A less complex and more straightforward program is a crucial factor that
enhances its maintainability and makes writing secure and bug-free programs
easier. However, due to its heavy workload and the risks of breaking the
working programs, programmers are reluctant to do code refactoring, and thus,
it also causes the loss of potential learning experiences. To mitigate this, we
demonstrate the application of using a large language model (LLM), GPT-3.5, to
suggest less complex versions of the user-written Python program, aiming to
encourage users to learn how to write better programs. We propose a method to
leverage the prompting with few-shot examples of the LLM by selecting the
best-suited code refactoring examples for each target programming problem based
on the prior evaluation of prompting with the one-shot example. The
quantitative evaluation shows that 95.68% of programs can be refactored by
generating 10 candidates each, resulting in a 17.35% reduction in the average
cyclomatic complexity and a 25.84% decrease in the average number of lines
after filtering only generated programs that are semantically correct.
Furthermore, the qualitative evaluation shows outstanding capability in code
formatting, while unnecessary behaviors such as deleting or translating
comments are also observed.

使用大型语言模型 GPT-3.5，通过以少量示例来提示用户编写结构更简单的 Python 程序，以提高程序可维护性和减少代码 bug，并通过定量和定性评估证明了其有效性。

使用少量示例的大语言模型重构程序

Refactoring Programs Using Large Language Models with Few-Shot Examples

There has been significant interest in zero and few-shot learning for
dialogue state tracking (DST) due to the high cost of collecting and annotating
task-oriented dialogues. Recent work has demonstrated that in-context learning
requires very little data and zero parameter updates, and even outperforms
trained methods in the few-shot setting (Hu et al. 2022). We propose RefPyDST,
which advances the state of the art with three advancements to in-context
learning for DST. First, we formulate DST as a Python programming task,
explicitly modeling language coreference as variable reference in Python.
Second, since in-context learning depends highly on the context examples, we
propose a method to retrieve a diverse set of relevant examples to improve
performance. Finally, we introduce a novel re-weighting method during decoding
that takes into account probabilities of competing surface forms, and produces
a more accurate dialogue state prediction. We evaluate our approach using
MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero
and few-shot settings.

本文提出了 RefPyDST，在零和少样本学习中通过将 DST 作为 Python 编程任务明确地建模语言交替参考，通过检索一组相关性更强的例子来提高性能，并在解码期间引入一种新颖的重新加权方法以获取更精确的对话状态预测，从而在 MultiWOZ 上实现了最新的多域联合目标准确性。

多样化的检索增强背景学习用于对话状态跟踪

Diverse Retrieval-Augmented In-Context Learning for Dialogue State  Tracking

We introduce PrivPy, a practical privacy-preserving collaborative computation
framework, especially optimized for machine learning tasks. PrivPy provides an
easy-to-use and highly compatible Python programming front-end which supports
high-level array operations and different secure computation engines to allow
for security assumptions and performance trade-offs. With PrivPy, programmers
can write modern machine learning algorithms conveniently and efficiently in
Python. We also design and implement a new efficient computation engine, with
which people can use competing cloud providers to efficiently perform general
arithmetics over real numbers. We demonstrate the usability and scalability of
PrivPy using common machine learning models (e.g. logistic regression and
convolutional neural networks) and real-world datasets (including a
5000-by-1-million matrix).

PrivPy 是一个优化的机器学习隐私保护计算框架，提供易于使用的 Python 编程前端，支持高级数组操作和不同的安全计算引擎，可以方便高效地编写现代机器学习算法。

PrivPy：可扩展和通用的隐私保护机器学习

PrivPy: Enabling Scalable and General Privacy-Preserving Machine  Learning

scikit-image is an image processing library that implements algorithms and
utilities for use in research, education and industry applications. It is
released under the liberal "Modified BSD" open source license, provides a
well-documented API in the Python programming language, and is developed by an
active, international team of collaborators. In this paper we highlight the
advantages of open source to achieve the goals of the scikit-image library, and
we showcase several real-world image processing applications that use
scikit-image.

本文介绍了 scikit-image 库，它是一个基于 Python 编写的开源图片处理库，实现了图像处理的算法和实用工具，为研究、教育和工业应用提供了便捷的 API 和丰富的应用场景案例。