DAMSS 2023

The DAMSS 2023 programme.

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DAMSS 2023: Plenary Speakers

Prof. Valentina Emilia Balas Valentina E. Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. Cum Laude, in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is the author of more than 400 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling and Simulation. She is the Editor-in Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is evaluator expert for national, international projects and PhD Thesis. Dr. Balas is the Head of Intelligent Systems Research Centre in Aurel Vlaicu University of Arad and Head of the Department of International Relations in the same university. She served as General Chair of the International Workshop Soft Computing and Applications (SOFA) in ten editions organized in the interval 2005-2022 and held in Romania and Hungary. Dr. Balas participated in many international conferences as Organizer, Honorary Chair, Session Chair, member in Steering, Advisory or International Program Committees and Keynote Speaker. Recently she was working in a national project with EU funding support: BioCell-NanoART = Novel Bio-inspired Cellular Nano-Architectures - For Digital Integrated Circuits, 3M Euro from National Authority for Scientific Research and Innovation. She is a member of European Society for Fuzzy Logic and Technology (EUSFLAT), member of Society for Industrial and Applied Mathematics (SIAM) and a Senior Member IEEE, member in Technical Committee – Fuzzy Systems (IEEE Computational Intelligence Society), chair of the Task Force 14 in Technical Committee – Emergent Technologies (IEEE CIS), member in Technical Committee – Soft Computing (IEEE SMCS). She is member in the Committee of IEEE Romania Section as Volunteers Training Coordinator and vice chair of IEEE Computational Intelligence Society Chapter – CIS 11. During the interval 2021-2022 she was a member of IEEE European Public Policy Committee Working Group on ICT. From May 2023 Dr. Balas is associate member of Romanian Academy of Scientists. Dr. Balas was past Vice President (awards) of IFSA - International Fuzzy Systems Association Council (2013-2015), is a Joint Secretary of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), - A Multidisciplinary Academic Body, India. She is the recipient of the "Tudor Tanasescu" Prize from the Romanian Academy for contributions in the field of soft computing methods (2019), “Stefan Odobleja” Prize from Romanian Academy of Scientists (2023) and AGIR Award, Information Technology Section (2023).

Talk title: Empowering Industrial Control: Soft Computing Paradigms and Practical Applications

Abstract: The lecture delves into the realm of soft computing paradigms for designing intelligent systems. Computational Intelligence, an emerging field, equips us with tools to model and dissect complex systems, encompassing techniques like fuzzy logic, neural networks, genetic algorithms, and more. Presently, fuzzy logic stands as a widely adopted approach to tackle control problems across various applications. The lecture also showcases how to craft and practically employ intelligent complex systems by integrating deterministic knowledge into the processes and leveraging simulations during the design phase. Through introduced case studies, it vividly illustrates the utility of intelligent control across a diverse array of applications. Soft computing comprises a suite of methods tailored to handling imprecise information and intricate human cognition. When addressing industrial control challenges, soft computing techniques exhibit notable qualities, including intelligence, robustness, and cost-effectiveness. This study undertakes the task of providing an extensive overview of soft computing techniques and their roles in industrial control systems. These soft computing methodologies are primarily categorized into fuzzy logic, neural computing, and genetic algorithms. The study identifies the modern challenges in industrial control systems, such as information acquisition hurdles, complexities in modeling control rules, optimization difficulties, and the need for robustness. Subsequently, it reviews the advancements in soft computing that have been devised to address these challenges. Moving forward, the study offers a retrospective analysis of practical industrial control applications spanning transportation, intelligent machinery, process industries, and energy engineering. The presentation underscores how soft computing methods bestow industrial control processes with numerous advantages, highlighting their significant potential applications.

Prof. Dr. Alfred M. Bruckstein Prof. Dr. Alfred M. Bruckstein holds the Ollendorff Chair in Science at the Technion, IIT, in the Deppartment of Computer Science. A graduate of the Technion, with a BSc and an MSc in EE, he earned a PhD at Stanford University, in the EE Department, in 1984. Since then he is on faculty at the Technion, with long-term visiting professorship positions at Bell Laboratories at Murray Hill, NJ, USA (from 1987 to 2000), and at Nanayang Technological University, in Singapore (from 2009-present). His shorter visiting positions include Tsing-Hua University in Beijing, China (2002-2003), and visiting positions at Universite d'Evry, Paris France, at CEREMADE, University Dauphine, Paris, france, and Karlsruhe University in Germany. At the Technion, Professor Bruckstein served as the elected Dean of the Graduate School, from 2002 to 2005, and as the Head of the Technion Excellence Program for Undergraduates, from 2006 to 2012. He is a member of the MAA and AMS, and in 2014 he was elected Fellow of SIAM for contributions to Signal Processing, Image Analysis and Ant Robotic.

Talk title: An Overview of Holographic Data Representations

Abstract: Holographic representations of data encode information in packets of equal importance that enable progressive recovery. The quality of recovered data improves as more and more packets become available. This progressive recovery of the information is independent of the order in which packets become available. Such representations are ideally suited for distributed storage and for the transmission of data packets over networks with unpredictable delays and or erasures.

Prof. Dr. Eligius M.T. Hendrix Prof. Dr. Eligius M.T. Hendrix is a European scientist with more than 35 years of experience in mathematical modelling and optimization algorithms. His research focuses on exploiting the mathematical structure of optimization problems in order to derive novel specific algorithms that can be implemented on modern computer platforms. Most of his work was related to practical problems in environmental and food science. Among others, he developed a new method for unmixing data from hyperspectral data and is interested in data selection of training sets for Deep learning on those data from the point of view of the design of experiments. Moreover, his studies enhance logistics, inventory control, competitive location problems, production scheduling, traffic control, minimizing the size of search trees, fisheries quota determination, offshore wind farm maintenance, pooling, water control, food supply chains, coalition formation, deforestation, economic behaviour, design of experiments, permit trading, biomass production, fodder production, farm management and plague control. He published more than 85 journal articles and several books and organized international conferences such as Global Optimization workshops and ICCSA. He is affiliated with the Universidad de Málaga.

Talk title: On computational challenges in value iteration for inventory control

Abstract: Inventory control has always been an interesting subject for mathematic and stochastic analysis, simulation and computation. The concept enhances rules that tell us how much to order (replenish) from which product in which situation of the inventory level. On a personal level, you should to decide how much beer to store in your fridge and when to replenish it by going to the shop. Challenges are getting bigger when we have perishable products like lettuce or strawberries in them as we have to take care of their age and consume them before a due date. On the level of retail, as one-third of world food production is disposed of every year, there is a renewed interest in deriving adequate inventory control policies for perishable products. Simulation-based optimization and Stochastic Programming approaches may be used to derive optimal control rules. A computational challenge appears when in the order rules we would like to take the age distribution of items in stock into account. Dynamic programming is an elegant tool to derive the best rule. Specifically, we will sketch the concept of Value Iteration for small cases. It is based on the so-called Bellman optimality criterion for dynamic programming. Under certain conditions, the value iteration may lead to the optimal rule. In a practical sense, dynamic programming is confronted with the so-called curse of dimensionality. This means that if more factors are taken into account in the state space, it is hard in an exponential sense to come to an optimal solution. Here, careful bounding is relevant. The target of the presentation is to sketch the idea with very small instances and gradually illustrate what happens if, for instance, the age of products is taken into account, or the lead-time of delivery or the amount sold, which can be returned to the shop.

Prof. Ing. Jan Flusser Prof. Dr.Jan Flusser received the M.Sc. degree in mathematical engineering from the Czech Technical University, Prague, Czech Republic, in 1985; the Ph.D degree in computer science from the Czechoslovak Academy of Sciences in 1990; and the Dr.Sc. degree in technical cybernetics in 2001. Since 1985 he has been with the Institute of Information Theory and Automation, Czech Academy of Sciences, Prague. In 2007–2017, he was holding the position of a Director of the Institute. Since 2017 he has been at the position of Research Director. He is a full professor of Applied Mathematics at the Czech Technical University, Prague, Czech Republic, where he gives undergraduate and graduate courses on Digital Image Processing, Pattern Recognition, and Moment Invariants and Wavelets. Jan Flusser’s research interest covers moments and moment invariants, image registration, image fusion, multichannel blind deconvolution, and super-resolution imaging. He has authored and coauthored more than 200 research publications in these areas, including the monographs "Moments and Moment Invariants in Pattern Recognition" (Wiley, 2009) and "2D and 3D Image Analysis by Moments" (Wiley, 2016). In 2010, Jan Flusser was awarded by the SCOPUS 1000 Award. He received the Felber Medal of the Czech Technical University for excellent contribution to research and education in 2015 and the Praemium Academicae of the Czech Academy of Sciences for outstanding researchers in 2017.

Talk title: Visual Object Recognition - Traditional Methods Along with Deep Learning Approaches

Abstract: The talk falls into the area of visual artificial intelligence (AI), particularly image recognition by deep networks. In AI applications such as surveillance systems, autonomous robots, unmanned vehicles, drones, etc., cameras and other visual sensors form the ``eyes'' of the system while image recognition algorithms substitute the visual cortex of the brain. The key requirement is a continuous (possibly real-time) analysis of the visual field and, in that way, preparing the basis for decision and next action planning. The visual analysis may comprise scene segmentation, detection of objects and persons of interest, recognition of their identity and their behaviour, and even prediction of their next actions. In this talk, we focus on the recognition part, where the image/object is classified as a member of one of the pre-defined classes. Current convolutional networks work with inefficient pixel-wise image representation, which does not provide almost any invariance. This leads to using very large training sets and massive augmentation. We propose to decompose intra-class variances into two degradation operators where one of them (image rotation, scaling, blurring, etc.) can be mathematically modelled by a superposition integral with a transformation of the coordinates. We propose to design hybrid network architectures that use both pixel-level and newly developed high-level invariant image representations such that the high-level representation will eliminate the influence of modelable degradations. This leads to a substantial reduction of the training set without sacrificing the recognition rate. The hybrid architectures could define new standards in image-oriented networks.

Academician Janusz Kacprzyk Janusz Kacprzyk is Professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, WIT – Warsaw School of Information Technology, AGH University of Science and Technology in Cracow, and Professor of Automatic Control at PIAP – Industrial Institute of Automation and Measurements in Warsaw, Poland. He is an Honorary Foreign Professor at the Department of Mathematics, Yli Normal University, Xinjiang, China. He is a Full Member of the Polish Academy of Sciences, Member of Academia Europaea, European Academy of Sciences and Arts, European Academy of Sciences, International Academy of Systems and Cybernetics (IASCYS), Foreign Member of the: Bulgarian Academy of Sciences, Spanish Royal Academy of Economic and Financial Sciences (RACEF), Finnish Society of Sciences and Letters, Flemish Royal Academy of Belgium of Sciences and the Arts (KVAB), Russian Academy of Sciences. National Academy of Sciences of Ukraine and Lithuanian Academy of Sciences. He was awarded with 8 honorary doctorates. He is a Fellow of IEEE (Life), IET, IFSA, EurAI, IFIP, AAIA, I2CICC, and SMIA. His main research interests include the use of modern computation computational and artificial intelligence tools, notably fuzzy logic, in systems science, decision-making, optimization, control, data analysis and data mining, with applications in mobile robotics, systems modelling, ICT etc. He authored 7 books, (co)edited more than 150 volumes, (co)authored more than 650 papers, including ca. 150 in journals indexed by the WoS. He was listed in 2020 and 2021 as”World’s 2% Top Scientists” by Stanford University, Elsevier (Scopus) and ScieTech Strategies and published in PLOS Biology Journal. He is the editor-in-chief of 8 book series at Springer and of 2 journals and is on the editorial boards of ca. 40 journals. He is President of the Polish Operational and Systems Research Society, Past President of the International Fuzzy Systems Association, and is a member of the Adcom (Administrative Committee) of the Computational Intelligence Society of the IEEE, and a member of the Board of Governors of the Systems, Man and Cybernetics Society of the IEEE.

Talk title: Towards Effective and Efficient AI-assisted/Enabled Decision Aid and Support Systems: A Challenge for the Human-AI Collaboration

Abstract: Artificial intelligence (AI), with its presumably most relevant subfield of machine learning, is often considered to be the next Industrial Revolution and is taking by storm virtually all areas of science and technology. Since decision-making is the most frequent act in human activities and its role in all aspects and activities in our life is crucial, it has become obvious that AI will also be a decisive factor in the quest for making better decisions. We consider decision-making processes in complex environments, with many stakeholders, criteria, dynamics, etc., and advocate the use of smart decision aid and decision support systems, and in particular, advocate the use of AI as their driving force. First, we present new directions in AI-assisted decision-making, showing the role of data-driven approaches to deal with both big data and small data type problems, notably aimed at supporting decision-making. We advocate the use of a labour division by including domain and tool specialists, which can be implemented via a decision aid architecture, and show problems with such a multi-stakeholder situation. Then, we assume a more „democratic” approach of an AI-enabled DSS (decision support system), which does not require as much expertise from the stakeholders as the decision aid. We also emphasize the data-driven approaches and a wide use of machine learning to derive all kinds of the users’ characteristic features. We also consider the context awareness and intention awareness aspects. Emphasis is on the broadly perceived human-AI collaboration exemplified by a proper account for the basic difference in the number crunching power of the computers and human capabilities of solving more complex problems, human deficiencies in the sense of cognitive biases, various dilemmas related to fast and slow decision making, etc. An important aspect will be devoted to a proper selection of problems or their parts that should be solved by either the human or a computer system. Some future challenges and directions will be briefly mentioned.

Prof. Dr. Jan W. Owsiñski Jan W. Owsiński is Ph.D., D.Sc., deputy director for research and professor at the Systems Research Institute, Polish Academy of Sciences. Also: Professor / Lecturer, Warsaw School of Information Technology and Management (WIT), Executive Editor of the English language quarterly Control and Cybernetics; Editor: Modern Problems in Management, journal of WIT Secretary General at the Polish Operations and Systems Research Society. Member of Editorial Boards of several international journals. Published altogether close to 300 papers and more than 50 own and edited volumes. Scientific interests: (i) advanced methods and algorithms of data analysis and AI (primarily cluster analysis and preference aggregation) and their applications, mainly in economics, ecology, biology; (ii) knowledge processing and management (data-information-knowledge feedback system); (iii) e-Government, e-Economy, e-Society; (iv) economics of the transition countries; (v) environmentally, socially and economically sustainable development, and life quality: notions, models and methods; (vi) transport and logistics – network optimisation. Headed several research projects or project work-packages, national and international, dealing with, chronologically: (a) modelling of regional agricultural systems; (b) interactive data analysis software for in-session use; (c) data analysis systems for Polish companies in transition; (d) modelling of Polish economy in the period of transition (developed the demographic module); (e) distance teaching on sustainable development (PHARE); (f) integrated DSS for transboundary catchments (FP5); (g) educational catching up in the information society for vulnerable graduates; (h) e-administration & local networks vis a vis local development in conditions of the information society and knowledge economy; (i) logistic and transport systems modelled through graph (and hypergraph) network representations.

Talk title: Back to the Roots: on the Use and Principles of Clustering under Various Circumstances

Prof. Dr. Álvaro Rocha Álvaro Rocha holds the title of Honorary Professor, and holds a D.Sc. in Information Science, Ph.D. in Information Systems and Technologies, M.Sc. in Information Management, and BCs in Computer Science. He is a Professor of Information Systems at the University of Lisbon - ISEG, researcher at the ADVANCE (the ISEG Centre for Advanced Research in Management), and a collaborator researcher at both LIACC (Laboratory of Artificial Intelligence and Computer Science) and CINTESIS (Center for Research in Health Technologies and Information Systems). His main research interests are maturity models, information systems quality, online service quality, requirements engineering, intelligent information systems, e-Government, e-Health, and information technology in education. He is also Vice-Chair of the IEEE Portugal Section Systems, Man, and Cybernetics Society Chapter, and Founder and Editor-in-Chief of both following Scopus and/or WoS journals: JISEM (Journal of Information Systems Engineering & Management) and RISTI (Revista Ibérica de Sistemas e Tecnologias de Informação / Iberian Journal of Information Systems and Technologies). Moreover, he has served as a Vice-Chair of Experts for the European Commission’s Horizon 2020 Program, and as an Expert at the COST - intergovernmental framework for European Cooperation in Science and Technology, at the European Commission’s Horizon Europe Program, at the Government of Italy’s Ministry of Universities and Research, at the Government of Latvia’s Ministry of Finance, at the Government of Mexico's National Council of Science and Technology, at the Government of Polish's National Science Centre, and at the Government of Cyprus's Research and Innovation Foundation. He has 339 of his publications indexed in Scopus database, having an H-Index = 26 and 2601 citations. In Google Scholar he has an H5-Index = 35, having 5892 citations. He has 197 of his publications indexed in the Web of Science database (Core Collection), having an H-Index = 20 and 1423 citations. And in ResearchGate, he has an H-Index = 30 and 4089 citations, being part of the group of the 2% best researchers in the world, considering all areas of investigation, and part of the group of the 1% best researchers in the world, considering only his area of research: Information Systems.

Talk title: A Health Data Analytics Maturity Model for Hospitals Information Systems

Abstract: In the last five decades, maturity models have been introduced as reference frameworks for Information System (IS) management in organizations within different industries. In the healthcare domain, maturity models have also been used to address a wide variety of challenges and the high demand for Hospital IS (HIS) implementations. The increasing volume of data exceeds the ability of health organizations to process it for improving clinical and financial efficiencies and quality of care. It is believed that careful and attentive use of Data Analytics in healthcare can transform data into knowledge that can improve patient outcomes and operational efficiency. A maturity model in this conjuncture, is a way of identifying strengths and weaknesses of the HIS maturity and thus, find a way for improvement and evolution. This speech presents a proposal to measure Hospitals Information Systems maturity regarding Data Analytics. The outcome is a maturity model, which includes six stages of HIS growth and maturity progression