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Keynote Speakers at ICCCC 2020 (online)

May 11-15, Oradea, Romania

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General Chair: Ioan DZITAC, Agora University of Oradea & Aurel Vlaicu University of Arad, Romania

  • Welcome Word from General Chair:  PDF
  • Anniversary diplomas "Agora 20":  PDF
  • Classic vs. Online in a Conference Organization. Case Study:  ICCCC (Draft) PDF

Keynote Speakers (in alphabetical order):

1. Prof. Razvan ANDONIE, Central Washington University, Ellensburg, USA

  • Title of lecture: "Practical Hyperparameter Optimization for Deep Learning"; Presentation:  PDF

2. Prof. Valeriu BEIU, Aurel Vlaicu University of Arad, Romania

  • Title of lecture: "Land of the Giants ... AI Chips"; Presentation :  PDF 

3. Prof. Alfred BRUCKSTEIN, Technion, Haifa, Israel

  • Title of lecture: "Erratic Extremists Induce Dynamic Consensus: A New Model for Opinion Dynamics"; Presentation:  PDF

4. Prof. Felisa CORDOVA, University Finis Terrae, Santiago, Chile

  • Title of lecture: "Trends and challenges of cyber-social-technological-cognitive approach in ecosystems"; Presentation :  PDF

5. Prof. Yezid DONOSO, Universidad de los Andes, Bogota, Colombia

  • Title of lecture: "Methodologies for Solving Complex Multi-Objective Combinatorial Problems in Engineering: An Evolutionary Approach Applied to Computer Networks"; Presentation :  PDF 

6.  Acad. Gintautas DZEMYDA, University of Vilnius,  Lithuania

  • Title of lecture: "Recent advances in data dimensionality reduction using multidimensional scaling"; Presentation:  PDF 

7. Acad. Florin Gheorghe FILIP, Romanian Academy, Bucharest, Romania

  • Title of lecture:  "10th commemoration since Acad. M. Draganescu (1929-2010) passed away. Mihai Draganescu and The Birth of the Romanian  ICT Sector"; Presentation:  PDF

8. Prof. Enrique HERERRA-VIEDMA, University of Granada,  Spain

  • Title of lecture: "Consensus in Group Decision Making and Social Networks": Presentation:  PDF 

9. Acad. Janusz KACPRZYK, Polish Academy of Sciences, Warsaw, Poland

  • Title of lecture :  "Decision aid, decision advice, decision support: when, why, for whom"; Presentation:  PDF

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1. Prof. Razvan ANDONIE

Prof. Răzvan ANDONIE, Central Washington University, 400 East University Way, Ellensburg, WA 98926, USA, Phone: (509) 963-1430, FAX: (509) 963-1449, andonie@ cwu.edu

Brief  Bio Sketch: Razvan ANDONIE received the M.S. degree in mathematics and computer science from University of Cluj-Napoca, Romania, and the Ph.D. degree from University of Bucharest, Romania. His Ph.D. advisor was Solomon Marcus, Fellow of the Romanian Academy. He is currently a Professor of Computer Science at both Central Washington University and Transilvania University of Brasov, Romania. He has published more than 130 research papers and was an invited professor at many universities. His actual research interests are computational intelligence techniques and applications, parallel/distributed computing, machine learning, and big data analytics.

Title of lecture: "Practical Hyperparameter Optimization for Deep Learning"

Abstract. While the training parameters of machine learning models are adapted during the training phase, the values of the hyperparameters (or meta-parameters) are specified before the learning phase.  For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. We present an integrated view of methods used in hyperparameter optimization of learning systems, with an emphasis on computational complexity aspects. Our thesis is that we should solve a hyperparameter optimization problem using a combination of techniques for: optimization, search space and training time reduction. Case studies from real-world applications illustrate the practical aspects. In previous work, we introduced the Weighted Random Search (WRS) method, a combination of Random Search and probabilistic greedy heuristic. This method outperforms many state-of-the-art hyperparameter optimization methods. We discuss practical applications of WRS in deep learning model optimization. 

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2. Dr. Valeriu BEIU

Department of Mathematics and Computer Science, Aurel Vlaicu University of Arad, Romania, valeriu.beiu@uav.ro

Valeriu Beiu (S’92–M’95–SM’96) received the MSc in CE from the University “Politehnica” Bucharest (UPB) in 1980, and the PhD summa cum laude in EE from the Katholieke Universiteit Leuven (KUL) in 1994.

His affiliations include the Research Institute for Computer Techniques, UPB, KUL, King’s College London, Los Alamos National Laboratory, Rose Research, Washington State University, United Arab Emirates University, and "Aurel Vlaicu" University of Arad, while his research interests have constantly been on biological-inspired nano-circuits and brain-inspired nano-architectures (low-power, highly reliable, massively parallel), being funded at over US$ 41M, and publishing over 250 papers (42 invited and 11 patents) as well as giving over 190 invited talks and organizing over 100 conferences.

Dr. Beiu received 5 fellowships and 7 best paper awards, and is a member of ACM, INNS, ENNS, and MCFA.  He was a member of the SRC-NNI Working Group on Novel Nano-architectures, the IEEE CS Task Force on Nano-architectures, and the IEEE Emerging Technologies Group on Nanoscale Communications, and has been an Associate Editor of the IEEE Transactions on Neural Networks, Nano Communication Networks, and IEEE Transactions for Very Large Scale Integration Systems.

Title of lecture: "Land of the Giants ... AI Chips" : Icon PDF (30.7 MB)

Abstract. This presentation will start with a brief historic overview of Artificial Intelligence (AI), explaining the earlier AI waves. Afterwards, the focus of the second part will be on the rapid rise of AI, narrowing it down to Deep Learning, currently perceived as an ubiquitous solution for a wide range of applications. This trend has had, and continues to have, massive financial support and vast substantial implications, which will be mentioned alongside the current “Cambrian explosion” of AI start-ups. The third part will be targeting a sub-class of these AI start-ups, namely those working on designing and building AI chips. These will be classified—into cloud and edge AI hardware—analyzed, and put into context. The fast pace growing number of players in this deceptively esoteric research field will be identified, and their latest results will be surveyed. Finally, we will comment on forthcoming trends of AI hardware while pinpointing its growth potential in the wider context of rebooting and quantum computing seen through larger and larger investments (triggered by the expected demise of Moore’s law). 

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3. Dr. Alfred M. BRUCKSTEIN

Alfred M. Bruckstein, Ollendorff Chair in Science, Technion, IIT, Haifa, Israel, freddy@cs.technion.ac.il

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.

Title of lecture: "Erratic Extremists Induce Dynamic Consensus: A New Model for Opinion Dynamics" : Icon PDF (4.1 MB)

Abstract. A society of agents, with ideological positions, or "opinions" expressed as real values ranging from −∞ (the "far left") to +∞ (the "far right"), is considered. At fixed (unit) time intervals agents repeatedly reconsider and change their opinions if and only if they find themselves at the extremes of the range of ideological positions. Extremist agents are erratic: they become either more radical, and move away from the positions of other agents, with probability ε, or more moderate, and move towards the positions held by peers, with probability (1−ε). The change in the opinion of the extremists is one unit on the real line. We prove that the agent positions cluster in time, so that non-extremist agents are eventually located within a unit interval. However, the “consensus opinion” is dynamic. Due to the extremists' erratic behavior the clustered opinion set performs a sluggish random walk on the entire range of possible ideological positions (the real line). The inertia of the group, i.e. the reluctance of the society's agents to change their consensus opinion, increases with the size of the group. The extremists perform biased random walk excursions to the right and left and, in time, their actions succeed to move the society of agents in random directions. The "far left" agent effectively pushes the group consensus toward the right, while the "far right" agent counter-balances the push and causes the consensus to move toward the left. We believe that this model, and some of its variations, has the potential to explain the real world swings in societal ideologies that we see around us. (Joint work with Dmitry Rabinovich)

4. Prof. Felisa CORDOVA

Prof. Felisa Córdova, Director School of Engineering, Faculty of Engineering, University Finis Terrae, Santiago, Chile, fcordova@uft.cl

Felisa CORDOVA is graduated in Electrical Engineering at the University of Santiago of Chile (USACH). She obtained the D.E.A. in Electronics and the Docteur Ingenieur degree at the University of Paris XI, France (1981). Now she is Director of the School of Engineering at University Finis Terrae. She was also professor, Director of the Department of Industrial Engineering and Academic Vice Rector at USACH. Her main research interests include Strategic and Operations Management and Knowledge Management of the Supply Chain. She has participated in several national and international research projects in the fields of Robotics, AGV and Virtual Operation Systems in underground mining. She has published more than 80 papers in conference proceedings and international journals in the areas of Robotics and Production Research, Knowledge and Strategic Management, Neuromanagement and Neuromarketing. She is past-president of the Chilean Association of Automatic Control ACCA (member of IFAC). She has participated in the organization of national and international Conferences (ACCA, LCA, LCR, SEPROSUL, ICCC, ICPR). She is national councilor and past Vice President of the Engineers College of Chile. She was president of the accreditation board at the Agency Acredita CI, and actually, she is member of the Directory of the Agency.

Title of lecture: "Trends and challenges of cyber-social-technological-cognitive approach in ecosystems" : Icon PDF (3.7 MB)

Abstract. This lecture aims to provide an international forum to discuss the main ideas about cyber-social-technological-cognitive approach present in indifferent ecosystems. Nowadays, investments that promote disruptive technologies and digital transformation accompanied by advances in Industry 4.0 enable the development of intelligent and smart systems in different domains. The hyperspace allows the interconnection of multiple spaces of computers and networks which are interlinked with each other in cyber, social, technological and cognitive domains.

In the Cyber Domain many digital platforms in the hyperspace collaborate with Internet of the Things (IoT), Internet of Computers (IoC), Internet of the Services (IoS), allowing the integration, interconnection and interaction of people, computers and networks. e-crowd cloud and data bases facilitates the storage media and the interconnections among actors participating in the different interconnected networks. Especially holographic data, semantic sensors, intelligent supervisory control and cooperative actuators all play an important role in dynamic monitoring and decision systems.

 In Social Domain it is perceived that many of the social management networks are used by citizens who wish to express their opinion or interact with other people participating in the network. Also, companies or public and private institutions are providing public service and supporting main activities, developing forums and crowd applications to share protocols, knowledge, regulations, ideas, procedures, among other things.  In this context legal, cultural, structural, and environmental factors are managed involving companies’ goals and objectives, structure, standards, values for the community, culture and socialization of services.

In Technological Domain Industry 4.0 and digital transformation provides a set of technologies participating in the strategic, business and operational levels. At strategic and business level network technologies such as LAN, WAN, WLAN enable efficient communication and networking in the cyber space for companies and institutions linked. Artificial intelligence (AI) allows the community members display their capabilities, skills, expectations and knowledge, generating heuristics that are mapped in the cyber space as models, methods, techniques, tools and practices. In this way, community learning is shared in the ecosystem. Cybersecurity helps in the transactions, and in the protection of data and information. At operational level automation of physical processes allows real time control of events at operational level activities. Internet of things (IoT) can perceive and sensitize a physical object to be mapped later on the cyber space by technologies such as wireless sensors working at real time, physical data sensors, environmental sensors, equipment and mobile device sensors. Robotics and teleoperation allows acting on fixed and mobile equipment optimizing and making the operation of automated systems more flexible. Drones as aerial vehicles allow a close monitoring of any movements made in a site and its surroundings. Positioning technologies such as passive tags, RFID systems and GPS ensure traceability of products and services, also the transportation media used.

In Cognitive Domain the available knowledge provided by a person, a group, or a community of actors can be stored in the cyber space facilitating the classification and management of social, structural and intellectual capital of each organization participating in the ecosystem. Intellectual capital considers the knowledge of the progress of both intellectual and social capital of the community allowing planning the training needs for different actors.

In this context, we have the opportunity to analyse and discuss trends in some of the main conceptual models and architectures that integrate relevant attributes in hyperspace in different ecosystems, like green energy systems, smart cities, We-Media, among others. In particular, it is important to highlight the role of the Internet of Services (IoS) as it stands out as an ecosystem that takes over complex collaborative applications using interoperable resources across platforms and cloud storage. It also addresses advances in Artificial Intelligence (AI), neuroscience, and cognitive sciences that enable the development of Human Body Communication (HBC) and Human Information Processing (HIP) and thus open the door to multiple applications in the Internet of the People (IoP) domain.

5. Dr. Yezid DONOSO

Professor, Academic ViceDirector, Head of the Master Program in Information Security Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia, South America, 57-1-3394949 Ext 1723, ydonoso@uniandes.edu.co

Yezid Donoso received the System and Computing Engineering degree from Universidad del Norte, Barranquilla, Colombia, in 1996 (IEEE M’02 – SM’05) , M.S. degree from Universidad de los Andes, Bogotá, Colombia, in 1998, D.E.A. in Information Technology from Girona University, Girona, Spain, in 2002 and the Ph.D. (Cum Laude) in Information Technology from Girona University, Spain, in 2005. He is currently Associate Professor at Universidad de los Andes, Colombia and Coordinator of the Information Security Master Program and Academic ViceDirector of the Department. He is Senior Member IEEE, DVP (Distinguished Visitor Professor) Computer Society IEEE from 2005 to 2009 and President IEEE Colombia Section 2013 – 2014. Dr. Donoso has several national and international awards and medals. Biography published in the books: “Who’s Who in the World”, edition 2006 and “Who's Who in Science and Engineering” by Marquis Who’s Who in the World and “2000 Outstanding Intellectuals of the 21th Century” by International Biographical Centre. Cambridge, England, 2006. “Distinguished  Professor”, given by Universidad del Norte. Colombia. Octubre 2004. National Award of Operations research given by The Colombian Society of Operations Research. 2004..He is the co-author of several books, including Multi-Objective Optimization in Computer Networks Using Metaheuristics and Network Design for IP Convergence and He has more than 240 papers published between journals and conferences.

Title of lecture: "Methodologies for Solving Complex Multi-Objective Combinatorial Problems in Engineering: An Evolutionary Approach Applied to Computer Networks" : Icon PDF (16.7 MB)

Abstract. In real problems in Engineering, solving a problem is not enough; the solution of the problem must be the best solution possible. In other words, it is necessary to find the optimal solution. The solution is the best possible solution because in the real world this problem may have certain constraints by which the solutions found may be feasible, that is, they can be implemented in practice and, unfeasible or that they cannot be implemented. Some of these problems in engineering can be MOP (Multi-Objective Optimization Problem). A general MOP includes a set of n parameters (decision variables), a set of k objective functions and a set of m restrictions. The objective and restriction functions are functions of the decision variables where is possible to obtain a set of optimal values.

Then the MOP can be expressed as:

Optimize         y = f(x) = (f1(x), f2(x), ... , fk(x))

Subject to        e(x) = (e1(x), e2(x), ... , em(x))  0

Where   x = (x1, x2, ... , xn)  X

y = (y1, y2, ... , yk)  Y

The method evolutionary algorithm (EA) refers to searching and optimization techniques based on the evolution model proposed by Charles Darwin. Genetic algorithms are used in several areas especially for searching and optimizations. In the real case the algorithm is implemented by choosing a coding for the possible solutions to the problem. The coding is done through chains of bits, numbers or characters that represent the chromosomes. The crossing and mutation operations are applied in a very simple way through functions of vector value manipulation. The EAs are interesting given the fact that at first glance they seem especially apt to deal with the difficulties presented by MOPs. The reason for this is that they can return an entire set of solutions after a simple run and they do not have any other of the limitations of traditional techniques. In addition, some researchers have suggested that the EAs would behave better than other blind searching techniques.

6. Prof.  Gintautas DZEMYDA

Gintautas Dzemyda
Prof. Dr. Habil.
Full member of the Lithuanian Academy of Sciences
Director of the Institute of Mathematics and Informatics
Head of the Systems Analysis Department
Institute of Mathematics and Informatics
Vilnius University
Akademijos St. 4
Vilnius, LT-08663
Lithuania
e-mail: gintautas.dzemyda@mii.vu.lt

Brief  Bio Sketch: Prof. Gintautas Dzemyda was born in Vilnius, Lithuania, on July 24, 1957. In 1984 he received the doctoral degree in technical sciences (Ph.D.), and in 1997 he received the degree of Doctor Habilius from the Kaunas University of Technology. He was conferred the title of Professor (1998) at the Kaunas University of Technology. Full member of the Lithuanian Academy of Sciences (2011).

Recent employment is at the System Analysis Department of the Vilnius University Institute of Mathematics and Informatics as a Head of System Analysis Department and Principal Researcher. Since 2005, he is a Director of the Vilnius University Institute of Mathematics and Informatics.

The research interests include visualization of multidimensional data, optimization theory and applications, data mining, multiple criteria decision support, neural networks, parallel computing, internet data mining, recommender systems, image analysis. Applied problems have been solved in various areas including medicine and technologies. A software is designed both for general purposes and for applications. International and national RTD projects are managed. The author of 220 research papers, one monograph, five textbooks and two electronic courses for students.

Editor in Chief of the international journals Informatica (http://www.mii.lt/informatica) and the Baltic Journal of Modern Computing (http://www.lu.lv/baltic-journal-of-modern-computing/), Executive Editor a member of Editorial Board of the international journals: - Nonlinear Analysis: Modelling and Control (Associate Editor), - Informatics in Education, - Journal of Civil Engineering and Management, - Vestnik BSU. Series 1. Physics. Mathematics. Informatics, - Information Technology and Management Science. The Journal of Riga Technical University. Member of Lithuanian Computer Society, Lithuanian Mathematical Society, Lithuanian Operational Research Society, IFIP Technical Committee 12 Artificial Intelligence.

Title of lecture: "Recent advances in data dimensionality reduction using multidimensional scaling" : Icon PDF (3.8 MB)

Abstract. Human participation plays an essential role in most decisions when analyzing data. The huge storage capacity and computational power of computers cannot replace the human flexibility, perceptual abilities, creativity, and general knowledge. A proper interaction between human and computer is essential. Moreover, such an interaction is one of the areas in computer science that has evolved a lot in recent years. Real data in technologies and sciences are often high-dimensional. So it is very difficult to understand these data and extract patterns. One way of such an understanding is to make a visual insight into the data set. Here, a hopeful view may be put on the visualization of multidimensional data. The goal of visualization methods is to represent the multidimensional data in a low-dimensional space so that certain properties (e.g. clusters, outliers) of the structure of the data set were preserved as faithfully as possible. The dimensionality reduction or visualization methods are recent techniques to discover knowledge hidden in multidimensional data sets. Multidimensional scaling (MDS) is one of the most popular methods for a visual representation of multidimensional data. Low-dimensional visualization requires holding proximities between multidimensional points (observations) as much as possible. MDS requires estimating the coordinates of new points in a lower-dimensional space by minimizing some stress function. The stress function has many local minima often, and iterative algorithms are used for its minimization. Various attempts are made to the global minimum of the stress function still now. Classical approaches to minimize the stress reached their limits. New viewpoint to the problem is necessary, including its formulation and ways of solving. A novel geometric interpretation of the stress function and multidimensional scaling in general (Geometric MDS) has been proposed. Following this interpretation, the step size and direction forward the minimum of the stress function are found analytically for a separate point without reference to the analytical expression of the stress function, numerical evaluation of its derivatives and the linear search. It is proved theoretically that the direction coincides with the steepest descent direction, and the analytically found step size guarantees the decrease of stress in this direction. A strategy of application of the discovered option to minimize the stress function is presented and examined. It is compared with SMACOF version of MDS. The novel geometric approach will allow developing a new class of algorithms to minimize MDS stress, including global optimization and high-performance computing.

7. Prof. Florin Gheorghe FILIP

Florin Gheorghe FILIP

President of  "Information Science and Technology"  Section  of Romanian Academy, Romania, ffilip@acad.ro

IPC Chair of ICCCC2020

Brief  Bio Sketch: Prof. Florin Gheorghe Filip is a full member of Romanian Academy, President of  "Information Science and Technology"  Section  of Romanian Academy, and director of Library of Romanian Academy. He was a Vice-President of the Romanian Academy (2000 - 2010), Managing director of National Institute for R&D in Informatics (19911997). He authored/coauthored over 250 papers published in international journals (IFAC J Automatica, IFAC J Control Engineering Practice,  Annual Reviews in Control, Computers in Industry, System Analysis Modeling Simulation, Large Scale Systems etc) and contributed  volumes printed by international publishing houses (Pergamon Press, Springer, Kluwer, Chapmann & Hall etc). He is also the author/coauthor of ten monographs. He was an IPC member of more than 50 international  conferences held in Europe, USA, South America, Asia and Africa and gave plenary papers at scientific conferences held in Chile, China, France, Poland, Portugal, Rep. of Moldova, Spain, Tunisia, UK. He is co-founder and Editor-in-Chief of International Journal of Computers Communications & Control and co-founder of International Conference Computers Communications & Control. He has received Doctor Honoris Causa title from "Lucian Blaga" Univers ity of Sibiu (November 2000), "Valahia" University, Targoviste (2007), "Ovidius" University, Constanta (2007), Ecolle Centrale de Lille (France) (2007), Technical University ”Traian Vuia”, Timisoara (2009), Agora University of  Oradea (2012) and Academy of  Economic Studies, Bucharest (2014).

Title of lecture:  "10th commemoration since Acad. M. Draganescu (1929-2010) passed away. Mihai Draganescu and The Birth of the Romanian  ICT Sector": Icon PDF (2.5 MB)

Abstract. This year we commemorate 10 years since Prof Draganescu, a former president of the Romanian Academy (1990-1994),  passed away. The present short paper is intended to highlight the decisive contribution of prof. Mihai Draganescu in designing and leading the creating a national, integrated, network-type, information and communication (ICT) sector in Romania and his subsequent contributions in the way towards a knowledge-based society.. The paper starts  by  reviewing  several milestones and efforts made by eminent  scientists  and research and education schools that anticipated  and set the stage  for  creating  and deploying a national programme  in the ICT domain.  Then, it is exposed the design of a complex national ICT system that integrated in a coherent manner various subsystems such as: education, research, fabrication and usage of computers. Prof . Draganescu led the over 100 persons working in the virtual team that designed the national programme in 1967.  He also led the deployment the programme and the paper reviews the  first results. Besides being an engineers and scientific leader, prof. Draganescu was a philosopher and farseeing thinker who anticipated and described various development stages of the human society such as the information, knowledge-based, and consciousness ones which are eventually briefly described. More details and works of prof. Draganescu can be found in a dedicated sector of the Institute of AI and NLP of the Academy:http://www.racai.ro/about-us/dragam/

8. Prof. Enrique HERERRA - VIEDMA

University of Granada, Spain

C/ Periodista Daniel Saucedo Aranda, s/n

18071- Granada
Phone: +34 958 244258
Fax: +34 958 243317
E-mail:viedma@decsai.ugr.es

Brief  Bio Sketch: Enrique Herrera-Viedma  is  Professor in Computer Science and A.I in University of Granada and  Vice-President for Research and Knowlegde Transfer. He is also Vice-President for Publications in the IEEE System Man and Cybernetic Society. His current research interests include group decision making, consensus models, linguistic modeling, aggregation of information, information retrieval, bibliometrics, digital libraries, web quality evaluation, recommender systems, block chain and social media. In these topics he has published more than 250 papers in JCR journals and coordinated more than 22 research projects. Dr. Herrera-Viedma is EiC of the journal Frontiers in Artificial Intelligence and an Associate Editor of more than 10 JCR journals such as the IEEE Trans. On Syst. Man, and Cyb.: Systems, IEEE Trans. On Fuzzy Systems, IEEE Trans. On Intelligence Transportation Systems, Knowledge Based Systems, Soft Computing, Fuzzy Optimization and Decision Making, Applied Soft Computing, Journal of Intelligent and Fuzzy Systems, and Information Sciences. According to Web of Science his h-index is 77 with more than 17000 citations received and according to Google Scholar his h-index is 84 with more than 28000 citations. He has been identified by Clarivate Analytics as a Highly Cited Author in both scientific categories “Computer Science” and “Engineering” during the years 2014, 2015, 2016, 2017, 2018 and 2019.

Title of lecture: "Consensus in Group Decision Making and Social Networks" : Icon PDF (803.9 KB)

 Abstract. The consensus reaching process is the most important step in a group decision making scenario. This step is most frequently identified as a process consisting of some discussion rounds in which several decision makers, which are involved in the problem, discuss their points of view with the purpose of obtaining the maximum agreement before making the decision. Consensus reaching processes have been well studied and a large number of consensus approaches have been developed. In recent years, the researchers in the field of decision making have shown their interest in social networks since they may be successfully used for modelling communication among decision makers. However, a social network presents some features differentiating it to the classical scenarios in which the consensus reaching processes have been applied. The objective of this talk is to investigate the main consensus methods proposed in social networks and bring out the new challenges that should be faced in this research field.

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9. Janusz  KACPRZYK

Professor Janusz Kacprzyk, Ph.D., D.Sc, dr h.c. mult.            

Fellow, IEEE, IET, IFSA, EurAI, SMIA

Full member, Polish Academy of Sciences

Member, Academia Europaea

Member, European Academy of Sciences and Arts

Member, International Academy for  Systems and Cybernetic Sciences (IASCYS)

Foreign member, Bulgarian Academy of Sciences

Foreign member, Finnish Society of Sciences and Letters

Foreign member, Royal Flemish Academy of Belgium for Sciences and the Arts (KVAB)

Foreign member, Spanish Royal Academy of Economic and Financial Sciences (RACEF)

Room: 220 (2nd floor)
Phone: +(48) 22 3810275
Fax: +(48) 22 3810103
E-mail: Janusz.Kacprzyk@ibspan.waw.pl

Brief  Bio Sketch: Professor Ph.D., D.Sc. Janusz Kacprzyk is Head of Intelligent Systems Laboratory, Systems Research Institute, Polish Academy of Sciences. His research interests are: Uncertainty and imprecision in systems modelling, Intelligent decision support systems, Data mining and knowledge discovery, Soft computing, theory and applications, Fuzzy logic. He receive Pioneer Awards: Pioneer Award, IEEE CIS (Institute of Electrical and Electronics Engineers, Computational Intelligence Society) for pioneering works on multistage fuzzy control, in particular fuzzy dynamic programming, 2005; 6-th Kaufmann Award and Gold Medal of pioneering works on the use of fuzzy logic in economics and management, 2006; Pioneer Award for Outstanding Contributions to Granular Computing and Computing with Words by the Silicon Valley, California, IEEE Computational Intelligence Society Chapter, 2007. He is Full member, Polish Academy of Sciences, since 2010 (Member correspondent since 2002); Foreign member, Spanish Royal Academy of Economic and Financial Sciences (RACEF), since 2007 and Foreign member, Bulgarian Academy of Sciences, since 2013.

Title of lecture :  "Decision aid, decision advice, decision support: when, why, for whom": Icon PDF (10.6 MB)

Abstract. We consider complex decision problems which concern difficult situations nd challenges that cannot be adequately represented by simple and traditional decision making  models which are based on a straightforward utility maximization, preference analyses, etc. We assume that the complexity of the problem, for instance of strategic planning in a company, calls for an implicit or explicit participation of the humans in the process of conceptualizing, derivation and implementation of decisions which are meant to be not only technically effective and efficient but also to fulfill important more general goals, notably social. For this purpose we will use some solutions advocated in the broadly perceived human centric systems, human-in/on/ot-the loop, etc. Moreover, we will employ the concept of a decision making process that involves more factors and aspects like: the use of own and external knowledge, involvement of various „actors”, aspects, etc., individual habitual domains, non-trivial rationality, different paradigms, etc. In general, we assume that we have a „client”, who may be exemplified by a business owner or manager, and who needs a help in solving his or her problems from an „analyst” who can be a human domain specialist, exemplified by an operations research expert, or an artificial, computer based system, exemplified by an advice giving system (recommender) or a decision support system. We assume that the client has a limited knowledge of formal and algorithmic tools and techniques, and needs an easy to use solution. On the one extreme, we assume that we can have a relatively effective and efficient model for solving it, for instance from a multiple criteria decision making class, and the client involved can provide us with some additional information or data so that we can use the paradigm of decision aid. On the other extreme, we assume two possible situations that, first,  the model is practically unavailable but we have either domain knowledge, mostly tacit but maybe even to some extent explicit, or – second – we have enough data to pursue the so called data driven modeling. In both case we resort to the use of some non-model-driven decision suport systems. We also advocate that in some difficult situations in which specifics of a particular customer can be relevant, the paradigm of a decision advice can be employed using some new ideas from recommenders, notably their recent versions which can also provide reasons, rationale and explanations for a particular advice.    

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