The development of “ABCD” technology (Artificial intelligence, Blockchain, Cloud computing, and Big data) has promoted the design and development of new and innovative technologies, which fundamentally revolutionized the ways organizations manage themselves. However, digital transformations have also brought in new challenges. Against the backdrop of growing internationalization, cross-border collaborations, and increasing global trade, it is of utmost importance to explore how technology renovates the intercultural dialogues, negotiations, trainings, and the subsequent knowledge transfer processes. Researchers have suggested that technology will play a vital role in facilitating the need to work with people from all around the world. And from the perspective of intercultural exchanges, and cross-border/cross-cultural collaborations,it is necessary to know how to navigate the intercultural challenges effectively. The emergence of social robots like the Furhat allows to design rich human-robot interactions including verbal- and non-verbal communication elements, which is promising to deliver artificial intelligence (AI) -based intercultural training capabilities. This research project has two objectives: first, this research will bring in the AI factor in human landscape to explore the actions and interactions of AI and intercultural environment in transition; second, this study aims to provide orientations and recommendations for implementing interactions with social robots to enable AI-based intercultural trainings. Therefore, design criteria for implementing effective verbal and nonverbal interactions are outlined for achieving high standards of intercultural interactions to provide better understanding and interpretations of the contexts in which certain intercultural actions are practiced.

Cloud Computing allows companies to scale seamlessly, providing a broad range of state-of-the-art services. Another promise is to free users from the operational and administrative burdens [1]. However, with the advent of cloud-native applications [2], this promise becomes questionable – especially when DevOps [3] principles are used during development. Experience from practice shows that teams often struggle dealing with both the infrastructure, finding the right architecture, and implementing business logic. When working in decentralized teams, things are even worse, as standardization across teams cannot be assumed. To tackle those issues, automation by means of techniques such as Infrastructure-as-code [4] help to ease to cope with infrastructural concerns. However, when working with several teams in a decentralized manner, operational overhead is still there. Organizations struggle with standardization of infrastructure code and there is no clear centralized visibility for security-related concerns within the development lifecycle. To address these issues, we propose two things: First, building up a Platform Team [5], which serves as an organizational structure for continuous delivery. A Platform Teams can be the size of a typical small DevOps Team and support the whole organization with standardized security-hardened modules. Second, an Ops-Platform is needed that is operated by the Platform Team to centrally provide and maintain those modules. Other Dev-Teams can then consume those modules. In this paper, we report insights from the implementation of this approach in practice. We find out that developers are 75% less focused on operations by using such a platform and name specific success factors.

Research into artificial intelligence is not a very young field; its precursors can be traced back as far as the 16th century. Today’s technical development, however, is virtually leaping forward, with new intelligent chat systems and social robots playing no small part in this. This is revolutionizing a wide range of scientific and social fields. The very large publication numbers in this field illustrate this as well. In order to keep track of the discourse in the field, the representatives of the field, the publications as well as the topics and their future development, it is indispensable for academics and scientists to prepare them in a bibliometric analysis. Only in this way it is possible to uncover thematic gaps as well as further points of contact and to drive research forward in a targeted and stringent manner. It is precisely this sorting and processing of the research discourse, the topics, and the authors, which is necessary for further research, that is carried out in this paper. For this purpose, using bibliometric analysis tools, an overview of the past, present, and future of the research field is created, and the general relevant topics are uncovered. The analysis includes as performance analysis a) the total number of publications and b) the total number of citations, and for science mapping c) a co-citation analysis (past), d) a bibliographic coupling (present) and e) a co-word analysis (future). The data needed for the analysis are identified and extracted from the SCOPUS or Web of Science (ISI) databases

“China expertise” has become the keyword in European foreign politics. A look at the latest government publications shows that a clear definition is yet to be expected. It can be summarized from reports about educational programs that there are differing trends among European countries as regards their Chinese language and other China-related skills as well as their infrastructures to gain these. A linguistic assessment shows that for learning this distant language, an early onset of acquisition is recommended. Given the almost pan-European desire for China expertise a clear definition is demanded so efficient international cooperation to build up this expertise over-regionally is possible.

Ongoing assessments in a course are crucial for tracking student performance and progress. However, generating and evaluating tests for each lesson and student can be time-consuming. Existing models for generating and evaluating question-answer pairs have had limited success. In recent years, large language models (LLMs) have become available as a service, offering more intelligent answering and evaluation capabilities. This research aims to leverage LLMs for generating questions, model answers, and evaluations while providing valuable feedback to students and decentralizing the dependency on faculty. Our approach is based on the development and interaction of advanced AI-powered generative agents, built on large language models like ChatGPT, GPT-4, and Vicuna, and designed to emulate human activities such as information abstraction, context refinement, and query rating. These agents interact autonomously in a network, employing techniques like Zero-Shot and Few-Shot Prompting to generate responses and adapt to various roles and contexts. The setup includes three key agents for question generation, refinement, and quality assurance, which leverage text vectorization, document selection and filtering, and cutting-edge language models to generate, refine, and evaluate questions and answers based on specific learning objectives. In conclusion, this paper demonstrates the versatility of LLMs for various learning tasks, including question generation, model answer generation, and evaluation, all while providing personalized feedback to students. By identifying and addressing knowledge gaps, LLMs can support continuous assessment and help students improve their understanding before semester exams.

This study demonstrates the need for novel gas engine control systems for com-
bined heat and power plants, also known as cogeneration power plants, connected to natural
gas grids. Hydrogen addition to natural gas grids in a range of up to 5% by volume is already
permitted throughout Europe. This offers the possibility to reduce carbon dioxide emissions
of end consumers connected to public natural gas grids and contributes to climate protec-
tion. However, conventional engine controls are not designed for natural gas/hydrogen mixture
operation. We tested fuels with up to 30% hydrogen by volume using a commercial six-cylinder
spark ignition engine, designed for natural gas or biogas operation in power plants. With engine
settings according to usual cogeneration operation, nitrogen oxide emissions increased expo-
nentially with increasing hydrogen amounts. We demonstrate that the usual approach of using
the lower heating value of the fuel mixture to regulate the engine is unable to accommodate the
hydrogen induced changes. For this reason, we developed a mathematical model to determine
the nitrogen oxide emissions based on boost pressure and power output. The idea behind this
novel approach is to regulate the engine based on emissions, regardless of the fuel gas. In this
work the approach for this virtual sensor is described and its performance demonstrated.

The wave of digitization has begun. Organizations deal with huge amounts of data, such as logs, websites, and documents. A common way to make the information contained in these sources machine-accessible for automated processing is to first extract the information and then store it in a knowledge graph. A key task in this approach is to recognize entities. While common named entity recognition (NER) models work well for common entity types, they typically fail to recognize custom entities. Custom entity recognition requires data to be manually annotated and custom NER models to be trained. To efficiently extract the information, this paper proposes an innovative solution: Our Gazetteer approach uses a knowledge graph to create a coarse and fast NER component, reducing the need for manual annotation and saving human effort. Focusing on a university use case, our Gazetteer is integrated into a chatbot for entity recognition. In addition, data can be annotated using the Gazetteer and an NER model can be trained. Subsequently, the NER model can be used to recognize unseen custom entities, which are then added to the knowledge graph. This will improve the knowledge graph and make it self-extending.

Extracting triples of subjects, objects, and predicates from text to populate knowledge bases traditionally involves several intermediate steps such as co-reference resolution, named entity recognition, and relationship extraction. Treating triple extraction as translation task from source sentences to sets of triples, we present an end-to-end solution for information extraction that uses task prefixes to prompts a fine-tuned large language model to extract triples from text. Thus, the need for data labeling and training multiple models is reduced.

Individuals and groups are frequently targets of bullying, sexual harassment, and hate speech on online platforms. Such norm violations can have detrimental negative consequences, for instance by causing psychological harm and damaging social cohesion. Finding ways to reduce and prevent online norm violations is hence crucial. Online users may play an important role in this context. We argue that it can be considered morally courageous if users decide to take a stand against perceived violations of their own moral beliefs and endorsed norms, as it may imply substantial risks for themselves. With this chapter, we aim to advance our understanding of online moral courage as a relatively new phenomenon. First, we provide an examination of critical characteristics of online environments that may facilitate or hinder moral courage. Second, we discuss consequences of online moral courage by considering its effects on perpetrators, further online users, and the general tonality of the online discourse. Last, we integrate insights on the facilitators and obstacles of online moral courage and its consequences to provide practical recommendations for the design and management of online platforms and user education and training.