In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes' predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.

准确的交通预测是使流量管理等流量管理的关键要素，例如重新路由汽车减少道路拥堵或通过动态速度限制来调节流量以保持稳定的流量。表示流量数据的一种方法是以时间更改的热图可视化流量的属性（例如速度和音量）的形式。在最近的作品中，U-NET模型在热图预测的交通预测上显示了SOTA性能。我们建议将U-NET体系结构与图层相结合，该层面可以改善与香草U-NET相比，将空间概括到看不见的道路网络。特别是，我们专门将现有的图形操作对地理拓扑敏感，并概括合并和升级操作以适用于图形。

Progress on many Natural Language Processing (NLP) tasks, such as text classification, is driven by objective, reproducible and scalable evaluation via publicly available benchmarks. However, these are not always representative of real-world scenarios where text classifiers are employed, such as sentiment analysis or misinformation detection. In this position paper, we put forward two points that aim to alleviate this problem. First, we propose to extend text classification benchmarks to evaluate the explainability of text classifiers. We review challenges associated with objectively evaluating the capabilities to produce valid explanations which leads us to the second main point: We propose to ground these benchmarks in human-centred applications, for example by using social media, gamification or to learn explainability metrics from human judgements.

在本文中，我们提出了一种基于对比学习的完全监督的预培训方案，特别针对密集的分类任务。所提出的上下文 - 自我对比损失（CSCL）了解嵌入空间，通过在训练样本中的每个位置与其本地上下文之间使用相似性度量来弹出语义边界。对于从卫星图像时间序列（坐）的作物类型语义分割我们在宗地边界中发现性能是一个关键的瓶颈，并解释CSCL如何解决该问题的潜在原因，从而提高本任务中的最先进的性能。此外，我们使用来自Sentinel-2（S2）卫星任务的图像，我们编写了我们的知识，坐在裁剪类型和包裹身份密集地注释的数据集，我们将与数据生成管道一起公开使用。使用我们发现CSCL的数据，即使具有最小的预训练，以改善所有相应的基线，并且在超级分辨率下提出语义分割的过程，以获得更粒度的茶几。下载数据的代码和说明可以在https://github.com/michaeltrs/deepsatmodels中找到。