Jaan Altosaar

21 papers A* 2A 2Misc 2Journal 10Unranked 4
YearRankTypeTitle / Venue / Authors
2023 J jnl
CoRR
Jacob S. Zelko, Sarah Gasman, Shenita R. Freeman, Dong Yun Lee, Jaan Altosaar, Azza Shoaibi, Gowtham Rao
2022 Misc conf
AMIA
Tony Y. Sun, Shreyas Bhave, Jaan Altosaar, Noemie Elhadad
2022 J jnl
CoRR
Tony Y. Sun, Shreyas Bhave, Jaan Altosaar, Noémie Elhadad
2022 J jnl
CoRR
Anton Stengel, Jaan Altosaar, Rebecca Dittrich, Noémie Elhadad
2021 Misc conf
AMIA
Tony Y. Sun, Oliver J. Bear Don't Walk IV, Jenny Chen, Jaan Altosaar, Harry Reyes Nieva, Noemie Elhadad
2021 J jnl
F1000Research
Gabriel Reder, Adamo Young, Jaan Altosaar, Jakub Rajniak, Noémie Elhadad, Michael Fischbach, Susan Holmes
2020 J jnl
CoRR
William F. Whitney, Min Jae Song, David Brandfonbrener, Jaan Altosaar, Kyunghyun Cho
2020
Jaan Altosaar
2020 conf
IntRS@RecSys
Rohan Bansal, Jordan Olmstead, Uri Bram, Robert Cottrell, Gabriel Reder, Jaan Altosaar
2019 J jnl
CoRR
Kexin Huang, Jaan Altosaar, Rajesh Ranganath
2018 A* conf
ICML
Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
2018 J jnl
CoRR
Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
2018 A conf
AISTATS
Jaan Altosaar, Rajesh Ranganath, David M. Blei
2017 J jnl
CoRR
Jaan Altosaar, Rajesh Ranganath, David M. Blei
2016 A conf
RecSys
Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei
2016 conf
NIPS
Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei
2016 J jnl
CoRR
Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei
2015 A* conf
EMNLP
Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, Richard Jean So
2015 J jnl
CoRR
Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, Richard Jean So
2015 conf
NIME
Ethan Benjamin, Jaan Altosaar
2015 conf
NIME
Andrew Mercer-Taylor, Jaan Altosaar
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)