LeadroyaL

package com.leadroyal.miuiusb;

import java.lang.reflect.Field;
import java.lang.reflect.Method;

import de.robv.android.xposed.IXposedHookLoadPackage;
import de.robv.android.xposed.XC_MethodHook;
import de.robv.android.xposed.XposedBridge;
import de.robv.android.xposed.XposedHelpers;
import de.robv.android.xposed.callbacks.XC_LoadPackage;

anestisb

diff --git a/dexlayout/Android.bp b/dexlayout/Android.bp
index 838510be37..34186d85d7 100644
--- a/dexlayout/Android.bp
+++ b/dexlayout/Android.bp
@@ -132,6 +132,30 @@ cc_defaults {
     ],
 }

+cc_defaults {
+    name: "compact_dex_converter_defaults",

vra

#!/bin/sh
hard_limit=$(git config hooks.filesizehardlimit)
soft_limit=$(git config hooks.filesizesoftlimit)
: ${hard_limit:=10000000}
: ${soft_limit:=1000000}

list_new_or_modified_files()
{
    git diff --staged --name-status|sed -e '/^D/ d; /^D/! s/.\s\+//'
}

anestisb

diff --git a/dexlayout/Android.bp b/dexlayout/Android.bp
index 33ba58f..f3b2a7e 100644
--- a/dexlayout/Android.bp
+++ b/dexlayout/Android.bp
@@ -74,6 +74,13 @@ cc_defaults {
     ],
 }

+cc_defaults {
+    name: "compact_dex_converter_defaults",

wd

{
  "log": {
    "loglevel": "none"
  },
  "inbound": {
      "port": 23456,
      "listen": "127.0.0.1",
      "protocol": "socks",
      "settings": {
        "udp": true

npjohnson

#!/bin/sh

# Copyright 2012 The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#

mayufo

var GB2260 = {
  "110000": "北京市",
  "110100": "北京市市辖区",
  "110101": "北京市东城区",
  "110102": "北京市西城区",
  "110103": "北京市崇文区",
  "110104": "北京市宣武区",
  "110105": "北京市朝阳区",
  "110106": "北京市丰台区",
  "110107": "北京市石景山区",

Thimira

'''
Using Bottleneck Features for Multi-Class Classification in Keras

We use this technique to build powerful (high accuracy without overfitting) Image Classification systems with small
amount of training data.

The full tutorial to get this code working can be found at the "Codes of Interest" Blog at the following link,
https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html

Please go through the tutorial before attempting to run this code, as it explains how to setup your training data.

oasisfeng

package com.oasisfeng.android.service;

import android.annotation.SuppressLint;
import android.app.Service;
import android.content.Intent;
import android.os.Binder;
import android.os.IBinder;
import android.os.IInterface;
import android.support.annotation.Nullable;
import android.util.Log;

oasisfeng

package com.oasisfeng.hack;

import android.support.annotation.CheckResult;
import android.support.annotation.NonNull;
import android.support.annotation.Nullable;
import android.util.Log;

import java.io.IOException;
import java.lang.reflect.AccessibleObject;
import java.lang.reflect.Constructor;