Embodiments of the present invention relate to the technical field of vehicle electronics and disclose an upgrading method and apparatus for a tire pressure monitoring module and a tire pressure sensor. The method is applicable to a tire pressure sensor including a signal receiving module. The method specifically includes: controlling the signal receiving module to receive an upgrade file and save the upgrade file; and waking up a tire pressure monitoring module, and controlling the tire pressure monitoring module to read the saved upgrade file and upgrade the tire pressure monitoring module based on the upgrade file. The embodiments of the present invention can realize wireless upgrading of the tire pressure sensor, thereby improving the upgrading efficiency, reducing the upgrading costs, and improving the safety in upgrading of the tire pressure sensor.

Secure digital assistant integration with web pages is provided. The system receives an intent manifest data structure that maps actions of a digital assistant with link templates of an electronic resource developed by a third-party developer device. The system validates the electronic resource based on the intent manifest data structure. The system receives, from a data exchange component of an iframe of the electronic resource loaded by a client computing device, an identifier of the client computing device. The system receives a foreground state of the electronic resource from an onsite state sharing API. The system selects a data value for a parameter based on the foreground state and the intent manifest data structure. The system provides the data value. An authorization component generates an authorization prompt, receives input, and transmits the data value to an onsite intent execution API of the electronic resource to execute an action.

Provided herein are systems, methods and devices for classifying nested content execution loaded by a webpage or an application executed by a client device, comprising a client device executing a webpage or an application loaded from a content server which embed nesting element(s) used for loading nested content from nested content server(s). The webpage/application embeds a host monitoring code executed to collect session data indicative of execution session of the webpage/application including execution of nested content loaded using the nesting element(s), transmitting a signature of the session data to server(s) configured to classify the execution according to at least part of the session data extracted from the signature, and transmitting transaction indicator(s) of execution of the nested content to one or more providers of the nested content which may verify execution of the nested content in the context of the webpage/application based on the classification obtained from the server(s).

A computer-implemented method for bytecode class verification includes: encountering a class requiring verification of its bytecode during a run of an application; determining whether class relationship data for the class exists in a shared classes cache; in response to a determination that the class relationship data for the class does not exist in the shared classes cache: performing a linear bytecode walk of the bytecode to identify relationship data for the class and verify that the bytecode is well-formed; and storing the identified relationship data as the class relationship data for the class in the shared classes cache; in response to a determination that the class relationship data for the class does exist in the shared classes cache: retrieving the class relationship data for the class from the shared classes cache; and processing the class relationship data.

An example system includes a memory store of aggregate definitions. Each aggregate definition specifies a key value, an output store, a feature, a half-life value, and an aggregate operation metric to apply to a cross of the feature and the half-life value to generate aggregate metrics. The system also includes an aggregation engine that generates aggregate feature records from the input source based on the aggregate definitions and stores the aggregate feature records in the output store. An aggregate feature record includes an aggregate of the metric for the feature decayed over time using the half-life. The system also includes a query service that identifies, using the aggregate definitions, responsive aggregate feature records that satisfy parameters of a received request, applies the half-life to the responsive feature records, and provides the responsive feature records to a requester, the requester using the responsive feature records as input for a neural network.

A program verification system of the invention includes program verification means 51 for verifying whether a verification target program input as a program operating in a secure environment does not include a program execution function which is a function of executing a new program in the same environment by a command in the corresponding program and/or whether the verification target program or a protection mechanism of the secure environment as an operation source of the verification target program includes an external input attack defense function which is a function of defending against an attack caused by an external data input during execution of the program; and signature means 52 for giving a signature to the program based on a result of the verification by the program verification means 51.

Computer-implemented methods, non-transitory, computer-readable media, and computer-implemented systems are provided for executing a smart contract in a blockchain network. A computer-implemented method includes: in response to determining that bytecodes of a smart contract are deployed on a first blockchain node in a blockchain network, starting, by the first blockchain node, to compile the bytecodes of the smart contract into machine codes of the smart contract through a Just-In-Time (JIT) compiler; determining, by the first blockchain node, that the machine codes of the smart contract are not locally stored and that execution results of the machine codes of the smart contract and the bytecodes of the smart contract are consistent; and in response to the determining, performing, by the first blockchain node, interpretation execution on the bytecodes of the smart contract.

Example robotic process automation systems and methods are described. In one implementation, a processing system receives a first automation scenario, where the first automation scenario is for execution by the processing system. The processing system identifies a list of plugins in the first automation scenario and identifies a version number associated with each of the plugins in the first automation scenario. Additionally, the processing system verifies the list of plugins and their associated version numbers. If the list of plugins and their associated version numbers are verified, the processing system builds a first virtual environment for the plugins in the first automation scenario.

A computer-implemented method for bytecode class verification includes: encountering a class requiring verification of its bytecode during a run of an application; determining whether class relationship data for the class exists in a shared classes cache; in response to a determination that the class relationship data for the class does not exist in the shared classes cache: performing a linear bytecode walk of the bytecode to identify relationship data for the class and verify that the bytecode is well-formed; and storing the identified relationship data as the class relationship data for the class in the shared classes cache; in response to a determination that the class relationship data for the class does exist in the shared classes cache: retrieving the class relationship data for the class from the shared classes cache; and processing the class relationship data.

A robotic process automation system employs centralized compilation to generate a platform independent executable version of a bot, which is encoded to perform user level operations. The system employs an extensible set of commands which can be user generated. The bots execute on devices that are separate and independent from a server processor that controls the system. The devices execute bots in an execution environment that is provided by the server processor. Change in a command in a bot requires recompilation of the bot which is then delivered upon request to a device. The execution environment does not require recompilation upon a change in a command.