The software industry is experiencing a surge in the adoption of Continuous
Integration (CI) practices, both in commercial and open-source environments. CI
practices facilitate the seamless integration of code changes by employing
automated building and testing processes. Some frameworks, such as Travis CI
and GitHub Actions have significantly contributed to simplifying and enhancing
the CI process, rendering it more accessible and efficient for development
teams. Despite the availability these CI tools , developers continue to
encounter difficulties in accurately flagging commits as either suitable for CI
execution or as candidates for skipping especially for large projects with many
dependencies. Inaccurate flagging of commits can lead to resource-intensive
test and build processes, as even minor commits may inadvertently trigger the
Continuous Integration process. The problem of detecting CI-skip commits, can
be modeled as binary classification task where we decide to either build a
commit or to skip it. This study proposes a novel solution that leverages Deep
Reinforcement Learning techniques to construct an optimal Decision Tree
classifier that addresses the imbalanced nature of the data. We evaluate our
solution by running a within and a cross project validation benchmark on
diverse range of Open-Source projects hosted on GitHub which showcased superior
results when compared with existing state-of-the-art methods.

该研究提出了一种利用深度强化学习技术构建最优决策树分类器的新型解决方案，以解决数据不平衡的问题，并通过对托管在 GitHub 上的多样化的开源项目进行内部和跨项目验证基准测试来展示了与现有最先进方法相比具有较好结果。

基于强化学习的连续集成跳过检测方法

Detecting Continuous Integration Skip : A Reinforcement Learning-based  Approach

Accurate and interpretable diagnostic models are crucial in the
safety-critical field of medicine. We investigate the interpretability of our
proposed biomarker-based lung ultrasound diagnostic pipeline to enhance
clinicians' diagnostic capabilities. The objective of this study is to assess
whether explanations from a decision tree classifier, utilizing biomarkers, can
improve users' ability to identify inaccurate model predictions compared to
conventional saliency maps. Our findings demonstrate that decision tree
explanations, based on clinically established biomarkers, can assist clinicians
in detecting false positives, thus improving the reliability of diagnostic
models in medicine.

使用临床确立的生物标志物，决策树分类器的解释能够协助医务人员检测错误阳性，从而提高医学诊断模型的可靠性。

利用生物标志物提高模型的可解释性和可靠性

Improving Model's Interpretability and Reliability using Biomarkers

We present an approach to explain the decisions of black box models for image
classification. While using the black box to label images, our explanation
method exploits the latent feature space learned through an adversarial
autoencoder. The proposed method first generates exemplar images in the latent
feature space and learns a decision tree classifier. Then, it selects and
decodes exemplars respecting local decision rules. Finally, it visualizes them
in a manner that shows to the user how the exemplars can be modified to either
stay within their class, or to become counter-factuals by "morphing" into
another class. Since we focus on black box decision systems for image
classification, the explanation obtained from the exemplars also provides a
saliency map highlighting the areas of the image that contribute to its
classification, and areas of the image that push it into another class. We
present the results of an experimental evaluation on three datasets and two
black box models. Besides providing the most useful and interpretable
explanations, we show that the proposed method outperforms existing explainers
in terms of fidelity, relevance, coherence, and stability.

通过在黑盒模型中采用对抗性自动编码器学习到的潜在特征空间，生成实例图像并学习决策树分类器的方法，从而对黑盒模型进行解释，并提供了更好的解释性能。

在潜在特征空间中通过学习图像示例实现黑匣子解释

Black Box Explanation by Learning Image Exemplars in the Latent Feature  Space

This paper focuses on finding spatial and temporal criminal hotspots. It
analyses two different real-world crimes datasets for Denver, CO and Los
Angeles, CA and provides a comparison between the two datasets through a
statistical analysis supported by several graphs. Then, it clarifies how we
conducted Apriori algorithm to produce interesting frequent patterns for
criminal hotspots. In addition, the paper shows how we used Decision Tree
classifier and Naive Bayesian classifier in order to predict potential crime
types. To further analyse crimes datasets, the paper introduces an analysis
study by combining our findings of Denver crimes dataset with its demographics
information in order to capture the factors that might affect the safety of
neighborhoods. The results of this solution could be used to raise awareness
regarding the dangerous locations and to help agencies to predict future crimes
in a specific location within a particular time.

通过统计分析以及利用 Apriori 算法找出犯罪热点、利用决策树分类器及朴素贝叶斯分类器预测潜在的犯罪类型，再结合人口统计数据对犯罪数据集进行分析，以识别可能影响社区安全的因素。通过该研究，可以提高人们对危险区域的认识，并帮助机构预测特定地点在特定时间内的犯罪行为。