Abstract: A loudspeaker assembly includes an enclosure having an open front side and a wall having an interior surface and an exterior surface with a wall thickness defined therebetween. The wall is integrally molded with a screw boss supported by a plurality of molded ribs that extend inwardly from the interior surface. The loudspeaker assembly also includes an audio transducer positioned within the enclosure and having an output side facing the open front side of the enclosure to emit sound therefrom; and a mount for securing the transducer to the enclosure via a screw threaded into the screw boss. At a meeting point with the interior surface, each of the plurality of molded ribs has a rib thickness measured perpendicular to the wall thickness, the rib thickness being less than 50 percent of the wall thickness to avoid sink marks in the injection molded part.

Abstract: A loudspeaker assembly includes an enclosure having an open front side and a wall having an interior surface and an exterior surface with a wall thickness defined therebetween. The wall is integrally molded with a screw boss supported by a plurality of molded ribs that extend inwardly from the interior surface. The loudspeaker assembly also includes an audio transducer positioned within the enclosure and having an output side facing the open front side of the enclosure to emit sound therefrom; and a mount for securing the transducer to the enclosure via a screw threaded into the screw boss. At a meeting point with the interior surface, each of the plurality of molded ribs has a rib thickness measured perpendicular to the wall thickness, the rib thickness being less than 50 percent of the wall thickness to avoid sink marks in the injection molded part.

Abstract: A loudspeaker assembly includes an enclosure defining a direction for projecting sound, a first transducer and a second transducer positioned within the enclosure, an acoustic horn coupled with the first transducer, a mounting baffle secured to the enclosure for supporting the second transducer, and a port establishing fluid communication along a port length between the interior of the enclosure and an outside environment in front of the open front side of the enclosure. The acoustic horn has a surface facing an interior of the enclosure. A forward portion of the port is formed by a forward face of the mounting baffle and an extension wall depending from the acoustic horn in a direction transverse to the direction. The port length is extended by a rearward port portion formed by a rearward extending flange of the mounting baffle and a rearward projection extending from the surface of the acoustic horn.

Abstract: Systems, methods, and non-transitory computer readable media can generate one or more first machine learning models, where each of the one or more first machine learning models is associated with a respective portion of a first period of time. A second machine learning model incorporating the one or more first machine learning models as features can be generated, where the second machine learning model is associated with a second period of time. A respective weight associated with each of the one or more first machine learning models can be determined. It can be determined whether a content item is associated with a category based on the second machine learning model.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a node graph comprising a plurality of user account nodes and a plurality of edge nodes connecting the plurality of user account nodes. A distance score is calculated for each user account node of the plurality of user account nodes. It is determined that a transaction is an illegitimate transaction based on the distance scores.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a node graph comprising a plurality of user account nodes and a plurality of edge nodes connecting the plurality of user account nodes. A distance score is calculated for each user account node of the plurality of user account nodes. It is determined that a transaction is an illegitimate transaction based on the distance scores.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a node graph comprising a plurality of user account nodes and a plurality of edge nodes connecting the plurality of user account nodes. A distance score is calculated for each user account node of the plurality of user account nodes. It is determined that a transaction is an illegitimate transaction based on the distance scores.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a plurality of accounts associated with a set of features. Each account in the plurality of accounts can be associated with a respective set of feature values for the set of features. A selection for a subset of features out of the set of features can be received. A group of clusters can be generated based on the selection for the subset of features. Each cluster in the group of clusters can include a respective collection of nodes representing at least some of the plurality of accounts. It can be determined whether a particular collection of nodes, included in at least one cluster out of the group of clusters, represents illegitimate accounts or legitimate accounts.

Abstract: Systems, methods, and non-transitory computer readable media can generate one or more first machine learning models, where each of the one or more first machine learning models is associated with a respective portion of a first period of time. A second machine learning model incorporating the one or more first machine learning models as features can be generated, where the second machine learning model is associated with a second period of time. A respective weight associated with each of the one or more first machine learning models can be determined. It can be determined whether a content item is associated with a category based on the second machine learning model.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire information associated with a set of rules and a set of activities identified by the set of rules as being potentially illegitimate. A priority order for a set of properties associated with the set of rules can be acquired. The set of rules can be ranked based on the priority order. A highest ranked rule can be stored into a record. The highest ranked rule can be removed from consideration out of the set of rules. At least one activity, identified by the highest ranked rule, can be removed from consideration out of the set of activities. In some instances, the record can be provided.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a plurality of accounts associated with a set of features. Each account in the plurality of accounts can be associated with a respective set of feature values for the set of features. A selection for a subset of features out of the set of features can be received. A group of clusters can be generated based on the selection for the subset of features. Each cluster in the group of clusters can include a respective collection of nodes representing at least some of the plurality of accounts. It can be determined whether a particular collection of nodes, included in at least one cluster out of the group of clusters, represents illegitimate accounts or legitimate accounts.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire information associated with a set of rules and a set of activities identified by the set of rules as being potentially illegitimate. A priority order for a set of properties associated with the set of rules can be acquired. The set of rules can be ranked based on the priority order. A highest ranked rule can be stored into a record. The highest ranked rule can be removed from consideration out of the set of rules. At least one activity, identified by the highest ranked rule, can be removed from consideration out of the set of activities. In some instances, the record can be provided.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.