Generating context specific data quality deficits is necessary to
experimentally assess data quality of data-driven (artificial intelligence (AI)
or machine learning (ML)) applications. In this paper we present badgers, an
extensible open-source Python library to generate data quality deficits
(outliers, imbalanced data, drift, etc.) for different modalities (tabular
data, time-series, text, etc.). The documentation is accessible at
this https URL and the source code at
this https URL

生成数据质量缺陷对于实验评估数据驱动 (人工智能或机器学习) 应用的数据质量是必要的。本文介绍了 badgers，这是一个可扩展的开源 Python 库，用于生成不同模态 (表格数据、时间序列、文本等) 的数据质量缺陷 (异常值、不平衡数据、偏移等)。文档可在此 https URL 访问，源代码可在此 https URL 获取。

獾：使用 Python 生成数据质量缺陷

Badgers: generating data quality deficits with Python

Based on the collective input of Dagstuhl Seminar (21342), this paper
presents a comprehensive discussion on AI methods and capabilities in the
context of edge computing, referred as Edge AI. In a nutshell, we envision Edge
AI to provide adaptation for data-driven applications, enhance network and
radio access, and allow the creation, optimization, and deployment of
distributed AI/ML pipelines with given quality of experience, trust, security
and privacy targets. The Edge AI community investigates novel ML methods for
the edge computing environment, spanning multiple sub-fields of computer
science, engineering and ICT. The goal is to share an envisioned roadmap that
can bring together key actors and enablers to further advance the domain of
Edge AI.

本文基于 Dagstuhl 研讨会 (21342) 的集体输入，全面讨论了边缘计算背景下的 AI 方法和能力，称之为边缘 AI，旨在为数据驱动的应用程序提供适应性，在网络和无线电访问方面进行增强，允许创建、优化和部署具有给定的体验质量、信任、安全性和隐私目标的分布式 AI/ML 管道。边缘 AI 社区研究适用于边缘计算环境的新型 ML 方法，涵盖计算机科学、工程和 ICT 的多个子领域，旨在分享一份愿景路线图，以进一步推进边缘 AI 领域。

边缘人工智能路线图：Dagstuhl 视角

Roadmap for Edge AI: A Dagstuhl Perspective

Outlier detection plays an essential role in many data-driven applications to
identify isolated instances that are different from the majority. While many
statistical learning and data mining techniques have been used for developing
more effective outlier detection algorithms, the interpretation of detected
outliers does not receive much attention. Interpretation is becoming
increasingly important to help people trust and evaluate the developed models
through providing intrinsic reasons why the certain outliers are chosen. It is
difficult, if not impossible, to simply apply feature selection for explaining
outliers due to the distinct characteristics of various detection models,
complicated structures of data in certain applications, and imbalanced
distribution of outliers and normal instances. In addition, the role of
contrastive contexts where outliers locate, as well as the relation between
outliers and contexts, are usually overlooked in interpretation. To tackle the
issues above, in this paper, we propose a novel Contextual Outlier
INterpretation (COIN) method to explain the abnormality of existing outliers
spotted by detectors. The interpretability for an outlier is achieved from
three aspects: outlierness score, attributes that contribute to the
abnormality, and contextual description of its neighborhoods. Experimental
results on various types of datasets demonstrate the flexibility and
effectiveness of the proposed framework compared with existing interpretation
approaches.

本文提出了一种名为 COIN 的解释方法，可以从异常得分、有助于异常性的属性和周边情境描述三个方面解释现有检测异常值的异常性，并对各种类型数据集进行了实验，展示了 COIN 框架相对于现有解释方法的灵活性和有效性。