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Titlebook: Handbuch Verkehrspolitik; Oliver Schwedes,Weert Canzler,Andreas Knie Book 2016Latest edition Der/die Herausgeber bzw. der/die Autor(en), e

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發(fā)表于 2025-3-27 00:26:06 | 只看該作者
32#
發(fā)表于 2025-3-27 04:12:24 | 只看該作者
Fritz Sager in the automotive industry is one of the achievements of this industrial revolution. Implementing a new manufacturing system requires continuous attention to the variables and conditions called Critical Success Factors (CSF). To successfully implement smart manufacturing, first, it is essential to
33#
發(fā)表于 2025-3-27 09:05:18 | 只看該作者
Frank Fichert,Hans-Helmut Grandjotsed to calculate the weights of criteria. The traditional BWM uses the accurate value based on Saaty’s scale to describe a decision maker (DM)’s preferences. However, a DM may be unsure about his preference and may give several possible values to express his preferences. In this situation, the hesit
34#
發(fā)表于 2025-3-27 13:25:10 | 只看該作者
35#
發(fā)表于 2025-3-27 13:58:54 | 只看該作者
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發(fā)表于 2025-3-27 19:04:20 | 只看該作者
37#
發(fā)表于 2025-3-27 21:59:00 | 只看該作者
Tilmann Heuser,Werner Rehbeen developed, but they were designed essentially to improve the accuracy or fairness of the prediction results. We focus herein on another aspect of fairness-aware predictors, i.e., the stability. We define that fairness-aware techniques are stable if the same models are learned when a training da
38#
發(fā)表于 2025-3-28 06:02:26 | 只看該作者
39#
發(fā)表于 2025-3-28 08:09:09 | 只看該作者
40#
發(fā)表于 2025-3-28 12:45:27 | 只看該作者
Manfred Wermuthy depend on the degree of satisfaction with the received recommendation results. Mainstream bias refers to the phenomenon that recommendation algorithms favor mainstream users and provide inferior results to non-mainstream users, which harms user fairness. In recent work, Zhu et al.?[.] explore seve
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