Wang Jie

18 papers B 2C 1Journal 8Unranked 7
YearRankTypeTitle / Venue / Authors
2026 J jnl
J. Appl. Math. Comput.
Liu Xing, Wang Jie, Yanhua Lang, Huizi Yang, Zhou Zhongxin
2024 J jnl
World Sci. Annu. Rev. Artif. Intell.
Richard Chang, Wang Jie, Namrata Thakur, Ramanpreet Singh Pahwa
2023 J jnl
Int. J. Mach. Learn. Cybern.
Xiaonan Li, Bo Ning, Guanyu Li, Wang Jie
2022 conf
M2VIP
Lingyan Liu, Wang Jie, Jiyuan Wu, Dong Lingjie, Xingsong Wang, Mengqian Tian
2022 J jnl
Int. J. Perform. Eng.
Deng Hongjing, Zhang Xuan, Jiang Jiahao, Wang Jie, Huang Hexiang
2022 J jnl
J. Ind. Inf. Integr.
Jinping Liu, Juanjuan Wu, Yongfang Xie, Wang Jie, Pengfei Xu, Zhaohui Tang, Huazhan Yin
2019 J jnl
Sensors
Xia Fang, Wang Jie, Tao Feng
2019 J jnl
Int. J. Wirel. Mob. Comput.
Wang Jie, Lu Jingyi
2014 C conf
DASC
Wang Jie, Yu Xiao, Zhao Ming, Wang Yong
2013 conf
ITSC
Shoufeng Lu, Wang Jie, Henk J. van Zuylen, Ximin Liu
2012 conf
BHI
Wang Longchen, Zhu Gaojie, Wang Jie, Li Bin
2012 conf
FSKD
Wang Jie, Zeng Yu
2012 conf
ICDMA
Wang Jie, Zhang Li, Kuang Jie, Wu Feng
2011 J jnl
Scientometrics
Huang Yi, Wang Jie
2008 B conf
SMC
Wang Jie, Fan Yao-Tian
2008 B conf
SMC
Wang Jie, Hu Xian-Zhong
2007 conf
APPT
Wang Jie, Ji Zhen-zhou, Mingzeng Hu
2005 conf
FSKD (1)
Xiaolu Li, Wang Jie, Shengli Xie
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)