Keynote Speakers
We are very pleased to have acquired the services of excellent keynote speakers for the conference. The speakers and the titles of their talks are shown below.
Prof Vladimir Tikhomirov
Moscow State University of Economics, Statistics and InformaticsRussia
Smart Education as the Main Paradigm of Development of an Information Society
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Prof George A. Tsihrintzis
University of PiraeusGreece
One Class Classification Problems : Applications in Recommender Systems
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Prof Nikos Tsourveloudis
Technical University of CreteGreece
Bio-inspired Robots: Learning from Nature
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Prof Maria Virvou
University of PiraeusGreece
Emotion-related features in personalised and adaptive e-learning
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Prof Vladimir Tikhomirov
Moscow State University of Economics, Statistics and InformaticsRussia
Smart Education as the Main Paradigm of Development of an Information Society
Abstract:
Knowledge has a big influence on the development of modern society and the economy. The rapid proliferation and active utilization of the Internet, Web and various information and communication technologies (ICT) help to significantly reduce the number of steps on the way from knowledge creation to its implementation. They have led to the emergence of a new world - Smart World - where there are no barriers to the creation, sharing and dissemination of knowledge, and where ICT make knowledge flow independent of rules and stereotypes.
The new challenge for a university is to provide students, faculty and administrators with smart learning, teaching, management, and safety environment - smart university. As a result, the current approaches to education should be completely reconsidered in terms of learning and teaching strategies, methods of knowledge management, and use of supporting technologies, hardware and software used; they should be based on smart technologies in order to meet the increasing public demand for high quality educational services.
The following topics are planned for detailed discussion in this presentation:
- Features of information society
- From e-learning to smart e-learning
- Massive Open Online Courses (MOOCs)
- Smart university: features and technologies
- MESI e-University model:
- development of "Electronic University" consortium, joint development and use of a common repository of learning content by various universities;
- creation of a single point of access to the scientific services using cloud technologies;
- e-University + companies close collaboration on learning content, analytical, technical, communications and managerial skills required by modern businesses, companies and industries;
- active international partnership in the area of design, development and use of quality e-learning, certification procedures, and participation in the development of regulatory documents concerning e-learning.
- From smart university to smart society
- Future of smart education
Biography:
Professor Vladimir P. Tikhomirov is the President of the "e-University" International Consortium, and the Academic Supervisor of the Moscow State University of Economics, Statistics and Informatics (MESI University). In 1992-2007 he was the President of MESI University. Under his leadership, the student body of MESI University increased from about 3,000 students in 1992 to 100,000+ students in 2007.
Prof. Vladimir P. Tikhomirov is the Scientist Emeritus of the Russian Federation, Honorary Worker of Higher Professional Education of the Russian Federation. In 2003, Prof. Vladimir P. Tikhomirov was appointed as Chairman of Expert Council for e-Learning and Information Technologies in Education in the Russian State Parliament (DUMA) Committee on Education.
Prof. Tikhomirov is the author of more than 120 professional publications. His current research areas include smart university, smart society, e-learning and knowledge management. Prof. Tikhomirov is one of the leading international experts in Smart University area; he has been keynote and invited speaker of multiple professional meetings and symposiums.
Prof. Vladimir P. Tikhomirov holds the positions of President of Association of Economic Higher Schools, President of Eurasian Association of Distance Learning, Vice-President of International Academy of Higher School, and Vice-President of Academy of Technological Sciences.
Prof George A. Tsihrintzis
University of PiraeusGreece
One Class Classification Problems : Applications in Recommender Systems
Abstract:
Classification is a very common supervised machine learning task, in which a piece of data needs to be assigned by the learning algorithm to one of a given number of potential classes of origin. More specifically, in classification, the machine is given a set of training samples for each of which the class of origin is known. The machine is then asked to learn inductively from the given samples and generalize into a rule for assigning data into classes of origin that allows it to classify samples other than the ones used for training. It is the usual assumption of the binary classification problem that the number of training samples available from one class is comparable to the number of training samples available from the other class. However, it is not uncommon in certain applications for the number of training samples from one class to be significantly higher than the number of training samples from the other class. For example, users of recommender systems are very willing to provide examples (samples) of items they like, but are reluctant to provide samples of items they do not like. Similarly, in a protected system, the number of samples of intruders may be relatively limited, while the number of available samples of allowed/legal users may be quite high. Classification problems with class imbalance arise in nature as well. For example, the immune system in vertebrate organisms needs to be able to discriminate between self cells and other antigens, so as to respond accordingly. A high number of samples from the class of self cells are available to train the immune system. On the other hand, the class of antigens is very broad, including cancer cells, cells from other organisms, molecules and other intruding substances, viruses, bacteria, and parasitic worms. The number of available training samples from the class of antigens is very limited when compared to the size and diversity of this class.
The imbalance in the number of samples from each class affects the performance of traditional binary classifiers. Indeed, in probabilistic terms, classification problems in which training samples from one class are significantly higher in number than training samples from the other class result in significantly uneven prior probabilities of the two classes. The class from which a higher number of samples is available (target class) will have higher prior probability, while the class from which only a limited number of samples is available (outlier class) will have much lower prior probability. In turn, this affects the posterior probabilities of a sample coming from one or the other class. As a result, a binary classifier will erroneously tend to decide more often that an unknown sample comes from the target class than from the outlier class. In recommender system applications, this would mean that the system would tend to recommend items that the user might not like. Similarly, in a protected system, intruders and other threats might not be recognized.
In this presentation, we will discuss one-class classification problems, i.e, classification problems with extreme class imbalance and investigate the applicability of one-class classification methodologies in the design of recommender systems.
Biography:
GEORGE A. TSIHRINTZIS is member of the Council, Director of Graduate Studies in 'Advanced Computing and Informatics Systems' and Full Professor in the University of Piraeus, Greece. He received the Diploma of Electrical Engineer from the National Technical University of Athens, Greece (with honors) and the M.Sc. and Ph.D. degrees in Electrical Engineering from Northeastern University, Boston, Massachusetts, USA. His current research interests include Pattern Recognition, Machine Learning, Decision Theory, and Statistical Signal Processing and their applications in Multimedia Interactive Services, User Modeling, Knowledge-based Software Systems, Human-Computer Interaction and Information Retrieval. He has authored or co-authored over 300 research publications in these areas, which include 5 monographs and 13 edited volumes.
He is the Editor-in-Chief of the International Journal of Computational Intelligence Studies (Inderscience) and a member of the editorial boards of 8 additional journals. He has chaired over 20 international conferences.
He has supervised 8 doctoral students who have received their doctoral degrees and is currently supervising an additional 7 students.
He has guest co-edited 8 special issues of international journals.
He won the Best Poster Paper Award of the 5th International Conference on Information Technology: New Generations, Las Vegas, USA, April 7-9, 2008, for co-authoring a paper titled: "Evaluation of a Middleware System for Accessing Digital Music Libraries in Mobile Services."
He also won one of the Best Applications Papers Award of the 29th Annual International Conference of the British Computer Society Specialist Group on Artificial Intelligence, Cambridge, UK, December 15-17, 2009, for co-authoring a paper titled: "On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information."
Prof Nikos Tsourveloudis
Technical University of CreteGreece
Bio-inspired Robots: Learning from Nature
Abstract:
The fundamental motivation behind the development of bio-inspired multi-robot teams is the ability of living organisms to successfully cope and provide good solutions to almost all robotic related problems. Navigation, material handling and sensors, machine learning are only some of the research areas benefited from examining and adopting methodologies, techniques or mimicking behaviors proved sustainable and successful for animals and humans.
The talk will follow the bio-inspired paradigm of hunting mammals in land (wolves) and the sea (dolphins), intending to make this knowledge applicable to the coordination problem of heterogeneous robotic teams. The objective will be to present, define and discuss the required level of inference capabilities needed for robotic navigation and coordination purposes. Emphasis will be given on the fact that humans and animals decide and conclude about unknown features of their world under constraints of limited time, knowledge, and computational capacity. And despite their "bounded rationality" (or cognitive limitations) tend to built and use domain specific heuristics that allow for fast problem solving (and task specific successful behaviors). Robots and agents may be benefited from this fact.
Biography:
Nikos Tsourveloudis is a professor of manufacturing technology at the Technical University of Crete (TUC), Chania, Greece, where he leads the Intelligent Systems and Robotics Laboratory and the Machine Tools Laboratory.
His research interests are mainly in the area of autonomous navigation of field robots. His teaching focuses on manufacturing and robotic technologies and he published more than 100 scientific papers on these topics. He serves in the editorial board of numerous scientific journals and conferences, is a member of professional and scientific organizations around the globe, and several public organizations and private companies have funded his research.
Dr. Tsourveloudis' research group has been honored with several prizes and awards for their research efforts and also for transferring academic knowledge into real industrial prototypes. The most recent awards include: the 3rd EURON/EUROP Robotic Technology Transfer award (2009), the 1st Car Safety award in 2010 and 2011, and the 1st Global Energy Challenge award, 2013, at the Shell Eco Marathon competition, the Excellent Research Achievements award by the TUC (2010). In 2011 he was awarded with a "Chair-of-Excellence" in Robotics by the University Carlos III of Madrid, Spain, and the Santander Bank. He is the Dean of the School of Production Engineering and Management at TUC.
Prof Maria Virvou
University of PiraeusGreece
Emotion-related features in personalised and adaptive e-learning
Abstract:
Recent advances of technology, wireless connections to the Internet as well as the compelling needs of society for lifelong education and learning at any time and any place have rendered e-learning practices as popular as ever. However, one problem that has attracted a lot of research energy during the last decade stems from the fact that human emotions had been overlooked for many years in the area of e-learning while remote learners faced impersonal e-learning systems that do not help the cognitive procedures. This talk will review advances in emotion related features of e-learning systems; how these features can be perceived, recognised and used automatically to adapt the educational process to the individual learners' needs taking into account the recognised emotions. Adaptivity in such cases includes emotion-related feedback through emotion generation mechanisms that render the user interface of the e-learning system more human-like and personal.
Biography:
Maria Virvou was born in Athens, Greece. She received a B.Sc. Degree in Mathematics from the University of Athens, Greece, a M.Sc. Degree in Computer Science from the University of London (University College London), U.K. and a Ph.D. Degree in Computer Science and Artificial Intelligence from the University of Sussex, U.K.
She is a FULL PROFESSOR, HEAD OF THE DEAPARTMENT and DIRECTOR OF POST- GRADUATE STUDIES in the Department of Informatics, University of Piraeus, Greece. She is also EDITOR-IN-CHIEF of the SpringerPlus Journal (Springer) for the whole area of Computer Science. Additionally, she is an ASSOCIATE EDITOR of the Knowledge and Information Systems (KAIS) Journal (Springer) and MEMBER OF THE EDITORIAL BOARD of the International Journal on Computational Intelligence Studies (Inderscience). She has been the GENERAL CHAIR / PROGRAM CHAIR of over twenty (20) International Conferences. She is the Director of a research lab. She has supervised 12 Ph.D. theses which have been completed successfully, all in the area of Computers in Education and she is currently supervising 6 Ph.D. students and 10 post-doctoral researchers. Moreover, she has supervised more than 100 M.Sc. theses in the area of Computers and Education. Her research interests are in the area of Computers and Education, Artificial Intelligence in Education, user and student modelling, e-learning and m-learning, Knowledge-Based Software Engineering and Human-Computer Interaction.
Professor Virvou is the sole author of five (5) books in Computer Science and AUTHOR/CO-AUTHOR of over 300 research papers published in international journals, books and conference proceedings. According to Microsoft Academic Search, she is ranked as 53 in the top 100 authors out of 58000 authors worldwide in the area of Computers and Education (http://academic.research.microsoft.com/). According to the same academic search, she is ranked as the top first author in the area of "student model" and "authoring tools". Additionally, she is ranked in the top ten authors in the area of "Intelligent Tutoring Systems" and "GUI (Graphical User Interface)" out of 28075 authors worldwide and she is ranked in the top 100 in the recent area of emotion recognition.