"name":"Beyond the C: Retargetable Decompilation using Neural Machine Translation",
"date":"2021-12-31T23:00:00Z",
"hyperdata":{
"abstract":" The problem of reversing the compilation process, decompilation, is an important tool in reverse engineering of computer software. Recently, researchers have proposed using techniques from neural machine translation to automate the process in decompilation. Although such techniques hold the promise of targeting a wider range of source and assembly languages, to date they have primarily targeted C code. In this paper we argue that existing neural decompilers have achieved higher accuracy at the cost of requiring language-specific domain knowledge such as tokenizers and parsers to build an abstract syntax tree (AST) for the source language, which increases the overhead of supporting new languages. We explore a different tradeoff that, to the extent possible, treats the assembly and source languages as plain text, and show that this allows us to build a decompiler that is easily retargetable to new languages. We evaluate our prototype decompiler, Beyond The C (BTC), on Go, Fortran, OCaml, and C, and examine the impact of parameters such as tokenization and training data selection on the quality of decompilation, finding that it achieves comparable decompilation results to prior work in neural decompilation with significantly less domain knowledge. We will release our training data, trained decompilation models, and code to help encourage future research into language-agnostic decompilation. ",
"authors":"Iman Hosseini, Brendan Dolan-Gavitt",
"bdd":"Arxiv",
"doi":"10.14722/bar.2022.23009",
"institutes":", ",
"language_iso2":"EN",
"publication_date":"2022-12-17T20:45:59Z",
"publication_year":2022,
"source":"",
"title":"Beyond the C: Retargetable Decompilation using Neural Machine Translation",
"url":"http://arxiv.org/pdf/2212.08950v1"
}
},
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{
"document":{
"id":7797,
"hash_id":null,
"typename":4,
"user_id":2,
"parent_id":null,
"name":"TREXIO: A File Format and Library for Quantum Chemistry",
"date":"2022-12-31T23:00:00Z",
"hyperdata":{
"abstract":" TREXIO is an open-source file format and library developed for the storage and manipulation of data produced by quantum chemistry calculations. It is designed with the goal of providing a reliable and efficient method of storing and exchanging wave function parameters and matrix elements, making it an important tool for researchers in the field of quantum chemistry. In this work, we present an overview of the TREXIO file format and library. The library consists of a front-end implemented in the C programming language and two different back-ends: a text back-end and a binary back-end utilizing the HDF5 library which enables fast read and write operations. It is compatible with a variety of platforms and has interfaces for the Fortran, Python, and OCaml programming languages. In addition, a suite of tools has been developed to facilitate the use of the TREXIO format and library, including converters for popular quantum chemistry codes and utilities for validating and manipulating data stored in TREXIO files. The simplicity, versatility, and ease of use of TREXIO make it a valuable resource for researchers working with quantum chemistry data. ",
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