Wangjie Li

12 papers A 1Journal 9Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Comput. Vis. Image Underst.
Xu-Hua Yang, Dong Wei, Wangjie Li, Hongxiang Hu
2025 conf
APSIPA
Wangjie Li, Lin Li, Qingyang Hong
2025 J jnl
CoRR
Wangjie Li, Lin Li, Qingyang Hong
2025 A conf
INTERSPEECH
Zhaoyang Li, Jie Wang, Xiaoxiao Li, Wangjie Li, Longjie Luo, Lin Li, Qingyang Hong
2025 J jnl
CoRR
Zhaoyang Li, Jie Wang, Xiaoxiao Li, Wangjie Li, Longjie Luo, Lin Li, Qingyang Hong
2025 J jnl
CoRR
Wangjie Li, Xingjia Xie, Yishuang Li, Wenhao Guan, Kaidi Wang, Pengyu Ren, Lin Li, Qingyang Hong
2024 J jnl
Ind. Manag. Data Syst.
Wenlong Liu, Wangjie Li, Jian Mou
2024 J jnl
Digit. Signal Process.
Hongye Liu, Xu Xu, Yue Yang, Wangjie Li
2023 J jnl
IEEE Signal Process. Lett.
Wangjie Li, Xu Xu, Xinyue Huang, Yue Yang
2023 J jnl
Digit. Signal Process.
Yue Yang, Xu Xu, Huichao Yang, Wangjie Li
2023 J jnl
Digit. Signal Process.
Xinyue Huang, Zhongfu Ye, Wangjie Li, Xu Xu
2018 conf
TURC
Zhiyao Niu, Wangjie Li, Xiangguo Yan, Ning Wu
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