Kaimeng Ding

13 papers Journal 13
YearRankTypeTitle / Venue / Authors
2024 J jnl
ISPRS Int. J. Geo Inf.
Kaimeng Ding, Yingying Wang, Chishe Wang, Ji Ma
2024 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Kaimeng Ding, Shiping Chen, Yue Zeng, Yanan Liu, Bei Xu, Yingying Wang
2023 J jnl
Remote. Sens.
Fan Meng, Guocan Zhao, Guojun Zhang, Zhi Li, Kaimeng Ding
2022 J jnl
Algorithms
Kaimeng Ding, Shiping Chen, Jiming Yu, Yanan Liu, Jie Zhu
2021 J jnl
Remote. Sens.
Kaimeng Ding, Shiping Chen, Yu Wang, Yueming Liu, Yue Zeng, Jin Tian
2021 J jnl
Int. J. Pattern Recognit. Artif. Intell.
Shoubao Su, Zhaorui Zhai, Chishe Wang, Kaimeng Ding
2021 J jnl
IEEE Access
Kaimeng Ding, Shoubao Su, Nan Xu, Tingting Jiang
2020 J jnl
Multim. Tools Appl.
Chengsong Yang, Chang-qing Zhu, Yingying Wang, Ting Rui, Jingwei Zhu, Kaimeng Ding
2020 J jnl
ISPRS Int. J. Geo Inf.
Kaimeng Ding, Yueming Liu, Qin Xu, Fuqiang Lu
2020 J jnl
IEEE Access
Qin Xu, Gaoyao Ma, Kaimeng Ding, Bin Xu
2018 J jnl
Algorithms
Kaimeng Ding, Shiping Chen, Fan Meng
2018 J jnl
Inf.
Yingying Wang, Chengsong Yang, Chang-qing Zhu, Kaimeng Ding
2018 J jnl
Inf.
Kaimeng Ding, Fan Meng, Yueming Liu, Nan Xu, Wenjun Chen
redb/extractors/decompiler/bninja/analysis/medium_level_normalization.py
← Index redb/extractors/decompiler/bninja/analysis/medium_level_normalization.py python
from binaryninja import (
    MediumLevelILInstruction,
    Variable, SSAVariable,
    ILIntrinsic,
)


class MediumLevelNormalization:
    def __init__(self):
        return

    def _collect_ops(self, il, ops):
        if il is None:
            return
        ops.append(int(il.operation))
        operands = getattr(il, "operands", None)
        if not operands:
            return
        for op in operands:
            if isinstance(op, MediumLevelILInstruction):
                self._collect_ops(op, ops)
            elif isinstance(op, (list, tuple)):
                for sub in op:
                    if isinstance(sub, MediumLevelILInstruction):
                        self._collect_ops(sub, ops)

    def normalize_instruction_all_levels(self, instr_il):
        ops = []
        self._collect_ops(instr_il, ops)
        return ops

    def _leaf_type(self, val):
        if isinstance(val, SSAVariable):
            return "SSA_VAR"
        if isinstance(val, Variable):
            return "VAR"
        if isinstance(val, ILIntrinsic):
            return "INTRINSIC"
        if isinstance(val, bool):
            return "BOOL"
        if isinstance(val, float):
            return "FLOAT_CONST"
        if isinstance(val, int):
            return "CONST"
        if isinstance(val, str):
            return "STR"
        return type(val).__name__.upper()

    def _collect(self, il, out_op):
        if il is None:
            return
        out_op.append(int(il.operation))
        operands = getattr(il, "operands", None)
        if not operands:
            return
        for op in operands:
            if isinstance(op, MediumLevelILInstruction):
                self._collect(op, out_op)
            elif isinstance(op, (list, tuple)):
                for sub in op:
                    if isinstance(sub, MediumLevelILInstruction):
                        self._collect(sub, out_op)
                    else:
                        out_op.append(self._leaf_type(sub))
            else:
                out_op.append(self._leaf_type(op))

    def normalize_instr_with_operands(self, instr_il):
        ops = []
        self._collect(instr_il, ops)
        return ops