Xiao Zang

21 papers A* 7A 4Journal 8Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
CoRR
Jinqi Xiao, Cheng Luo, Lingyi Huang, Cheng Yang, Yang Sui, Huy Phan, Xiao Zang, Yibiao Ying, Zhexiang Tang, Anima Anandkumar, Bo Yuan
2024 A* conf
DAC
Lingyi Huang, Cheng Yang, Yu Gong, Yang Sui, Xiao Zang, Anthony Goeckner, Qi Zhu, Bo Yuan
2024 A* conf
HPCA
Lingyi Huang, Yu Gong, Yang Sui, Xiao Zang, Bo Yuan
2023 A* conf
ICML
Jinqi Xiao, Miao Yin, Yu Gong, Xiao Zang, Jian Ren, Bo Yuan
2023 J jnl
CoRR
Jinqi Xiao, Miao Yin, Yu Gong, Xiao Zang, Jian Ren, Bo Yuan
2023 A conf
IROS
Wenjin Zhang, Xiao Zang, Lingyi Huang, Yang Sui, Jingjin Yu, Yingying Chen, Bo Yuan
2023 J jnl
CoRR
Xiao Zang, Jie Chen, Bo Yuan
2023 A* conf
NeurIPS
Xiao Zang, Miao Yin, Jinqi Xiao, Saman A. Zonouz, Bo Yuan
2022 J jnl
Signal Image Video Process.
Linyu Wang, Xiao Zang, Jianhong Xiang, Yu Zhong
2022 A* conf
AAAI
Miao Yin, Huy Phan, Xiao Zang, Siyu Liao, Bo Yuan
2022 A* conf
CVPR
Miao Yin, Yang Sui, Wanzhao Yang, Xiao Zang, Yu Gong, Bo Yuan
2022 A conf
ICCAD
Lingyi Huang, Xiao Zang, Yu Gong, Bo Yuan
2022 conf
ACM Great Lakes Symposium on VLSI
Lingyi Huang, Xiao Zang, Yu Gong, Chunhua Deng, Jingang Yi, Bo Yuan
2022 A conf
IROS
Xiao Zang, Miao Yin, Lingyi Huang, Jingjin Yu, Saman A. Zonouz, Bo Yuan
2022 J jnl
CoRR
Xiao Zang, Miao Yin, Lingyi Huang, Jingjin Yu, Saman A. Zonouz, Bo Yuan
2022 conf
IEEECONF
Lingyi Huang, Xiao Zang, Yu Gong, Boyang Zhang, Bo Yuan
2021 A* conf
IJCAI
Xiao Zang, Yi Xie, Jie Chen, Bo Yuan
2021 J jnl
CoRR
Xiao Zang, Yi Xie, Siyu Liao, Jie Chen, Bo Yuan
2020 J jnl
CoRR
Xiao Zang, Yi Xie, Jie Chen, Bo Yuan
2015 J jnl
Multim. Tools Appl.
Zhiyong Wu, Yishuang Ning, Xiao Zang, Jia Jia, Fanbo Meng, Helen Meng, Lianhong Cai
2014 A conf
INTERSPEECH
Xiao Zang, Zhiyong Wu, Helen M. Meng, Jia Jia, Lianhong Cai
redb/extractors/decompiler/bninja/similarity/minhasher.py
← Index redb/extractors/decompiler/bninja/similarity/minhasher.py python
import logging
import random
from enum import Enum

from ..analysis.medium_level_normalization import MediumLevelNormalization

try:
    from .minhashcustom import MinHashCustom
    from ..analysis.low_level_normalization import LowLevelNormalization
except ImportError:
    # Fallback to absolute imports (for multiprocessing spawned processes)
    from redb.extractors.decompiler.bninja.similarity.minhashcustom import MinHashCustom
    from redb.extractors.decompiler.bninja.analysis.low_level_normalization import LowLevelNormalization

## Values for this configuration were extracted from https://github.com/danielplohmann/mcrit/blob/main/mcrit/config/MinHashConfig.py#L10
# Length in number of Shingles of which a minhash consists
# this value represents the length of sha256sum hash truncated
MINHASH_SIGNATURE_LENGTH: int = 64
# Number of bits per signature element (1-32 bits)
MINHASH_SIGNATURE_BITS: int = 8


class TokenKind(Enum):
    LLIL = "llil"
    TYPED_LLIL = "typed_llil"
    MLIL = "mlil"
    TYPED_MLIL = "typed_mlil"


class MinHasher:
    # stick to the default method
    MINHASH_STRATEGY_HASH_ALL = 1

    def __init__(self, seed, il_function, kind: TokenKind = TokenKind.LLIL):
        self._minhash_seeds = []
        self.il_func = il_function
        self.kind = kind
        self._minhash_permutation = []
        self._signature_segments = []
        self._initMinhashing(seed)

    def _initMinhashing(self, MINHASH_SEED=None):
        random.seed(MINHASH_SEED)
        # init sequence of seeds
        self._minhash_seeds = [
            random.randint(0, MinHashCustom.getHashMax()) for _ in range(MINHASH_SIGNATURE_LENGTH)
        ]

    def make_ngrams(self, tokens, n=3):
        """Take the ngrams of the IL we try to pass into the functions"""
        return [tuple(tokens[i:i+n]) for i in range(len(tokens) - n + 1)]

    def _extract_tokens(self):
        """Extract the IL tokens from the IL function, picking the right
        normalizer (LLIL/MLIL) and the right normalization mode
        (skeleton/typed) based on self.kind."""
        if self.kind in (TokenKind.LLIL, TokenKind.TYPED_LLIL):
            normalizer = LowLevelNormalization()
        elif self.kind in (TokenKind.MLIL, TokenKind.TYPED_MLIL):
            normalizer = MediumLevelNormalization()
        else:
            raise ValueError(f"Unsupported token kind: {self.kind}")

        # typed variants include operand type info, skeleton variants don't
        if self.kind in (TokenKind.TYPED_LLIL, TokenKind.TYPED_MLIL):
            normalize = normalizer.normalize_instr_with_operands
        else:
            normalize = normalizer.normalize_instruction_all_levels

        instructions = []
        for basic_block in self.il_func.basic_blocks:
            for il in basic_block:
                instructions.append(normalize(il))

        return instructions

    def calculateMinHash(self):
        """Calculate hash function every time, then take minimum shingle per shingler"""
        minhash_result = MinHashCustom(minhash_bits=MINHASH_SIGNATURE_BITS)
        minhash_signature = []

        tokens = self._extract_tokens()
        shingles = self.make_ngrams(tokens, n=3)

        # Functions with fewer than 3 IL instructions can't produce n-grams
        # Return empty minhash for such small functions (thunks, stubs, etc.)
        # Triggered by 39d8ad95b0323c37bd3134ab93ac4af44c66a1a8443a41c1ac02cec19bb2816a
        if not shingles:
            return []

        # Generate the MinHash
        for seed in self._minhash_seeds:
            hashed_shingles = [
                self.shingle_hash(shingle, seed) for shingle in shingles
            ]
            min_value = min(hashed_shingles)

            if MINHASH_SIGNATURE_BITS < 32:
                min_value %= (2 ** MINHASH_SIGNATURE_BITS)

            minhash_signature.append(min_value)

        minhash_result.setMinHash(minhash_signature)
        return minhash_result.getMinHashInt()

    def shingle_hash(self, shingle, hash_seed=0):
        """Produce a single 32bit UINT hash for a given shingle"""
        return MinHashCustom.hashData(shingle, hash_seed)