Xiaofei Shi

23 papers Misc 1Journal 12Unranked 10
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Weibin Gu, Chenrui Feng, Lian Liu, Chen Yang, Xingchi Jiao, Yuhe Ding, Xiaofei Shi, Chao Gao, Alessandro Rizzo, Guyue Zhou
2025 J jnl
CoRR
Anastasis Kratsios, Xiaofei Shi, Qiang Sun, Zhanhao Zhang
2023 J jnl
Remote. Sens.
Xiangyu Jiao, Xiaofei Shi, Ziyang Shen, Kuiyuan Ni, Zhiyu Deng
2023 J jnl
Remote. Sens.
Derui Song, Cheng Zhu, Jingzhe Tao, Xiaofei Shi, Xiang-Hai Wang
2023 J jnl
Comput. Aided Des.
Long Liu, Bing Yi, Xiaofei Shi
2021 J jnl
CoRR
Wenxing Liao, Xiaofei Shi
2021 J jnl
CoRR
Xiaofei Shi, Wenxing Liao
2020 J jnl
EURASIP J. Wirel. Commun. Netw.
Xiaofei Shi, Zhiyu Deng, Xing Ding, Li Li
2020 J jnl
Int. J. Distributed Sens. Networks
Wenxing Liao, Xiaofei Shi, Xinying Chen
2019 conf
CSPS
Yucheng Qiu, Donghui Li, Xiaofei Shi
2019 conf
CSPS
Hui Lin, Yan Wang, Zhenzhen Wang, Mengli Sun, Shiqiang Zhang, Xiaofei Shi, Xiaokai Liu
2019 conf
EMNLP/IJCNLP (1)
Xiaofei Shi, Yanghua Xiao
2019 conf
CSPS
Hongzhi Wang, Qiumin Luo, Zunyi Shang, Gang Li, Xiaofei Shi
2017 J jnl
BMC Syst. Biol.
Xiaofei Shi, Ruiqi Wang
2016 J jnl
Circuits Syst. Signal Process.
Li Li, Tianshuang Qiu, Xiaofei Shi
2016 conf
GRMSE (2)
Xiaofei Shi, Yunfeng Ma, Qi Wang, Tingshuai Wang, Ping Wang, Shuai Wang, Xuezong Xu, Weike Xu, Zhongyi Wei, Nan Xiao, Caina Zhang, Xiaorui Ma, Yanwei Qian, Kunyu Gao
2015 J jnl
Sensors
Shuguo Pan, Weirong Chen, Xiaodong Jin, Xiaofei Shi, Fan He
2015 conf
GRMSE
Xiaofei Shi, Yunfeng Ma, Qi Wang, Kunyu Gao, Xu Liu
2014 Misc conf
WSC
Junhai Cao, Feng Yang, Zongyu Geng, Xiaofei Shi
2014 conf
GRMSE
Yunfeng Ma, Qi Wang, Xiaofei Shi, Zhihong Sun
2009 conf
SoCPaR
Chang Liu, Xiaofei Shi
2006 conf
ISNN (1)
Xiaofei Shi, Jidong Suo, Chang Liu, Li Li
2005 conf
ISSPA
Xiaofei Shi, Ren-Jie Liu, Yao-liang Huang
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)