Ranchao Wu

52 papers Journal 51Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
Math. Comput. Simul.
Ali Rehman, Ranchao Wu
2026 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Ali Rehman, Qigang Deng, Ranchao Wu
2026 J jnl
Appl. Math. Lett.
Qigang Deng, Ali Rehman, Ranchao Wu
2025 J jnl
Appl. Math. Lett.
Quanli Ji, Ranchao Wu, Federico Frascoli, Zhenzhen Chen
2024 J jnl
Comput. Appl. Math.
Biao Liu, Quanli Ji, Ranchao Wu
2024 J jnl
IEEE Trans. Circuits Syst. II Express Briefs
Liping Chen, Min Xue, António M. Lopes, Ranchao Wu, Xiaohua Zhang, YangQuan Chen
2024 J jnl
Comput. Appl. Math.
Jianjiang Ge, Ranchao Wu, Zhaosheng Feng
2024 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Mengxin Chen, Xuezhi Li, Ranchao Wu
2023 J jnl
Appl. Intell.
Liping Chen, Jinhui Gao, António M. Lopes, Zhiqiang Zhang, Zhaobi Chu, Ranchao Wu
2023 J jnl
Int. J. Bifurc. Chaos
Ranchao Wu, Lingling Yang
2023 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Mengxin Chen, Ranchao Wu, Xiaohui Wang
2023 J jnl
Appl. Math. Lett.
Quanli Ji, Ranchao Wu
2023 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Liping Chen, Xiaobo Wu, António M. Lopes, Xin Li, Penghua Li, Ranchao Wu
2023 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Liping Chen, Wenliang Guo, António M. Lopes, Ranchao Wu, Penghua Li, Lisheng Yin
2023 J jnl
Int. J. Bifurc. Chaos
Mengxin Chen, Ranchao Wu
2022 J jnl
Appl. Math. Comput.
Liping Chen, Xiaomin Li, YangQuan Chen, Ranchao Wu, António M. Lopes, Suoliang Ge
2021 J jnl
Signal Process. Image Commun.
Liping Chen, Hao Yin, Liguo Yuan, J. A. Tenreiro Machado, Ranchao Wu, Zeeshan Alam
2021 J jnl
Int. J. Bifurc. Chaos
Ranchao Wu, Chuanying Zhang, Zhaosheng Feng
2021 J jnl
Integr.
Fei Qi, Yi Chai, Liping Chen, YangQuan Chen, Ranchao Wu
2021 J jnl
Math. Comput. Simul.
Naveed Iqbal, Ranchao Wu, Wael W. Mohammed
2020 J jnl
Frontiers Inf. Technol. Electron. Eng.
Liping Chen, Hao Yin, Liguo Yuan, António M. Lopes, J. A. Tenreiro Machado, Ranchao Wu
2020 J jnl
IEEE Trans. Circuits Syst. II Express Briefs
Liping Chen, Ranchao Wu, Yuan Cheng, YangQuan Chen
2020 J jnl
Comput. Appl. Math.
Liping Chen, Tingting Li, Ranchao Wu, António M. Lopes, J. A. Tenreiro Machado, Kehan Wu
2020 J jnl
Int. J. Bifurc. Chaos
Mengxin Chen, Ranchao Wu, Liping Chen
2020 J jnl
Appl. Math. Comput.
Mengxin Chen, Ranchao Wu, Liping Chen
2019 J jnl
Neural Networks
Liping Chen, Tingwen Huang, J. A. Tenreiro Machado, António M. Lopes, Yi Chai, Ranchao Wu
2019 J jnl
J. Frankl. Inst.
Weiwei Zhang, Jinde Cao, Ranchao Wu, Fuad E. Alsaadi, Ahmed Alsaedi
2019 J jnl
Int. J. Syst. Sci.
Liping Chen, Tingting Li, YangQuan Chen, Ranchao Wu, Suoliang Ge
2019 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Mengxin Chen, Ranchao Wu, Biao Liu, Liping Chen
2018 J jnl
J. Frankl. Inst.
Penghua Li, Liping Chen, Ranchao Wu, J. A. Tenreiro Machado, António M. Lopes, Liguo Yuan
2017 J jnl
Int. J. Syst. Sci.
Song Liang, Ranchao Wu, Liping Chen
2017 J jnl
Appl. Math. Comput.
Naveed Iqbal, Ranchao Wu, Biao Liu
2017 J jnl
Int. J. Control
Liping Chen, Ranchao Wu, Zhaobi Chu, Yigang He, Lisheng Yin
2017 J jnl
Neural Networks
Liping Chen, Jinde Cao, Ranchao Wu, J. A. Tenreiro Machado, António M. Lopes, Hejun Yang
2017 J jnl
Int. J. Bifurc. Chaos
Biao Liu, Ranchao Wu, Naveed Iqbal, Liping Chen
2016 J jnl
Neural Comput. Appl.
Liping Chen, Cong Liu, Ranchao Wu, Yigang He, Yi Chai
2015 J jnl
Neurocomputing
Song Liang, Ranchao Wu, Liping Chen
2015 J jnl
Neurocomputing
Ranchao Wu, Yanfen Lu, Liping Chen
2015 J jnl
Appl. Math. Comput.
Liping Chen, Ranchao Wu, Yigang He, Lisheng Yin
2015 J jnl
Appl. Math. Comput.
Ranchao Wu, Tianbao Fang
2015 J jnl
Neural Networks
Liping Chen, Ranchao Wu, Jinde Cao, Jia-Bao Liu
2013 J jnl
Neurocomputing
Liping Chen, Yi Chai, Ranchao Wu, Tiedong Ma, Houzhen Zhai
2013 J jnl
Entropy
Liping Chen, Jianfeng Qu, Yi Chai, Ranchao Wu, Guoyuan Qi
2012 J jnl
Expert Syst. Appl.
Liping Chen, Yi Chai, Ranchao Wu
2012 J jnl
J. Appl. Math.
Yi Chai, Liping Chen, Ranchao Wu
2012 J jnl
IEEE Trans. Circuits Syst. II Express Briefs
Liping Chen, Yi Chai, Ranchao Wu, Jing Yang
2011 J jnl
Expert Syst. Appl.
Liping Chen, Ranchao Wu, Donghui Pan
2009 J jnl
Int. J. Bifurc. Chaos
Ranchao Wu
2009 J jnl
Int. J. Bifurc. Chaos
Liping Chen, Ranchao Wu
2009 J jnl
Expert Syst. Appl.
Ranchao Wu, Weiwei Zhang
2008 conf
CSSE (4)
Ranchao Wu, Liping Chen
2006 J jnl
Int. J. Bifurc. Chaos
Ranchao Wu, Jianhua Sun
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)