The growing interest in developing smart diagnostic systems to help medical
experts process extensive data for treating incurable diseases has been
notable. In particular, the challenge of identifying thyroid cancer (TC) has
seen progress with the use of machine learning (ML) and big data analysis,
incorporating transformers to evaluate TC prognosis and determine the risk of
malignancy in individuals. This review article presents a summary of various
studies on AIbased approaches, especially those employing transformers, for
diagnosing TC. It introduces a new categorization system for these methods
based on artifcial intelligence (AI) algorithms, the goals of the framework,
and the computing environments used. Additionally, it scrutinizes and contrasts
the available TC datasets by their features. The paper highlights the
importance of AI instruments in aiding the diagnosis and treatment of TC
through supervised, unsupervised, or mixed approaches, with a special focus on
the ongoing importance of transformers in medical diagnostics and disease
management. It further discusses the progress made and the continuing obstacles
in this area. Lastly, it explores future directions and focuses within this
research feld.

利用机器学习和大数据分析，特别是采用 transformers 评估甲状腺癌（TC）预后和确定个体恶性风险，该综述文章介绍了人工智能算法、框架目标和计算环境构建的新分类方法，以及对可用的 TC 数据集的审查和对比，强调了 AI 工具在辅助甲状腺癌的诊断和治疗中的重要性，特别关注 transformers 在医学诊断和疾病管理中的持续重要性，并探讨了该领域的进展和持续的障碍，最后还探讨了该研究领域的未来方向和重点。

机器学习与视觉变换器在甲状腺癌诊断中的应用：一项综述

Machine Learning and Vision Transformers for Thyroid Carcinoma  Diagnosis: A review

The correctness of software systems is vital for their effective operation.
It makes discovering and fixing software bugs an important development task.
The increasing use of Artificial Intelligence (AI) techniques in Software
Engineering led to the development of a number of techniques that can assist
software developers in identifying potential bugs in code. In this paper, we
present a comprehensible comparison and analysis of the efficacy of two
AI-based approaches, namely single AI models and ensemble AI models, for
predicting the probability of a Java class being buggy. We used two open-source
Apache Commons Project's Java components for training and evaluating the
models. Our experimental findings indicate that the ensemble of AI models can
outperform the results of applying individual AI models. We also offer insight
into the factors that contribute to the enhanced performance of the ensemble AI
model. The presented results demonstrate the potential of using ensemble AI
models to enhance bug prediction results, which could ultimately result in more
reliable software systems.

本文介绍了两种基于人工智能的方法，即单个人工智能模型和集成人工智能模型的效果比较和分析，用于预测 Java 类存在错误的概率。实验结果表明，集成人工智能模型的性能优于单个人工智能模型，同时揭示了提升集成人工智能模型性能的因素，这为提高错误预测结果的可靠性提供了潜力。

集成模型在缺陷预测中的功效的可理解性分析

A comprehensible analysis of the efficacy of Ensemble Models for Bug  Prediction

The growing advancements in Autonomous Vehicles (AVs) have emphasized the
critical need to prioritize the absolute safety of AV maneuvers, especially in
dynamic and unpredictable environments or situations. This objective becomes
even more challenging due to the uniqueness of every traffic
situation/condition. To cope with all these very constrained and complex
configurations, AVs must have appropriate control architectures with reliable
and real-time Risk Assessment and Management Strategies (RAMS). These targeted
RAMS must lead to reduce drastically the navigation risks. However, the lack of
safety guarantees proves, which is one of the key challenges to be addressed,
limit drastically the ambition to introduce more broadly AVs on our roads and
restrict the use of AVs to very limited use cases. Therefore, the focus and the
ambition of this paper is to survey research on autonomous vehicles while
focusing on the important topic of safety guarantee of AVs. For this purpose,
it is proposed to review research on relevant methods and concepts defining an
overall control architecture for AVs, with an emphasis on the safety assessment
and decision-making systems composing these architectures. Moreover, it is
intended through this reviewing process to highlight researches that use either
model-based methods or AI-based approaches. This is performed while emphasizing
the strengths and weaknesses of each methodology and investigating the research
that proposes a comprehensive multi-modal design that combines model-based and
AI approaches. This paper ends with discussions on the methods used to
guarantee the safety of AVs namely: safety verification techniques and the
standardization/generalization of safety frameworks.

本文调查研究自动驾驶汽车的安全性保障问题，重点关注控制架构、风险评估与管理策略、基于人工智能和基于模型的方法的研究，并探讨了安全保障技术和安全框架的标准化 / 概括方法。