Abstract: A method for computing device maintenance, an apparatus, a storage medium and a program product. The method includes: monitoring an operating status of a computing device; and sending a notification message to a maintenance device if the computing device operates abnormally, where the notification message is used to notify that the computing device operates abnormally, and is used to notify to restart the computing device. The cost of human resources can be reduced, and stability and safety of operation of the computing device is improved to some extent.

Abstract: Provided are an electrochemical gas sensor and a fabrication method therefor. A sensing electrode, a reference electrode and an auxiliary electrode in a planar arrangement are arranged on an upper layer in a sensor housing, and an electrolyte and a membrane material are arranged on a lower layer in the sensor housing. An upper part of the sensor housing is provided with a vent, and a lower part of the sensor housing is provided with a connector for connecting three electrodes and connected to an external circuit. Compared with the traditional cylindrical stacked electrochemical gas sensor, the fabrication method for a plate-body thin-film type sensor is simple, is less affected by the change of ambient pressure, temperature and humidity, and is beneficial to the flattening and miniaturization of electronic instruments and apparatus for gas analysis.

Abstract: Embodiments of this application disclose a network key processing system, including user equipment, a security anchor network element, and an access and mobility management network element, where the security anchor network element is configured to: obtain a first key parameter from a slice selection network element, where the first key parameter includes identifier information of N network slices; generate N slice-dedicated keys based on the first key parameter; and send the N slice-dedicated keys to the corresponding N network slices respectively; the access and mobility management network element is configured to: obtain the first key parameter, and send the first key parameter to the user equipment; and the user equipment is configured to: generate the N slice-dedicated keys for the N network slices based on the first key parameter, and access the N network slices based on the generated N slice-dedicated keys.

Abstract: A data processing method and device are provided. The method includes: extracting a plurality of data sets from unlabeled data; and for each data set, creating a plurality of sample sets by assigning labels to data samples in the data set, respectively training, for each sample set created from the data set, a classifier by using the sample set and labeled data, obtaining a sample set that corresponds to a trained classifier with the highest performance, and adding the obtained sample set to a candidate training set. Each sample set includes the first preset number of data samples with respective labels, the labels of the data samples in each sample set constitutes a label combination, and label combinations corresponding to different sample sets are different from each other. The method also includes adding a second preset number of sample sets in the candidate training set to the labeled data.

Abstract: A machine learning model training method includes: training a machine learning model using features of samples in a training set, where a sample in the training set corresponds to an initial first weight and an initial second weight. In one iteration, the method includes: determining a first sample set comprising one or more samples whose corresponding target variables are incorrectly predicted; determining an overall predicted loss of the first sample set based on the predicted losses and corresponding first weights of samples in the first sample set; updating the first weights and second weights of the samples in the first sample set based on the overall predicted loss of the first sample set; and inputting the second weights, the features, and the target variables of the samples in the training set to the machine learning model, and initiating a next iteration of training the machine learning model.

Abstract: A machine learning model training method includes: training a machine learning model using features of samples in a training set, where a sample in the training set corresponds to an initial first weight and an initial second weight. In one iteration, the method includes: determining a first sample set comprising one or more samples whose corresponding target variables are incorrectly predicted; determining an overall predicted loss of the first sample set based on the predicted losses and corresponding first weights of samples in the first sample set; updating the first weights and second weights of the samples in the first sample set based on the overall predicted loss of the first sample set; and inputting the second weights, the features, and the target variables of the samples in the training set to the machine learning model, and initiating a next iteration of training the machine learning model.

Abstract: A machine learning model training method includes: training a machine learning model using features of each sample in a training set based on an initial first weight and an initial second weight. In one iteration, the method includes determining a first sample set in which a target variable is incorrectly predicted, and a second sample set in which a target variable is correctly predicted, based on a predicted loss of each sample; and determining overall predicted loss of the first sample set based on a predicted loss and a first weight of each sample in the first sample set. The method also includes updating the first weight and a second weight of each sample in the first sample set based on the overall predicted loss; and inputting the updated second weight, the features, and the target variable of each sample to the machine learning model, and initiating a next iteration.

Abstract: A text deduplication method and apparatus, and a storage medium are provided. The method includes: obtaining a text set, the text set including a plurality of to-be-deduplicated texts; capturing, for each to-be-deduplicated text, a corresponding subtext string from the to-be-deduplicated text; and determining, in the text set, to-be-deduplicated texts having a same subtext string, to obtain text subsets. Each subtext string corresponds to a text subset, and each text subset includes one or more to-be-deduplicated texts that have the corresponding subtext string. The method also includes performing text deduplication processing on the text subset corresponding to each subtext string, to obtain a deduplicated text set corresponding to each subtext string; and obtaining, according to the deduplicated text set corresponding to each subtext string, a result text set of the text set after the deduplication.

Abstract: A method and a device for recommendation of media content are described. The method includes: calculating scores of media content labels in a computer media content library; in descending order of the scores, selecting a first threshold media content label as a candidate media content label; for the candidate media content label, finding out a media content corresponding to the candidate media content label from the media content library; for the media content corresponding to the candidate media content label, in descending order of the amount of page views, selecting a second threshold media content as a media content to be recommended corresponding to the candidate media content label; and recommending the media content to be recommended corresponding to the candidate media content label to a user. The method and device can recommend popular media contents to users, and reduce the style difference between the media contents.

Abstract: The disclosure provides a text information processing method. Training textual data is determined according to text information, and characters and strings are identified from the training textual data. For each of the identified characters, a respective independent probability of appearance among the training textual data is calculated. For each of the identified strings, a respective joint probability of appearance among the training textual data is calculated. Whether a particular string of the identified strings corresponds to a candidate neologism is determined according to independent probabilities of various characters of the particular string and the joint probability of the particular string. Moreover, the candidate neologism is determined as a neologism when the candidate neologism is not in a preset dictionary and a joint probability of the candidate neologism is greater than a preset threshold.

Abstract: A machine learning model training method includes: training a machine learning model using features of each sample in a training set based on an initial first weight and an initial second weight. In one iteration, the method includes determining a first sample set in which a target variable is incorrectly predicted, and a second sample set in which a target variable is correctly predicted, based on a predicted loss of each sample; and determining overall predicted loss of the first sample set based on a predicted loss and a first weight of each sample in the first sample set. The method also includes updating the first weight and a second weight of each sample in the first sample set based on the overall predicted loss; and inputting the updated second weight, the features, and the target variable of each sample to the machine learning model, and initiating a next iteration.

Abstract: A text deduplication method and apparatus, and a storage medium are provided. The method includes: obtaining a text set, the text set including a plurality of to-be-deduplicated texts; capturing, for each to-be-deduplicated text, a corresponding subtext string from the to-be-deduplicated text; and determining, in the text set, to-be-deduplicated texts having a same subtext string, to obtain text subsets. Each subtext string corresponds to a text subset, and each text subset includes one or more to-be-deduplicated texts that have the corresponding subtext string. The method also includes performing text deduplication processing on the text subset corresponding to each subtext string, to obtain a deduplicated text set corresponding to each subtext string; and obtaining, according to the deduplicated text set corresponding to each subtext string, a result text set of the text set after the deduplication.

Abstract: A method and a device for recommendation of media content are described. The method includes: calculating scores of media content labels in a computer media content library; in descending order of the scores, selecting a first threshold media content label as a candidate media content label; for the candidate media content label, finding out a media content corresponding to the candidate media content label from the media content library; for the media content corresponding to the candidate media content label, in descending order of the amount of page views, selecting a second threshold media content as a media content to be recommended corresponding to the candidate media content label; and recommending the media content to be recommended corresponding to the candidate media content label to a user. The method and device can recommend popular media contents to users, and reduce the style difference between the media contents.

Abstract: A data processing method and device are provided. The method includes: extracting a plurality of data sets from unlabeled data; and for each data set, creating a plurality of sample sets by assigning labels to data samples in the data set, respectively training, for each sample set created from the data set, a classifier by using the sample set and labeled data, obtaining a sample set that corresponds to a trained classifier with the highest performance, and adding the obtained sample set to a candidate training set. Each sample set includes the first preset number of data samples with respective labels, the labels of the data samples in each sample set constitutes a label combination, and label combinations corresponding to different sample sets are different from each other. The method also includes adding a second preset number of sample sets in the candidate training set to the labeled data.

Abstract: A method and a device for recommendation of media content are described. The method includes: calculating scores of media content labels in a computer media content library; in descending order of the scores, selecting a first threshold media content label as a candidate media content label; for the candidate media content label, finding out a media content corresponding to the candidate media content label from the media content library; for the media content corresponding to the candidate media content label, in descending order of the amount of page views, selecting a second threshold media content as a media content to be recommended corresponding to the candidate media content label; and recommending the media content to be recommended corresponding to the candidate media content label to a user. The method and device can recommend popular media contents to users, and reduce the style difference between the media contents.

Abstract: The disclosure provides a text information processing method. Training textual data is determined according to text information, and characters and strings are identified from the training textual data. For each of the identified characters, a respective independent probability of appearance among the training textual data is calculated. For each of the identified strings, a respective joint probability of appearance among the training textual data is calculated. Whether a particular string of the identified strings corresponds to a candidate neologism is determined according to independent probabilities of various characters of the particular string and the joint probability of the particular string. Moreover, the candidate neologism is determined as a neologism when the candidate neologism is not in a preset dictionary and a joint probability of the candidate neologism is greater than a preset threshold.

Abstract: A method for grouping network service users includes acquiring attribute and/or behavior data of multiple users within a current period, and converting the attribute and/or behavior data into standardized data; determining multiple group central points according to the standardized data, and placing the standardized data in a group where a group central point having a shortest distance is located; determining group features of groups according to standardized data in the groups; and separately pushing corresponding service push information to users in the groups according to the group features of the groups. In addition, an apparatus for grouping network service users is further described.

Abstract: A method for grouping network service users includes acquiring attribute and/or behavior data of multiple users within a current period, and converting the attribute and/or behavior data into standardized data; determining multiple group central points according to the standardized data, and placing the standardized data in a group where a group central point having a shortest distance is located; determining group features of groups according to standardized data in the groups; and separately pushing corresponding service push information to users in the groups according to the group features of the groups. In addition, an apparatus for grouping network service users is further described.

Abstract: Devices and methods are provided for preventing user churn, wherein the methods include: collecting target user data corresponding to one or more target users associated with a target application program (101), the target user data including user basic attribute information, user behavioral indicator information and user active indicator information; determining a target user type of the one or more target users based on at least information associated with the target user data of the one or more target users (102), the target user type including a normal active user, an approximately silent user and a silent user; and in response to the target user type of the one or more target users being an approximately silent user, pushing first data for promoting activeness to the one or more target users associated with the target application program (103).

Abstract: A method and a device for recommendation of media content are described. The method includes: calculating scores of media content labels in a computer media content library; in descending order of the scores, selecting a first threshold media content label as a candidate media content label; for the candidate media content label, finding out a media content corresponding to the candidate media content label from the media content library; for the media content corresponding to the candidate media content label, in descending order of the amount of page views, selecting a second threshold media content as a media content to be recommended corresponding to the candidate media content label; and recommending the media content to be recommended corresponding to the candidate media content label to a user. The method and device can recommend popular media contents to users, and reduce the style difference between the media contents.