Provided herein are implementations to sign files via a publish-subscribe message service. In particular, a payload extractor receives, from a message broker via a publish-subscribe message service, a new file event. The message broker receives messages from publisher components and routes the messages to subscriber components based on content of the messages. The payload extractor extracts a payload of the new file and then sends, via the message broker, a signing request with the payload (and metadata) to a signing service. The signing service sends a signed notice with a signature (and metadata), via the message broker, to a signature inserter, which inserts the signature into the new file. Such a configuration provides a secure and scalable signing architecture with decreased network traffic.

A method for generating a signature of a spam message includes determining one or more classification attributes and one or more clustering attributes contained in successively intercepted first and second electronic messages. The first electronic message is classified using a trained classification model for classifying electronic messages based on the one or more classification attributes. The first electronic message is classified as spam if a degree of similarity of the first electronic message to one or more spam messages is greater than a predetermined value. A determination is made whether the first electronic message and the second electronic message belong to a single cluster based on the determined one or more clustering attributes. A signature of a spam message is generated based on the the identified single cluster of electronic messages.

Systems, methods and products for increasing efficiency of resource usage by determining the reliability of reporters of unwanted messages and prioritizing evaluation of messages based on the reliability scores. Reports of unwanted messages are evaluating to determine whether they are bad. If an unwanted message is bad, a score for the reporter is updated to reflect a positive credit. A set of safe rules are applied to the message to determine whether it is safe and if the message is determined to be safe, the reporter score corresponding to the reporter is updated to reflect a non-positive (zero or negative) credit. If the message is determined to be neither bad nor safe, the message is entered in a reevaluation queue and, after a waiting period, the message evaluation is repeated using updated threat information, and the reporter score is updated according to the reevaluation.

A method for managing avatar usage rights including transmitting a request to use an avatar of a second user who is registered as a messenger friend of a first user of a user terminal on an instant messaging service, and in response to the request to use the avatar of the second user being accepted by the second user, allowing the use of the avatar of the second user in the user terminal associated with the first user may be provided.

Non-volatile memory may contain definitions of: (i) a plurality of message queue implementations respectively associated with different queue behaviors, the different queue behaviors specified by corresponding sets of modes, and (ii) an application programming interface (API) through which applications can access one or more message queues. One or more processors may be configured to: create a message queue of a particular message queue type, supported by a corresponding message queue implementation, by specifying a set of modes corresponding to a queue behavior; receive, from a producing application and by way of the API, one or more messages for the message queue; store the one or more messages in a data structure associated with the message queue; receive, from a consuming application and by way of the API, a request to read from the message queue; and provide a message from the message queue to the consuming application.

A computer-implemented method for sending email messages with dynamically-determined content, the method comprising: transmitting email messages to a plurality of recipient groups each comprising at least one recipients, each message comprising one or more dynamic content network references associated with dynamically-selected content items; receiving, by a network-connected server, a dynamic content network request responsive to the rendering of one of said dynamic content network references within an email message received by a recipient device, the dynamic content network request comprising one or more content selection criteria, the content selection criteria comprising an embedded user identifier uniquely associated with a user to whom an email among the email messages was transmitted; dynamically selecting a content item from amongst a plurality of candidate content items based at least in part on the content selection criteria and the recipient's recipient group; implementing an exclusion service by cross-referencing a dynamic content item with static content associated with the embedded user identifier and, based on a determination that the dynamic content item is duplicative of an associated static content item within a same email communication, serving an alternative non-duplicative content item in response to the dynamic content request; returning the selected or the alternative content item to the email recipient device for rendering within the same email communication; and determining relative effectiveness of content by recipient group.

A method and apparatus for operating an instant messaging server are disclosed. The method for operating an instant messaging server according to an example embodiment includes managing a message sent and received through a first chat room of a messenger application, managing a message sent and received through a second chat room of a 3.sup.rd party application receiving support a chat function through the instant messaging server, identifying a user account of an instant messaging service (IMS) linked with a user account of the 3.sup.rd party service participating in the second chat room, and providing, based on the identified user account of the IMS, the second chat room of the 3.sup.rd party application to the messenger application.

Verification of entities associated with a communication platform are described. An instruction to create a communication channel on a communication platform is received. The communication channel can be associated with an organization that is registered with the communication platform. Data associated with an entity (e.g., the organization and/or a user associated therewith) can be analyzed to determine whether to verify the entity. Based at least partly on a determination that the entity is verified, an indicator can be presented in association with the entity on a user interface associated with the communication platform and a first set of permissions can be associated with the entity. The first set of permissions can be different than a second set of permissions associated with unverified entities. Use of the communication channel and/or the communication platform by the entity can be controlled based at least partly on the first set of permissions.

In one embodiment, a method includes accessing visual data from a client system associated with a user, wherein the visual data comprises images portraying one or more objects, receiving, from the client system, a user request, wherein the user request comprises a coreference to a target object, resolving the coreference to the target object from among the one or more objects, resolving the target object to a specific entity, and sending, to the client system, instructions for providing a response to the user request, wherein the response comprises attribute information about the specific entity.

Disclosed herein are systems and methods for clustering email messages identified as spam using a trained classifier. In one aspect, an exemplary method comprises, selecting at least two characteristics from each received email message, for each received email message, using a classifier containing a neural network, determining whether or not the email message is a spam based on the at least two characteristics of the email message, for each email message determined as being a spam email, calculating a feature vector, the feature vector being calculated at a final hidden layer of the neural network, and generating one or more clusters of the email messages identified as spam based on similarities of the feature vectors calculated at the final hidden layer of the neural network.