Generative Artificial Intelligence (AI), particularly tools like OpenAI's
popular ChatGPT, is reshaping the landscape of computer science research. Used
wisely, these tools can boost the productivity of a computer research
scientist. This paper provides an exploration of the diverse applications of
ChatGPT and other generative AI technologies in computer science academic
research, making recommendations about the use of Generative AI to make more
productive the role of the computer research scientist, with the focus of
writing new research papers. We highlight innovative uses such as brainstorming
research ideas, aiding in the drafting and styling of academic papers and
assisting in the synthesis of state-of-the-art section. Further, we delve into
using these technologies in understanding interdisciplinary approaches, making
complex texts simpler, and recommending suitable academic journals for
publication. Significant focus is placed on generative AI's contributions to
synthetic data creation, research methodology, and mentorship, as well as in
task organization and article quality assessment. The paper also addresses the
utility of AI in article review, adapting texts to length constraints,
constructing counterarguments, and survey development. Moreover, we explore the
capabilities of these tools in disseminating ideas, generating images and
audio, text transcription, and engaging with editors. We also describe some
non-recommended uses of generative AI for computer science research, mainly
because of the limitations of this technology.

生成人工智能，尤其是像 OpenAI 流行的 ChatGPT 这样的工具正在重塑计算机科学研究的领域。本文提供了对 ChatGPT 和其他生成人工智能技术在计算机科学学术研究中多样应用的探索，提出使用生成人工智能来提高计算机科学研究科学家的工作效率的建议，重点是撰写新的研究论文。我们强调了创新性应用，如头脑风暴研究想法，辅助学术论文的起草和格式化，以及协助综述文献。此外，我们深入探讨了在理解跨学科方法、简化复杂文本以及推荐适合学术期刊发表方面应用这些技术的可能性。我们还着重讨论了生成人工智能在合成数据创建、研究方法论和指导以及任务组织和文章质量评估方面的贡献。本文还讨论了人工智能在文章审查、适应文本长度限制、构建对立观点和调查开发方面的实用性。此外，我们探讨了这些工具在传播观念、生成图像和音频、文本转录和与编辑人员互动方面的能力。我们还描述了一些不推荐在计算机科学研究中使用生成人工智能的用途，主要是由于该技术的局限性。

ChatGPT 和生成式人工智能在计算机科学研究中的最佳应用

Best uses of ChatGPT and Generative AI for computer science research

Data augmentation is classically used to improve the overall performance of
deep learning models. It is, however, challenging in the case of medical
applications, and in particular for multiparametric datasets. For example,
traditional geometric transformations used in several applications to generate
synthetic images can modify in a non-realistic manner the patients' anatomy.
Therefore, dedicated image generation techniques are necessary in the medical
field to, for example, mimic a given pathology realistically. This paper
introduces a new data augmentation architecture that generates synthetic
multiparametric (T1 arterial, T1 portal, and T2) magnetic resonance images
(MRI) of massive macrotrabecular subtype hepatocellular carcinoma with their
corresponding tumor masks through a generative deep learning approach. The
proposed architecture creates liver tumor masks and abdominal edges used as
input in a Pix2Pix network for synthetic data creation. The method's efficiency
is demonstrated by training it on a limited multiparametric dataset of MRI
triplets from $89$ patients with liver lesions to generate $1,000$ synthetic
triplets and their corresponding liver tumor masks. The resulting Frechet
Inception Distance score was $86.55$. The proposed approach was among the
winners of the 2021 data augmentation challenge organized by the French Society
of Radiology.

介绍了一种新的数据增广框架，通过生成式深度学习方法生成大规模宏小结肝细胞癌的多参数（T1 动脉，T1 门静脉和 T2）磁共振图像及其相应的肿瘤掩膜，所提出的方法在有限的多参数 MRI 三元组数据集上训练，生成了 1000 个合成三元组及其相应的肝脏肿瘤掩膜，Frechet Inception Distance 得分为 86.55，此方法是由法国放射学学会组织的 2021 年数据增广挑战中的获胜者之一。

面具引导的多参数磁共振成像数据增强方法在一种罕见肝细胞癌中的应用

Mask-guided Data Augmentation for Multiparametric MRI Generation with a  Rare Hepatocellular Carcinoma

We propose a method that augments a simulated dataset using diffusion models
to improve the performance of pedestrian detection in real-world data. The high
cost of collecting and annotating data in the real-world has motivated the use
of simulation platforms to create training datasets. While simulated data is
inexpensive to collect and annotate, it unfortunately does not always closely
match the distribution of real-world data, which is known as the sim2real gap.
In this paper we propose a novel method of synthetic data creation meant to
close the sim2real gap for the challenging pedestrian detection task. Our
method uses a diffusion-based architecture to learn a real-world distribution
which, once trained, is used to generate datasets. We mix this generated data
with simulated data as a form of augmentation and show that training on a
combination of generated and simulated data increases average precision by as
much as 27.3% for pedestrian detection models in real-world data, compared
against training on purely simulated data.

该研究提出了一种使用扩散模型增强模拟数据集的方法，以改善行人检测在现实世界数据中的性能，并使用扩散基础架构来生成具有真实分布的数据集，以填补模拟数据和真实数据之间的差距，通过使用生成数据与模拟数据进行的训练可以将行人检测模型在真实数据中的平均精度提高 27.3%。