Abstract: The present invention belongs to the technical field of nanofiber emulsion, in particular to application of nanofiber emulsion to preparation of baked food. The nanofiber emulsion is nanofiber emulsion of a pomelo peel sponge layer, and is PCNF emulsion obtained by emulsification of a nanofiber emulsion stabilizer of the pomelo peel sponge layer and an oil phase; the amount of the nanofiber emulsion in the baked food is 18-90%, and the nanofiber emulsion replaces oil in traditional baked food; when the PCNF emulsion is added to the batter, bubble density of the batter is larger, and proper addition of the PCNF emulsion can reduce specific gravity of the batter, improve aeratability of the batter with soybean oil, reduce a baking loss rate and water activity, and prolong the shelf life of the cakes with the PCNF emulsion; hardness and chewiness of the cakes can be reduced.

Abstract: The present invention belongs to the technical field of nanofiber emulsion, in particular to application of nanofiber emulsion to preparation of baked food. The nanofiber emulsion is nanofiber emulsion of a pomelo peel sponge layer, and is PCNF emulsion obtained by emulsification of a nanofiber emulsion stabilizer of the pomelo peel sponge layer and an oil phase; the amount of the nanofiber emulsion in the baked food is 18-90%, and the nanofiber emulsion replaces oil in traditional baked food; when the PCNF emulsion is added to the batter, bubble density of the batter is larger, and proper addition of the PCNF emulsion can reduce specific gravity of the batter, improve aeratability of the batter with soybean oil, reduce a baking loss rate and water activity, and prolong the shelf life of the cakes with the PCNF emulsion; hardness and chewiness of the cakes can be reduced.

Abstract: A computer-implemented for information processing based on a large language model (LLM) includes: obtaining a query request; determining, by the LLM and based on the query request, a query condition, a source of a query result, and a select field for generating a structured query language (SQL) statement; generating by the LLM, the SQL statement by combining the query condition, the source of the query result, and the select field; and obtaining the query result corresponding to the query request, by inputting the SQL statement into the LLM for execution

Abstract: A method for pushing a resource includes obtaining a collection of users to which a resource is to be pushed, in which the collection includes a plurality of users; obtaining respective predicted conversion rates of the resource for the plurality of users; obtaining user groups by dividing the plurality of users into groups based on respective predicted conversion rates for the plurality of users, in which the user groups correspond to different conversion rate ranges; and for each user group, selecting a target user from the user group based on a real conversion rate related to pushed users corresponding to the conversion rate range of the user group, and pushing the resource to the target user.

Abstract: A computing system includes a host device and an acceleration device. The host device is in a communication connection with the acceleration device. The host device is configured to send invocation information to the acceleration device. The invocation information indicates a storage address of the first data. The acceleration device is configured to receive the invocation information from the host device, obtain the first data from a memory based on the storage address, and perform the to-be-processed task based on the first data to obtain a processing result. The host device notifies the acceleration device of the storage address, and the acceleration device directly obtains the data from the memory based on the storage address such that the host device is prevented from obtaining the data from the memory.

Abstract: This application provides a data processing method. The method is applied to a server, the server includes a first container and an Android driver (240), and a first service manager is deployed in the first container. Each container is allocated its respective private memory (2412, 2422, and 2432) and private service linked list (2411, 2421, and 2431), so as to avoid a problem in a conventional technology of data inconsistency and affected service processing caused by sharing of same memory by a plurality of containers.

Abstract: The present disclosure provides a method and apparatus for iterating a credit scorecard model, electronic device, and storage medium, and relates to the technical field of machine learning, in particular, to the field of smart finance and risk control technologies. A method can include: dividing multiple to-be-classified samples into a first part sample and a second part sample; classifying the first part sample by using a first credit scorecard model to obtain a first prediction result, and classifying the second part sample by using the first credit scorecard model to obtain a second prediction result; and determining an input sample of a second credit scorecard model based on the first prediction result and the second prediction result, and iterating the first credit scorecard model according to the second prediction result and multiple history samples, to obtain a target credit scorecard model.

Abstract: The present disclosure provides a sample classification method and apparatus, an electronic device and a storage medium, and relate to the technical field of data mining, in particular to the field of machine learning. The method includes that: a sample to be classified is acquired, and a sample feature dimension of the sample to be classified is greater than a preset threshold; feature encoding is performed on a sample feature of the sample to be classified according to various feature encoding modes to obtain multiple feature vectors; and clustering analysis is performed on the multiple feature vectors to determine a target class of the sample to be classified.

Abstract: A method of processing a feature information is provided, which relates to a field of data processing, in particular to fields of artificial intelligence and big data. The method includes: determining at least one candidate division point in a value range of the feature information, and determining an information value corresponding to each candidate division point; determining a target division point based on the information value; dividing the value range based on the target division point, so as to obtain two sub-ranges; determining a sub-range meeting a termination condition in the two sub-ranges as a target interval, determining a sub-range not meeting the termination condition in the two sub-ranges as a new value range, and returning to perform the step of determining at least one candidate division point in a value range until both sub-ranges meet the termination condition, so as to obtain a plurality of target intervals.

Abstract: A data processing method and apparatus, and a heterogeneous system, pertaining to the field of computer technologies are provided. The heterogeneous system includes a processor connected to an accelerator. A secondary memory is connected to the accelerator. The processor is configured to write to-be-processed data into the secondary memory and trigger the accelerator to access and process the to-be-processed data stored in the secondary memory according to a processing instruction. The accelerator is configured to write a processing result of the to-be-processed data into the secondary memory and to trigger the processor to read the processing result. Processing efficiency is enhanced by reducing the number of times of interaction between the processor and the accelerator and simplifying the procedure for data processing.

Abstract: Data processing techniques are provided. The techniques include: obtaining a first prediction data set, a model feature list and configuration information, wherein the model feature list indicates a plurality of features required by a data analysis model; generating a second prediction data set based on the model feature list and the first prediction data set, wherein the feature dimension of prediction data in the second prediction data set is smaller than the feature dimension of prediction data in the first prediction data set; performing feature transformation on a feature of the prediction data in the second prediction data set based on the configuration information to generate a third prediction data set; and inputting the third prediction data set into the data analysis model to obtain a prediction result.

Abstract: A method for pushing a resource includes obtaining a collection of users to which a resource is to be pushed, in which the collection includes a plurality of users; obtaining respective predicted conversion rates of the resource for the plurality of users; obtaining user groups by dividing the plurality of users into groups based on respective predicted conversion rates for the plurality of users, in which the user groups correspond to different conversion rate ranges; and for each user group, selecting a target user from the user group based on a real conversion rate related to pushed users corresponding to the conversion rate range of the user group, and pushing the resource to the target user.

Abstract: A method and apparatus for processing risk-management feature factors based on user generated content (UGC), an electronic device and a storage medium are disclosed, which relates to the fields of artificial intelligence and cloud computing. An implementation includes generating a feature expression of the UGC based on the UGC; and extracting the risk-management feature factors of the UGC according to a pre-generated risk-management-feature-factor extracting model and the feature expression of the UGC.

Abstract: The present disclosure relates to a technical field of user portraits, and provides a user portrait processing method, a user portrait processing device, a server and a storage medium, for a purpose of solving a problem that it is difficult to trace historical label features of a user since an original label feature is replaced by an updated label feature when the user portrait is updated in the related art. The method includes: when receiving a message for updating user portrait data of a user account, obtaining the updated user portrait data. The user portrait data includes a label feature. The method further includes storing the updated user portrait data in association with an update time as user portrait information of the user account. The present disclosure is applicable for performing statistics on historical information of the user portrait.

Abstract: A method and apparatus for processing risk-management feature factors based on user generated content (UGC), an electronic device and a storage medium are disclosed, which relates to the fields of artificial intelligence and cloud computing. An implementation includes generating a feature expression of the UGC based on the UGC; and extracting the risk-management feature factors of the UGC according to a pre-generated risk-management-feature-factor extracting model and the feature expression of the UGC.

Abstract: A method and an apparatus for outputting information are provided. The method may include: acquiring feature data of a user, where the feature data includes a user identifier, values of feature variable, and label values corresponding to the user identifiers; determining a discrete feature variable and a continuous feature variable in the feature variables; determining sets of values of the discrete feature variable corresponding to different label values, and determining sets of values of the continuous feature variable corresponding to the different label values; determining sets of values of the feature variables corresponding to the different label values based on the sets of values of the discrete feature variable corresponding to the different label values and the sets of values of the continuous feature variable corresponding to the different label values; and outputting the sets of values of the feature variables corresponding to the different label values.

Abstract: The present disclosure discloses a method, apparatus and device for generating a model and a storage medium. A method can include: acquiring sample resource features and sample labels; determining a first screening factor according to the sample resource features and the sample labels, and determining first resource features from the sample resource features according to the first screening factor; determining a second screening factor, and determining second resource features from the first resource features based on the second screening factor, and obtaining features of a target model based on the second resource features; and training a machine learning model, by taking the features of the target model as an input of the target model, and taking the sample labels corresponding to the features of the target model as an output of the target model, to obtain the trained target model.

Abstract: This application provides a data processing method. The method is applied to a server, the server includes a first container and an Android driver (240), and a first service manager is deployed in the first container. Each container is allocated its respective private memory (2412, 2422, and 2432) and private service linked list (2411, 2421, and 2431), so as to avoid a problem in a conventional technology of data inconsistency and affected service processing caused by sharing of same memory by a plurality of containers.

Abstract: A method and apparatus for training an online prediction model are provided. The method may include: acquiring an offline sample feature and an online sample feature of a user, the offline sample feature including a user portrait feature; offline training to obtain an offline recommendation model, based on the offline sample feature and the online sample feature of the user; acquiring a latest online feature of the user, and online training to obtain an online learning model based on the latest online feature of the user, the online learning model being used to adapt the latest online feature for use as an online sample feature to be input into the trained offline recommendation model; and synchronizing the offline recommendation model to online, and inputting the latest online feature output by the online learning model into the offline recommendation model to generate an online prediction model.

Abstract: The present disclosure relates to a technical field of user portraits, and provides a user portrait processing method, a user portrait processing device, a server and a storage medium, for a purpose of solving a problem that it is difficult to trace historical label features of a user since an original label feature is replaced by an updated label feature when the user portrait is updated in the related art. The method includes: when receiving a message for updating user portrait data of a user account, obtaining the updated user portrait data. The user portrait data includes a label feature. The method further includes storing the updated user portrait data in association with an update time as user portrait information of the user account. The present disclosure is applicable for performing statistics on historical information of the user portrait.