Abstract: Provided is a method for moderating abnormal users. The method includes: acquiring history behavior data of a plurality of to-be-moderated users, wherein the history behavior data includes behavior data of interactions of the plurality of to-be-moderated users; extracting a plurality of predetermined valid features from the history behavior data for each of the plurality of to-be-moderated users, wherein the predetermined valid features are features predetermined in sample data; calculating, based on predetermined probabilities of events associated with the plurality of valid features, a probability that each of the plurality of to-be-moderated users is the abnormal user; establishing a total probability function based on probabilities that the plurality of to-be-moderated users are the abnormal users; determining candidate users by solving, based on predetermined conditions, a maximum value of the total probability function; and determining the abnormal users by moderating the candidate users.

Abstract: Provided is a method for detecting object poses. The method includes: acquiring image data, wherein the image data includes a target object; detecting two-dimensional first pose information of a three-dimensional bounding box in response to being projected onto the image data by inputting the image data into a two-dimensional detection model, wherein the bounding box is configured to detect the target object; mapping the two-dimensional first pose information to three-dimensional second pose information; and detecting third pose information of the target object based on the three-dimensional second pose information.

Abstract: Provided is a method for moderating live streaming. The method includes: performing preliminary violation moderation on a current live streaming frame in a to-be-moderated live streaming room based on a high-accuracy moderation model and a high-recall moderation model that are cascaded with each other; and acquiring a target violation score of the current live streaming frame by inputting a first violation score of the current live streaming frame under the high-accuracy moderation model, a second violation score of the current live streaming frame under the high-recall moderation model, and a multi-dimensional behavior feature of the current live streaming frame in the to-be-moderated live streaming room to a pre-constructed behavior moderation model in response to the current live streaming frame passing the preliminary violation moderation.

Abstract: Provided is a method for moderating live streaming. The method includes: performing preliminary violation moderation on a current live streaming frame in a to-be-moderated live streaming room based on a high-accuracy moderation model and a high-recall moderation model that are cascaded with each other; and acquiring a target violation score of the current live streaming frame by inputting a first violation score of the current live streaming frame under the high-accuracy moderation model, a second violation score of the current live streaming frame under the high-recall moderation model, and a multi-dimensional behavior feature of the current live streaming frame in the to-be-moderated live streaming room to a pre-constructed behavior moderation model in response to the current live streaming frame passing the preliminary violation moderation.

Abstract: Provided is a method for moderating abnormal users. The method includes: acquiring history behavior data of a plurality of to-be-moderated users, wherein the history behavior data includes behavior data of interactions of the plurality of to-be-moderated users; extracting a plurality of predetermined valid features from the history behavior data for each of the plurality of to-be-moderated users, wherein the predetermined valid features are features predetermined in sample data; calculating, based on predetermined probabilities of events associated with the plurality of valid features, a probability that each of the plurality of to-be-moderated users is the abnormal user; establishing a total probability function based on probabilities that the plurality of to-be-moderated users are the abnormal users; determining candidate users by solving, based on predetermined conditions, a maximum value of the total probability function; and determining the abnormal users by moderating the candidate users.

Abstract: In a method for allocating a file to a user, a first computer receives, over a network, data monitored by a second computer. The first computer comprises an IDE client. The data comprises data of usage of the file by one or more users in a group of users. The first computer receives a request to allocate the file to a first user in the group. In response to the first computer determining that, based on the data of usage, the file is allocated to a second user in the group, the first computer denies the request. The first computer adds the first user to an end of a queue identifying one or more users in the group that are waiting for the second user to relinquish allocation of the file. The first computer utilizes the data of usage to display the queue in the IDE client.

Abstract: A lighting-emitting diode (LED) lamp string device with a battery box is provided. The LED lamp string device includes a power supply system and a lamp string light emission system connected together. The lamp string light emission system includes an LED lamp string that is composed of a plurality of LED lamps and electrical wires that electrically connect the LED lamps. The power supply system includes batteries and a light controller that are connected to the LED lamp string through the electrical wires and further includes a battery box, in which the light controller and the batteries are received.