In the realm of Autonomous Systems, the absence of direct human oversight introduces novel challenges as these systems start forming complex relationships. The emergent dynamics raise concerns regarding the fair distribution of resources and the promotion of altruistic behavior, which are traditionally moderated by human intervention. This paper envisions an innovative approach that integrates the monetization of acts of generosity in autonomous ecosystems to foster benevolent actions among autonomous agents and, therefore, promote the fairness of the ecosystem as a whole.

Manufacturing production heavily depends on the processes that need to be followed during manufacturing. As there might be many reasons behind possible deviations from these processes, the deviations can also cover ongoing insider attacks, e.g., intended to perform sabotage or espionage on these infrastructures. Insider attacks can cause tremendous damage to a manufacturing company because an insider knows how to act inconspicuously, making insider attacks very hard to detect. In this paper, we examine the potential of process-mining methods for insider-attack detection in the context of manufacturing, which is a new and promising application context for process-aware methods. To this end, we present five manufacturing-related scenarios of insider threats identified in cooperation with a manufacturing company, where the process mining could be most helpful in the detection of their respective attack events. We describe these scenarios and demonstrate the utilization of process mining in this context, creating ground for further future research.

With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes.

In this paper, we introduce a new updated version of the Czech Question Answering database SQAD v2.1 (Simple Question Answering Database) with the update being devoted to improved question and answer classification. The SQAD v2.1 database contains more than 8,500 question-answer pairs with all appropriate metadata for QA training and evaluation. We present the details and changes in the database structure as well as a new algorithm for detecting the question type and the actual answer type from the text of the question. The algorithm is evaluated with more than 4,000 question answer pairs reaching the F1-measure of 88% for question typed and 85% for answer type detection.