Abstract: The present disclosure provides a joint training method and apparatus for models, a device and a storage medium. The method may include: training a first-party model to be trained using a first sample quantity of first-party training samples to obtain first-party feature gradient information; acquiring second-party feature gradient information and second sample quantity information from a second party, where the second-party feature gradient information is obtained by training, by the second party, a second-party model to be trained using a second sample quantity of second-party training samples; and determining model joint gradient information according to the first-party feature gradient information, the second-party feature gradient information, first sample quantity information and the second sample quantity information, and updating the first-party model and the second-party model according to the model joint gradient information.

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: A web page grabbing method is provided. A target web page on a website is grabbed, the target web page including a web page corresponding to a Hypertext Markup Language 5 (H5) content and a web page corresponding to a non-H5 content. The web page corresponding to the H5 content is detected according to web page source code of the target web page. Dynamic rendering is performed on the web page corresponding to the H5 content, to obtain a rendered web page. Content details information corresponding to the H5 content is extracted from the rendered web page.

Abstract: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated, and unbalanced workloads are identified. Based on a comparison of the unbalanced workloads to patterns in the updated pattern decision tree, an orchestration topology is generated that specifies a new placement of the computing resources in the first and second computing environments.

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: The present disclosure provides a multi-party data joint query method, a device, a server and a storage medium. The multi-party datajoint query method executed by a manager includes: analyzing a multi-party joint query sentence to obtain a logical execution plan; processing the logical execution plan according to providers of respective nodes in the logical execution plan to obtain a physical execution plan of each provider; and generating a query instruction of each provider according to the physical execution plan of each provider, and sending the query instruction to respective provider. The query instruction is configured to instruct the providers to perform a query cooperatively.

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: 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: 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: 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.

Abstract: Embodiments of the present disclosure disclose a server management method and a management device. The method comprises determining whether a USB device is connected to the server, the USB device including a permission of a respective user to the server. The method further comprises: in response to determining that the USB device is connected to the server, transmitting the permission to the server to enable the server to be operated based on the permission.

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: A web page grabbing method is provided. A target web page on a website is grabbed, the target web page including a web page corresponding to a Hypertext Markup Language 5 (H5) content and a web page corresponding to a non-H5 content. The web page corresponding to the H5 content is detected according to web page source code of the target web page. Dynamic rendering is performed on the web page corresponding to the H5 content, to obtain a rendered web page. Content details information corresponding to the H5 content is extracted from the rendered web page.

Abstract: A computerized method useful for generating an animated GIF related to an educational content based on a learning context in an online educational platform including the step of obtaining a digital image. The method includes the step of receiving a set of administrator edits to the digital image. The method includes the step of generating animated Graphics Interchange Format (GIF) image. The GIF image comprises the digital image and the set of user edits to said digital image. The method includes the step of generating a digital tutorial to a learning user with respect to an online educational. The method includes the step of including the animated GIF file within a flow of the digital tutorial.

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: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated, and unbalanced workloads are identified. Based on a comparison of the unbalanced workloads to patterns in the updated pattern decision tree, an orchestration topology is generated that specifies a new placement of the computing resources in the first and second computing environments.

Abstract: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated. Based on the updated pattern decision tree and a user request that specifies requirements of the computing resources, an orchestration topology is generated that specifies a placement of the computing resources in the first and second computing environments.

Abstract: A server includes a tray, a handle structure, a first hard disk assembly and a second hard disk assembly. The tray is removably disposed on a server cabinet. Two ends of the handle structure are respectively slidably pivoted to two inner walls of the tray. The first hard disk assembly is movably disposed in the tray. The second hard disk assembly is disposed in the tray. The first hard disk assembly and the second hard disk assembly are placed side by side. Two sides of the first hard disk assembly, which are opposite to each other, are pivoted to the handle structure. When the handle structure is pivoted and slide relative to the tray, the first hard disk assembly is pivotable between a stored position and an inclined position by the handle structure.

Abstract: A computer-implemented method includes saving a copy of a client computing environment to a computer memory on the host system. The processor writes an image of the client computing environment based on the saved copy of the client computing environment. The image includes an application layer, a middleware layer, and a system layer, and is based on the copy of the client computing environment. The image is extractable as a sub-image that includes one or more of the application layer, the middleware layer, and the system layer. The image includes a resource pattern having dependencies that associate two or more of the application layer, the middleware layer, and the system layer, such that the sub-image is combinable with an existing active container operating on a second computing system. The combination results in a functional copy of the client computing system with the changes extracted from the sub-image.

Abstract: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated. Based on the updated pattern decision tree and a user request that specifies requirements of the computing resources, an orchestration topology is generated that specifies a placement of the computing resources in the first and second computing environments.