Title
Distributed Intelligence in the Computing Continuum

Abstract
Modern distributed systems also deal with uncertain scenarios, where environments, infrastructures, and applications are widely diverse. In the scope of IoT-Edge-Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments. A captivating set of hypotheses from the field of neuroscience suggests that human and animal brain mechanisms result from a few powerful principles. If proved to be accurate, these assumptions could open a deep understanding of the way humans and animals manage to cope with the unpredictability of events and imagination.

Biodata
Schahram Dustdar is a Full Professor of Computer Science at the TU Wien, heading the Research Division of Distributed Systems, Austria. He holds several honorary positions: University of California (USC) Los Angeles; Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, and University Pompeu Fabra, Barcelona, Spain. From Dec 2016 until Jan 2017 he was a Visiting Professor at the University of Sevilla, Spain and from January until June 2017 he was a Visiting Professor at UC Berkeley, USA.
From 1999 – 2007, he worked as the co-founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by ProjectNetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. He is the co-founder of edorer.com (an EdTech company based in the US) and co-founder and chief scientist of Sinoaus.net, a Nanjing, China-based R&D organization focusing on IoT and Edge Intelligence.
He serves as Editor-in-Chief of Computing (Springer). Dustdar is the recipient of multiple awards: IEEE TCSVC Outstanding Leadership Award (2018), IEEE TCSC Award for Excellence in Scalable Computing (2019), ACM Distinguished Scientist (2009), ACM Distinguished Speaker (2021), IBM Faculty Award (2012). He is an elected member of the Academia Europaea: The Academy of Europe, as well as an IEEE Fellow(2016) and an Asia-Pacific Artificial Intelligence Association (AAIA) Fellow (2021) and the was AAIA president (from 2020-2021).

Title
Data Stream Methodology For Speeding Up Training Deep Neural Networks

Abstract
Speeding up the training of artificial neural networks is crucial due to the growing complexity of models and expanding datasets. Researchers worldwide are working on different methods to make this training process faster. In the lecture, it will be shown how to develop faster deep neural network learning algorithms by making use of the data stream methodology. First, the basic concepts of stream data mining are outlined with a special emphasis put on concept drift – the phenomenon describing the time-varying nature of streaming data. Next, a novel paradigm for faster deep neural network learning is described and its performance is investigated and illustrated, showing our ongoing research results.

Biodata
Leszek Rutkowski received the M.Sc., Ph.D., and D.Sc. degrees from the Wrocław University of Technology, Wrocław, Poland, in 1977, 1980, and 1986, respectively, and the Honoris Causa degree from the AGH University of Science and Technology, Kraków, Poland, in 2014. He is a professor at the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland, and at the AGH University of Krakow, Poland. His research interests include data stream mining, big data analysis, neural networks, agent systems, fuzzy systems, image processing, pattern classification, and expert systems. He has authored 7 books and over 300 papers, including more than 50 in various IEEE Transactions series. He is the president and founder of the Polish Neural Networks Society. He organized and served as a General Chair of the International Conference on Artificial Intelligence and Soft Computing, held in the period 1995-2024. He is on the editorial board of several prestigious international journals. A recipient of the IEEE Transactions on Neural Networks Outstanding Paper Award, he served in the IEEE Computational Intelligence Society as the chair of the Distinguished Lecturer Program. Awarded the IEEE Fellow membership grade for contributions to neurocomputing and flexible fuzzy systems, he received an honorary degree from the prestigious AGH University of Krakow "in recognition of outstanding scientific achievements in the field of artificial intelligence." He is an Academician of the Polish Academy of Sciences and a Member of the Academia Europaea.

Title
Utilizing Machine Learning and Social Network Analysis to Counter Disinformation on Online Social Networks

Abstract
The proliferation of disinformation and misleading content within online social networks (OSNs) has emerged as a pressing concern, posing detrimental effects at personal, societal, and national levels. The unchecked and swift dissemination of such content has fostered an environment conducive to the amplification of falsehoods, rumors, propaganda, and hoaxes, thereby significantly impacting economic, political, and public health landscapes in recent years. Tackling this multifaceted challenge necessitates a holistic, collaborative approach involving various stakeholders, including individuals, media entities, governmental bodies, technology firms, and researchers. This keynote seeks to shed light on the associated complexities and delve into the utilization of Machine Learning, and Social Network Analysis techniques in countering disinformation. Specifically, the keynote will center on two primary domains: Natural Language Processing (NLP) and Multimodal Deep Learning (DL) architectures. In the realm of NLP, emphasis will be placed on the facter-check architecture, a solution employing ensembles and deep learning rooted in Transformer technology. This framework showcases the effective integration of NLP and SNA methodologies in identifying misleading content and tracing its dissemination across OSNs. Conversely, the presentation will also spotlight novel deep learning models tailored for the analysis and fusion of multimodal information, encompassing images and textual information (comments, posts, metadata). Analyzing posts poses a formidable challenge due to the diverse modalities of information, including textual and visual components. Finally, the keynote will explore future trends and challenges associated with the detection and mitigation of misinformation, underscoring the ongoing efforts in combating this pervasive issue.

Biodata
David Camacho is Full Professor at Computer Systems Engineering Department of Universidad Politécnica de Madrid (UPM), he is the head of the Applied Intelligence and Data Analysis research group (AIDA: https://aida.etsisi.uam.es), the Director of the PhD program in Computer Science and Technologies of Smart Cities, and the Director of the Master program in Machine Learning and Big Data at UPM. He has published more than 300 journals, books, and conference papers (https://scholar.google.es/citations?hl=en&user=fpf6EDAAAAA). His research interests include Machine Learning (Clustering/Deep Learning), Computational Intelligence (Evolutionary Computation, Swarm Intelligence), Social Network Analysis, Fake News and Disinformation Analysis. He has participated/led more than 60 AI-based R&D projects (National and International: H2020, MCSA ITN-ETN, DG Justice, ISFP, NRF Korea), applied to real-world problems in areas as aeronautics, aerospace engineering, cybercrime/cyber intelligence, social networks applications, disinformation countering, or video games among others. He serves as Editor in Chief of Expert Systems from 2023 and sits on the Editorial Board of several journals including Information Fusion, Human-centric Computing and Information Sciences (HCIS), and Cognitive Computation, IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), among others. Contact at: David.Camacho@upm.es

Title
Multimodal Graph Generative AI and applications to biomedical domain

Abstract
Graph generative models are recently gaining significant interest in current application domains. They are commonly used to model social networks, knowledge graphs, molecules and proteins. In this talk we will present the potential of graph generative models and our recent relevant efforts in the biomedical domain. More specifically we present a novel architecture that generates medical records as graphs with privacy guarantees. We capitalize and modify the graph Variational autoencoders (VAEs) architecture. We train the generative model with the well known MIMIC medical database and achieve generated data that are very similar to the real ones yet provide privacy guarantees. We achieve there as well promising results with potential for future application in broader biomedical related tasks. Finally we present ongoing research directions for multi modal generative models involving graphs and applications to protein function text generation – the prot2text model.

Biodata
M. Vazirgiannis is a Distinguished Professor at Ecole Polytechnique in France. He has been intensively involved in data science and AI related research. His broad research area is in methods for data mining and machine/deep learning methods for diverse data types and applications (including graphs, text, time series). Recently he is working on i. GNNs and aspects including expressiveness, efficiency, generation ii. Pretrained models and resources for multilingual NLP and Biomedical applications. His research and industrial impact is spanning different domains such as web advertising, social networks, online gambling, insurance, legal text applications, aviation and maritime industry and the bio/medical domain. He also pioneered at the teaching/training level having introduced new machine/deep learning and AI courses for academic and executive training studies. Pr. Vazirgiannis has published more than 250 papers in international journals and proceedings of international conferences and his work is highly cited with his h-index 60 (Google scholar, as of March 2024). On the side of supervision he has supervised 30 completed PhD theses. Finally he has been able to attract significant funding for research from national and international sources, from research agencies and industrial partners (including Google, Airbus, Huawei, Deezer, BNP, LVMH). He lead(s) academic research chairs (DIGITEO 2013-15, ANR/HELAS 2020-26) and an industrial one (AXA, 2015-2018). He has received several awards and distinctions including i. Marie Curie Intra European Fellowship (2006-8) ii. "Rhino-Bird International Academic Expert Award" in recognition of his academic/professional work @ Tencent (2017), iii. best paper awards in international conferences (such as IJCAI 2018, CIKM2013). He has been invited to media interviews in France, USA and China and published popularized articles in French and Greek magazines/newspapers on Artificial Intelligence topics. More info at: http://www.lix.polytechnique.fr/dascim/