Methods, systems, and apparatus, including computer programs stored on computer-readable media, for integrating electronic card display at a client via a software development kit (SDK) provided at one or more data servers are disclosed. A client-side application such as a web browser may request the SDK based on a code snippet included in a data file, such as a HyperText Markup Language (HTML) file for a web page. Once the SDK is received at the client, the client-side application may perform various functionalities enabled by the SDK, including determining terms in the data file that match keywords associated with electronic cards, requesting and receiving electronic card rendering data associated with the electronic cards, and modifying a display generated based on the data file to display an electronic card, upon detecting a trigger event in relation to a term within the web page that corresponds to the electronic card.

Systems and methods for code generation for a plurality of architectures. At a host architecture, a JIT compile operation is performed for a received JavaScript or Web Assembly file. The JIT compiler references a host library that has been updated to include at least one new JIT instruction. Output from the JIT compile operation is compiled machine code for the host architecture that has new opcodes (OPX) added, responsive to the new JIT instruction. The JIT compiler executes the opcodes (OPX) in XuCode mode, meaning that the host architecture switches into a hardware protected private ISA (Instruction Set Architecture) called XuCode to implement the new JIT opcode instruction in XuCode.

Systems and methods for code generation for a plurality of architectures. At a host architecture, a JIT compile operation is performed for a received JavaScript or Web Assembly file. The JIT compiler references a host library that has been updated to include at least one new JIT instruction. Output from the JIT compile operation is compiled machine code for the host architecture that has new opcodes (OPX) added, responsive to the new JIT instruction. The JIT compiler executes the opcodes (OPX) in XuCode mode, meaning that the host architecture switches into a hardware protected private ISA (Instruction Set Architecture) called XuCode to implement the new JIT opcode instruction in XuCode.

A method for automatically migrating infrastructure as code (IaC) from a first cloud infrastructure platform to a second cloud infrastructure platform is provided. The method may include receiving an original IaC comprising a first type of coding language. The method may further include using natural language processing to map a connection between the first type of coding language and a second type of coding language. The method may further include based on the mapped connection, using the NLP to automatically generate a partial translation of the first type of coding language to the second type of coding language. The method may further include using a machine learning algorithm to correct at least one inaccuracy in the partial translation. The method may further include generating a complete translation and implementing a second IaC on the second cloud infrastructure platform based on the complete translation.

A method for automatically migrating infrastructure as code (IaC) from a first cloud infrastructure platform to a second cloud infrastructure platform is provided. The method may include receiving an original IaC comprising a first type of coding language. The method may further include using natural language processing to map a connection between the first type of coding language and a second type of coding language. The method may further include based on the mapped connection, using the NLP to automatically generate a partial translation of the first type of coding language to the second type of coding language. The method may further include using a machine learning algorithm to correct at least one inaccuracy in the partial translation. The method may further include generating a complete translation and implementing a second IaC on the second cloud infrastructure platform based on the complete translation.

A method for task mobilization may include a server receiving user inputs from a desktop application. A user input may correspond to a respective function performed by a user on the desktop application. A processor may identify the respective functions of the user inputs. Further, the method may include translating the respective functions into respective mobile view functions. Based on the respective mobile view functions, the processor may generate a mobile application and provide the mobile application to a mobile user device.

A method for task mobilization may include a server receiving user inputs from a desktop application. A user input may correspond to a respective function performed by a user on the desktop application. A processor may identify the respective functions of the user inputs. Further, the method may include translating the respective functions into respective mobile view functions. Based on the respective mobile view functions, the processor may generate a mobile application and provide the mobile application to a mobile user device.

An embedded GPU-based wideband parallel channelized receiving method includes: constructing an OpenCL platform; decimating a wideband signal read in the OpenCL platform at an interval indicated by the number of channels; assigning data in each row to one of multiple work groups for processing; filtering data on each of channels based on a coefficient of a polyphase filter on a branch; multiplying the filtered data by a factor; and performing an FFT on the formed two-dimensional matrix by columns to obtain data outputted from each of the channels.

An embedded GPU-based wideband parallel channelized receiving method includes: constructing an OpenCL platform; decimating a wideband signal read in the OpenCL platform at an interval indicated by the number of channels; assigning data in each row to one of multiple work groups for processing; filtering data on each of channels based on a coefficient of a polyphase filter on a branch; multiplying the filtered data by a factor; and performing an FFT on the formed two-dimensional matrix by columns to obtain data outputted from each of the channels.

A machine learning (ML) based code transformation system that transforms a source programming code developed using a source library for execution on a source platform into remediated code for execution on a target platform is disclosed. Metadata extracted from the source programming code is used to detect the source programming language, source libraries, and the source platform. The metadata also enables modularizing the source programming code based on the functionality and identifying a node from a plurality of nodes in a communication network to execute the various source code modules. A similarity map is generated mapping the source libraries to the target libraries and the source code modules that are incompatible with the target platform are identified and remediated with similar target code modules using the similarity map.