This paper introduces and presents a new language named MAIL (Malware
Analysis Intermediate Language). MAIL is basically used for building malware
analysis and detection tools. MAIL provides an abstract representation of an
assembly program and hence the ability of a tool to automate malware analysis
and detection. By translating binaries compiled for different platforms to
MAIL, a tool can achieve platform independence. Each MAIL statement is
annotated with patterns that can be used by a tool to optimize malware analysis
and detection.

本文介绍了一种名为 MAIL 的新语言，其主要用于构建恶意软件的分析和检测工具，MAIL 提供了汇编程序的抽象表示形式，因此具有自动化分析和检测恶意软件的能力，并且通过将不同平台编译的二进制文件转换为 MAIL，可以实现平台独立性，并且每个 MAIL 语句都带有可以用于优化恶意软件分析和检测的模式。

MAIL: 恶意软件中间语言

MAIL: Malware Analysis Intermediate Language

User Interface (UI) prototyping is a necessary step in the early stages of
application development. Transforming sketches of a Graphical User Interface
(UI) into a coded UI application is an uninspired but time-consuming task
performed by a UI designer. An automated system that can replace human efforts
for straightforward implementation of UI designs will greatly speed up this
procedure. The works that propose such a system primarily focus on using UI
wireframes as input rather than hand-drawn sketches. In this paper, we put
forward a novel approach wherein we employ a Deep Neural Network that is
trained on our custom database of such sketches to detect UI elements in the
input sketch. Detection of objects in sketches is a peculiar visual recognition
task that requires a specific solution that our deep neural network model
attempts to provide. The output from the network is a platform-independent UI
representation object. The UI representation object is a dictionary of
key-value pairs to represent the UI elements recognized along with their
properties. This is further consumed by our UI parser which creates code for
different platforms. The intrinsic platform-independence allows the model to
create a UI prototype for multiple platforms with single training. This
two-step approach without the need for two trained models improves over other
methods giving time-efficient results (average time: 129 ms) with good
accuracy.

使用深度神经网络从 UI 草图中检测 UI 元素并生成平台无关的 UI 表现形式，进而使用 UI 解析器创建多平台 UI 原型，从而加速 UI 原型开发（平均时间：129 毫秒）。