Carola Eschenbach

26 papers A* 1A 1B 1C 2Misc 2Journal 6Unranked 10
YearRankTypeTitle / Venue / Authors
2014 conf
Robophilosophy
Felix Lindner, Carola Eschenbach
2013 B conf
ICSR
Felix Lindner, Carola Eschenbach
2011 conf
COSIT
Kris Lohmann, Carola Eschenbach, Christopher Habel
2011 conf
COSIT
Felix Lindner, Carola Eschenbach
2010 J jnl
Log. J. IGPL
Carola Eschenbach, Özgür L. Özçep
2008 C ed.
FOIS
Carola Eschenbach, Michael Grüninger
2005 ch.
Functional Features in Language and Space
Carola Eschenbach
2004 A* conf
KR
Carola Eschenbach
2003 conf
Spatial Cognition
Ladina Tschander, Hedda Rahel Schmidtke, Carola Eschenbach, Christopher Habel, Lars Kulik
2002 conf
GIScience
Lars Kulik, Carola Eschenbach, Christopher Habel, Hedda Rahel Schmidtke
2001 C conf
FOIS
Carola Eschenbach
2000 conf
Spatial Cognition
Carola Eschenbach, Ladina Tschander, Christopher Habel, Lars Kulik
1999 conf
COSIT
Carola Eschenbach
1999 J jnl
Spatial Cogn. Comput.
Carola Eschenbach
1999 Misc conf
FLAIRS
Carola Eschenbach, Christopher Habel, Lars Kulik
1999 J jnl
Künstliche Intell.
Carola Eschenbach, Kerstin Schill
1998 conf
Spatial Cognition
Carola Eschenbach, Christopher Habel, Lars Kulik, Annette Leßmöllmann
1997 conf
Foundations of Computer Science: Potential - Theory - Cognition
Christopher Habel, Carola Eschenbach
1997 Misc conf
KI
Carola Eschenbach, Lars Kulik
1995 J jnl
Int. J. Hum. Comput. Stud.
Carola Eschenbach, Wolfgang Heydrich
1995 book
Carola Eschenbach
1994
Carola Eschenbach
1993 J jnl
J. Semant.
Carola Eschenbach
1989 conf
GWAI
Carola Eschenbach
1989 A conf
EACL
Carola Eschenbach, Christopher Habel, Michael Herweg, Klaus Rehkämper
1988 J jnl
Über Ansätze zur Darstellung von Konzepten und Prototypen
LILOG-Report
Carola Eschenbach
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)