Tootfinder

This https://arxiv.org/abs/2210.02435 has been replaced.
link: https://scholar.google.com/scholar?q=a

IRJIT: A Simple, Online, Information Retrieval Approach for Just-In-Time Software Defect Prediction
Just-in-Time software defect prediction (JIT-SDP) prevents the introduction of defects into the software by identifying them at commit check-in time. Current software defect prediction approaches rely on manually crafted features such as change metrics and involve expensive to train machine learning or deep learning models. These models typically involve extensive training processes that may require significant computational resources and time. These characteristics can pose challenges when att…

TikTag: Breaking ARM's Memory Tagging Extension with Speculative Execution
Juhee Kim, Jinbum Park, Sihyeon Roh, Jaeyoung Chung, Youngjoo Lee, Taesoo Kim, Byoungyoung Lee
https://arxiv.org/abs/2406.08719

TikTag: Breaking ARM's Memory Tagging Extension with Speculative Execution
ARM Memory Tagging Extension (MTE) is a new hardware feature introduced in ARMv8.5-A architecture, aiming to detect memory corruption vulnerabilities. The low overhead of MTE makes it an attractive solution to mitigate memory corruption attacks in modern software systems and is considered the most promising path forward for improving C/C++ software security. This paper explores the potential security risks posed by speculative execution attacks against MTE. Specifically, this paper identifies n…

Language Models are Crossword Solvers
Soumadeep Saha, Sutanoya Chakraborty, Saptarshi Saha, Utpal Garain
https://arxiv.org/abs/2406.09043 https://

Language Models are Crossword Solvers
Crosswords are a form of word puzzle that require a solver to demonstrate a high degree of proficiency in natural language understanding, wordplay, reasoning, and world knowledge, along with adherence to character and length constraints. In this paper we tackle the challenge of solving crosswords with Large Language Models (LLMs). We demonstrate that the current generation of state-of-the art (SoTA) language models show significant competence at deciphering cryptic crossword clues, and outperfo…

AsterX: a new open-source GPU-accelerated GRMHD code for dynamical spacetimes
Jay V. Kalinani, Liwei Ji, Lorenzo Ennoggi, Federico G. Lopez Armengol, Lucas Timotheo Sanches, Bing-Jyun Tsao, Steven R Brandt, Manuela Campanelli, Riccardo Ciolfi, Bruno Giacomazzo, Roland Haas, Erik Schnetter, Yosef Zlochower
https://arxiv.org/abs/2…

AsterX: a new open-source GPU-accelerated GRMHD code for dynamical spacetimes
We present AsterX, a novel open-source, GPU-accelerated, fully general relativistic magnetohydrodynamic (GRMHD) code designed for dynamic spacetimes in 3D Cartesian coordinates, and tailored for exascale computing. We utilize block-structured adaptive mesh refinement (AMR) through CarpetX, the new driver for the Einstein Toolkit, which is built on AMReX, a software framework for massively parallel applications. AsterX employs the Valencia formulation for GRMHD, coupled with the 'Z4c' formalism …