Abstract
E-commerce has become an essential aspect of modern life, providing consumers worldwide with convenience and accessibility. However, the high volume of short and noisy product descriptions in text streams of massive e-commerce platforms translates into an increased number of clusters, presenting challenges for standard model-based stream clustering algorithms. This is the case of a dataset extracted from the Brazilian NF-e Project containing electronic invoice product descriptions, including many product clusters. While LDA-based clustering methods have shown to be crucial, they have been mainly evaluated on datasets with few clusters. We propose the Topic Model with Contextual Outlier Handling (TMCOH) method to overcome this limitation. This method combines the Dirichlet Process, specific word representation, and contextual outlier detection techniques to recycle identified outliers aiming to integrate them into appropriate clusters later on. The experimental results for our case study demonstrate the effectiveness of TMCOH when compared to state-of-the-art methods and its potential for application to text clustering in large datasets.

Abstract
Over the past few decades, road transportation emissions have increased. Vehicles are among the most significant sources of pollutants in urban areas. As such, several studies and public policies emerged to address the issue. Estimating greenhouse emissions and air quality over space and time is crucial for human health and mitigating climate change. In this study, we demonstrate that it is feasible to utilize raw GPS data to measure regional pollution levels. By applying feature engineering techniques and using a microscopic emissions model to calculate vehicle-specific power (VSP) and various specific pollutants, we identify areas with higher emission levels attributable to a fleet of taxis in Porto, Portugal. Additionally, we conduct network analysis to uncover correlations between emission levels and the structural characteristics of the transportation network. These findings can potentially identify emission clusters based on the network's connectivity and contribute to developing an emission inventory for an urban city like Porto.

Abstract
Advances in artificial intelligence trigger transformations that make more and more companies enter Industry 4.0 and 5.0 eras. In many cases, these transformations are gradual and performed in a bottom-up manner. This means that in the first step, the industrial hardware is upgraded to collect as much data as possible without actual planning of the utilization of the information. Furthermore, the data storage and processing infrastructure is prepared to keep large volumes of historical data accessible for further analysis. Only in the last step are methods for processing the data developed to improve or gain more insight into the industrial and business processes. Such a pipeline makes many companies face a problem with huge amounts of data, an incomplete understanding of how the existing knowledge is represented in the data, under which conditions the knowledge no longer holds, or what new phenomena are hidden inside the data. We argue that this gap needs to be addressed by the next generation of XAI methods which should be expert-oriented and focused on knowledge generation tasks rather than model debugging. The paper is based on the findings of the EU CHIST-ERA project on Explainable Predictive Maintenance (XPM). © 2023 CEUR-WS. All rights reserved.

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The field of Explainable Predictive Maintenance (PM) is concerned with developing methods that can clarify how AI systems operate in the PM domain. One of the challenges of creating maintenance plans is integrating AI output with human decision-making processes and expertise. For AI to be helpful and trustworthy, fault predictions must be contextualized and easily comprehensible to humans. This involves providing tailored explanations to different actors depending on their roles and needs. For example, engineers can be connected to technical installation blueprints, while managers can evaluate system downtime costs, and lawyers can assess safety-threatening failures' potential liability. In many industries, black-box AI systems analyze sensor data to predict failures by detecting anomalies and deviations from typical behavior with impressive accuracy. However, PM is just one part of a broader context that aims to identify the most probable causes, develop a recovery plan, and estimate remaining useful life while providing alternative solutions. Achieving this requires complex interactions among various actors in industrial and decision-making processes. Our tutorial explores current trends, promising research directions in Explainable AI (XAI) relevant to Explainable Predictive Maintenance (XPM), and future challenges and open issues on this topic. We will also present three case studies that highlight XPM's challenges in bus and train operations and steel factories.