The present invention provides a method for collecting system logs, applied to an intelligent device with an Android system, wherein providing a daemon process for log collecting, and the daemon process is started when the system of an Android device is started; providing an application process for log processing; providing an external storage device for accessing the intelligent device; the method comprises the following steps: the daemon process collects the application framework layer and logs of the Linux kernel, and saves the logs in a first storage path of the Android system; the application process creating a second storage path in the external storage device after identifying the accessed external storage device; and the application process obtaining the logs from the first storage path and saving the logs in the second storage path.

A cross-domain guard is disclosed that includes a field programmable gate array (FPGA). The FPGA includes a rule database containing one or more rules, a memory interconnect configured to send control data or rule processing data, media access control logic, and a plurality of filter engines configured to receive an incoming message and generate a processed message. Each of the plurality of filter engines may contain a message processing allocation element configured to receive and distribute the incoming message, and a plurality of rule processor kernels. Each of the plurality of rule processor kernels includes a rule processor kernel control element, a plurality of data operator kernels configured to perform a data comparison operation, a ternary lookup table processor configured to perform a logic operation based upon a result of the data comparison operation, and a processed message arbiter. A method for filtering incoming messages is also disclosed.

The present disclosure provides an operator registration method and apparatus for a deep learning framework, a device and a storage medium, relates to the field of computer technologies, and specifically to the field of artificial intelligence such as deep learning. The operator registration method for a deep learning framework includes: receiving registration information provided by a user for registering operators with the deep learning framework, the registration information including: a custom calculation function, the custom calculation function being written in a manner irrelevant to the deep learning framework; building operator meta-information in the deep learning framework based on the registration information; and constructing a to-be-registered operator within the deep learning framework based on the operator meta-information, and registering the to-be-registered operator in a global operator table within the deep learning framework. The present disclosure can simplify an operator registration process.

A computation unit for performing a computation of a neural network layer is disclosed. A number of processing element (PE) units are arranged in an array. First input values are provided in parallel in an input dimension of the array during a first processing period, and a second input values are provided in parallel in the input dimension during a second processing period. Computations are performed by the PE units based on stored weight values. An adder coupled to the first set of PE units generates a first sum of results of the computations by the first set of PE units during the first processing cycle, and generates a second sum of results of the computations during the second processing cycle. A first accumulator coupled to the first adder stores the first sum, and further shifts the first sum to a second accumulator prior to storing the second sum.

There is provided a method for generating a representation for behavior similarity comparison by generating a program-level stateful model of one or more entities in a computer operating system operating on a computer system, the program-level stateful model having a data structure representing a state of a program; generating an updated representation of the program based on the program-level stateful model; searching for at least one other representation of another program-level stateful model similar to the updated representation of the program; and comparing the updated representation of the program to the at least one other representation of another program-level stateful model.

An approach is provided in which a method, system, and computer program product load a first program and a second program on a target host that includes a host kernel. The first program and the second program are both pre-compiled on a build system that is different from the target host. The method, system, and computer program product execute at least a subset of the first program on the host kernel and the subset of the first program captures a set of kernel structure information from the host kernel. The method, system, and program product load, at the target host, the set of kernel structure information into the second program at one or more placeholder locations. Then, the method, system and program product execute at least a subset of the second program with the set of kernel structure information on the target kernel.

Example embodiments provide a client live keeping method and apparatus, and a storage medium. The method includes: receiving, based on a client being switched to a background, a request including state information of a target state of the client at a current time and a target live keeping duration corresponding to the target state; transmitting the request to a terminal server; receiving, from the terminal server in response to the request, target information indicating live keeping information of the target state; and determining whether to perform live keeping on the target state based on the target information.

A shared computing system for serving a plurality of tenants using container pools. Each container pool has a filesystem service configured to service one or more applications within the container pool. A shared memory is used to facilitate interprocess communication between the application and the filesystem service, both of which along with the interprocess communication itself are run at user level.

A method includes receiving a system call from an application within a container executing on an operating system, the system call comprising a synchronization operation to synchronize memory of the application to storage. The method further includes determining, by the kernel, whether a system call filtering policy associated with the container indicates that the system call is to be prevented. preventing, by the kernel, performance of the synchronization operation in view of the system call filtering policy.

An image processing system for convolving an image includes processing circuitry that is configured to retrieve the image including a set of rows, a merged kernel, multiple skip values and a pixel base address. The merged kernel includes all non-zero coefficients of a set of kernels. Each skip value corresponds to a location offset of each non-zero coefficient with respect to a previous non-zero coefficient. Further, the processing circuitry is configured to execute a multiply-accumulate (MAC) instruction and a load instruction parallelly in one clock cycle for multiple times, on the set of rows and the merged kernel to convolve the image with the merged kernel. Each row on which the MAC and load instructions are executed is associated with a corresponding non-zero coefficient and a corresponding skip value. The load instruction is executed based on the pixel base address, the corresponding skip value, and a width of each row.