automated component repair service. Repair files are downloaded from an external storage location and used to repair the corruption. The downloaded files are preferably the smallest amount of data necessary to repair the identified corruption. The process of repairing corrupt files is used in conjunction with a software updating service to resolve problems that occur when corrupt software is updated by allowing a corrupt component to be repaired and to then uninstalled such that an updated component can be properly installed.