Abstract: Embodiments bypass Address Space Layout Randomization (ASLR) executed on a web server that implements a web function. Embodiments, from a client remote from the web server, construct a stack layout of the web function. Embodiments identify memory locations of the stack layout that are writable and read a currently stored library instruction address of a library at the identified memory locations. Embodiments then iteratively increment the currently stored library instruction address by one and overwriting the identified memory locations until a remote shell is successfully spawned or another malicious instruction is executed.

Abstract: Techniques discussed herein relate to providing in-memory workflow management at an edge device (e.g., a computing device distinct from and operating remotely with respect to a data center). The edge device can operate as a computing node in a computing cluster of edge devices and implement a hosting environment (e.g., a distributed data plane). A work request can be obtained by an in-memory workflow manager of the edge device. The work request may include an intended state of a data plane resource (e.g., a computing cluster, a virtual machine, etc.). The in-memory workflow manager can determine the work request has not commenced and initialize an in-memory execution thread to execute orchestration tasks to configure a data plane of the computing cluster according to the intended state. Current state data corresponding to the configured data plane may be provided to the user device and eventually displayed.

Abstract: A campaign profile specifies products and/or content items associated with a campaign. A target group selection engine applies an affinity attribute model to user information of a user. The affinity attribute model is used to determine the user's affinity towards (a) product attributes of the products associated with the campaign and/or (b) content attributes of the content items associated with the campaign. The affinity attribute model may be generated using machine learning. A user interface accepts target user tuning parameters that specify weights to be applied to the affinity attributes determined by the affinity attribute model. Based at least on applying the weights to the affinity attributes, an inclusion score and/or exclusion score for the user is determined. The user is included in a target group, for engaging with the campaign, based on the inclusion score and/or exclusion score.

Abstract: Disclosed systems relate to automatically instrumenting a web application for tracing. In an example, a method includes providing a web page application to a web browser on a client device. The web page application includes a tracer application. The method further includes detecting a start of the web page application and instantiating tracer application. The tracer application is configured to log tracing data for the web page application. The method further includes detecting an event initiated by interaction with the web page application. The method further includes automatically logging a start of a span based on the detection, the logging associating the span with the tracer application. The method further includes executing operations corresponding to the event. The method further includes automatically logging an end of the span based upon a completion of the operations corresponding to the event.

Abstract: “Semi-supervised” machine learning relies on less human input than a supervised algorithm to train a machine learning algorithm to perform entity recognition (NER). Starting with a known entity value or known pattern value for a specific entity type, phrases in a training data corpus are identified that include the known entity value. Context-value patterns are generated to match selected phrases that include the known entity value. One or more context-value patterns may be validated based on human input. The validated patterns identify additional entity values. A subset of the additional entity values may also be validated based on human input. Occurrences of validated entity values may be labeled in the training corpus. Sample phrases from the labeled training dataset may be extracted to form a reduced-size training set for a supervised machine learning model which may be further used in production to label data for any named entity recognition application.

Abstract: Techniques for implementing an infrastructure orchestration service are described. In certain embodiments, a cloud infrastructure orchestration system (CIOS) is disclosed that generates customized flock configurations for services to be deployed to different regions supported by the CIOS. The CIOS receives generic configuration information describing a set of infrastructure assets associated with a service and identifies first portions of the generic configuration information for deploying the set of infrastructure assets associated with the service that are configurable. The CIOS receives region configuration information for configuring the generic configuration information and updates the generic configuration information based on the region configuration information. The CIOS then transmits the updated configuration information to set of regions managed by the CIOS.

Abstract: In accordance with an embodiment, described herein is a system and method for supporting multi-tenancy in an application server, cloud, on-premise, or other environment, which enables categories of components and configurations to be associated with particular application instances or partitions. Resource group templates define, at a domain level, collections of deployable resources that can be referenced from resource groups. Each resource group is a named, fully-qualified collection of deployable resources that can reference a resource group template. A partition provides an administrative and runtime subdivision of the domain, and contains one or more resource groups. Each resource group can reference a resource group template, to bind deployable resources to partition-specific values, for use by the referencing partition. A tenant of the application server or cloud environment can be associated with a partition, or applications deployed therein, for use by that tenant.

Abstract: The present embodiments relate to providing near real-time communications from a public network to a private network. A first computing device in a public network can obtain data packets to be provided to the private network from an application executing on the first computing device. A trust module executed by the first computing device can authenticate the user, application, and the data packets to be provided to the private network and add metadata relating to the sending user, recipient user, etc. The data packets can be forwarded to the private network via a cross-domain system (CDS). The metadata and the digital signature on the data packets can be verified by a trust module executing on a second computing device in the private network. The second computing device can receive the data packets and store the data packets for subsequent actions to be performed in the private network.

Abstract: A campaign profile specifies products and/or content items associated with a campaign. A target group selection engine applies an affinity attribute model to user information of a user. The affinity attribute model is used to determine the user's affinity towards (a) product attributes of the products associated with the campaign and/or (b) content attributes of the content items associated with the campaign. The affinity attribute model may be generated using machine learning. A user interface accepts target user tuning parameters that specify weights to be applied to the affinity attributes determined by the affinity attribute model. Based at least on applying the weights to the affinity attributes, an inclusion score and/or exclusion score for the user is determined. The user is included in a target group, for engaging with the campaign, based on the inclusion score and/or exclusion score.

Abstract: Systems and methods for a bouncing replication protocol are described herein. The system can include a replication cluster including a plurality of servers and a master controller that can receive a first request to replicate a first transaction and execute a batching process to replicate the first transaction. The batching process can include selecting a first server for replication of the first transaction, determining that a pending acknowledgement from the selected first server has not been identified, adding the first transaction to a first batch for the first server, and sending the first batch including only the first transaction.

Abstract: Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.

Abstract: Techniques for configuring one or more applications based on a detected wakefulness state of a user are disclosed. A system trains and applies a machine learning model to wakefulness data to compute a wakefulness state of a user. The system obtains the wakefulness data from wearable devices worn by the user and environmental devices in a user's environment. The system configures applications and/or devices based on the computed wakefulness state of the user. The system configures the ability of devices or applications to generate visual, audible, or tactile notifications in response to determining that a user is awake or asleep.

Abstract: Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.

Abstract: Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.

Abstract: Techniques for preprocessing data assets to be used in a natural language to logical form model based on scalable search and content-based schema linking. In one particular aspect, a method includes accessing an utterance, classifying named entities within the utterance into predefined classes, searching value lists within the database schema using tokens from the utterance to identify and output value matches including: (i) any value within the value lists that matches a token from the utterance and (ii) any attribute associated with a matching value, generating a data structure by organizing and storing: (i) each of the named entities and an assigned class for each of the named entities, (ii) each of the value matches and the token matching each of the value matches, and (iii) the utterance, in a predefined format for the data structure, and outputting the data structure.

Abstract: Described herein are dialog systems, and techniques for providing such dialog systems, that are suitable for use on standalone computing devices. In some embodiments, a dialog system includes a dialog manager, which takes as input an input logical form, which may be a representation of user input. The dialog manager may include a dialog state tracker, an execution subsystem, a dialog policy subsystem, and a context stack. The dialog state tracker may generate an intermediate logical form from the input logical form combined with a context from the context stack. The context stack may maintain a history of a current dialog, and thus, the intermediate logical form may include contextual information potentially missing from the input logical form. The execution subsystem may execute the intermediate logical form to produce an execution result, and the dialog policy subsystem may generate an output logical form based on the execution result.

Abstract: Systems, methods, and machine-readable media may place workloads of source systems in a migration of data and applications from the source systems to target systems. Data relating to a first number of source nodes and a second number of target nodes may be received and analyzed. A migration plan that specifies placement of workloads from source systems into target systems may be created. The placement may include packing a pluggable environment or a clustered environment. The packing the pluggable environment or the clustered environment may include: when the second number of target nodes is greater than the first number of source nodes, placing workload from the source nodes to the target nodes; and, when the second number of target nodes is less than the first number of source nodes, the workload from the source nodes is not placed to the target nodes.

Abstract: Embodiments optimize display ordering of reservable hotel room choices for a hotel. Embodiments receive a trained prediction demand model for the hotel, the trained prediction model including estimated coefficients, and receive a total inventory of hotel rooms for the hotel. Embodiments determine optimal Lagrangian coefficients from the estimated coefficients using a first iterative gradient search and determine optimized prices per customer based on the estimated coefficients and the optimal Lagrangian coefficients using a second iterative gradient search. Embodiments determine an offer order optimization per customer based on the optimal Lagrangian coefficients and using linear programming. Embodiments receive a request for a hotel room from a first customer, the request including one or more attributes. Based on the one or more attributes and the optimized prices per customer and the offer order optimization per customer, embodiments display an optimized ordered list of hotel room choices.

Abstract: Techniques are disclosed for training a model, using multi-task learning, to transform natural language to a logical form. In one particular aspect, a method includes accessing a first set of utterances that have non-follow-up utterances and a second set of utterances that have initial utterances and associated one or more follow-up utterances and training a model for translating an utterance to a logical form. The training is a joint training process that includes calculating a first loss for a first semantic parsing task based on one or more non-follow-up utterances from the first set of utterances, calculating a second loss for a second semantic parsing task based on one or more initial utterances and associated one or more follow-up utterances from the second set of utterances, combining the first and second losses to obtain a final loss, and updating model parameters of the model based on the final loss.

Abstract: Protecting data in non-volatile storages provided to clouds against malicious attacks. According to an aspect, multiple malicious patterns indicating respective malicious attacks to access non-volatile storages provided to clouds in a cloud infrastructure are maintained. When an access request is received, the data stream representing the access request is examined to determine whether the data stream contains any of the malicious patterns. If the data stream is found not to contain any malicious pattern, it is concluded that the access request is free of the malicious attacks. If the data stream is found to contain at least one malicious pattern, it is concluded that the access request is a malicious attack corresponding to the malicious pattern.