Abstract: This application relates to a face image processing method, apparatus, computer device, and storage medium. The method includes acquiring a first face image and a second face image, the first face image and the second face image being images of real faces; generating a first updated face image with non-real face image characteristics based on the first face image; adjusting color distribution of the first updated face image according to color distribution of the second face image to obtain a first adjusted face image; acquiring a target face mask of the first face image, the target face mask being generated by randomly deforming a face region of the first face image; and blending the first adjusted face image and the second face image according to the target face mask to obtain a target face image. Accordingly, a diversity of target face images can be generated.

Abstract: A method and apparatus for generating a training model based on feedback are provided. The method for generating a training model based on feedback, includes calculating an eigenvector of a sample among a plurality of samples; obtaining scores granted by a user for one or more of the plurality of samples in a round, obtaining scores granted by the user for a first number of samples; obtaining scores granted by the user for a second number of samples in response to detecting, based on the eigenvector, an inconsistency between the scores granted by the user for the first number of samples; and generating a training model based on the scores granted by the user for the first and second numbers of samples. A corresponding apparatus is also provided.

Abstract: Techniques for neural network training are provided. One computer-implemented method comprises: obtaining, by an electronic device operatively coupled to a processing unit, based on a similarity between two images, a first image and a second image. The computer-implemented method also comprises training, by the electronic device, a neural network based on the first image and the second image such that a distance between a first vector and a second vector generated respectively from the first image and the second image in the trained neural network is associated with the similarity.

Abstract: Techniques for model based data processing are proposed. In one example, a computer-implemented method is as follows. A first model is determined from a first category of data processing models and a second model is determined from a second category of data processing models. The second category can be different from the first category in some instances. Performance of a first combined model of the first and second models can be compared with performance of the first model. A target model for processing data can be selected from the first combined model and the first model based on the comparing.

Abstract: One or more sensors associated with a vehicle detect a roadway condition of a first roadway and an evasive maneuver is performed by the vehicle to avoid the detected roadway condition. In response to one or more processors determining that the evasive maneuver was successful, a record of the successful maneuver and the roadway condition are stored in a database. Subsequent to storing the record in the database, one or more computers associated with one or more vehicles is trained to execute the evasive maneuver, in response to determining that the one or more vehicles is exposed to the roadway condition experienced by the vehicle.

Abstract: A method and apparatus for generating a training model based on feedback are provided. The method for generating a training model based an feedback, includes calculating an eigenvector of a sample among a plurality of samples; obtaining scores granted by a user for one or more of the plurality of samples in a round, obtaining scores granted by the user for a first number of samples; obtaining scores granted by the user for a second number of samples in response to detecting, based on the eigenvector, an inconsistency between the scores granted by the user for the first number of samples; and generating a training model based on the scores granted by the user for the first and second numbers of samples. A corresponding apparatus is also provided.

Abstract: A method, system and computer program product are disclosed for recommending terms in a document based on a specified interaction with the document. In one embodiment, the method comprises displaying a document on a device; detecting a specified interaction with the document displayed on the device; identifying text associated with the specified interaction with the document displayed on the device; performing object recognition with the identified text to recognize one or more defined interested objects; and recommending terms in the document based on the recognized defined interested objects. In an embodiment, the object recognition includes identifying one or more objects from the text, and performing the object recognition with these objects to recognize the one or more interested objects. In an embodiment, one or more terms are extracted from the text, and these terms are partitioned to identify the one or more objects from the text.

Abstract: The present invention relates to a method and apparatus for genome compression and decompression. In one embodiment of the present invention, there is a method for genome compression, including: selecting from a reference database a reference genome that matches the genome; building an index based on positions of the reference genome's multiple segments in the reference genome; aligning the genome with the reference genome based on the multiple segments so as to identify difference data between the genome and the reference genome; and generating a compressed genome, the compressed genome including at least the index and the difference data. In other embodiments, there is provided an apparatus for genome compression. Further, there is a method and apparatus for decompressing the genome that has been compressed using the above method and apparatus.

Abstract: A method of generating a predictor to classify data includes: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data. The first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively.

Abstract: An approach for a computer to evaluate a predictive model includes identifying features of training samples in a set of training samples. The approach selects evaluation metrics from a set of evaluation metrics as available metrics using identified features and includes determining recommended metrics using the predictive model, the available metrics, and a predetermined set of user-preferred metrics. The approach applies the predictive model created using the set of training samples to a set of test samples to calculate values of the available metrics. The approach evaluates the predictive model by using the available metrics and the values of the available metrics to evaluate the predictive model by evaluating the predictive model using the recommended metrics and the values of the recommended metrics.

Abstract: Processing GPS drifting. A method and system for processing GPS drifting is included. The method includes: determining predicted error information on position correction of a device within a region; obtaining a GPS initial position and a corrected position of the device within the region; and determining GPS drifting of the region based on the predicted error information, the GPS initial position, and the corrected position of at least one device within the region. The method and system of the present application can effectively ameliorate GPS drifting issues.

Abstract: A method and apparatus for determining similarity among gene sequences, for compressing a gene sequence, and for decompressing a gene sequence. The method for determining similarity between a first gene sequence and a second gene sequence includes: moving a sliding window of a predefined length on the first gene sequence and the second gene sequence respectively; extracting a first part String1i of the first gene sequence within the sliding window, and a second part String2i of the second gene sequence within the sliding window during the ith movement of the sliding window; and determining similarity between the first gene sequence and the second gene sequence based on the first part String1i and the second part String2i. Also provided is an apparatus for the above method.

Abstract: A method and apparatus for generating a training model based on feedback are provided. The method for generating a training model based on feedback, includes calculating an eigenvector of a sample among a plurality of samples; obtaining scores granted by a user for one or more of the plurality of samples in a round, obtaining scores granted by the user for a first number of samples; obtaining scores granted by the user for a second number of samples in response to detecting, based on the eigenvector, an inconsistency between the scores granted by the user for the first number of samples; and generating a training model based on the scores granted by the user for the first and second numbers of samples. A corresponding apparatus is also provided.

Abstract: Systems for database optimization include a database rebuild module configured to compact columns in an unoptimized database that co-appear in queries to the unoptimized database into a single hyper-column to form an optimized database. A database interface module is configured to accept queries according to a schema for the unoptimized database and to translate the accepted queries into queries according to a schema for the optimized database.

Abstract: Methods and systems for database optimization include compacting columns in an unoptimized database that co-appear in queries to the database into a single hyper-column using a processor to form an optimized database. Queries are received according to a schema for the unoptimized database. The received queries are translated into queries according to a schema for the optimized database.

Abstract: A method for determining an object referenced within a set of one or more informal online communications comprises: generating a knowledge graph for a company based at least on formal online communications, the knowledge graph comprising a plurality of node elements, and the knowledge graph further comprising, for each node element of the knowledge graph, a corresponding halo comprising one or more words which are temporally proximate to that node element within the formal online communications; for each node element of the knowledge graph which is determined to be present in a given informal online communication, detecting a halo comprising one or more words which are temporally proximate to that node element within the given informal online communication; and identifying which of the plurality of node elements has a corresponding halo within the knowledge graph most similar to the detected halo, wherein the identified node element is the referenced object.

Abstract: A method and apparatus for estimating a wave velocity of negative pressure wave in a fluid transportation pipeline. The method including: receiving a plurality of pressure signals from a plurality of sensors; determining time differences produced by the negative pressure wave reaching the adjacent sensors based on the received pressure signals; determining a wave source sensor segment where a wave source of the negative pressure wave is located; and estimating the wave velocities of the negative pressure wave in a non-wave source sensor segment and the wave source sensor segment.

Abstract: Methods and systems for database optimization include compacting columns in an unoptimized database that co-appear in queries to the database into a single hyper-column using a processor to form an optimized database. Queries are received according to a schema for the unoptimized database. The received queries are translated into queries according to a schema for the optimized database.

Abstract: One or more sensors associated with a vehicle detect a roadway condition of a first roadway and an evasive maneuver is performed by the vehicle to avoid the detected roadway condition. In response to one or more processors determining that the evasive maneuver was successful, a record of the successful maneuver and the roadway condition are stored in a database. Subsequent to storing the record in the database, one or more computers associated with one or more vehicles is trained to execute the evasive maneuver, in response to determining that the one or more vehicles is exposed to the roadway condition experienced by the vehicle.

Abstract: Methods and systems for database optimization include detecting a plurality of columns in an unoptimized database that co-appear in queries to the database. The plurality of columns are compacted into a single hyper-column to form an optimized database. Each entry in the hyper-column includes data from a corresponding entry in each of the plurality of columns. An interface is generated for the optimized database that accepts queries according to a schema for the unoptimized database and translates the queries into queries according to a schema for the optimized database.