Abstract: The present application relates to a method for evaluating an operation of a distribution network based on data capture and Spark. The method includes: acquiring a packet from a terminal, and mirroring the packet through a mirroring port of a switch to acquire a mirror packet of the packet; analyzing the mirror packet, acquiring a 101/104 protocol packet, and storing the 101/104 protocol packet into an HDFS; acquiring the 101/104 protocol packet from the HDFS, parsing the 101/104 protocol packet on a Spark platform to acquire a parameter in the 101/104 protocol packet, and analyzing and processing the parameter to obtain a key indicator for evaluating the operation of the distribution network; and displaying the key indicator through a web page.

Abstract: Provided are a closed-loop system and a method for testing an intelligent alarm application of an integrated monitoring system. The system includes: a host computer and a slave computer, where the host computer and the slave computer perform downloading files and command interactions through an internal protocol, the host computer includes a visual signal configuration module, a scheduling protocol analysis and simulation module and an evaluation module, and the slave computer includes an intelligent electronic devices (IEDs) server simulation module.

Abstract: The present application relates to a method for evaluating an operation of a distribution network based on data capture and Spark. The method includes: acquiring a packet from a terminal, and mirroring the packet through a mirroring port of a switch to acquire a mirror packet of the packet; analyzing the mirror packet, acquiring a 101/104 protocol packet, and storing the 101/104 protocol packet into an HDFS; acquiring the 101/104 protocol packet from the HDFS, parsing the 101/104 protocol packet on a Spark platform to acquire a parameter in the 101/104 protocol packet, and analyzing and processing the parameter to obtain a key indicator for evaluating the operation of the distribution network; and displaying the key indicator through a web page.

Abstract: A social networking system presents advertisements and recommendation units to its users. The recommendation units suggest actions for the users to increase their engagement with the social networking system or otherwise interact with other users, while the social networking system receives revenue from advertisers for displaying advertisements based on bid values associated with the advertisements. The social networking system determines values for the advertisements and for the recommendation units, where the values are measured in a comparable fashion. This allows the system to rank and select the advertisements and recommendation units together in a unified auction model. For example, the social networking system uses a pacing value to determine values of recommendation units having a common unit of measurement with expected values of advertisements to the social networking system.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: In one embodiment, a method includes receiving a request from a first user for a content page; in response to the request, identifying at least one content page, wherein the content page is associated with a page identifier; identifying a plurality of content items based at least in part on a plurality of content features associated with the content page; ranking the plurality of content items based at least in part on a plurality of user features associated with the first user; and delivering to the first user, with the requested content page, one or more of the plurality of content items as recommendations to the first user based on the ranking of the content items.

Abstract: In one embodiment, a system includes one or more computing systems that implement a social networking environment containing a large number of heterogeneous objects type, each of the plurality of object types having varying features, the system implementing a generic object recommendation engine for scoring objects and recommending the objects to users of the social networking system. In particular embodiments, the user and content object features are fed as inputs into a heuristic model that generates an expected value for the content object and user. In particular embodiments, the object recommendation engine includes an online learner that may log a user's actions after the initial impression to determine the relatively degree of interest to the user.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: A social networking system presents recommendation units to its users. The recommendation units suggest actions for the users to increase their engagement with the social networking system or otherwise interact with other users. The social networking system establishes internal goals and associates bids for recommendation units with different goals. The bids reflect the value to the goal of a user interacting with a recommendation unit. Based on bids for recommendation units associated with one or more goals, expected values of the recommendation units arid determined. The recommendation units are ranked based on the expected values and one or more recommendation units are selected based on the ranking.

Abstract: As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.

Abstract: As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.

Abstract: A social networking system presents advertisements and recommendation units to its users. The recommendation units suggest actions for the users to increase their engagement with the social networking system or otherwise interact with other users, while the social networking system receives revenue from advertisers for displaying advertisements based on bid values associated with the advertisements. The social networking system determines values for the advertisements and for the recommendation units, where the values are measured in a comparable fashion. This allows the system to rank and select the advertisements and recommendation units together in a unified auction model. For example, the social networking system uses a pacing value to determine values of recommendation units having a common unit of measurement with expected values of advertisements to the social networking system.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: Methods, apparatuses and systems for generating a name-word dictionary that includes associations between names of users and candidate words (e.g., nicknames) based on statistical analysis of user communications observed at a network communications facility, such as a social network system, an email provider and the like.