Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for updating embeddings for user accounts on a social messaging platform. One of the methods includes receiving data having multiple data tuples, each data tuple indicating a user account and an item that the user account engaged with on the social messaging platform. A portion of the data is assigned to a respective multiple clusters. Embeddings are updated using an assigned cluster for each portion of the data. The updating includes: for each iteration step of multiple iteration steps, first embeddings are updated based on a first variable coefficient and a second variable coefficient. The updated first embeddings are stored into respective temporary data structures, which are then aggregated to generate aggregated first embeddings in a preserved data structure. Second embeddings are updated based on the aggregated first embeddings.

Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: A handheld point-of-sale (POS) terminal) is provided that has a housing, a touchscreen display on a face of the housing, and a credit card reader that is configurable for use disposed within the housing, the credit card reader including a first assembly comprising a mag stripe card reader and a second assembly comprising a Europay Mastercard Visa (EMV) reader, where the second assembly is coupled to the first assembly. The first assembly rotates about an axis perpendicular to a plane in which the assemblies are disposed to one of a plurality of angular positions, the plurality of angular positions indicating an offset angle of the first assembly relative to the second assembly, where the credit card reader is disposed within the housing in the one of the plurality of positions, and where the housing is configured to allow for access to credit card insertion points on the credit card reader.

Abstract: A credit card reader that is configurable for use is provided that includes a first assembly and a second assembly. The first assembly has a mag stripe card reader. The second assembly has a Europay Mastercard Visa (EMV) reader. The second assembly is coupled to the first assembly, where the first assembly rotates about an axis perpendicular to a plane in which the assemblies are disposed to one of a plurality of angular positions, the plurality of angular positions indicating an offset angle of the first assembly relative to the second assembly.

Abstract: A fixed point-of-sale (POS) terminal is provided that includes a mounting device for affixing the fixed POS terminal in a fixed location, a housing that is coupled the mounting device, a touchscreen display on a face of the housing, and a credit card reader that is configurable for use disposed within the housing. The credit card reader has a first assembly including a mag stripe card reader, and a second assembly including a Europay Mastercard Visa (EMV) reader, where the second assembly is coupled to the first assembly. The first assembly rotates about an axis perpendicular to a plane in which the assemblies are disposed to one of a plurality of angular positions, the plurality of angular positions indicating an offset angle of the first assembly relative to the second assembly, where the credit card reader is coupled to the housing in the one of the plurality of positions.

Abstract: Various embodiments illustrated and described herein include systems, methods and software for content type classification. Some such embodiments include determining a potential state of classification for packets associated with a session based at least in part on a packet associated with the session that is a packet other than the first packet of the session.

Abstract: Various embodiments illustrated and described herein include systems, methods and software for content type classification. Some such embodiments include determining a potential state of classification for packets associated with a session based at least in part on a packet associated with the session that is a packet other than the first packet of the session.

Abstract: Various embodiments illustrated and described herein include systems, methods and software for content type classification. Some such embodiments include determining a potential state of classification for packets associated with a session based at least in part on a packet associated with the session that is a packet other than the first packet of the session.

Abstract: Various embodiments illustrated and described herein include systems, methods and software for content type classification. Some such embodiments include determining a potential state of classification for packets associated with a session based at least in part on a packet associated with the session that is a packet other than the first packet of the session.

Abstract: Various embodiments illustrated and described herein include systems, methods and software for content type classification. Some such embodiments include determining a potential state of classification for packets associated with a session based at least in part on a packet associated with the session that is a packet other than the first packet of the session.