Title
Impact on information dissemination processes in complex networks - from influence maximization towards continuous efforts to improve or suppress process dynamics

Abstract
Research on information dissemination processes in complex networks for a long time was focused on maximizing the influence associated with the selection of initial nodes that initiated the dissemination process. Classic methods in this area assumed a single-stage process initiation without further influence on its course. This was a certain simplification that was inappropriate to the actual processes related, for example to social campaigns or marketing actions that are continuously influenced. Extensions towards continuous influence open research directions related to the use of various impact methods, additional seeding, network topological changes or examining the impact of multiple transmissions for static, temporal and multi-layer networks. Solutions in this area have the potential to reduce the impact of harmful processes and increase the dynamics of, for example, social campaigns or preventive activities.

Biodata
Jaroslaw Jankowski works as an associate professor in the Faculty of Computer Science at West Pomeranian University of Technology in Szczecin in Poland. His research interests are in the interdisciplinary field of human centered computing with focus on performance of web systems, usability and user experience, conversion optimization, personalisation. The work uses perceptual research, eye tracking, EEG systems and simulation methods. He also carries out projects related to methods of influencing information diffusion processes in complex networks and researching the visual impact on users of Internet systems. Author and co-author of articles published in scientific journals like Internet Research, International Journal of Human-Computer Studies, Scientific Reports, Computers in Human Behavior, Cognitive Processing and others.

Title
Deciphering the Evolution of Collective Intelligence: A Bibliometric Journey

Abstract
Professor Klaus Solberg Söilen presents a concise yet profound analysis titled "Deciphering the Epresents volution of Collective Intelligence: A Bibliometric Journey." This session promises to offer a unique lens into the field of collective intelligence, combining academic rigor with practical insights as we ask what the value is of this field of study.
Professor Söilen utilizes bibliometric analysis to trace the development and trajectory of collective intelligence as a field of study. The talk reveals the most influential research, key trends, and emerging themes in collective intelligence, providing a comprehensive overview of its academic landscape and importance.
A significant focus of the talk will be on the practical implications of these findings. Professor Söilen will discuss how the insights gained from bibliometric analysis can inform real-world applications of collective intelligence in various sectors, including business, technology, and public policy. He highlights the potential of collective intelligence to enhance decision-making processes, foster innovation, and address complex challenges in today's interconnected world.

Biodata
Professor Klaus Solberg Söilen was born in Norway and has been a professor at Halmstad University, Sweden, since 2014. He also holds positions as a guest professor at the University of Agder and at the University of Latvia. His primary area of interest is intelligence studies in business, but his academic pursuits also extend to teaching and conducting research in digital marketing and scientific methods. He served as the editor-in-chief of the Journal of Intelligence Studies in Business (JISIB) for 12 years, concluding his tenure in 2022. Before embarking on his academic career, Professor Söilen spent a decade working in the industry in the United States, France, and Norway, with his last three years as an auditor for KPMG. He has played a pivotal role in developing educational programs at several universities in many countries and has been a management consultant for over 75 companies worldwide

Title
Driving Towards the Future: Hybrid Models for Human Behavior Prediction in Traffic

Abstract
The prediction of human road user behavior is a pivotal challenge in the deployment of autonomous vehicles. Traffic systems represent complex socio-technical multi-agent environments, shaped by physics, social norms, and human behavior. These systems are inherently open and continuously evolving. Effective behavior models and predictions for human-driven vehicles and pedestrians are essential not only as part of autonomous driving systems but also for simulation environments used in training, verification, and validation. This keynote will begin by defining the problem space and the inherent challenges. It will provide an overview of recent advancements utilizing both data-driven methods, such as machine learning models leveraging neural networks, and model-based approaches, like interpretable game-theoretic frameworks. The discussion will highlight the integration of these two approaches to address the multifaceted challenges of predicting human road user behavior. Key topics will include foundational models, digital twins, and innovative strategies that combine data-driven and model-driven methodologies. In particular, the talk will explore the synergy between neural networks and symbolic reasoning, drawing on the Type I/Type II classification of human cognitive processes as an inspiration for hybrid modeling of human behavior.

Biodata
Krzysztof Czarnecki is a Professor of Electrical and Computer Engineering and a University Research Chair at the University of Waterloo, where he heads the Waterloo Intelligent Systems Engineering (WISE) Laboratory. He is a leading expert in the engineering of automated driving systems (ADS), with focus on assuring the safety of driving behavior and machine-learned functions. He co-lead the development of the first ADS tested on public roads in Canada in 2018. As a member of standardization committees, he has contributed to ISO 21448 (2nd edition), ISO 8800 (under development), and SAE J3164. Before coming to Waterloo, he was a researcher at DaimlerChrysler Research (1995-2002), Germany, focusing on improving software development practices and technologies in enterprise, automotive, and aerospace sectors. While at Waterloo, he held the NSERC/Bank of Nova Scotia Industrial Research Chair in Requirements Engineering of Service-oriented Software Systems (2008-2013). He received the Premier's Research Excellence Award in 2004 and the British Computing Society in Upper Canada Award for Outstanding Contributions to IT Industry in 2008. He has also received twelve Best Paper Awards, two ACM Distinguished Paper Awards, and five Most Influential Paper Awards.

Title
Towards Neuro-Symbolic AI with Knowledge Graphs and Generative AI

Abstract
In this talk, we discuss the topic of Neuro-Symbolic Artificial Intelligence (AI), focusing on the synergistic integration of Knowledge Graphs and Generative AI such as Large Language Models. Neuro-Symbolic AI represents an approach that combines the robust, interpretable reasoning capabilities of symbolic AI with the adaptive, data-driven strengths of neural networks. The talk will illuminate how this fusion offers a promising pathway towards more intelligent, explainable, and reliable AI systems. As a showcase of our approach towards neuro-symbolic AI we will demonstrate Corporate Memory, an enterprise ready Knowledge Graph and Neuro-Symbolic AI platform used by major Enterprises as well as the Open Research Knowledge Graph. The ORKG is representing research contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques leveraging Large Language Models. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for enterprises and researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches.

Biodata
Following stations at the universities of Dresden, Ekaterinburg, Leipzig, Pennsylvania, Bonn and the Fraunhofer Society, Prof. Auer was appointed Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB in 2017. Prof. Auer has made important contributions to semantic technologies, knowledge engineering and information systems. He is the author (resp. co-author) of over 200 peer-reviewed scientific publications. He has received several awards, including an ERC Consolidator Grant from the European Research Council, a SWSA ten-year award, the ESWC 7-year Best Paper Award, and the OpenCourseware Innovation Award. He has led several large collaborative research projects, such as the EU H2020 flagship project BigDataEurope. He is co-founder of high potential research and community projects such as the Wikipedia semantification project DBpedia, the Open Research Knowledge Graph ORKG.org and the innovative technology start-up eccenca.com. Prof. Auer was founding director of the Big Data Value Association, led the semantic data representation in the Industrial/International Data Space, is an expert for industry, European Commission, W3C, the German National Research Data Infrastructure (NFDI) and the European Open Science Cloud (EOSC).