Abstract: A method for improving communication network performance comprises identifying a favorability status of individual predictions and/or decisions of a plurality of decisions of a machine-learning algorithm acting on the communication network. The favorability statuses are stored with corresponding values of network parameters used as features in the algorithm. A counterfactual algorithm is generated, e.g., by generating a tree-based classification algorithm, based on the stored favorability statuses and network parameter values, to derive rules for producing a favorable status based on one or more of the network parameters. A proposed recourse action comprising a change in at least one of the network parameters is identified, based on the rules, and a decision network, such as a Bayesian inference network, is generated for determining a confidence level estimating a reliability of achieving a favorable status by changing the network parameter(s).

Abstract: A method by a network function of a wireless communication system includes receiving structured non-tabular data relating to the wireless communication system, wherein the structured non-tabular data includes network topology data in graph format. The structured non-tabular data is converted into tabular data. Converting the structured non-tabular data into tabular data includes identifying a baseline object of a graph of the network topology data and generating a row of tabular data for each baseline object in the graph of the network topology data. The method further includes storing the tabular data in a data store for access by consumer applications in the wireless communication system.

Abstract: Resolving conflicting changes to structured data files. A method includes for a structured data file which has both a generic structure and dialects built on top of the generic structure, for which conflicting changes have been made, and where an original version of the structured data, a first version of the structured data and a second version of the structured data exist, determining a dialect of the structured data. The method further includes, based on the determined dialect performing at least one of merge or diff operations on the original version of the structured data, the first version of the structured data and the second version of the structured data.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Resolving conflicting changes to structured data files. A method includes for a structured data file which has both a generic structure and dialects built on top of the generic structure, for which conflicting changes have been made, and where an original version of the structured data, a first version of the structured data and a second version of the structured data exist, determining a dialect of the structured data. The method further includes, based on the determined dialect performing at least one of merge or diff operations on the original version of the structured data, the first version of the structured data and the second version of the structured data.

Abstract: Resolving conflicting changes to structured data files. A method includes for a structured data file which has both a generic structure and dialects built on top of the generic structure, for which conflicting changes have been made, and where an original version of the structured data, a first version of the structured data and a second version of the structured data exist, determining a dialect of the structured data. The method further includes, based on the determined dialect performing at least one of merge or diff operations on the original version of the structured data, the first version of the structured data and the second version of the structured data.

Abstract: Systems and methods of leveraging machine learning to predict user generated content are provided. For instance, first entity data associated with an entity can be received. The first entity data can include user specified data associated with an attribute of the entity. The first entity data can be input into a machine-learned content prediction model. Inferred entity data can be received as output of the machine-learned content prediction model. The inferred entity data can include inferred data descriptive of the attribute of the entity.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Methods and system are disclosed that manage behavior of a graphical user interface associated with an application during a runtime of the application. In one aspect, the graphical user interface (GUI) may be configured with attributes associated with the application by a GUI configuration manager. Upon determining application configuration information, a data field metadata manager may determine data fields to be mapped onto the GUI. The data field metadata manager may read the metadata information associated with the data fields that may include data field attributes and domain values. A GUI metadata manager may retrieve metadata information associated with the mapped data fields. A GUI runtime manager may manage the behavior of the GUI and the data received by the data fields may be saved in a data store in a data format associated with the application.

Abstract: Systems and methods of leveraging machine learning to predict user generated content are provided. For instance, first entity data associated with an entity can be received. The first entity data can include user specified data associated with an attribute of the entity. The first entity data can be input into a machine-learned content prediction model. Inferred entity data can be received as output of the machine-learned content prediction model. The inferred entity data can include inferred data descriptive of the attribute of the entity.

Abstract: Resolving conflicting changes to structured data files. A method includes for a structured data file which has both a generic structure and dialects built on top of the generic structure, for which conflicting changes have been made, and where an original version of the structured data, a first version of the structured data and a second version of the structured data exist, determining a dialect of the structured data. The method further includes, based on the determined dialect performing at least one of merge or diff operations on the original version of the structured data, the first version of the structured data and the second version of the structured data.

Abstract: Methods and system are disclosed that manage behavior of a graphical user interface associated with an application during a runtime of the application. In one aspect, the graphical user interface (GUI) may be configured with attributes associated with the application by a GUI configuration manager. Upon determining application configuration information, a data field metadata manager may determine data fields to be mapped onto the GUI. The data field metadata manager may read the metadata information associated with the data fields that may include data field attributes and domain values. A GUI metadata manager may retrieve metadata information associated with the mapped data fields. A GUI runtime manager may manage the behavior of the GUI and the data received by the data fields may be saved in a data store in a data format associated with the application.

Abstract: Resolving conflicting changes to structured data files. A method includes for a structured data file which has both a generic structure and dialects built on top of the generic structure, for which conflicting changes have been made, and where an original version of the structured data, a first version of the structured data and a second version of the structured data exist, determining a dialect of the structured data. The method further includes, based on the determined dialect performing at least one of merge or diff operations on the original version of the structured data, the first version of the structured data and the second version of the structured data.

Abstract: A system and method for the acquisition of access to, and display of, information graphics on a computer processing device comprising a data interface, central processor unit, one or more input devices, storage, and memory. The electronic computing device is configured to display media content data to one or more users on demand. Media content is made accessible for download following successful completion of one or more payment transactions. Access to interactive media content, including animation having recursive action playback attributes, is made optionally available to users on a “pay-per-play” basis, thus providing efficiencies in the distribution of educational content. Access to viewing instances of information graphics playback may be constrained by a content provider according to a predetermined time period, a predetermine number of playback instances, or a predetermine subject matter associated with the media content.

Abstract: Tools and techniques assist developers with the detection of memory leaks by using correlation of data type memory usage trends. In particular, investigations of memory leaks can be prioritized without always resorting to the use of bulky and performance-degrading memory dumps, by using these tools and techniques to identify leaky correlated data types. Data about a program's memory usage is processed to identify memory usage trends over time for respective data types, and the trends are searched for significant correlations. Correlated trends (and hence their corresponding data types) are grouped. Memory usage analysis information is displayed for grouped data types, such as the names of the most rapidly leaking data types, the names of correlated data types, leak rates, and leak amounts in terms of memory size and/or data object counts. Memory usage data may also be correlated with processing load requests to indicate which requests have associated memory leaks.

Abstract: The present invention extends to methods, systems, and computer program products for identifying impacted tests from statically collected data. In general, static dependency data, possibly augmented with some dynamic data, is used to find an appropriate set of impacted tests for code changes. In some embodiments, static dependency analysis is used to identify tests impacted by a code change. Heuristics can be used to assist with identifying an appropriate set of impacted tests to run for a code change. Dynamic data can be used to augment static dependency data to identify more optimal sets of impacted tests to run for a code change.

Abstract: Tools and techniques assist developers with the detection of memory leaks by using correlation of data type memory usage trends. In particular, investigations of memory leaks can be prioritized without always resorting to the use of bulky and performance-degrading memory dumps, by using these tools and techniques to identify leaky correlated data types. Data about a program's memory usage is processed to identify memory usage trends over time for respective data types, and the trends are searched for significant correlations. Correlated trends (and hence their corresponding data types) are grouped. Memory usage analysis information is displayed for grouped data types, such as the names of the most rapidly leaking data types, the names of correlated data types, leak rates, and leak amounts in terms of memory size and/or data object counts. Memory usage data may also be correlated with processing load requests to indicate which requests have associated memory leaks.