Abstract: An apparatus is disclosed. The apparatus comprises a processor to generate a launch control policy (LCP) comprising a plurality of measured launch environment (MLE) elements associated with one or more libraries of a software application and a cryptographic processor to deploy the LCP to generate root of trust (ROT) measurements, wherein the processor to perform launch verification to execute the one or more libraries upon execution and to verify integrity of the one or more libraries based on the associated MLEs and the ROT measurements.

Abstract: An apparatus is disclosed. The apparatus comprises a system on chip (SOC), including a plurality of hardware components and a processor to launch a secure execution environment to verify integrity of the plurality of hardware components using an expected integrity measurement generated based on properties of the plurality of hardware components.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an inferred subscription level based at least in part on the type associated with each notification; classifying the notifications based on the inferred subscription of each notification; and sending one or more of the notifications to the user. Each of the sent notifications has an inferred subscription level higher than a pre-determined threshold subscription level.

Abstract: Methods and apparatus for selection of radio access technology (RAT) based on device usage patterns are provided. A User Equipment (UE) obtains information relating to one or more Quality of Service (QoS) metrics for communication of data by the UE. The UE designates a Radio Access Technology (RAT) from a plurality of available RATs as a preferred RAT for the communication, based on the obtained information.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an interest of each activity to the user based at least in part on the type of each notification; ranking the notifications based at least in part on the calculated interest; and sending one or more of the notifications to the user. Each of the sent notifications has a ranking higher than a pre-determined threshold ranking.

Abstract: Disclosed are embodiments for information barriers that are conditional on the type of information being communicated. Information barrier polices provided by the disclosed embodiments selectively allow communication between accounts or groups based on characteristics of the content of the communication. For example, communication between a marketing department and an engineering department may be conditional on the communication not including any sensitive information. The determination of whether the communication includes sensitive information is further designed to provide good performance even in environments that maintain substantial portions of data in an offsite or cloud environment, where latencies associated with searching large datastores can be prohibitive.

Abstract: A method for controlling transactional processing system having transactions that include multiple tasks, a throughput limit a transaction processing time limit includes allocating a plurality of threads to be used by multiple tasks to achieve a throughput approximating the throughput limit. The method assigns the multiple tasks to the plurality of threads and assigns respectively different processing delays to the plurality of threads. The processing delays span an interval less than the transaction processing time limit. The method processes the multiple tasks within the transaction processing time limit by executing the plurality of threads at times determined by the respective processing delays.

Abstract: A method of forwarding packets by a physical network switch is provided. The method assigns egress ports that connect the network switch to each particular next hop to a weighted-cost multipathing (WCMP) group associated with the particular next hop. The method assigns weights to each egress port in each WCMP group according to the capacity of each path that connects the egress port to the next hop associated with the WCMP group and normalizes the weights over a range of values. For each packet received at the network switch, the method identifies the WCMP group associated with a next hop destination of the packet. The method calculates a hash value of a set of fields in the packet header and uses the hash value to perform a range lookup in the identified WCMP group to select an egress port for forwarding the packet to the next hop.

Abstract: In one embodiment, a method includes receiving a number of notifications of one or more activities relevant to a user. Each notification has an associated receipt time and type of notification. The method also includes aggregating one or more of the notifications based on the type of notification; determining a sending time to send the aggregated notifications based at least in part on determining that a pre-determined amount of time that has elapsed from a receipt time of a most recent one of the aggregated notifications; and sending the aggregated notifications to the user based on the sending time.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating a level of interest in content of each notification to the user based at least in part on the type of each notification; modifying the content of one or more of the notifications based at least in part on the calculated level of interest; and sending one or more of the notifications with modified content to the user.

Abstract: In one embodiment, a method includes one or more computing devices accessing a notification to be sent to a user, where the notification has a context. The method also includes one or more computing devices sending a request to a history service for historical notification data associated with the user with respect to the context of the notification and a ranking of the notification where the ranking indicates a probability of the user interacting with the notification. The method also includes one or more computing devices receiving the historical notification data and the ranking from the history service. Moreover, the method also includes one or more computing devices determining a notification policy to apply to the notification based at least in part on the context of the notification, the historical notification data, and the ranking. Furthermore, the method also includes one or more computing devices applying the notification policy to the notification to be sent to the user.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A method of forwarding packets by a physical network switch is provided. The method assigns egress ports that connect the network switch to each particular next hop to a weighted-cost multipathing (WCMP) group associated with the particular next hop. The method assigns weights to each egress port in each WCMP group according to the capacity of each path that connects the egress port to the next hop associated with the WCMP group and normalizes the weights over a range of values. For each packet received at the network switch, the method identifies the WCMP group associated with a next hop destination of the packet. The method calculates a hash value of a set of fields in the packet header and uses the hash value to perform a range lookup in the identified WCMP group to select an egress port for forwarding the packet to the next hop.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an estimated click-through rate (CTR) for each notification based at least in part on the type associated with each notification; determining a push threshold value for each notification based at least in part on the estimated CTR for each notification; and sending one or more of the notifications to the user. Each of the sent notifications has a push threshold value higher than a pre-determined push threshold value.