Abstract: There is a need for more effective and efficient seasonally-adjusted predictive data analysis solutions. This need can be addressed by, for example, solutions for performing seasonally-adjusted predictive data analysis that use autoregressive integrated moving average (ARIMA) machine learning models.

Abstract: Systems and techniques are described for a unified data pipeline interface. The pipeline interface provides an interface illustrating key metrics, activities, insight, and/or other categories. The pipeline interface provides such data as to a specific opportunity. The systems and techniques described herein allow for the determination, presentation, and standardization of such data through a specifically configured graphical user interface.

Abstract: Systems and methods for reducing network traffic associated with generating event predictions based on cognitive image analysis are disclosed. The system receives a user input indicating a location identifier of a location of interest. The system queries a data store to determine whether an image associated with the location of interest satisfies a data recency criterion. In response to the image failing to satisfy the data recency criterion, the system queries a remote data store to determine whether another, additional image associated with the location of interest satisfies the data recency criterion. In response to the additional image satisfying the data recency criterion, the system extracts one or more image-based attributes of the additional image and determines a distance between objects of the image. The system generates an event prediction based on the image-based attributes, the distance, and location-based attributes using a machine learning model to be displayed.

Abstract: Exemplary system, method, and computer-accessible medium for protecting at least one integrated circuit (IC) design, includes generating an abstract syntax tree (“AST”) based on a hardware description language and a first register-transfer level (RTL) design. The method also includes selecting semantic elements in the AST to lock and locking the selected semantic elements. Additionally, the method includes a procedure for generating a second RTL design.

Abstract: There is a need for more effective and efficient seasonally-adjusted predictive data analysis solutions. This need can be addressed by, for example, solutions for performing seasonally-adjusted predictive data analysis that use autoregressive integrated moving average (ARIMA) machine learning models.

Abstract: Technologies are generally described for methods and systems effective to assign a job to be executed in a multi-core processor that includes a first set of cores with a first size and a second set of cores with a second size different from the first size. The multi-core processor may receive the job at an arrival time and may determine a job arrival rate based on the arrival time. The job arrival rate may indicate a frequency that the multi-core processor receives a plurality of jobs. The multi-core processor may select the first set of cores and may select a degree of parallelism based on the job arrival rate and based on a performance metric relating to execution of the job on the first set of cores. In response to the selection, the multi-core processor may assign the job to be executed on the first set of cores.

Abstract: Technologies are generally described for methods and systems effective to assign a job to be executed in a multi-core processor that includes a first set of cores with a first size and a second set of cores with a second size different from the first size. The multi-core processor may receive the job at an arrival time and may determine a job arrival rate based on the arrival time. The job arrival rate may indicate a frequency that the multi-core processor receives a plurality of jobs. The multi-core processor may select the first set of cores and may select a degree of parallelism based on the job arrival rate and based on a performance metric relating to execution of the job on the first set of cores. In response to the selection, the multi-core processor may assign the job to be executed on the first set of cores.

Abstract: A method is performed by one or more devices in a provider network. The method includes providing, to a user device, selected media content (such as television content) from a group of content options available from different content providers. A user device tracks viewer selections and provides the viewer selections as device tracking data that includes a viewing history for the user device. The method includes receiving, from the user device, the device tracking data and generating a billing record, for a user account associated with the user device, that includes a billing amount calculated using the device tracking data. The method includes compiling tracking data from multiple user accounts, including the device tracking data associated with the billing record and calculating, based on the compiled tracking data, proportional revenue shares for the different content providers.

Abstract: A method is performed by one or more devices in a provider network. The method includes providing, to a user device, selected media content (such as television content) from a group of content options available from different content providers. A user device tracks viewer selections and provides the viewer selections as device tracking data that includes a viewing history for the user device. The method includes receiving, from the user device, the device tracking data and generating a billing record, for a user account associated with the user device, that includes a billing amount calculated using the device tracking data. The method includes compiling tracking data from multiple user accounts, including the device tracking data associated with the billing record and calculating, based on the compiled tracking data, proportional revenue shares for the different content providers.