Search Results for "machine learning platform"

NASM (Netwide Assembler) is a widely used, open-source assembler for the x86 architecture that provides a flexible and portable way to write low-level machine code using an Intel-style syntax. It is designed to support a wide variety of output formats, making it suitable for different operating systems and development environments. NASM is known for its simplicity and clarity, allowing developers to write assembly code that is both readable and efficient. It is commonly used in system...

BinExport is a disassembly export plugin developed by Google as part of the BinDiff ecosystem, designed for reverse engineering and binary analysis. It works with popular disassemblers including IDA Pro, Binary Ninja, and Ghidra, enabling the export of disassembly data into a structured Protocol Buffer format. This exported data can then be used for binary comparison, diffing, and advanced analysis tasks through BinDiff or other compatible tools. BinExport captures detailed information such...

...Selfie is a project of the Computational Systems Group at the Department of Computer Sciences of the University of Salzburg in Austria. The Selfie Project provides an educational platform for teaching undergraduate and graduate students the design and implementation of programming languages and runtime systems. The focus is on the construction of compilers, libraries, operating systems, and even virtual machine monitors. The common theme is to identify and resolve self-reference in systems code which is seen as the key challenge when teaching systems engineering, hence the name. ...

This is a C compiler, assembler, linker and virtual machine for computing based on encrypted working and obfuscated compilation and execution. The idea is that the platform machine `works encrypted' for the user and unencrypted for the operator, running a machine code instruction set in user mode that is provably safe against computational attacks, for code that is generated by a compiler that has introduced random variations into the code at each recompilation so that the attacker has no statistical leverage from human programming biases.