Abstract: A method and a system for applying a machine learning model that uses an artificial intelligence technique to determine that a software code commit is risky and therefore likely to result in a production issue and to provide an explanation for the risk are provided. The method includes: receiving a first code commit; analyzing the first code commit in order to determine features that relate to the first code commit; applying a machine learning model that uses an artificial intelligence technique to project a result of executing the first code commit; assessing whether the first code commit is risky based on an output of the machine learning model; and when the first code commit is assessed as being risky, determining an explanation that relates to the assessment.

Abstract: A database server may perform reach potential analysis for a local segment, or a target audience, of a data set. The local segment may include user devices which share a specific, common attribute. The database server may calculate similarities and correlations between a first data set for a user and a second data set from a data provider. The database server may calculate a reach index using the second data set from the data provider to determine whether user devices are likely to join the local segment by taking on the specific attribute which defines the local segment. Using the data set from the data provider, the database server may determine a reach potential within the first data set, outside of the first data set, or both.

Abstract: Methods, systems, and devices for determining device associations are described. Some database systems may store information related to device characteristics. Each of these devices may be operated by one or more users, and each user may operate one or more devices. In some cases, information about users may be more valuable than information about devices. As such, a system may determine probable associations between devices, where an association can correspond to operation by a same user. To determine device associations, the system may perform a machine-learning process (e.g., using probabilistic soft logic (PSL) and a hinge-loss Markov Random Field (HL-MRF) model) on input device characteristics and connection information to generate a probability density function. The probability density function may indicate associations between devices within the system.

Abstract: A database server may perform reach potential analysis for a local segment, or a target audience, of a data set. The local segment may include user devices which share a specific, common attribute. The database server may calculate similarities and correlations between a first data set for a user and a second data set from a data provider. The database server may calculate a reach index using the second data set from the data provider to determine whether user devices are likely to join the local segment by taking on the specific attribute which defines the local segment. Using the data set from the data provider, the database server may determine a reach potential within the first data set, outside of the first data set, or both.

Abstract: Methods, systems, and devices for determining device associations are described. Some database systems may store information related to device characteristics. Each of these devices may be operated by one or more users, and each user may operate one or more devices. In some cases, information about users may be more valuable than information about devices. As such, a system may determine probable associations between devices, where an association can correspond to operation by a same user. To determine device associations, the system may perform a machine-learning process (e.g., using probabilistic soft logic (PSL) and a hinge-loss Markov Random Field (HL-MRF) model) on input device characteristics and connection information to generate a probability density function. The probability density function may indicate associations between devices within the system.

Abstract: A process for making paper, comprising contacting an anionic binder with a cationizing agent to make a cationized binder, contacting the cationized binder with an anionic pigment to form a binder/pigment agglomerate, contacting the agglomerate with an aqueous slurry of fibers, and forming a paper product from the slurry. The agglomerate may reduce the total cost of the paper product being produced while maintaining the strength of the paper at acceptable levels.

Abstract: A process for making paper, comprising contacting an anionic binder with a cationizing agent to make a cationized binder, contacting the cationized binder with an anionic pigment to form a binder/pigment agglomerate, contacting the agglomerate with an aqueous slurry of fibers, and forming a paper product from the slurry. The agglomerate may reduce the total cost of the paper product being produced while maintaining the strength of the paper at acceptable levels.