Abstract: The disclosure relates to a video anomaly detection method based on human-machine cooperation, in which video frames and traditional descriptors of optical stream of an image are utilized as an input for auto-encoder neural network coding, and converted into a representation content of a hidden layer, and then the representation content of the hidden layer is decoded, reconstructed and output. The auto-encoder network is trained with normal samples. In a test stage, if an input is a normal sample, a final reconstructed error keeps high similarity with an input sample; on the contrary, if the input is an abnormal sample, the final reconstructed error deviates greatly from the input sample.

Abstract: The disclosure relates to a method for generating personalized dialogue content, in which an implicit association between personalized characteristics and corresponding dialogue replies is extracted by collecting a set of personalized dialogue data; a vector representation of a dialogue context and texts of the personalized characteristics is learned with a Transformer model; finally, through learning a sequence dependency between natural languages, a subsequent content may be automatically predicted and generated from a previous text, so that the generating of corresponding reply content may be achieved according to the dialogue context. With various optimization algorithms added, a generation probability of universal reply can be reduced and a diversity of the generated dialogue content can be improved.

Abstract: This disclosure provides a sequential recommendation method based on long-term and short-term interests, in which an interaction sequence between a user and products is obtained by processing a purchase sequence of the user and a question data of the user in a dataset, characteristics of the products are represented with extracted comments of the user on the products; next, a stable long-term preference of the user is learned from a historical purchase sequence of the user with a recursive neural network, and immediate interests of the user are modeled with the question data. For the stable long-term preference and dynamic immediate interests, a dependence of different users on the two characteristics is described with an Attention mechanism, so as to effectively solve a problem of an inaccurate recommendation caused by an evolution of the preference of the user, while different dependence degrees of the different users on the long-term preference and immediate interests can represented effectively.

Abstract: A method including performing data mining according to a first domain name bandwidth and a local buffer hit ratio of a server, so as to obtain a threshold corresponding to the first domain name bandwidth; according to the threshold and the size of a second domain name bandwidth, obtaining the number of servers to be allocated. The present disclosure solves the technical problem in the conventional techniques that the configuration of the number of servers for a domain name in a region needs to be adjusted on the basis of experiences or manually in real time according to actual situations, resulting in high operation and maintenance costs and unguaranteed configuration accuracy.

Abstract: The disclosure provides an identification method based on an expert feedback mechanism, in which the expert properly give a feedback to results of a static model, the model is dynamically adjusted and updated according to the feedback of the expert each time, so that identifications for similar objects can be changed from a wrong identification to a correct identification. The model can adapt to dynamic changes of the environment, so that an identification accuracy and robustness of the model under the dynamic environment are improved with an expertise. The accuracy of the identification model is improved without repeated training, which solves a problem that the accuracy of the static model decreases in the dynamic environment, raising an adaptability of the identification model to environmental changes, shortening updating time of the model and improving working efficiency of the identification application system.

Abstract: The disclosure relates to a video anomaly detection method based on human-machine cooperation, in which video frames and traditional descriptors of optical stream of an image are utilized as an input for auto-encoder neural network coding, and converted into a representation content of a hidden layer, and then the representation content of the hidden layer is decoded, reconstructed and output. The auto-encoder network is trained with normal samples. In a test stage, if an input is a normal sample, a final reconstructed error keeps high similarity with an input sample; on the contrary, if the input is an abnormal sample, the final reconstructed error deviates greatly from the input sample.

Abstract: This disclosure provides a method for generating a personalized product description based on multi-source crowd data, which includes following steps: collecting data required for the personalized product description, the required data including reviews for crowd products and historical reviews of a crowd of users; portraiting the product and user to obtain a user preference label and a product label, which are then matched to obtain a personalized preference label; and generating the personalized product description in conjunction with the personalized preference labels. For different product attributes, different text generation methods are employed, and with different characteristics of the text generation methods such as extracted text generation and generated text generation, multi-source data are fused, so that the generated product description is smoother.

Abstract: This disclosure provides an accurate and personalized recommendation method based on a knowledge graph, which includes following steps: acquiring relevant knowledge of objects from a knowledge base according to historical behaviors of a user, and constructing a knowledge graph; initializing a vector representation of each node and its connection, and determining a receptive field of the node; generating training samples according to the historical behaviors of the user, and initializing a vector representation of all users and objects; acquiring a receptive field of an entity in the knowledge graph corresponding to the object in the training sample, then inputting the receptive field and the training sample to a graph neural network model to obtain predicted values of a possibility of an interaction between the user and the object.

Abstract: Disclosed herein is a pharmaceutical composition containing a recombinant human GLP-1 peptide and use thereof. The human GLP-1 peptide and the pharmaceutical composition can be used for preventing or treating obesity, bulimia, and/or overweight. Other related uses include weight management (e.g. inducing weight loss), slowing stomach emptying, increasing satiety, etc.

Abstract: A method including performing data mining according to a first domain name bandwidth and a local buffer hit ratio of a server, so as to obtain a threshold corresponding to the first domain name bandwidth; according to the threshold and the size of a second domain name bandwidth, obtaining the number of servers to be allocated. The present disclosure solves the technical problem in the conventional techniques that the configuration of the number of servers for a domain name in a region needs to be adjusted on the basis of experiences or manually in real time according to actual situations, resulting in high operation and maintenance costs and unguaranteed configuration accuracy.