This paper describes a rapid feasibility study of using GPT-4, a large
language model (LLM), to (semi)automate data extraction in systematic reviews.
Despite the recent surge of interest in LLMs there is still a lack of
understanding of how to design LLM-based automation tools and how to robustly
evaluate their performance. During the 2023 Evidence Synthesis Hackathon we
conducted two feasibility studies. Firstly, to automatically extract study
characteristics from human clinical, animal, and social science domain studies.
We used two studies from each category for prompt-development; and ten for
evaluation. Secondly, we used the LLM to predict Participants, Interventions,
Controls and Outcomes (PICOs) labelled within 100 abstracts in the EBM-NLP
dataset. Overall, results indicated an accuracy of around 80%, with some
variability between domains (82% for human clinical, 80% for animal, and 72%
for studies of human social sciences). Causal inference methods and study
design were the data extraction items with the most errors. In the PICO study,
participants and intervention/control showed high accuracy (>80%), outcomes
were more challenging. Evaluation was done manually; scoring methods such as
BLEU and ROUGE showed limited value. We observed variability in the LLMs
predictions and changes in response quality. This paper presents a template for
future evaluations of LLMs in the context of data extraction for systematic
review automation. Our results show that there might be value in using LLMs,
for example as second or third reviewers. However, caution is advised when
integrating models such as GPT-4 into tools. Further research on stability and
reliability in practical settings is warranted for each type of data that is
processed by the LLM.

该文章介绍了使用 GPT-4（一个大型语言模型）快速可行性研究来（半）自动化系统性综述中的数据提取，在设计和评估 LLM-based 自动化工具方面仍然存在欠缺的问题。

使用大型语言模型进行系统综述中的数据提取探索：快速可行性研究

Exploring the use of a Large Language Model for data extraction in  systematic reviews: a rapid feasibility study

With the emergence of Artificial Intelligent chatbot tools such as ChatGPT
and code writing AI tools such as GitHub Copilot, educators need to question
what and how we should teach our courses and curricula in the future. In
reality, automated tools may result in certain academic fields being deeply
reduced in the number of employable people. In this work, we make a case study
of cybersecurity undergrad education by using the lens of ``Understanding by
Design'' (UbD). First, we provide a broad understanding of learning objectives
(LOs) in cybersecurity from a computer science perspective. Next, we dig a
little deeper into a curriculum with an undergraduate emphasis on cybersecurity
and examine the major courses and their LOs for our cybersecurity program at
Miami University. With these details, we perform a thought experiment on how
attainable the LOs are with the above-described tools, asking the key question
``what needs to be enduring concepts?'' learned in this process. If an LO
becomes something that the existence of automation tools might be able to do,
we then ask ``what level is attainable for the LO that is not a simple query to
the tools?''. With this exercise, we hope to establish an example of how to
prompt ChatGPT to accelerate students in their achievements of LOs given the
existence of these new AI tools, and our goal is to push all of us to leverage
and teach these tools as powerful allies in our quest to improve human
existence and knowledge.

人工智能聊天机器人工具、代码编写人工智能工具、网络安全本科教育、学习目标和自动化工具是该研究论文的关键词，并且论文主要探讨了新型人工智能工具对教育和就业的影响，以及如何在培养学生能力的同时与这些工具合作。

ChatGPT 与学习目标是否需要重写 —— 网络安全中的 CASE 研究

With ChatGPT, do we have to rewrite our learning objectives -- CASE  study in Cybersecurity

Following the successful debut of polyp detection and characterization, more
advanced automation tools are being developed for colonoscopy. The new
automation tasks, such as quality metrics or report generation, require
understanding of the procedure flow that includes activities, events,
anatomical landmarks, etc. In this work we present a method for automatic
semantic parsing of colonoscopy videos. The method uses a novel DL multi-label
temporal segmentation model trained in supervised and unsupervised regimes. We
evaluate the accuracy of the method on a test set of over 300 annotated
colonoscopy videos, and use ablation to explore the relative importance of
various method's components.

本研究提出了一种用于结肠镜视频的自动语义分析方法，包括使用新型 DL 多标签时间分割模型在监督和无监督环境下进行训练，通过对 300 多个带标注的结肠镜视频测试集进行评估，并使用消融研究来探索各种方法组件的相对重要性。

多标签时序网络对结肠镜检查视频进行语义解析

Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal  Networks

Artificial Intelligence (AI) is making a significant impact in multiple areas
like medical, military, industrial, domestic, law, arts as AI is capable to
perform several roles such as managing smart factories, driving autonomous
vehicles, creating accurate weather forecasts, detecting cancer and personal
assistants, etc. Software testing is the process of putting the software to
test for some abnormal behaviour of the software. Software testing is a
tedious, laborious and most time-consuming process. Automation tools have been
developed that help to automate some activities of the testing process to
enhance quality and timely delivery. Over time with the inclusion of continuous
integration and continuous delivery (CI/CD) pipeline, automation tools are
becoming less effective. The testing community is turning to AI to fill the gap
as AI is able to check the code for bugs and errors without any human
intervention and in a much faster way than humans. In this study, we aim to
recognize the impact of AI technologies on various software testing activities
or facets in the STLC. Further, the study aims to recognize and explain some of
the biggest challenges software testers face while applying AI to testing. The
paper also proposes some key contributions of AI in the future to the domain of
software testing.

本研究旨在探讨人工智能技术在软件测试活动和方面中的影响，以及 AI 在软件测试领域未来的一些关键贡献和应用面临的挑战。