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IF 11.2 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Communications Magazine

Pub Date : 2026-02-09 DOI: 10.1109/mcom.2026.11373812

引用次数: 0

RACER: Fast and Accurate Time Series Clustering with Random Convolutional Kernels and Ensemble Methods RACER：基于随机卷积核和集成方法的快速准确时间序列聚类

IF 10.6 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Internet of Things Journal

Pub Date : 2026-02-09 DOI: 10.1109/jiot.2026.3662758

Haowen Zhang, Juan Li, Qing Yao

引用次数: 0

IEEE Transactions on Industrial Electronics Publication Information IEEE工业电子出版信息汇刊

IF 7.7 1区工程技术 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Industrial Electronics

Pub Date : 2026-02-09 DOI: 10.1109/tie.2026.3654287

引用次数: 0

IEEE Industrial Electronics Society Information IEEE工业电子学会信息

IF 7.2 1区工程技术 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Industrial Electronics

Pub Date : 2026-02-09 DOI: 10.1109/TIE.2026.3654291

引用次数: 0

Quantifying Model Uncertainty with AutoML and Rashomon Partial Dependence Profiles: Enabling Trustworthy and Human-centered XAI 用AutoML和Rashomon部分依赖谱量化模型不确定性：实现可信赖和以人为中心的XAI

IF 5.9 3区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Frontiers

Pub Date : 2026-02-09 DOI: 10.1007/s10796-026-10698-3

Mustafa Cavus, Jan N. van Rijn, Przemysław Biecek

Trustworthiness of AI systems is a core objective of Human-Centered Explainable AI, and relies, among other things, on explainability and understandability of the outcome. While automated machine learning tools automate model training, they often generate not only a single “best” model but also a set of near-equivalent alternatives, known as the Rashomon set. This set provides a unique opportunity for human-centered explainability: by exposing variability among similarly performing models, we can offer users richer and more informative explanations. In this paper, we introduce Rashomon partial dependence profiles , a model-agnostic technique that aggregates feature effect estimates across the Rashomon set. Unlike traditional explanations derived from a single model, Rashomon partial dependence profiles explicitly quantify uncertainty and visualize variability, further enabling user trust and understanding model behavior to make informed decisions. Additionally, under high-noise conditions, the Rashomon partial dependence profiles more accurately recover ground-truth feature relationships than a single-model partial dependence profile. Experiments on synthetic and real-world datasets demonstrate that Rashomon partial dependence profiles reduce average deviation from the ground truth by up to 38%, and their confidence intervals reliably capture true feature effects. These results highlight how leveraging the Rashomon set can enhance technical rigor while centering explanations on user trust and understanding aligned with Human-centered explainable AI principles.

人工智能系统的可信度是以人为中心的可解释人工智能的核心目标，它依赖于结果的可解释性和可理解性。虽然自动化机器学习工具可以自动进行模型训练，但它们通常不仅会生成一个“最佳”模型，还会生成一组近乎等效的替代模型，即罗生门集。这个集合为以人为中心的可解释性提供了一个独特的机会：通过暴露类似执行模型之间的可变性，我们可以为用户提供更丰富、更有信息的解释。在本文中，我们介绍了罗生门部分依赖概况，这是一种模型不可知论技术，可以聚合整个罗生门集的特征效应估计。与源自单一模型的传统解释不同，Rashomon部分依赖剖面明确量化了不确定性，并可视化了可变性，进一步增强了用户的信任和对模型行为的理解，从而做出明智的决策。此外，在高噪声条件下，Rashomon部分依赖剖面比单一模型部分依赖剖面更准确地恢复地真特征关系。在合成数据集和真实数据集上的实验表明，Rashomon部分依赖剖面将与地面真实的平均偏差降低了38%，其置信区间可靠地捕获了真实的特征效果。这些结果突出了如何利用罗生门集来提高技术严谨性，同时将解释集中在用户信任和理解上，与以人为中心的可解释人工智能原则保持一致。

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引用次数: 0

Cramér-Rao Bound Optimization With Security Constraints in IRS-Enabled MU-ISAC Systems 基于irs的MU-ISAC系统中安全约束的cram<s:1> - rao界优化

IF 6.8 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Vehicular Technology

Pub Date : 2026-02-09 DOI: 10.1109/tvt.2026.3662730

Jie Gao, Hui-Ming Wang, Jiale Bai

引用次数: 0

SOH Estimation Using Contrastive Learning to Extract Cross-Battery Transferable Degradation Representations 利用对比学习提取跨电池可转移退化表征的SOH估计

IF 7.7 1区工程技术 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Industrial Electronics

Pub Date : 2026-02-09 DOI: 10.1109/tie.2026.3658686

Rui Yue, Xuemei Wang, Longyun Kang, Yuqi Li, Di Xie