Abstract: Embodiments of the present disclosure disclose a machine learning training method and a server. The method includes: acquiring training data uploaded by the terminal; creating a trusted execution environment in response to a machine learning training request from the terminal; and performing machine learning training based on the trusted execution environment and the training data.

Abstract: Systems, methods, and devices securely boot processors and nonvolatile memories. Methods include implementing, using a controller of a secured nonvolatile memory, a validation operation on a first portion of code stored in a first secured storage region of the secured nonvolatile memory, the validation operation comprising computing a validation value. Methods also include retrieving a second portion of code from a second secured storage region, the second portion of code comprising a pre-computed validation value, the first and second portion of code being associated with booting a processor, and implementing a comparison operation of the validation value and the pre-computed validation value. Methods further include generating, using the controller, a signal based on a result of the comparison operation, the signal being provided to the processor via an interface of the secured nonvolatile memory, and the signal enabling booting of the processor in response to a matching comparison operation.

Abstract: A method and apparatus for processing network usage of a cloud product, a device, a storage medium and a product, and relates to cloud computing and big data in data processing. The specific implementation is: acquiring traffic usage data of a target cloud product within a preset time interval; determining an algorithm list corresponding to the target cloud product; and invoking all pre-configured algorithms corresponding to identifiers of all the algorithms according to the algorithm list to perform corresponding data processing operations on the traffic usage data to obtain a target network usage corresponding to the target cloud product.

Abstract: A computer-implemented method includes detecting, by a bridge container running inside a container scope, connection information about a first service instance running to provide a respective first service outside the container scope. A first virtual container is initialized inside the container scope. The first virtual container is connected to the first service instance, utilizing the connection information about the first service instance, to virtualize the first service instance inside the container scope. It is detected that that a first source container inside the container scope requires the first service of the first service instance. The first source container is connected to the first virtual container to enable the first source container to access the first service instance through the first virtual container.

Abstract: A method for updating a capacity of a battery for the information of a user includes detecting a state of charge-1 of the battery and an actual capacity-1 of the battery and determining a first state of charge according to the state of charge-1 of the battery. The method further includes determining a second state of charge according to the state of charge-1 of the battery and the actual capacity-1 of the battery and updating an actual capacity-2 of the battery according to the first state of charge and the second state of charge. An electronic device and a non-transitory storage medium are further provided.

Abstract: An image processing method is provided. The image processing method includes: acquiring first second input images; extracting a content feature of the first input image; extracting an attribute feature of the second input image; performing feature fusion and mapping processing on the content feature of the first input image and the attribute feature of the second input image by using a feature transformation network to obtain a target image feature, the target image feature having the content feature of the first input image and the attribute feature of the second input image; and generating an output image based on the target image feature.

Abstract: An electrical connector includes: an insulative housing having a rear base and a front tongue; plural contacts extending rearward through the base and exposing to the tongue; an outer cover enclosing the insulative housing to define a rear chamber therebetween; and a sealing member formed in the chamber to seal an interface between the base and the outer cover and an interface between the base and the contacts, wherein the base has a rear face and a rib protruding rearward from the rear face into the rear chamber.

Abstract: The present disclosure provides a method of federated computing. The method of federated computing includes: acquiring a plurality of metadata from a plurality of computing nodes and publishing the plurality of metadata; determining, from the plurality of computing nodes, at least two computing nodes that agree to execute a federated computing task, wherein the at least two computing nodes are computing nodes that agree to execute the federated computing task based on the plurality of metadata; receiving the federated computing task submitted by at least one of the at least two computing nodes, and splitting the federated computing task into a plurality of subtasks; and distributing the plurality of subtasks to the at least two computing nodes so as to execute the plurality of subtasks between the at least two computing nodes. The present disclosure also provides a device, an apparatus of federated computing, and a non-transitory storage medium.

Abstract: Embodiments of a secure multi-party computation method are provided. The method can include: dynamically converting a multi-party computation program segment into a first garbled circuit by using a multi-party computation operator of a first main body, and executing garbled gates of the first garbled circuit in sequence through an execution engine of the first main body, to encrypt data of the first main body; transmitting to a second main body the encrypted data of the first main body and identifiers for garbled gates of the first garbled circuit; performing a second encryption on the encrypted data of the first main body by the second main body in sequence according to the received identifiers for the garbled gates of the first garbled circuit, and returning to the first main body a result of the second encryption on the encrypted data of the first main body.

Abstract: The schematic flow chart diagrams included herein are generally set forth as logical flow chart diagrams. As such, the depicted order and labeled steps are indicative of one embodiment of the presented method. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more steps, or portions thereof, of the illustrated method. Additionally, the format and symbols employed are provided to explain the logical steps of the method and are understood not to limit the scope of the method. Although various arrow types and line types may be employed in the flow chart diagrams, and they are understood not to limit the scope of the corresponding method.

Abstract: A data visual analysis method, system and terminal, and a computer readable storage medium are provided. The method includes: obtaining to-be-analyzed parameters and generating a data analysis model, the data analysis model including a plurality of execution units; the data sources collecting data information related to the to-be-analyzed parameters; the execution units performing analysis on the data information collected by the data sources, to obtain execution results of the execution units; and visually outputting the execution results of the execution units.

Abstract: In one aspect, a computerized method useful for showing, testing and certifying users in-application on cloud application workflows includes the step of triggering a test. The test tests and certifies a user on a specified software task with a specified software application. The method includes the step of tracking a set of user test data from the test. The method includes the step of using the set of user test data that is tracked to improve in application training workflows.

Abstract: The present disclosure provides a spark query method and system supporting trusted computing. The method comprises receiving a SQL query statement input by a user; performing parsing, logical plan analysis, logical plan optimization and physical plan generation for the SQL query statement; re-implementing an operator in the physical plan by using SGX API, and generating an execution code; invoking an enclave trusted environment to implement trusted computing for the operator, and executing the execution code. Through the present disclosure, safety of data during the calculation is ensured while the query request is processed in parallel through the spark architecture. Moreover, the spark interface provides the user with more available service and is user-friendly.

Abstract: A face recognition method includes: extracting a first identity feature of a first face image by using a feature extraction module, and extracting a second identity feature of a second face image by using the feature extraction module, wherein the feature extraction module is implemented by using a neural network, and pre-trained in a manner such that a correlation coefficient of training batch data is obtained based on an identity feature and an age feature of a sample face image in the training batch data, and decorrelated training of the identity feature and the age feature is performed on the feature extraction module based on the correlation coefficient; and performing a face recognition based on determining a similarity between faces in the first face image and the second face image according to the first identity feature and the second identity feature.

Abstract: The disclosure provides an image recognition network model training method, including: acquiring a first image feature corresponding to an image set; acquiring a first identity prediction result by using an identity classifier, and acquiring a first pose prediction result by using a pose classifier; obtaining an identity classifier according to the first identity prediction result and an identity tag, and obtaining a pose classifier according to the first pose prediction result and a pose tag; performing pose transformation on the first image feature by using a generator, to obtain a second image feature corresponding to the image set; acquiring a second identity prediction result by using the identity classifier, and acquiring a second pose prediction result by using the pose classifier; and training the generator.

Abstract: Embodiments of the disclosure relate to a method, a device, and a computer-readable storage medium for testing a basic input output system (BIOS). The method comprises: acquiring a target attribute of a target menu of a BIOS of a test machine; determining a path to the target menu based on a menu map of the BIOS, wherein the menu map is obtained by performing image identification on a BIOS interface of the test machine; and causing the BIOS of the test machine to enter the target menu based on the path so as to modify the attribute of the target menu into the target attribute.

Abstract: Embodiments of the disclosure relate to a method, a device, and a computer-readable storage medium for testing a basic input output system (BIOS). The method comprises: acquiring a target attribute of a target menu of a BIOS of a test machine; determining a path to the target menu based on a menu map of the BIOS, wherein the menu map is obtained by performing image identification on a BIOS interface of the test machine; and causing the BIOS of the test machine to enter the target menu based on the path so as to modify the attribute of the target menu into the target attribute.

Abstract: Embodiments of the present disclosure provide a data mining system, a data mining method, and a storage medium. The data mining system includes a transfer device, a first trusted execution space and a second trusted execution space. The transfer device is configured to receive a data calling request of the second trusted execution space, obtain data to be called from the first trusted execution space according to the data calling request, and provide the data to be called to the second trusted execution space, so as to perform data mining based on the data to be called and the mining-related data to obtain a data mining result and to provide the data mining result to a device of the data user.

Abstract: A method and apparatus for obtaining a privacy set intersection are provided. The method may include: encrypting a privacy set of an intersection initiator by using a homomorphic encryption algorithm to generate a cipher text, a cipher text function, a public key, and a private key of the intersection initiator; delivering the cipher text, the cipher text function, and the public key of the intersection initiator to an intersection server; receiving a to-be-decrypted function value of a privacy set of the intersection server from the intersection server; and decrypting the to-be-decrypted function value of the privacy set of the intersection initiator by using the private key, to obtain an intersection element of the privacy set of the intersection initiator and the privacy set of the intersection server.

Abstract: A method of data processing for an object identification system comprising a neural network. The method comprises, in a secure environment, obtaining first sensed data representative of a physical quantity measured by a sensor. The first sensed data is processed, using the neural network in the secure environment, to identify an object in the first sensed data. The method includes determining that the identified object belongs to a predetermined class of objects. In response to the determining, a first portion of the first sensed data is classified as data to be secured, and a second portion of the first sensed data is classified as data which is not to be secured. The second sensed data, derived from at least the second portion, is outputted as non-secure data.