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The fifth generation of mobile networks evolved to serve applications with distinct requirements, which results in a high management complexity due to simultaneous real-time tasks. In the physical layer, code words that allow proper data exchange between the Base Station (BS) and the served users must be chosen. While, in higher layers, the BS must choose users to be served in a given transmission opportunity. There are approaches based on Machine Learning (ML) to solve these combined tasks. However, due to the high amount of possible inputs, a challenge is the availability of data to train the models. In some cases, there may not even exist a predefined optimal answer to use as a "label" for supervised approaches. In this paper, we evaluate solutions for the combined problems of beam selection and user scheduling with Reinforcement Learning (RL), which does not need labels, as a solution for problems without a predefined answer. The algorithms were proposed for Problem Statement 6 of the challenge organized by the International Telecommunication Union (ITU) in 2021, which ranked as the finalists. We compare the approaches in relation to the cumulative reward received by the agents and show a performance comparison of different RL approaches by comparing them with baselines developed for the challenge. The paper also shows how the action taken by the trained agents affect network operation by comparing the number of packets transmitted, which is highly related to the proper selection of users and code words.

Cross-organizational process mining aims to discover an entire process model across multiple organizations where their identifier (ID) systems are not managed uniformly, and each organization has an independent ID system. Cross-organizational process mining has been gaining popularity as information systems increase in complexity. However, previous methods have limitations in that they do not work well for event logs that contain only common items, or cyclic orchestrations, which indicates that the model contains loops. In this paper, we propose an accurate cross-organizational process mining technique based on a stepby-step case ID identification mechanism that uses only common items in event logs and can handle cyclic orchestrations.Step-by-step case ID identification repeats the following steps: 1) identification of case IDs based on activity connection of adjacent event pairs, and 2) extraction of additional activity connections by leveraging the newly identified case IDs. We alternately identify the most probable case ID pairs and remove events belonging to these identified case IDs from the event log, which contributes to extracting additional activity connections and narrowing down the candidates of case ID pairs. Evaluation using real-world event logs showed that the proposed method generates the process model with more than 98.4% precision and more than 94.2% recall for two datasets, outperforming previous methods.INDEX TERMS Process mining, cross-organizational process mining, integrating event logs, identifying case IDs.

Real-time flood prediction in urban areas is an important tool for city emergency planning. Earlier studies suggest two approaches to predict flooding: a supervised machine learning approach based on observed data and a modeling approach for urban environments based on hydraulics. However, the first approach can only be applied in areas where there is sufficient data on flooding and is not accurate enough for prediction. The second approach can provide accurate predictions even for cities that have never experienced flooding, but models of complex urban environments are not suitable for real-time prediction owing to significant computational complexity. Therefore, we propose a third approach, machine learning supervised by a program. This approach consists of training a lightweight neural network using an integrated flood analysis program composed of multiple hydrologically based models. We demonstrate that this trained neural network is 19.8 times faster and and has accuracy comparable to that of the previous modeling approaches.