Haixia Mao

12 papers B 2Journal 7Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Expert Syst. Appl.
Jianglin Dong, Yiyi Zhao, Shangqun Mu, Haixia Mao, Jiangping Hu
2025 J jnl
Inf. Fusion
Jianglin Dong, Yiyi Zhao, Haixia Mao, Ya Yin, Jiangping Hu
2025 J jnl
Appl. Soft Comput.
Haixia Mao, Yiyi Zhao, Min Xu, Jianglin Dong, Jiangping Hu
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Yue Shen, Xianmin Wang, Xiaoyu Yi, Li Cao, Haixiang Guo, Haixia Mao, Xubing Zhang, Chen Liu, Xuewen Wang
2025 J jnl
Expert Syst. Appl.
Jianglin Dong, Yiyi Zhao, Haixia Mao, Junyi Yang, Jiangping Hu
2024 B conf
SMC
Jianglin Dong, Haixia Mao, Yiyi Zhao, Yuan Peng, Junyi Yang, Jiangping Hu
2019 J jnl
Comput. Hum. Behav.
Haixia Mao, Xiaopeng Fan, Jinping Guan, Yeh-Cheng Chen, Haoran Su, Wenzhong Shi, Yubin Zhao, Yang Wang, Cheng-Zhong Xu
2016 conf
CCBD
Manhua Jiang, Xiaopeng Fan, Fan Zhang, Chengzhong Xu, Haixia Mao, Renkai Liu
2016 B conf
MDM
Xiaopeng Fan, Jiannong Cao, Haixia Mao, Weigang Wu, Yubin Zhao, Cheng-Zhong Xu
2016 conf
CCBD
Luyan Xiao, Xiaopeng Fan, Haixia Mao, Cheng-Zhong Xu, Ping Lu, Shengmei Luo
2013 J jnl
J. Parallel Distributed Comput.
Xiaopeng Fan, Jiannong Cao, Haixia Mao, Yunhuai Liu
2009 conf
ICIRA
Bo Hu, Yi Lu, Haixia Mao
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