Special Session 1: Network Optimization and Security
Organizer and Chair:, University de los Andes, Colombia
Prof. Yezid DONOSO, Head of the Information Security Postgraduate and Master Program, Systems and Computing Engineering Department, Universidad de los Andes, Cra. 1 Este No. 19A-40, Phone 57-1-3394949 Ext 1723, Bogotá, Colombia, South America, ydonoso@ uniandes.edu.co
Brief Bio-Sketch: Dr. Eng. Yezid DONOSO is an Associate Professor at the Universidad de los Andes in the Computing and System Engineering Department in Bogota, Colombia, South America. He is a consultant in computer network and optimization. He holds a degree in System and Computer Engineering, a M.Sc. degree in System and Computer Engineering, a D.E.A. in Information Technology and a Ph.D. (Cum Laude) in Information Technology from Girona University, Girona, Spain. IEEE Senior Member. Distinguished Professor, given by Universidad del Norte, Colombia, October 2004. National Award of Operations research given by the Colombian Society of Operations Research, 2004. He is co-author of the book Multi-Objective Optimization in Computer Networks Using Metaheuristics (2007) and Network Design for IP Convergence.
The convergence in communication networks and computing has led the exponential growth of new applications and information systems. Nowadays, users and applications generate and request more data demanding efficient and secure management. New algorithms are needed to manage the network resource allocation improving the network performance, response against failures, congestion and attacks; and to avoid loss of confidentiality, integrity or availability in the network.
T.2.1. Network Optimization: Advanced Network Architecture; Computational complexity and data structures; Distributed Algorithms for control and management in Communication Systems; Energy Efficiency in Wireless Networks; Mobility, Handoff, and Location Management; Network Algorithm analysis; Network Structure, Routing and Resource Management; Networks Survivability against Failures, Congestion and Attacks; Network Planning; Quality of Service / Quality of Experience Optimization; Software Define Network; Scheduling and Network Optimization; Self-Organizing Networks; Reliable Networks; Special Topics in Network Optimization.
T.2.2. Security: Intrusion; Detection and Prevention Systems; Network Authentication and Key Management; Network Reliability; Privacy and Anonymity; Secure Networking; Secure Network Protocols; Security for Cloud Networking; Security for Internet Applications; Security for Wireless Sensor networks; Security for Smart Grids; Security for Vehicular Networks; Security for Critical Infrastructures; Special Topics in Security.
Special Session 2: Data Mining and Intelligent Knowledge Management
Organizers and Chairs: Gang KOU and Yi PENG
Prof. Gang KOU,
Executive Dean of School of Business Administration,
Southwestern University of Finance and Economics,
Chengdu, 611130, China, E-mail: email@example.com
Brief Bio Sketch:
Dr. Gang Kou (b. December 12, 1975) is a Professor and Executive Dean
of School of Business Administration, Southwestern University of Finance
and Economics. He is managing editor of International Journal of
Information Technology & Decision Making and series editor of
Quantitative Management (Springer). Previously, he was a research
scientist in Thomson Co., R&D. He received his Ph.D. in Information
Technology from the College of Information Science & Technology,
University of Nebraska at Omaha; got his Master degree in Department of
Computer Science, University of Nebraska at Omaha; and B.S.degree in
Department of Physics, Tsinghua University, Beijing, China.
He has participated in various data mining projects, including data mining for software engineering, network intrusion detection, health insurance fraud detection and credit card portfolio analysis. His research interests are in Data mining, Multiple Criteria Decision
Making and Information management. He has published more than eighty papers in various peer-reviewed journals and conferences. He has over 1,700 citations in Google Scholar.
Prof. Yi PENG,
School of Management and Economics,
Chengdu 610054, China, pengyi@ uestc.edu.cn
Brief Bio Sketch: Dr. Yi PENG (b. March 21, 1975) is a professor of School of Management and Economics, University of Electronic Science and Technology of China. Previously, she worked as Senior Analyst for West Co., USA. Dr. Peng received her Ph.D. in Information Technology from the College of Information Science & Technology, Univ. of Nebraska at Omaha and got her Master degree in Dept of Info. Science & Quality Assurance, Univ. of Nebraska at Omaha and B.S. degree in Department of Management Information Systems, Sichuan University, China. Her research interests cover Knowledge Discover in Database and data mining, multi-criteria decision making, data mining methods and modeling, knowledge discovery in real-life applications. She published more than forty papers in various peer-reviewed journals and conferences. She is the Workshop Chair of the 20th International Conference on Multiple Criteria Decision Making (2009), guest editor of Annals of Operations Research special issue on Multiple Criteria Decision Making on Operations Research.
Session Scope :
Data mining (DM) and knowledge management (KM) are two important research areas, but with different emphasis. Research and practice in these two areas have been largely conducted in parallel. Although both data mining and knowledge management have been active areas in research and practice, there is still a lack of idea exchange between these two camps. The first goal of this workshop is to bridge this gap. It has been well-known that data mining algorithms can discover hidden patterns from large-scale databases. However, the results of data mining may not be regarded as “knowledge”. To elicit explicit knowledge from the hidden patterns of data mining, which is useful to the end-users, the theory of human knowledge management should be adopted. Such a "special" knowledge, different from traditional knowledge since it can be stored, transformed, disseminated and expanded, is called intelligent knowledge. The second goal of this workshop is to discuss the research issues beyond data mining, foundation of intelligent knowledge management, and the process of identifying intelligent knowledge.
The workshop welcomes both high-quality academic (theoretical or empirical) and practical papers in the broad ranges of data mining and intelligent knowledge management related topics including, but not limited to the following: Data mining interpretation; Data mining and knowledge transfer; Data analysis and knowledge management; Data mining and risk management; Data warehousing in knowledge management; Evaluations of hidden patterns; Optimization based data mining for knowledge management; Practical issues of data mining; Integration of data mining and knowledge management; Intelligent knowledge algorithms; Man-machine interaction in data mining; Intelligent knowledge management.
Special Session 3: Computational Intelligence Methods
Organizers and Chairs:& , Central Washington University, USA & , Aurel Vlaicu University of Arad, Romania
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.
Dr. Donald DAVENDRA, Central Washington University, 400 East University Way, Ellensburg, WA 98926, USA, Phone: (509) 963-1430, FAX: (509) 963-1449, donald.davendra@ vsb.cz
Brief Bio Sketch: Donald DAVENDRA is an Associate Professor of Computing Science at Central Washington University, USA. His education background includes a Bachelor of Science, Postgraduate Diploma and Master of Science in Computing Science and Engineering from the University of the South Pacific, Fiji Islands and a Doctor of Philosophy in Technical Cybernetics from Tomas Bata University in Zlin, Czech Republic. His research areas are in evolutionary systems, chaotic systems, complex networks and its application to combinatorial optimisation problems. His publication list includes two edited monographs and around 100 papers in various peer-reviewed journals and conferences.
Prof. Valeriu BEIU, Aurel Vlaicu University of Arad,
str. Elena Drăgoi, nr. 2, 310330, Ara, firstname.lastname@example.org
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.
Computational intelligence is relatively new to industry. It On top of that, computational intelligence is based on a smorgasbord of approaches with very different theoretical bases, such as fuzzy logic, neural networks, evolutionary computation, statistical learning theory, swarm intelligence, and intelligent agents.
There is little consensus as to a precise definition of Computational Intelligence (CI). Nevertheless, most practitioners would include neural networks, fuzzy and evolutionary techniques, swarm intelligence (and perhaps others), and more especially hybrids of these. Essentially, CI comprises concepts, paradigms, algorithms and implementations of systems that are supposed to exhibit intelligent behavior in complex environments. It relies heavily on numerical, predominantly nature-inspired methods. These methods have the advantage that they tolerate incomplete, imprecise and uncertain knowledge.
CI is still a fast- growing research area in the category of emerging technologies, with thousands of applications. For most of the scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more difficult task. One of the reasons is that it is hard to choose from this huge variety of techniques the most appropriate one for a given real-world problem. Moreover, we sometimes do not even know if a CI technique is the appropriate approach.
Therefore, this section will adopt a very different definition of CI. We focus on problems instead of tools, looking at methods for problems not at problems for methods. This will enable us to compete with other methods for various applications, facilitating real progress towards more difficult problems.
This approach inspired by Włodzisław Duch’s definition, “CI is a branch of computer science studying problems for which there are no effective computational algorithms”. If we define CI by the problems that the field studies, then we do not have to restrict the types of methods used for solutions.
Topics and Keywords
We seek original and high-quality contributions on the general theme of Computational Intelligence Methods. The session is focused on both CI algorithmic developments and applications. The following is a non-exhaustive list of problem environments for which we look for CI solutions: incomplete and imprecise data; big data; data streams; real-time processing; hybrid data; parallel/distributed processing; scheduling, logistic and assignment problems optimization; complex network analysis; social network analysis; bioinformatics.
Special Session 4:
Advanced Decision Support Systems with particular emphasis on sustainable energy
Organizers and Chairs:
, Lucian Blaga University of Sibiu, Romania
, University of Santiago de Chile, Chile
Prof. Marius Cioca, Faculty of Engineering, "Lucian Blaga" University of Sibiu, Emil Cioran Street, 4, 550025 - Sibiu, ROMANIA, Phone (office): +40 - 0269 - 217928, int. 1438, Fax (office): +40 - 0269 - 212245, marius.cioca@ ulbsibiu.ro
Prof. Felisa Cordova, Department of Industrial Engineering, University of Santiago of Chile, Santiago, Chile, email@example.com
The concept of DSS is extremely large and there are multiple definitions depending on the viewpoint of researchers. Passing over definition and concept, this session aims types of users, ways of use and implications of using DSS. Thus the components of DSS must be highlighted, and, considering the new paradigm in field, ways of tehnological integration and emphasis (news) technologies used to build them, especially, focused on communications as well an approach of DSS based on web. Sure, beside concepts, ways and principles are welcome also exemplification of DSS already used/tested both in industry but also in other fields (ex. e-Health, e-Governance, e-Business, DSS Group etc.)
Session Topics: Challenges of complex decision making in an organization; An overview of the decision making methods of a human; The relation of knowledge and data from different perspectives; DSS Architecture and types; Requirements of decision makers in an organization; Data warehousing and DSS; Data mining and Web mining in DSS; Multi-criteria decision making; Group decision making; Organizational decision making; DSS using Knowledge management.
Special Session 5: Fuzzy Control, Modeling and Optimization
Organizer and Chair:, Politehnica University of Timisoara, Romania
Prof. Radu-Emil PRECUP, Department of Automation and Applied Informatics, Politehnica University of Timisoara, Bd. V. Parvan 2, 300223 Timisoara, Romania, firstname.lastname@example.org
Brief Bio-Sketch: Radu-Emil PRECUP is currently with the Politehnica University of Timisoara, Romania, where he became a Professor in the Department of Automation and Applied Informatics, in 2000, and he is currently a Doctoral Supervisor of automation and systems engineering. He is also an Honorary Professor and a Member of the Doctoral School of Applied Informatics with the Óbuda University, Budapest, Hungary. He has been an Editor-in-Chief of the International Journal of Artificial Intelligence since 2008 and he is also on the editorial board of several other prestigious journals including Applied Soft Computing (Elsevier) and Evolving Systems (Springer). He is the author or coauthor of more than 300 papers published in various scientific journals, refereed conference proceedings, and contributions to books. His current research interests include intelligent control systems, data-driven control, and nature-inspired algorithms for optimization. He is a senior member of IEEE, the vice-chair of the Virtual Reality Task Force of the Intelligent Systems Applications Technical Committee (TC) of the IEEE Computational Intelligence Society, a member of the International Federation of Automatic Control (IFAC) TC on Computational Intelligence in Control, and the Romanian Society of Control Engineering and Technical Informatics. He was the recipient of the “Grigore Moisil” Prize from the Romanian Academy in 2005 for his contribution on fuzzy control.
Fuzzy control has long been applied to very large areas that exhibit important results. Originally introduced as model-free control approach, model-based fuzzy control has gained widespread significance recently. A systematic way to meet the performance specifications of control systems in complex applications is solving optimization problems with variables represented by the tuning parameters of the controllers. The very good quality of fuzzy models is necessary for the description of nonlinear dynamic processes and for getting simple models that are useful in the model-based design of fuzzy controllers. Once the fuzzy models are obtained, their parameters can be tuned on the basis of adequate optimization problems that target the minimization of objective functions in order to reduce as much as possible the modeling errors. The objective of this session is to provide papers about the recent advances of fuzzy control, modeling and optimization in various industrial and non-industrial applications. The combination of two of these three approaches is encouraged.
The papers submitted to this session will include the following topics: Stable and model-based design of fuzzy control systems; Fuzzy modeling and simulation; Nature-inspired optimization in fuzzy modeling and control; Data-driven fuzzy control; Adaptive, predictive and robust fuzzy control; Type-2 fuzzy control and modeling; Evolving soft computing techniques for modeling, fault detection and isolation; Hybrid intelligent control including fuzzy control, neural networks, evolutionary-based optimization.
Special Session 6: Membrane Computing - Theory and Applications
Organizers and Chairs:&
Prof. Marian Gheorghe - The University of Bradford, Bradford BD7 1DP, United Kingdom, email@example.com
Brief Bio Sketch: Marian Gheorghe received the bachelor degree in mathematics and computer science and the PhD degree from the University of Bucharest. He moved to academia in 1991 as lecturer with the University of Bucharest and after a short spell with the University of Pitesti he took up a lectureship position with the University of Sheffield. Since 2015 he is a professor of computational models and software engineering with the University of Bradford, UK. His main research interests are in computational models, like automata, rewriting mechanisms, membrane systems and their applications in modelling complex systems, especially in biology. He is also interested in formal verification and model based testing. He was for five consecutive years the chair of the steering committee of the membrane computing conference, the main research forum of the membrane computing community. He edited special issues of prestigious journals and volumes of various conference proceedings.
Prof. Florentin Ipate - The University of Bucharest, Str Academiei 14, 010014, Bucharest, Romania, firstname.lastname@example.org
Brief Bio Sketch: Florentin IPATEreceived his bachelor degree in computer science from the "Politehnica" university of Bucharest. He received his MSc (in Software Systems Technology, with distinction) and his PhD (with a thesis nominated for the Distinguished Disertation Award in UK) from the University of Sheffield. Since 2002 he is Professor of Computer Science (at Pitesti University 2002-2012) and since 2012 at the University of Bucharest. His main research interests are in model based testing, agent based modelling, verification and testing, membrane computing. He published over 100 papers in prestigious journals and conferences and a research monograph with Springer.
Session Scope: Membrane computing is a new nature inspired computational paradigm abstracting from the structure and functionality of the living cell. It has a well-established corpus of research topics covering computational power, complexity and efficiency aspects, relationships with other classes of computational models, formal semantics and verification. Applications in systems and synthetic biology, cryptography, graphics, natural language processing and parallel and distributed computing have been investigated. A handbook presenting the key research topics has been published with Oxford University Press and two monographs on applications have appeared with Springer.
Session Topics: New computational models in membrane computing; Complexity aspects; Formal verification; Membrane algorithms; Fuzzy membrane systems; Applications.
Special Session 7: Stereovision Based Perception for Autonomous Mobile Systems and Advanced Driving Assistance
Organizer and Chair: Sergiu Nedevschi, Technical University of Cluj-Napoca, Romania
Prof. Sergiu NEDEVSCHI , Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului st., 400114, Cluj-Napoca, Romani Sergiu.Nedevschi@cs.utcluj.ro
Brief Bio-Sketch: Sergiu Nedevschi received the M.S. degree in E.E. from Technical University Cluj-Napoca, Romania, in 1975, and the Ph.D. degree in E.E. from the same university in 1993. From 1976 to 1983 he worked as researcher at the Research Institute for Computer Technologies from Cluj-Napoca. In 1998 he was appointed Professor in Computer Science and he founded the Image Processing and Pattern Recognition Research Center at Technical University of Cluj-Napoca (TUCN). From 2000 to 2004 he was the Head of Computer Science Department, from 2004 to 2012 the Dean of Faculty of Automation and Computer Science, and now is Vice–President for Scientific Research and ICT of the Technical University of Cluj-Napoca. His research interests include image processing, pattern recognition, computer vision, stereovision based perception, intelligent vehicles and driving assistance systems. He leaded more than 75 research projects and published more than 300 papers (80+ journal papers). Since 2001 he has coordinated a series of research contracts funded by Volkswagen AG in the field of “Stereovision for Driving Assistance and Autonomous Driving”. From 2013 he is continuing the research activity in Stereovision for Driving Assistance in cooperation with Bosch. He has been involved in EU projects like as INTERSAFE 2, INSEMTIVES, LARKC, DRIVE C2X, PAN-Robots. He is associate editor of IEEE Transactions on Intelligent Transportation Systems, the organizer of the IEEE Intelligent Computer Communication and Processing Conference (ICCP). He has been PC member, member in the local organizing committee, session chair of more than 50 international conferences and workshops including IEEE IV Symposium and IEEE ITSC.
Session Scope: The autonomous mobile systems and the advanced driving assistance systems sense their surrounding with devices such as radar, lidar, video camera. The sensory information is captured, perceived and represented in a convenient way for navigation path identification, and collision avoidance. The main perception functions are: environment geometry detection; painted objects detection, localization and classification; traffic signs and lights detection and classification; landmarks detection, localization and classification; detection, tracking, relative localization, relative speed estimation, and classification of the objects from traffic environment; objects behavior understanding; scene understanding; risk assessment.
The stereo sensor is the most powerful and reach in information sensor providing at least 3 different modalities: depth image, optical flow image, intensity or color image. The current research is focused firstly on improving dense stereo reconstruction, dense optical flow estimation, stereovision based visual odometry, and secondly on developing more robust and effective perception functions.
The current trend is to use probabilistic approaches to solve problems as estimation, recursive state estimation, parameters learning, classifiers learning, temporal and multi-sensor fusion, object behavior and scene understanding.
The objective of this session is to provide papers about the recent advances in stereovision based perception with applications in autonomous navigation and advanced driving assistance.
The papers submitted to this session will include the following topics: dense stereo reconstruction; dense optical flow estimation; visual odometry; error models; scene parsing; environment geometry detection; obstacle detection, tracking, classification; pedestrian detection, tracking and classification, behavior understanding, scene understanding, environment representation, risk assessment.